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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• Knowledge Base
• Methodology
• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, January 30). Case Study | Definition, Examples & Methods. Scribbr. Retrieved 24 June 2024, from https://www.scribbr.co.uk/research-methods/case-studies/

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Case Study Research Method in Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

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Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Case Reports Vs Clinical Studies

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This post discusses questions validity if authored by an employee of the reporting company, Roho. This blog will answer these questions regarding clinical studies and clinical evidence:

• What is the difference between a clinical study and a case report?
• Who can observe and document the results of a clinical study?
• What circumstance would beg questioning the validity of a case report?

(Information below was take from the site clinical trials .gov a service of the National Institute of Health)

Definition of case report and clinical study

In medicine, a  case report  is a detailed  report of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient.  Case reports  may contain a demographic profile of the patient, but usually describe an unusual or novel occurrence.

The case report is written on one individual patient.

Clinical Study

A research study using human subjects to evaluate biomedical or health-related outcomes. Two types of clinical studies are Interventional Studies  (or clinical trials) and  Observational Studies . A clinical study involves multiple patients.

Observational Clinical Studies have a qualified investigator.

In an observational study, investigators assess health outcomes in groups of participants according to a research plan or protocol. Participants may receive interventions (which can include medical products such as drugs or devices) or procedures as part of their routine medical care, but participants are not assigned to specific interventions by the investigator (as in a clinical trial).

The Key Responsibilities of a Clinical Study Investigator:

• Be qualified to practice medicine or psychiatry and meet the qualifications specified by applicable national regulatory requirements(s)
• Be qualified by education, training, and experience to assume responsibility for the proper conduct of the study,
• Be familiar with and compliant with Good Clinical Practice (GCP)  ICH E6 Guideline  and applicable ethical and regulatory requirements prior to commencement of work on the study.
• Provide evidence of his/her qualification using the Abbreviated  TransCelerate Curriculum Vitae (CV) form

The internal validity of a medical device case report is questioned if bias is present. One must consider bias in a case report authored by an employee of the company that makes the device described in the report.

These are the facts on clinical studies published on the roho website. 

• There are 15 of what roho calls clinical studies on the roho website.  Based on the above definitions, these are not clinical studies but rather case reports. 
• Of these 15 case reports only one pertains a seat cushion improving a pressure ulcer.

This one single case report is written by Cynthia Fleck, an employee of crown therapeutics which is a division of roho

After selling 1 million cushions over the span of 45 years in business roho has exactly 1 case report which was written by an employee of roho which then begs the question of validity of this report.

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• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Peer Review reports

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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Sarah Crowe & Anthony Avery

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AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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DOI : https://doi.org/10.1186/1471-2288-11-100

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• Seward County Community College/Area Technical School, Liberal, KS
• Address for Correspondence: Suzanne Campbell, PhD, MLS(ASCP) CM , STEM Project Director, Medical Laboratory Technician Program Coordinator, Seward County Community College/Area Technical School, Liberal, KS 67901, 620-417-1403, suzanne.campbell{at}sccc.edu

Describe when the case study approach is the most appropriate qualitative research method.

Outline the components of a case study research method.

Discuss data coding and analysis and how categories and themes are developed.

Identify considerations for reporting the findings of case study research.

INTRODUCTION As medical laboratory professionals, we compare patient results to reference ranges and determine the clinical significance of the findings. Those numbers indicate whether a patient is healthy or will be diagnosed with a disease process. Even after the diagnosis, the numbers still have meaning as they reflect the outcome of the treatment. The result of the analytical process provides the healthcare team vital information regarding diagnosis and treatment. Because of the nature of our profession, quantitative research may be more readily accepted. We incorporate the results of quantitative research when we consider the likelihood of developing the disease, treatment success/failure rate, and prognosis. However, do we ever consider “how” the patient reacts to the diagnosis or “why” some patients have a better prognosis than others? 1 A quantitative research method would not provide the data needed to respond to those questions. Therefore, we should consider conducting a qualitative research method.

As previously identified, there are five approaches to qualitative research methods: narrative inquiry, phenomenological, grounded theory, ethnographic and case study research. 2 It is vital that the researcher consider the research questions and research design so the appropriate qualitative research method is selected. Qualitative research methods are used in psychology, sociology, philosophy, political science, medicine, social science, anthropology, government, business and education. 1,3,4,5 Let us explore in more detail the case study research method.

Case study research is an “…intensive study of a single case where the purpose of that study is… to shed light on a larger class of cases.” 4 Being…

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Case Study Method – 18 Advantages and Disadvantages

The case study method uses investigatory research as a way to collect data about specific demographics. This approach can apply to individuals, businesses, groups, or events. Each participant receives an equal amount of participation, offering information for collection that can then find new insights into specific trends, ideas, of hypotheses.

Interviews and research observation are the two standard methods of data collection used when following the case study method.

Researchers initially developed the case study method to develop and support hypotheses in clinical medicine. The benefits found in these efforts led the approach to transition to other industries, allowing for the examination of results through proposed decisions, processes, or outcomes. Its unique approach to information makes it possible for others to glean specific points of wisdom that encourage growth.

Several case study method advantages and disadvantages can appear when researchers take this approach.

List of the Advantages of the Case Study Method

1. It requires an intensive study of a specific unit. Researchers must document verifiable data from direct observations when using the case study method. This work offers information about the input processes that go into the hypothesis under consideration. A casual approach to data-gathering work is not effective if a definitive outcome is desired. Each behavior, choice, or comment is a critical component that can verify or dispute the ideas being considered.

Intensive programs can require a significant amount of work for researchers, but it can also promote an improvement in the data collected. That means a hypothesis can receive immediate verification in some situations.

2. No sampling is required when following the case study method. This research method studies social units in their entire perspective instead of pulling individual data points out to analyze them. That means there is no sampling work required when using the case study method. The hypothesis under consideration receives support because it works to turn opinions into facts, verifying or denying the proposals that outside observers can use in the future.

Although researchers might pay attention to specific incidents or outcomes based on generalized behaviors or ideas, the study itself won’t sample those situations. It takes a look at the “bigger vision” instead.

3. This method offers a continuous analysis of the facts. The case study method will look at the facts continuously for the social group being studied by researchers. That means there aren’t interruptions in the process that could limit the validity of the data being collected through this work. This advantage reduces the need to use assumptions when drawing conclusions from the information, adding validity to the outcome of the study over time. That means the outcome becomes relevant to both sides of the equation as it can prove specific suppositions or invalidate a hypothesis under consideration.

This advantage can lead to inefficiencies because of the amount of data being studied by researchers. It is up to the individuals involved in the process to sort out what is useful and meaningful and what is not.

4. It is a useful approach to take when formulating a hypothesis. Researchers will use the case study method advantages to verify a hypothesis under consideration. It is not unusual for the collected data to lead people toward the formulation of new ideas after completing this work. This process encourages further study because it allows concepts to evolve as people do in social or physical environments. That means a complete data set can be gathered based on the skills of the researcher and the honesty of the individuals involved in the study itself.

Although this approach won’t develop a societal-level evaluation of a hypothesis, it can look at how specific groups will react in various circumstances. That information can lead to a better decision-making process in the future for everyone involved.

5. It provides an increase in knowledge. The case study method provides everyone with analytical power to increase knowledge. This advantage is possible because it uses a variety of methodologies to collect information while evaluating a hypothesis. Researchers prefer to use direct observation and interviews to complete their work, but it can also advantage through the use of questionnaires. Participants might need to fill out a journal or diary about their experiences that can be used to study behaviors or choices.

Some researchers incorporate memory tests and experimental tasks to determine how social groups will interact or respond in specific situations. All of this data then works to verify the possibilities that a hypothesis proposes.

6. The case study method allows for comparisons. The human experience is one that is built on individual observations from group situations. Specific demographics might think, act, or respond in particular ways to stimuli, but each person in that group will also contribute a small part to the whole. You could say that people are sponges that collect data from one another every day to create individual outcomes.

The case study method allows researchers to take the information from each demographic for comparison purposes. This information can then lead to proposals that support a hypothesis or lead to its disruption.

7. Data generalization is possible using the case study method. The case study method provides a foundation for data generalization, allowing researches to illustrate their statistical findings in meaningful ways. It puts the information into a usable format that almost anyone can use if they have the need to evaluate the hypothesis under consideration. This process makes it easier to discover unusual features, unique outcomes, or find conclusions that wouldn’t be available without this method. It does an excellent job of identifying specific concepts that relate to the proposed ideas that researchers were verifying through their work.

Generalization does not apply to a larger population group with the case study method. What researchers can do with this information is to suggest a predictable outcome when similar groups are placed in an equal situation.

8. It offers a comprehensive approach to research. Nothing gets ignored when using the case study method to collect information. Every person, place, or thing involved in the research receives the complete attention of those seeking data. The interactions are equal, which means the data is comprehensive and directly reflective of the group being observed.

This advantage means that there are fewer outliers to worry about when researching an idea, leading to a higher level of accuracy in the conclusions drawn by the researchers.

9. The identification of deviant cases is possible with this method. The case study method of research makes it easier to identify deviant cases that occur in each social group. These incidents are units (people) that behave in ways that go against the hypothesis under consideration. Instead of ignoring them like other options do when collecting data, this approach incorporates the “rogue” behavior to understand why it exists in the first place.

This advantage makes the eventual data and conclusions gathered more reliable because it incorporates the “alternative opinion” that exists. One might say that the case study method places as much emphasis on the yin as it does the yang so that the whole picture becomes available to the outside observer.

10. Questionnaire development is possible with the case study method. Interviews and direct observation are the preferred methods of implementing the case study method because it is cheap and done remotely. The information gathered by researchers can also lead to farming questionnaires that can farm additional data from those being studied. When all of the data resources come together, it is easier to formulate a conclusion that accurately reflects the demographics.

Some people in the case study method may try to manipulate the results for personal reasons, but this advantage makes it possible to identify this information readily. Then researchers can look into the thinking that goes into the dishonest behaviors observed.

List of the Disadvantages of the Case Study Method

1. The case study method offers limited representation. The usefulness of the case study method is limited to a specific group of representatives. Researchers are looking at a specific demographic when using this option. That means it is impossible to create any generalization that applies to the rest of society, an organization, or a larger community with this work. The findings can only apply to other groups caught in similar circumstances with the same experiences.

It is useful to use the case study method when attempting to discover the specific reasons why some people behave in a specific way. If researchers need something more generalized, then a different method must be used.

2. No classification is possible with the case study method. This disadvantage is also due to the sample size in the case study method. No classification is possible because researchers are studying such a small unit, group, or demographic. It can be an inefficient process since the skills of the researcher help to determine the quality of the data being collected to verify the validity of a hypothesis. Some participants may be unwilling to answer or participate, while others might try to guess at the outcome to support it.

Researchers can get trapped in a place where they explore more tangents than the actual hypothesis with this option. Classification can occur within the units being studied, but this data cannot extrapolate to other demographics.

3. The case study method still offers the possibility of errors. Each person has an unconscious bias that influences their behaviors and choices. The case study method can find outliers that oppose a hypothesis fairly easily thanks to its emphasis on finding facts, but it is up to the researchers to determine what information qualifies for this designation. If the results from the case study method are surprising or go against the opinion of participating individuals, then there is still the possibility that the information will not be 100% accurate.

Researchers must have controls in place that dictate how data gathering work occurs. Without this limitation in place, the results of the study cannot be guaranteed because of the presence of bias.

4. It is a subjective method to use for research. Although the purpose of the case study method of research is to gather facts, the foundation of what gets gathered is still based on opinion. It uses the subjective method instead of the objective one when evaluating data, which means there can be another layer of errors in the information to consider.

Imagine that a researcher interprets someone’s response as “angry” when performing direct observation, but the individual was feeling “shame” because of a decision they made. The difference between those two emotions is profound, and it could lead to information disruptions that could be problematic to the eventual work of hypothesis verification.

5. The processes required by the case study method are not useful for everyone. The case study method uses a person’s memories, explanations, and records from photographs and diaries to identify interactions on influences on psychological processes. People are given the chance to describe what happens in the world around them as a way for researchers to gather data. This process can be an advantage in some industries, but it can also be a worthless approach to some groups.

If the social group under study doesn’t have the information, knowledge, or wisdom to provide meaningful data, then the processes are no longer useful. Researchers must weigh the advantages and disadvantages of the case study method before starting their work to determine if the possibility of value exists. If it does not, then a different method may be necessary.

6. It is possible for bias to form in the data. It’s not just an unconscious bias that can form in the data when using the case study method. The narrow study approach can lead to outright discrimination in the data. Researchers can decide to ignore outliers or any other information that doesn’t support their hypothesis when using this method. The subjective nature of this approach makes it difficult to challenge the conclusions that get drawn from this work, and the limited pool of units (people) means that duplication is almost impossible.

That means unethical people can manipulate the results gathered by the case study method to their own advantage without much accountability in the process.

7. This method has no fixed limits to it. This method of research is highly dependent on situational circumstances rather than overarching societal or corporate truths. That means the researcher has no fixed limits of investigation. Even when controls are in place to limit bias or recommend specific activities, the case study method has enough flexibility built into its structures to allow for additional exploration. That means it is possible for this work to continue indefinitely, gathering data that never becomes useful.

Scientists began to track the health of 268 sophomores at Harvard in 1938. The Great Depression was in its final years at that point, so the study hoped to reveal clues that lead to happy and healthy lives. It continues still today, now incorporating the children of the original participants, providing over 80 years of information to sort through for conclusions.

8. The case study method is time-consuming and expensive. The case study method can be affordable in some situations, but the lack of fixed limits and the ability to pursue tangents can make it a costly process in most situations. It takes time to gather the data in the first place, and then researchers must interpret the information received so that they can use it for hypothesis evaluation. There are other methods of data collection that can be less expensive and provide results faster.

That doesn’t mean the case study method is useless. The individualization of results can help the decision-making process advance in a variety of industries successfully. It just takes more time to reach the appropriate conclusion, and that might be a resource that isn’t available.

The advantages and disadvantages of the case study method suggest that the helpfulness of this research option depends on the specific hypothesis under consideration. When researchers have the correct skills and mindset to gather data accurately, then it can lead to supportive data that can verify ideas with tremendous accuracy.

This research method can also be used unethically to produce specific results that can be difficult to challenge.

When bias enters into the structure of the case study method, the processes become inefficient, inaccurate, and harmful to the hypothesis. That’s why great care must be taken when designing a study with this approach. It might be a labor-intensive way to develop conclusions, but the outcomes are often worth the investments needed.

Home » Pros and Cons » 12 Case Study Method Advantages and Disadvantages

12 Case Study Method Advantages and Disadvantages

A case study is an investigation into an individual circumstance. The investigation may be of a single person, business, event, or group. The investigation involves collecting in-depth data about the individual entity through the use of several collection methods. Interviews and observation are two of the most common forms of data collection used.

The case study method was originally developed in the field of clinical medicine. It has expanded since to other industries to examine key results, either positive or negative, that were received through a specific set of decisions. This allows for the topic to be researched with great detail, allowing others to glean knowledge from the information presented.

Here are the advantages and disadvantages of using the case study method.

List of the Advantages of the Case Study Method

1. it turns client observations into useable data..

Case studies offer verifiable data from direct observations of the individual entity involved. These observations provide information about input processes. It can show the path taken which led to specific results being generated. Those observations make it possible for others, in similar circumstances, to potentially replicate the results discovered by the case study method.

2. It turns opinion into fact.

Case studies provide facts to study because you’re looking at data which was generated in real-time. It is a way for researchers to turn their opinions into information that can be verified as fact because there is a proven path of positive or negative development. Singling out a specific incident also provides in-depth details about the path of development, which gives it extra credibility to the outside observer.

3. It is relevant to all parties involved.

Case studies that are chosen well will be relevant to everyone who is participating in the process. Because there is such a high level of relevance involved, researchers are able to stay actively engaged in the data collection process. Participants are able to further their knowledge growth because there is interest in the outcome of the case study. Most importantly, the case study method essentially forces people to make a decision about the question being studied, then defend their position through the use of facts.

4. It uses a number of different research methodologies.

The case study method involves more than just interviews and direct observation. Case histories from a records database can be used with this method. Questionnaires can be distributed to participants in the entity being studies. Individuals who have kept diaries and journals about the entity being studied can be included. Even certain experimental tasks, such as a memory test, can be part of this research process.

5. It can be done remotely.

Researchers do not need to be present at a specific location or facility to utilize the case study method. Research can be obtained over the phone, through email, and other forms of remote communication. Even interviews can be conducted over the phone. That means this method is good for formative research that is exploratory in nature, even if it must be completed from a remote location.

6. It is inexpensive.

Compared to other methods of research, the case study method is rather inexpensive. The costs associated with this method involve accessing data, which can often be done for free. Even when there are in-person interviews or other on-site duties involved, the costs of reviewing the data are minimal.

7. It is very accessible to readers.

The case study method puts data into a usable format for those who read the data and note its outcome. Although there may be perspectives of the researcher included in the outcome, the goal of this method is to help the reader be able to identify specific concepts to which they also relate. That allows them to discover unusual features within the data, examine outliers that may be present, or draw conclusions from their own experiences.

List of the Disadvantages of the Case Study Method

1. it can have influence factors within the data..

Every person has their own unconscious bias. Although the case study method is designed to limit the influence of this bias by collecting fact-based data, it is the collector of the data who gets to define what is a “fact” and what is not. That means the real-time data being collected may be based on the results the researcher wants to see from the entity instead. By controlling how facts are collected, a research can control the results this method generates.

2. It takes longer to analyze the data.

The information collection process through the case study method takes much longer to collect than other research options. That is because there is an enormous amount of data which must be sifted through. It’s not just the researchers who can influence the outcome in this type of research method. Participants can also influence outcomes by given inaccurate or incomplete answers to questions they are asked. Researchers must verify the information presented to ensure its accuracy, and that takes time to complete.

3. It can be an inefficient process.

Case study methods require the participation of the individuals or entities involved for it to be a successful process. That means the skills of the researcher will help to determine the quality of information that is being received. Some participants may be quiet, unwilling to answer even basic questions about what is being studied. Others may be overly talkative, exploring tangents which have nothing to do with the case study at all. If researchers are unsure of how to manage this process, then incomplete data is often collected.

4. It requires a small sample size to be effective.

The case study method requires a small sample size for it to yield an effective amount of data to be analyzed. If there are different demographics involved with the entity, or there are different needs which must be examined, then the case study method becomes very inefficient.

5. It is a labor-intensive method of data collection.

The case study method requires researchers to have a high level of language skills to be successful with data collection. Researchers must be personally involved in every aspect of collecting the data as well. From reviewing files or entries personally to conducting personal interviews, the concepts and themes of this process are heavily reliant on the amount of work each researcher is willing to put into things.

These case study method advantages and disadvantages offer a look at the effectiveness of this research option. With the right skill set, it can be used as an effective tool to gather rich, detailed information about specific entities. Without the right skill set, the case study method becomes inefficient and inaccurate.

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• Open access
• Published: 26 June 2024

Characterization of bacterial and viral pathogens in the respiratory tract of children with HIV-associated chronic lung disease: a case–control study

• Prince K. Mushunje 1   na1 ,
• Felix S. Dube 1 , 2   na1 ,
• Courtney Olwagen 3 ,
• Shabir Madhi 3 , 4 ,
• Jon Ø Odland 5 , 6 , 7 ,
• Rashida A. Ferrand 8 , 9 ,
• Mark P. Nicol 10 ,
• Regina E. Abotsi 1 , 11 &

The BREATHE study team

BMC Infectious Diseases volume  24 , Article number:  637 ( 2024 ) Cite this article

Metrics details

Introduction

Chronic lung disease is a major cause of morbidity in African children with HIV infection; however, the microbial determinants of HIV-associated chronic lung disease (HCLD) remain poorly understood. We conducted a case–control study to investigate the prevalence and densities of respiratory microbes among pneumococcal conjugate vaccine (PCV)-naive children with (HCLD +) and without HCLD (HCLD-) established on antiretroviral treatment (ART).

Nasopharyngeal swabs collected from HCLD + (defined as forced-expiratory-volume/second < -1.0 without reversibility postbronchodilation) and age-, site-, and duration-of-ART-matched HCLD- participants aged between 6–19 years enrolled in Zimbabwe and Malawi (BREATHE trial-NCT02426112) were tested for 94 pneumococcal serotypes together with twelve bacteria, including Streptococcus pneumoniae (SP), Staphylococcus aureus (SA), Haemophilus influenzae (HI), Moraxella catarrhalis (MC), and eight viruses, including human rhinovirus (HRV), respiratory syncytial virus A or B, and human metapneumovirus, using nanofluidic qPCR (Standard BioTools formerly known as Fluidigm). Fisher's exact test and logistic regression analysis were used for between-group comparisons and risk factors associated with common respiratory microbes, respectively.

A total of 345 participants (287 HCLD + , 58 HCLD-; median age, 15.5 years [IQR = 12.8–18], females, 52%) were included in the final analysis. The prevalence of SP (40%[116/287] vs. 21%[12/58], p  = 0.005) and HRV (7%[21/287] vs. 0%[0/58], p  = 0.032) were higher in HCLD + participants compared to HCLD- participants. Of the participants positive for SP (116 HCLD + & 12 HCLD-), 66% [85/128] had non-PCV-13 serotypes detected. Overall, PCV-13 serotypes (4, 19A, 19F: 16% [7/43] each) and NVT 13 and 21 (9% [8/85] each) predominated. The densities of HI (2 × 10 4 genomic equivalents [GE/ml] vs. 3 × 10 2 GE/ml, p  = 0.006) and MC (1 × 10 4 GE/ml vs. 1 × 10 3 GE/ml , p  = 0.031) were higher in HCLD + compared to HCLD-. Bacterial codetection (≥ any 2 bacteria) was higher in the HCLD + group (36% [114/287] vs. (19% [11/58]), ( p  = 0.014), with SP and HI codetection (HCLD + : 30% [86/287] vs. HCLD-: 12% [7/58], p  = 0.005) predominating. Viruses (predominantly HRV) were detected only in HCLD + participants. Lastly, participants with a history of previous tuberculosis treatment were more likely to carry SP (adjusted odds ratio (aOR): 1.9 [1.1 -3.2], p  = 0.021) or HI (aOR: 2.0 [1.2 – 3.3], p  = 0.011), while those who used ART for ≥ 2 years were less likely to carry HI (aOR: 0.3 [0.1 – 0.8], p  = 0.005) and MC (aOR: 0.4 [0.1 – 0.9], p  = 0.039).

Children with HCLD + were more likely to be colonized by SP and HRV and had higher HI and MC bacterial loads in their nasopharynx. The role of SP, HI, and HRV in the pathogenesis of CLD, including how they influence the risk of acute exacerbations, should be studied further.

Trial registration

The BREATHE trial (ClinicalTrials.gov Identifier: NCT02426112 , registered date: 24 April 2015).

Peer Review reports

In 2019, over 2.8 million children and adolescents were living with HIV globally, 90% in sub-Saharan Africa [ 1 ]. Respiratory infections remain the most common manifestation of HIV among these children and adolescents [ 2 , 3 ]. The scale-up of antiretroviral therapy (ART) has increased survival so that growing numbers of children are entering adulthood. In addition, ART has resulted in a reduction in the rate of respiratory disorders, including tuberculosis and lymphocytic interstitial pneumonitis [ 4 , 5 , 6 , 7 ]. However, studies in sub-Saharan Africa revealed that approximately 30% of HIV-infected older children experience chronic respiratory symptoms, including chronic cough and reduced tolerance to exercise, which often leads to presumptive tuberculosis treatment [ 8 ]. The clinical and radiological picture of this chronic lung disease is consistent with small airway disease, predominantly constrictive obliterative bronchiolitis [ 9 ].

The pathogenesis of this condition is incompletely understood. It is speculated that HIV-induced chronic inflammation and dysregulated immune activation may play a role [ 10 , 11 , 12 ]. A previous study of older children with HIV-associated chronic lung disease (HCLD) conducted by our group demonstrated that there was increased inflammatory activation in children with HCLD (HCLD +) compared to their HIV-infected counterparts without HCLD (HCLD-) [ 13 ]. In the same cohort, there was an association between the carriage of specific bacteria in the nasopharynx and HCLD [ 14 ]. Specifically, we observed that older children with HCLD were more likely to be colonized with Streptococcus pneumoniae (SP) and Moraxella catarrhalis (MC) than their HCLD- counterparts [ 14 ]. The study utilized bacterial culture, which is limited by viability and a narrow spectrum of culturable bacterial species. Although we observed that SP was associated with HCLD, we did not investigate the specific serotypes that may be involved in this condition, which is important to inform pneumococcal immunization. Furthermore, the prevalence of respiratory viruses was also not studied.

Viruses facilitate bacterial infections in the host through various mechanisms, including damaging the respiratory epithelium, modifying the immune response, and altering cell membranes [ 15 ]. Coinfection of viruses and bacteria leads to increased bacterial load, thus making individuals more susceptible to complications related to upper respiratory tract infections [ 16 ]. Prior to COVID-19, respiratory syncytial virus, influenza virus and human rhinovirus (HRV) were the most common causative agents of upper respiratory infection and have been linked to exacerbations of COPD [ 17 , 18 ], asthma development [ 17 ], and severe bronchiolitis in children [ 19 , 20 , 21 ].

To overcome these limitations, we investigated the prevalence of respiratory pathogens in both HCLD + and HCLD- participants using real-time quantitative polymerase chain reaction (qPCR) to detect and quantify a large number of bacterial and viral targets and elucidate common SP serotypes (94 serotypes). We also assessed clinical and sociodemographic factors associated with microbial carriage and density.

Materials and methods

Study design, population, and setting.

This case–control study was nested within the BREATHE trial (ClinicalTrials.gov Identifier: NCT02426112, registered date: 24 April 2015) investigating whether azithromycin therapy could improve lung function and reduce the risk of exacerbations among children with HCLD [ 22 ]. BREATHE was a two-site, double-blinded, placebo-controlled, individually randomized trial conducted in Harare (Zimbabwe) and Blantyre (Malawi). The study setting, population, and trial procedures are described elsewhere [ 22 , 23 , 24 ]. Briefly, we enrolled perinatally HIV-infected participants aged 6 – 19 years with HCLD. HCLD was defined as a forced expiratory volume in 1 s (FEV1) z score < -1, with no reversibility (< 12% improvement in FEV1 after salbutamol 200 µg inhaled using a spacer) [ 22 ]. A group of perinatally HIV-infected children without HCLD (FEV1 z score > 0) was also recruited at the same time as the enrollment of trial participants using frequency matching for site, sex, age, and duration of ART to serve as a comparison group for pathogenesis studies. Both groups were on ART for at least six months. All participants were most likely not vaccinated due to the introduction of PCV13 in 2012 in Zimbabwe [ 25 ] and in Malawi in 2011 [ 26 ], making them ineligible for vaccination at that time because of their older age. Swabs were collected between June 1, 2016 and September 31, 2019. Sociodemographic data and clinical history were recorded through an interviewer-administered questionnaire.

Nasopharyngeal swab collection

Nasopharyngeal swabs were collected at baseline from all participants using sterile flocked flexible nylon swabs (Copan Italia, Brescia, Italy). Swabs were immediately immersed in 1 mL PrimeStore® Molecular Transport Medium (MTM) (Longhorn Vaccines & Diagnostics LLC, Bethesda, USA), transported on ice and stored at -80 °C at the diagnostic laboratory at each site. PrimeStore® MTM was used because it is a medium optimized for transporting and storing samples for molecular analyses; it also inactivates potential pathogens and stabilizes nucleic acids [ 27 ]. The samples were batched and transported on dry ice to Cape Town, South Africa, where they were stored at -80 °C until further processing.

Total nucleic acid extraction

Total nucleic acid (TNA) extraction for microbial identification was conducted on NP swabs stored in Primestore® MTM. Briefly, the samples were thawed and vortexed for 10 s, and 400 µl aliquots were transferred into ZR BashingBead™ Lysis Tubes containing 0.5 mm beads (catalog no. ZR S6002–50, Zymo Research Corp., Irvine, CA, United States) for the mechanical lysis steps. Lysis was conducted on a Qiagen Tissue lyser LTTM (Qiagen, FRITSCH GmbH, Idar-Oberstein, Germany) for 5 min at 50 Hz, followed by centrifugation (Eppendorf F-45–30-11, Merck KgaA, Darmstadt, Germany) for 1 min at 10,000 rpm (10,640 g). The supernatants (250 µl) were extracted using the QIAsymphony® DSP Virus/Pathogen Kit (Qiagen GmbH, Hilden, Germany) on the QIAsymphony SP/AS instrument (Qiagen GmbH, Hilden, Germany) following the manufacturer’s instructions. The total nucleic acid was eluted in 70 µl DNA elution buffer into the Elution Microtube (Qiagen GmbH, Hilden, Germany) and immediately stored at -80 °C until further analysis.

Real-time qPCR using the biomark HD system (Fluidigm assay)

Nanofluidic qPCR testing was performed at the WITS-VIDA Research Unit, Witwatersrand University, Johannesburg, South Africa as previously described [ 28 , 29 ]. Briefly, all extracts were tested for 94 SP serotypes together with 12 bacterial species (SP, HI, MC, Staphylococcus aureus [SA], Neisseria lactamica , Neisseria meningitidis , Streptococcus pyogenes , Bordetella pertussis , Bordetella holmesii , Klebsiella pneumoniae , Acinetobacter baumanii and Streptococcus oralis ), 6 HI serotypes and 8 viruses (respiratory syncytial virus A and B, human rhinovirus, influenza A and B, human parainfluenza 1 and 3, and human metapneumovirus). Furthermore, all samples were previously cultured for SP, HI, MC and SA as described elsewhere [ 14 ]. These microbial targets (Table S1) included on the nanofluidic panel might be associated with HCLD and are the most frequent pathobionts in the nasopharynx. A detailed list of these microbial targets can be found in the supplementary material (Table S1). For SP, positive samples were defined as those with a Cycle of quantification (Cq) value ≤ 36 for each serotype-specific qPCR target and positive for both Lyt A and Pia B. Negative samples were defined as those with Cq values ≥ 36 for each target.

The bacterial or serotype densities were determined following the method outlined by Downs et al . [ 28 ]. Briefly, culture controls and synthetic double-stranded DNA (dsDNA) template gene fragments (gBlocks) were included in the assay as external calibrators, reported as copy numbers or gene equivalents, respectively. A DNA library was prepared for the targeted pneumococcal serotypes or other bacterial species at an average concentration ranging from 10 3 to 10 4  CFU/ml. For assay-sets meeting the defined efficiency criteria (90–110%), the relative quantification of bacterial density was determined by extrapolating using the linear equation derived from standard curves of the calibrators (control strains and gBlocks with known densities), employing the equation and reported as log 10 genomic equivalents/ml:

Data management and statistical analysis

Clinical and sociodemographic data were electronically captured using Google NexusTM tablets (Google, Mountain View, CA, USA) running OpenDataKit software, managed on Microsoft Access databases (Microsoft, Redmond, WA, USA) and analyzed using Stata ((StataCorp, College Station, TX). Comparisons between groups were performed with the Student T test or Mann–Whitney U test for continuous data and chi-squared or Fisher’s exact tests for categorical data where appropriate with no further adjustment of multiplexity. Multivariate logistic regression, adjusting for age category, duration of ART, site, sex, height-for-age, HIV viral suppression, history of TB treatment, Medical Research Council dyspnea score and ART regimen, was used to investigate the factors associated with microbial carriage and density. The following were excluded from the multivariate model because of colinearity: Enrollment BMI-for-age z score, weight-for-age z score, and CD4 count. A p value of less than 0.05 was considered statistically significant.

Clinical and sociodemographic characteristics

The study included 345 participants, HCLD + ( n  = 287) and HCLD- ( n  = 58), with a median age [IQR] of 15.5 (12.8 – 18.0) years and 52% (180/345)] female (Table  1 ). The median BMI-for-age- z score for the HCLD + group was lower compared to the HCLD- group (-1.1 vs -0.4), p  < 0.001. A higher proportion of the participants from the HCLD + group were previously treated for tuberculosis (31% vs. 12%, p  = 0.001), stunted (49% vs . 29%, p  = 0.009) and underweight (52% vs . 14%, p  < 0.001) compared to the HCLD- group. A higher number of HCLD + participants were on a second-line ART (protease inhibitor-based) regimen (25% vs. 10%, p  = 0.01) compared to HCLD-. Ten percent of HCLD + participants compared to HLCD- (2%) participants had an MRC dyspnea score of 3 or above. None of the participants reported smoking.

Prevalence and densities of selected nasopharyngeal microbes in participants with and without HCLD

The prevalence and median densities of selected microbes detected in the nasopharynx of HCLD + and HCLD- participants are summarized in Table  2 . The prevalence of SP colonization was significantly higher in the HCLD + group (40%, 116/278) compared to the HCLD- group (21%, 12/58; p  = 0.005). However, there were no statistically significant differences between the two groups in colonization prevalence of pneumococcal serotypes covered by the 13-valent pneumococcal conjugate vaccine (PCV13 vaccine types, VT) or those not covered (non-vaccine types, NVT).

Of the 128 participants colonized with SP (116 HCLD + , 12 HCLD-), 66% (85/128) carried NVT serotypes, while 34% (43/128) carried PCV13 VT serotypes (Fig.  1 ). A total of 150 pneumococcal serotypes was detected in the 128 participants colonized with SP including 134 serotypes from the HCLD + group and 16 from the HCLD- group (Fig.  1 ). Multiple serotypes were detected in 14% (16/116) of HCLD + participants and 17% (2/12) of the HCLD- participants colonized with SP.

Pneumococcal serotypes recovered from nasopharyngeal swabs of HCLD + and HCLD- participants. Abbreviations: PCV, polysaccharide-conjugated vaccine; n, number of swabs serotyped using the fluidigm assay from the HCLD + group ( n  = 116) and HCLD- group ( n  = 12). Denominator for prevalence is the total number of SP serotypes grouped into PCV 13 and non-PCV 13 serotypes. Others* HCLD + group: PCV13 serotype [3 (0.7%), 5 (0.7%), 6B (0.7%), 9AV (0.7%), 4 (5.2%), Others**: HCLD + group non-PCV 13 serotype [18B (0.7%), 19 atypical (0.7%), 20 (0.7%), 23B (1.5%), 27 (0.7%), 25AF/38 (0.7%), 45 (0.7%), 29 (0.7%), 31 (0.7%), 33C (1.5%), 38 (0.7%)]; HCLD- group non-PCV13 serotype [10A (6.3%), 10B (6.3%), 22A (6.3%), 33B (6.3%)]. 15: HCLD + group non-PCV 13 serotype [15AF (3.7%), 15BC (2.2%), 15like (1.5%)]; HCLD- group non-PCV13 serotype [15like (6.3%)]. 11: HCLD + group non-PCV 13 serotype [11AD [3.7%), 11E (1.5%)]; HCLD- group non-PCV13 serotype [11E (6.3%)]

NVT serotypes predominated over PCV13 VTs in both groups, accounting for 69% (93/134) in HCLD + and 81% (13/16) in HCLD- (Fig.  1 ). While not statistically significant ( p  = 0.398), the prevalence of PCV13 VT serotypes trended higher in HCLD + (31%, 41/134) compared to HCLD- (19%, 3/16).

The most common PCV13 VT serotypes in both groups were 4 (16%, 7/44), 19F (16%, 7/44), 19A (16%, 7/44), and 18C (14%, 6/44). The predominant NVTs were 13 and 21 (8% each, 8/106). There were no statistically significant differences in serotype-specific colonization prevalence between HCLD + and HCLD- groups. Likewise, the median densities of the composite NVT and PCV13 VT serotypes did not differ significantly between the two groups (Figure S1). The overall median serotype density across all samples was 8.8 genomic equivalents/ml.

There were no significant differences in the colonization prevalence of any other bacteria tested. Despite there being no difference in the colonization prevalence for both HI and MC, the mean log density was higher in the HCLD + ( 2 × 10 4− gene equivalents [GE]/ml & 1 × 10 4 GE/ml) compared to the HCLD- (3 × 10 2 GE/ml; p  = 0.006 & 0.5 × 10 3 GE/ml; p  = 0.031,) groups, respectively (Table  2 ). There was no significant difference in the mean log densities between the groups for the other tested bacteria. There was a low prevalence of the viruses detected amongst the group, with HRV (7% [21/287] vs . 0% [0/58], p  = 0.032) detected in the HCLD + group only. The bacterial species Klebsiella pneumoniae , Neisseria meningitidis , Actinobacter baumanii , Bordetella pertussis/holmesii , and viruses influenza A, influenza B, human parainfluenza type 1 & 3, and human metapneumovirus were not detected in any participants.

Nasopharyngeal bacterial and viral co-colonization in participants with and without HCLD

Bacterial and viral co-colonization detected in HIV-infected participants with or without HCLD is summarized in Table  3 and Table S2. Bacterial detection (any) was significantly higher in HCLD + (61% [175/287]) than in HCLD- (43.1% [25/58]) ( p  = 0.013). Moreover, the concurrent carriage of multiple bacterial species was higher in the HCLD + group (35.9% [103/287]) than in the HCLD- group (19% [11/58]) ( p  = 0.014). The most frequent bacteria detected concurrently with SP included SP were HI (HCLD + : 30% [87/287]) vs. HCLD-: 12.1% [7/58], p  = 0.013) and MC (HCLD + : 23.3% [67/287] vs. HCLD-: 12.1% [7/58], p  = 0.078). Viruses were detected only in the HCLD + group (8% [23/287]), with viral and bacterial co-colonization reported in 6.6% (19/287) of HCLD + participants. To determine whether the concurrent detection of bacteria in HCLD + participants was due to a true interaction or simply by chance, we compared the observed and expected values based on marginal probabilities. The results showed that the co-colonization of SP with HI (Observed [86/287] vs Expected [50.1/287], p  < 0.001); SP with MC (Observed [60/287] vs Expected [30.3/287]], p  < 0.001) and MC with HI (Observed [54/287] vs Expected [32.4/287], p  < 0.001]) was a result of true interactions with HCLD (Table S3).

Factors associated with carriage of selected bacteria at baseline in participants with HCLD

The results of the univariate and multivariate analyses of the clinical and sociodemographic factors associated with the carriage of SP and SA are displayed in Table  4 , while those for HI and MC are shown in Table  5 . On multivariate analysis, participants previously treated for TB (adjusted odds ratio were more likely to carry SP (aOR): 1.9 [1.1 -3.2], p  = 0.021) or HI (aOR: 2.0 [1.2 – 3.3], p  = 0.011). Participants on ART for ≥ 2 years (aOR: 0.3 [0.1 – 0.8], p  = 0.005) and living in Zimbabwe (aOR: 0.5 [0.3 – 0.9], p = 0.026) were less likely to carry HI (Table  5 ). Similarly, MC carriage was less likely in participants who had been on ART for ≥ 2 years (aOR: 0.4 [0.1 – 0.9], p  = 0.039) (Table  5 ). Participants who were attending school were more likely to carry MC (aOR: 2.5 [1.0 -6.4], p  = 0.050) (Table  5 ).

In this study, we used quantitative PCR to determine the prevalence and density of bacterial and viral carriage in HIV-infected African children. As previously shown [ 14 ], microbial colonization was more frequently detected in HCLD + than HCLD- participants, with the former more likely to carry SP or HRV. Strikingly, viruses (predominantly HRV) were detected only in HCLD + children. Moreover, we observed that HCLD + participants had a higher HI and MC density than their HCLD- counterparts. The prevalence and densities of all SP serotypes tested were similar between the two groups, with more of the recovered SP serotypes (79%) being non-PCV 13. Study participants with a history of previous tuberculosis treatment were more likely to carry SP or HI, while those who used ART for ≥ 2 years were less likely to carry HI and MC. Furthermore, those living in Zimbabwe were less likely to carry HI.

The prevalence of HI in the current study in both HCLD + (43%) and HCLD- (33%) participants was higher than that observed in our previous study of the same cohort by Abotsi et al. [ 14 ] (12% and 5%, respectively). Similar studies conducted in India [ 30 ] and Zambia [ 31 ] in HIV-infected children observed similar prevalence (26% and 29%, respectively) to our current study (33%). The discrepancy in results could be attributed to the more sensitive PCR detection method used in our study compared to the culture method employed in previous studies as well as age and pathological differences.

Furthermore, HCLD + participants showed a higher density of HI than their counterparts. Previous studies have associated HI carriage in participants with other lung diseases, including asthma [ 32 ], bronchiectasis [ 33 , 34 ] and chronic obstructive pulmonary disease [ 35 , 36 , 37 ]. HI has been identified as a biomarker for predicting the response to azithromycin treatment in adults with persistent uncontrolled asthma [ 32 ]. It has also been associated with negative outcomes in children suffering from respiratory viral infections [ 38 ], including hospitalization among RSV-positive children [ 39 ]. The bacterium’s ability to invade host epithelial cells, evade host defense mechanisms, form biofilms and survive as an intracellular pathogen contributes to its pathogenic nature [ 40 ], which may suggest an important role that it may play in HCLD + pathogenesis. However, further studies are needed to further elucidate this observation.

The prevalence of carriage of SA in this study (HCLD + [6%] and HCLD- [5%]) was markedly lower than that observed using bacterial culture in the same cohort (HCLD + [23%] and HCLD- [19%]) [ 14 ]. This difference in prevalence may be related to the efficiency of the nucleic acid extraction method used. The extraction of nucleic acids requires extended and vigorous lysis steps for some bacterial species (gram-positive such as SA) compared to others (gram-negative such as HI and MC) [ 41 ]; however, for this study, although we used a rigorous extraction protocol incorporating bead-beating, the inherent complexity and resilience of the SA bacterial cell wall may have contributed to the low yield. This underscores the need for tailored approaches and ongoing refinement of extraction methods to include enzymatic lysis alternatives such as lysostaphin or lysozyme in NP swabs DNA extraction for enhanced yield. Additionally, carriage may have been influenced by the efficiency of the annealing of PCR primers [ 42 ].

High SP, HI and MC density in the nasopharynx has been associated with respiratory infections in children [ 43 , 44 ]. This is consistent with our study, where a higher HI and MC density was observed in HCLD + participants than in their HCLD- counterparts. The HCLD + participants may have a chronic lung infection, as evidenced by the isolation of bacteria from their sputum in our previous study [ 14 ]. Microbiota dominated by Haemophilus , Moraxella or Neisseria species are associated with chronic lung diseases, including chronic obstructive pulmonary disease and asthma [ 45 , 46 , 47 , 48 ]. Bhadriraju et al . [ 40 ] observed that HIV-infected children with a sputum bacteriome dominated by Haemophilus , Moraxella or Neisseria species were 1.5 times more likely to have HCLD than those with Streptococcus or Prevotella spp. [ 40 ]. These bacterial genera were also associated with enhanced inflammatory effects [ 40 ]. Interestingly, we detected Neisseria species ( N. lactamica ) in HCLD + participants only. Taken together, these findings support the important role of HI and MC in HCLD.

Our observation of a higher SP carriage in the HCLD + group than in the HCLD- group is consistent with our culture-based study of the same cohort [ 14 ]. Furthermore, SP carriage in the HCLD- participants (21%) is comparable to studies of HIV-infected children in South Africa (22.2%) [ 49 ] and Cambodia (17.6%) [ 42 , 50 ]. Nevertheless, the prevalence is higher than that observed in children living with HIV in Ethiopia (10.3%) [ 51 ] and lower than that in studies from Ghana (27.1%) and Tanzania (81%) [ 52 ]. The differences in SP prevalence observed between studies can again be related to differences in the age of participants—younger children have a higher carriage prevalence[ 31 , 52 ], socioeconomic factors [ 53 ] and the geographical location of the participants.

The prevalence of PCV 13 serotypes and densities in both study groups (HCLD + : 30.4% and HCLD-: 16.7%) did not differ significantly. The most prevalent PCV-13 serotypes were serotypes 4 (15.9%), 19F (15.9%), 19A (15.9%) and 18C (14%). A study of HIV-infected children in Malawi [ 54 ] reports 19F and 6A among the most predominant serotypes. The relatively high prevalence of serotype 19A in PCV-vaccinated children has been suggested by Kamng’ona et al . [ 54 ] to occur due to an inversion in the rmlD gene at the CPS locus. This may downregulate the rmlD gene on the CPS locus, causing an altered 19A capsule [ 55 ] that is not recognized by the PCV vaccine.

There was a higher prevalence of non-PCV13 serotypes in both the HCLD + (69% [93/134]) and HCLD- (81% [13/16]) compared to PCV13 serotypes. Similar findings were reported in Malawi [ 54 ], Nigeria [ 56 ] and Ghana [ 57 ]. We assume that community herd protection from vaccinated siblings, neighbors, and playmates may be responsible for the low prevalence of vaccine-type serotypes in our cohort. Continued surveillance of SP and its non-PCV 13 serotypes is warranted to inform future vaccine formulation and roll-out strategies, especially in this vulnerable population.

There is evidence suggesting a relationship between SP and other pathogens co-colonizing the nasal and pharyngeal mucosae [ 58 ]. Our analysis, based on expected values, revealed a positive positive association between SP with HI and MC carriage in participants with CLD, which is consistent with previous reports by Madhi et al . [ 59 ] in HIV-infected South African children. A similar positive association between SP and HI was observed in a study of HIV-infected children in India (median age was 6.5 years, IQR [4.5 – 9]) [ 30 ]. HI modulates the expression of SP genes in biofilms primarily by upregulating the type IV pilus structural protein, which is essential for adhesion and stability [ 60 , 61 ]. Polymicrobial infections involving these microbes and others have been demonstrated to exacerbate higher disease severity and increased tolerance to antimicrobials [ 62 , 63 ]. Further studies are warranted to comprehensively understand the mechanisms underlying these interactions and their implications for CLD + pathogenesis and treatment strategies.

The major risk factors associated with the development of pneumococcal disease are demographic (age and sex) and immune status (CD4 count and HIV viral load) [ 64 ]. We observed no association between these common factors and most bacterial species, including SP. This is supported by previous studies that reported a lack of association between CD4 count and the prevalence of pneumococcal carriage [ 31 , 65 , 66 ]. A longer period on an ART regimen (two years or more) was associated with reduced carriage of MC and HI. Similar results were obtained from a study among HIV-infected adults in Brazil [ 67 ]. ART therapy could help reduce the risk of infection and carriage through immune reconstitution [ 67 ].

The presence of viruses increases bacterial adherence, and the difference in the prevalence of viruses in HCLD +  vs HCLD- children may partially explain the increased HI and MC densities we observed. Our findings are consistent with a study by Binks et al . [ 44 ], who reported an increased SP and HI density during coinfection with respiratory viruses within the nasopharynx of Australian children with otitis media. However, no significant difference in the bacterial load was detected in SP from the HCLD + and HCLD- groups. Viruses expose the host to bacterial infection through various mechanisms, including the destruction of the respiratory epithelium, modulation of innate defenses and alteration of cell membranes, which facilitates bacterial adherence [ 15 ]. Ishizuka et al . [ 68 ], in their in vitro studies, observed increased SP adherence to epithelial cells after infection with HRV. They suggested that this observation may explain why pneumonia develops following an HRV infection [ 68 ]. Interestingly, we found no association between any virus (HRV, RSVA and RSVB) and the prevalence or density of carriage of SP or other bacterial species tested. This contrasts with several in vitro and in vivo studies that have suggested that respiratory virus infection increases bacterial adherence and subsequent bacterial superinfection within the nasopharynx [ 68 , 69 , 70 ]. This discrepancy may be explained by the few viruses we detected due to the limited sample size, especially in the HCLD- group.

HRV is responsible for most upper respiratory tract infections and their complications, including bronchitis [ 15 ]. In a study of HIV-infected children in India [ 71 ], HRV was the most prevalent virus in these participants when asymptomatic. The GABRIEL multicenter case‒control study in Africa and Asia also found HRV in healthy control groups of pneumonia childhood studies [ 72 ]. In contrast, our study detected HRV (7%) in only HCLD + participants. Notably, RSV infection was uncommon, consistent with previous studies conducted in Africa and Asia (PERCH case‒control studies [ 73 ] and DCHS case‒control studies [ 74 ]), which showed its infrequency except during acute respiratory infections.

In conclusion, our study findings indicate that HCLD + participants were more commonly colonized by any of the bacteria tested compared to HCLD- participants. Specifically, the HCLD + group had a higher prevalence of carriage of SP bacteria, as well as a higher density of MC and HI bacteria. Interestingly, viruses, particularly HRV, were detected only in the HCLD + group. Moreover, our research revealed that previous treatment for tuberculosis was positively associated with the carriage of HI or SP bacteria among study participants. On the other hand, being a female participant was found to be less likely to be associated with SA carriage. Additionally, longer periods on the ART regimen were associated with reduced carriage of HI or MC bacteria. Our study sheds light on the quantitative information on microbial carriage and nasopharyngeal carriage of viruses and serotypes of HI and SP in children with HCLD + . The limitations of our study included a small sample size of HCLD participants, potentially impacting the statistical power and generalizability of our findings. Additionally, our statistical analysis did not correct for multiplexity. While one approach to address multiplexity is adjusting the p-value threshold to α = 0.05 divided by the number of tests conducted, this method may result in a considerable reduction in the number of statistically significant findings. Therefore, there is a need for more comprehensive studies in this population to further investigate the role of SP, HI, MC, and HRV in the pathogenesis of CLD and the underlying mechanisms behind these bacterial associations.

Availability of data and materials

The datasets used and analyzed during the current study are available from Felix Dube ([email protected]) on reasonable request and ethical approval.

Abbreviations

Antiretroviral therapy

Azithromycin

HIV-associated chronic lung disease

HIV-infected participants with HIV-associated chronic lung disease

HIV-infected participants without HIV-associated chronic lung disease

Nasopharyngeal

Haemophilus influenza

Moraxella catarrhalis

Staphylococcus aureus

Streptococcus pneumoniae

• Human rhinovirus

Respiratory syncytial virus

Non PCV 13 vaccine serotypes

PCV 13 vaccine serotypes

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Acknowledgements

We would like to acknowledge the BREATHE trial participants, their families, and the study team. We would also like to thank the staff of the Division of Medical Microbiology and the Department of Molecular and Cell Biology, particularly members of Dube Lab and the UCT community, for providing all the resources required for the project and training whenever needed. We are grateful to Lara Van Der Merwe of the WITS-VIDA research team for assistance with the Fluidigm assay and data analysis.

BREATHE Study Team Members: Prince K. Mushunje 1 , Msc; Felix S. Dube 1,2 , PhD; Jon Ø Odland 5,6,7 , PhD; Rashida A Ferrand 8,9 , PhD; Mark P. Nicol 10 , PhD; Regina E. Abotsi 1,11 , Tsitsi Bandason 8 , MSc; Ethel Dauya 8 , MPH; Tafadzwa Madanhire 8 , PhD; Elizabeth L. Corbett 9, 17 , PhD; Katharina Kranzer 8,9 , PhD; Edith D. Majonga 8 , PhD; Victoria Simms 8,12 ; PhD, Andrea M Rehman 8,12 ; PhD; Helen A.Weiss 12 , PhD; Hilda Mujuru 13 , MSc; Dan Bowen 14 , MSc; Louis-Marie Yindom 14 , PhD; Sarah L. Rowland-Jones 14 , DM; Trond Flaegstad 15 , PhD; Tore J. Gutteberg 15 , PhD; Jorunn Pauline Cavanagh 15 , PhD; Trym Thune Flygel 15 , MD; Evegeniya Sovarashaeva 15 , PhD; Jessica Chikwana 16 , MBBS; Gugulethu Newton Mapurisa 16 , MBBS; Carmen Gonzalez-Martinez 16, 17 , MSc; Robina Semphere 16 , MBBS; Brewster Wisdom Moyo 17 , MSc; Lucky Gift Ngwira 17 , MPH; 18 Slindile Mbhele, MSc

Affiliations: 1 Department of Molecular and Cell Biology & Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa; 2 School of Medicine, University of Lusaka, Lusaka, Zambia; 5 Nord University, Faculty of Biosciences and Aquaculture, Bodø, Norway; 6 International Research Laboratory for Reproductive Ecotoxicology (IL RET), The National Research University Higher School of Economics, Moscow, Russia; 7 School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa; 8 Biomedical Research and Training Institute, Harare, Zimbabwe; 9 Clinical Research Department, London School of Hygiene and Tropical Medicine, London, United Kingdom; 10 Marshall Centre, Division of Infection and Immunity, School of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia; 11 Department of Pharmaceutical Microbiology, School of Pharmacy, University of Health and Allied Sciences, Ho, Ghana; 12 MRC International Statistics and Epidemiology Group, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom; 13 Department of Paediatrics, University of Zimbabwe, Harare, Zimbabwe; 14 Nuffield Department of Medicine, University of Oxford, Oxford, UK; 15 Faculty of Health Sciences, UiT, The Arctic University of Norway, Tromsø, Norway; and Department of Paediatrics, University Hospital of North Norway, Tromsø, Norway); 16 Department of Paediatrics and Child Health, University of Malawi College of Medicine, Blantyre, Malawi; 17 Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi; 18 Division of Medical Microbiology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa

This parent study was funded by the Global Health and Vaccination Research (GLOBVAC) Programme of the Medical Research Council of Norway. This substudy was funded by the Royal Society through the Future Leaders African Independent Research award and the National Institute for Health Research (NIHR) Global Health Research Unit on Mucosal Pathogens using UK aid from the UK Government (Project number 16/136/46). The views expressed are those of the authors and not necessarily those of the NIHR, the Department of Health and Social Care. PM received funding from the UCT Postgraduate Funding, UCT's Building Research Active Academic Staff (B.R.A.A.S.) award, the Molecular and Cell Biology_Equity Development Programme scholarship, and the Dube-lab scholarship. REA acknowledges the financial support of the Swedish International Development Cooperation Agency (SIDA) through the Organization of Women in Science for the Developing World (OWSD) PhD Fellowship, Margaret McNamara Education Grants and L'Oréal UNESCO For Women in Science PhD Fellowship. MN is supported by an Australian National Health and Medical Research Council Investigator Grant [APP1174455]. FSD is supported by the National Research Foundation of South Africa (112160), Future Leaders – African Independent Research (FLAIR) Fellowship, the National Institute for Health Research (NIHR) Global Health Research Unit on Mucosal Pathogens using UK aid from the UK Government, the University of Cape Town and the Allergy Society of South Africa (ALLSA). RAF is funded by the Wellcome Trust (206316_Z_17_Z). The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Prince K. Mushunje and Felix S. Dube contributed equally to this work.

Authors and Affiliations

Department of Molecular and Cell Biology & Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa

Prince K. Mushunje, Felix S. Dube & Regina E. Abotsi

School of Medicine, University of Lusaka, Lusaka, Zambia

Felix S. Dube

South Africa Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Courtney Olwagen & Shabir Madhi

Infectious Diseases and Oncology Research Institute, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Shabir Madhi

Faculty of Biosciences and Aquaculture, Nord University, Bodø, Norway

Jon Ø Odland

International Research Laboratory for Reproductive Ecotoxicology (IL RET), The National Research University Higher School of Economics, Moscow, Russia

School of Health Systems and Public Health, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa

Biomedical Research and Training Institute, Harare, Zimbabwe

Rashida A. Ferrand

Clinical Research Department, London School of Hygiene and Tropical Medicine, London, UK

Marshall Centre, Division of Infection and Immunity, School of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia

Mark P. Nicol

Department of Pharmaceutical Microbiology, School of Pharmacy, University of Health and Allied Sciences, Ho, Ghana

Regina E. Abotsi

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• Prince K. Mushunje
• , Felix S. Dube
• , Jon Ø Odland
• , Rashida A. Ferrand
• , Mark P. Nicol
• , Regina E. Abotsi
• , Tsitsi Bandason
• , Ethel Dauya
• , Tafadzwa Madanhire
• , Elizabeth L. Corbett
• , Katharina Kranzer
• , Edith D. Majonga
• , Victoria Simms
• , Andrea M. Rehman
• , Helen A.Weiss
• , Hilda Mujuru
• , Dan Bowen
• , Louis-Marie Yindom
• , Sarah L. Rowland-Jones
• , Trond Flaegstad
• , Tore J. Gutteberg
• , Jorunn Pauline Cavanagh
• , Trym Thune Flygel
• , Evegeniya Sovarashaeva
• , Jessica Chikwana
• , Gugulethu Newton Mapurisa
• , Carmen Gonzalez-Martinez
• , Robina Semphere
• , Brewster Wisdom Moyo
• , Lucky Gift Ngwira
•  & Slindile Mbhele

Contributions

FSD conceived the study. PKM conducted the laboratory experiments and data analysis and wrote the first draft of the manuscript supervised by REA and FSD. CO and SM supervised the Fluidigm assay. JOO, MN, and RAF conceived and led the parent BREATHE study. JOO secured funding from GLOBVAC on behalf of the consortium. All authors contributed to, read, and approved the final manuscript.

Corresponding author

Correspondence to Prince K. Mushunje .

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Ethics approval and consent to participate.

The parent study (BREATHE) was approved by the Human Research and Ethics Committee of the University of Cape Town—UCT HREC (HREC/REF:754/2015), the London School of Hygiene and Tropical Medicine Ethics Committee (reference 8818), the Harare Central Hospital Ethics Committee and Medical Research Council of Zimbabwe (reference MRCZ/A/1946), the College of Medicine Research Ethics Committee Malawi (reference P.04/15/1719) and the Regional Committee for Medical and Health Research Ethics, Northern Norway (reference 2015/1650). The University of Oxford waived approval. Additional ethical approval was received for this substudy from the UCT HREC (HREC/REF: 092/2019). No additional data were collected other than those approved in the parent study. Written informed consent and assent were given by guardians and participants, respectively. Participants who were 18 years old and above consented independently at the time of enrollment. All data obtained and generated during the study were kept confidential. This research was conducted in accordance with the Declaration of Helsinki.

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Mushunje, P.K., Dube, F.S., Olwagen, C. et al. Characterization of bacterial and viral pathogens in the respiratory tract of children with HIV-associated chronic lung disease: a case–control study. BMC Infect Dis 24 , 637 (2024). https://doi.org/10.1186/s12879-024-09540-5

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DOI : https://doi.org/10.1186/s12879-024-09540-5

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A Clinical Diagnostic Test for Calcium Release Deficiency Syndrome

• 1 Libin Cardiovascular Institute, Department of Physiology and Pharmacology, University of Calgary, Calgary, Alberta, Canada
• 2 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
• 3 Jesselson Integrated Heart Center, Eisenberg R&D Authority, Shaare Zedek Medical Center, and Hebrew University Faculty of Medicine, Jerusalem, Israel
• 4 Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, England
• 5 Oxford Heart Centre, John Radcliffe Hospital, Oxford, England
• 6 Department of Cardiology, Faculty of Medicine and Health Sciences, Antwerp University Hospital, Antwerp, Belgium
• 7 Cardiovascular Research, Departments of Genetics, Pharmacology and Physiopathology of Heart, Blood Vessels and Skeleton, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
• 8 Member of the European Reference Network for Rare, Low Prevalence, and Complex Diseases of the Heart (ERN GUARD-Heart)
• 9 Department of Clinical Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
• 10 Heart Failure and Arrhythmias, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
• 11 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, Ontario, Canada
• 12 Montreal Heart Institute and Université de Montréal, Montreal, Quebec, Canada
• 13 Department of Cardiology, Aarhus University Hospital, Aarhus N, Denmark
• 14 Institut Universitaire de Cardiologie et de Pneumologie de Québec, Laval University, Quebec City, Quebec, Canada
• 15 Department of Cardiac Pacing and Electrophysiology, Hopital Cardiologique du Haut-Leveque, Centre Hospitalier Universitaire de Bordeaux, Pessac, France
• 16 Division of Cardiology and Centre for Cardiovascular Innovation, University of British Columbia, Vancouver, Canada
• 17 Department of Molecular Cardiology, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
• 18 Department of Molecular Medicine, University of Pavia, Pavia, Italy
• 19 Windland Smith Rice Genetic Heart Rhythm Clinic, Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
• 20 Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
• 21 Section of Cardiac Electrophysiology, Division of Cardiology, University of Washington Medical Center, Seattle
• 22 Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
• 23 Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, University of California, San Francisco
• 24 Windland Smith Rice Sudden Death Genomics Laboratory, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota
• 25 Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota
• 26 Inherited Arrhythmia and Cardiomyopathy Program, Arrhythmia Service, Division of Cardiology, Toronto General Hospital and the University of Toronto, Toronto, Ontario, Canada
• 27 Leviev Heart Institute, Chaim Sheba Medical Center, Ramat Gan, Israel
• 28 Tel Aviv University, Tel Aviv, Israel
• 29 Oxford Biomedical Research Centre and Wellcome Centre for Human Genetics, University of Oxford, Oxford, England
• 30 Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark
• 31 Heart Institute, Hadassah University Hospital, Jerusalem, Israel
• Editor's Note Clinical Test for Calcium Release Deficiency Syndrome? Gregory M. Marcus, MD, MAS; Gregory Curfman, MD; Kirsten Bibbins-Domingo, PhD, MD, MAS JAMA

Question   Cardiac arrest frequently occurs without explanation, even after a thorough clinical evaluation. Can a simple maneuver clinically diagnose calcium release deficiency syndrome (CRDS), a newly described cause of sudden death?

Findings   In this international, multicenter, case-control study, a provoked measure of T-wave amplitude on an electrocardiogram ascertained cases of CRDS with high accuracy. The genetic mouse models recapitulated the human findings and suggested a pathologically large systolic calcium release from the sarcoplasmic reticulum was responsible.

Meaning   These preliminary results suggest that the repolarization response on an electrocardiogram to brief tachycardia followed by a pause may effectively diagnose CRDS. Given the frequency of unexplained cardiac arrest, should these findings be confirmed in larger studies, this readily available maneuver may provide clinically actionable information.

Importance   Sudden death and cardiac arrest frequently occur without explanation, even after a thorough clinical evaluation. Calcium release deficiency syndrome (CRDS), a life-threatening genetic arrhythmia syndrome, is undetectable with standard testing and leads to unexplained cardiac arrest.

Objective   To explore the cardiac repolarization response on an electrocardiogram after brief tachycardia and a pause as a clinical diagnostic test for CRDS.

Design, Setting, and Participants   An international, multicenter, case-control study including individual cases of CRDS, 3 patient control groups (individuals with suspected supraventricular tachycardia; survivors of unexplained cardiac arrest [UCA]; and individuals with genotype-positive catecholaminergic polymorphic ventricular tachycardia [CPVT]), and genetic mouse models (CRDS, wild type, and CPVT were used to define the cellular mechanism) conducted at 10 centers in 7 countries. Patient tracings were recorded between June 2005 and December 2023, and the analyses were performed from April 2023 to December 2023.

Intervention   Brief tachycardia and a subsequent pause (either spontaneous or mediated through cardiac pacing).

Main Outcomes and Measures   Change in QT interval and change in T-wave amplitude (defined as the difference between their absolute values on the postpause sinus beat and the last beat prior to tachycardia).

Results   Among 10 case patients with CRDS, 45 control patients with suspected supraventricular tachycardia, 10 control patients who experienced UCA, and 3 control patients with genotype-positive CPVT, the median change in T-wave amplitude on the postpause sinus beat (after brief ventricular tachycardia at ≥150 beats/min) was higher in patients with CRDS ( P  < .001). The smallest change in T-wave amplitude was 0.250 mV for a CRDS case patient compared with the largest change in T-wave amplitude of 0.160 mV for a control patient, indicating 100% discrimination. Although the median change in QT interval was longer in CRDS cases ( P  = .002), an overlap between the cases and controls was present. The genetic mouse models recapitulated the findings observed in humans and suggested the repolarization response was secondary to a pathologically large systolic release of calcium from the sarcoplasmic reticulum.

Conclusions and Relevance   There is a unique repolarization response on an electrocardiogram after provocation with brief tachycardia and a subsequent pause in CRDS cases and mouse models, which is absent from the controls. If these findings are confirmed in larger studies, this easy to perform maneuver may serve as an effective clinical diagnostic test for CRDS and become an important part of the evaluation of cardiac arrest.

• Editor's Note Clinical Test for Calcium Release Deficiency Syndrome? JAMA

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Introduction, background and methods, limitations, acknowledgements, data availability.

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Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

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Md Mamunur Rashid, Kumar Selvarajoo, Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data, Briefings in Bioinformatics , Volume 25, Issue 4, July 2024, bbae300, https://doi.org/10.1093/bib/bbae300

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The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class–specific feature selection algorithms, which identifies multi-modal and -omics–associated interpretable components. MOMLIN was applied to 147 patients’ breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context–specific multi-omics network biomarkers and better predict drug-response classifications.

The advent of high-throughput sequencing technologies has revolutionized our ability to collect various ‘omics’ data types, such as deoxyribonucleic acid (DNA) methylations, ribonucleic acid (RNA) expressions, proteomics, metabolomics and bioimaging datasets, from the same samples or patients with unprecedented details [ 1 ]. By far, most studies have performed single omics analytics, which capture only a fraction of biological complexity. The integration of these multiple omics datasets offers a more comprehensive understanding of the underlying complex biological processes than single-omic analyses, particularly in human diseases like cancer and cardiovascular disease, where it significantly enhances prediction of clinical outcomes [ 2 , 3 ].

Cancer is a highly complex and deadly disease if left unchecked, and its heterogeneity poses significant challenges for treatment [ 4 ]. Standard treatments, including chemotherapy with or without targeted therapies, aim to reduce tumor burden and improve patient outcomes such as survival rate and quality of life [ 5–7 ]. However, even for the most advanced therapies, such as immunotherapies, treatment effectiveness varies widely across cancer types and even between patients with same diagnosis [ 8 ]. This heterogeneity is believed to be due to tumor microenvironment heterogeneity and their effects on the resultant complex and myriad molecular interactions within cells and tissues [ 9 , 10 ]. This variability underscores the urgent need to identify precise biomarkers to predict individual patient responses and potential adverse reactions to a particular therapy [ 11 ]. This can be made possible through multi-omics data integration analyses at the individual patient scale [ 12 ].

To assess treatment response, such as pathologic complete response (pCR) and residual cancer burden (RCB), current clinical practice relies on clinical parameters (e.g. tumor size/volume and hormone receptor status), along with genetic biomarkers (e.g. TP53 mutations) [ 13–15 ]. However, these approaches do not fully capture the complex intracellular regulatory dynamics [ 16 , 17 ] or the tumor-immune microenvironment (TiME) interactions that influence outcomes [ 18 , 19 ]. Thus, to enhance personalized cancer treatments, we need novel methodologies that can handle large, complex molecular (omics) and clinical datasets. Machine learning (ML) methods integrating multi-omics data offer a promising avenue to improve prediction accuracy and uncover robust biomarkers across drug-response classes [ 20 ], which may be overlooked by single-omics analytics. This approach can predict patients benefiting from standard treatments and those requiring alternative plans like combination therapies or clinical trials.

The current drug-response prediction methods can be broadly categorized into ML-based and network-based approaches. ML methods often analyze each data type (e.g. mutations and gene expression) independently using univariable selection [ 21 , 22 ] or dimension reduction methods [ 23 ]. These results are then integrated using various classifiers or regressors [e.g. support vector machine, elastic-net regressor, logistic regression (LR) and random forest (RF)] [ 24–26 ] and ensemble classifier to make predictions [ 9 ]. However, these methods often overlooked the crucial interactions among different data modalities. Deep learning methods, while gaining popularity, are limited by the need for large clinical sample sizes to achieve sufficient accuracy [ 27 ]. Recent ML advancements have focused on integrating multimodal omics features with patient phenotypes to improve predictive performance [ 28 , 29 ]. To discover multimodal biomarker, techniques such as multi-omics factor analysis (MOFA) and sparse canonical correlation analysis (SCCA), including its variant multiset SCCA (SMCCA) offer realistic strategies for integrating diverse data modalities [ 30–32 ]. However, although these methods are suitable for classification tasks, they are unsupervised and do not directly incorporate phenotypic information (e.g. disease status) to integrate diverse data types. As a result, they are limited to identify phenotype-specific biomarkers.

Recently, advanced supervised approaches like data integration analysis for biomarker discovery using latent components (DIABLO) by Sing et al. (2019) have emerged to overcome these limitations [ 28 ]. DIABLO is an extension of generalized SCCA (GSCCA), considers cross-modality relationships and extracts a set of common factors associated with different response categories. Network-based methods, like unsupervised network fusion or random walk with restart approaches construct drug–target interaction and sample similarity networks that are effective for patient stratification [ 20 , 33 ]. However, these methods lack a specific feature selection design, limiting their utility for identifying biomarkers for patient classification. Nevertheless, none of these ML methods are rigorous in terms of task/class-specific biomarker discovery and interpretability, and both SMCCA and GSCCA struggle with gradient dominance problem due to naive data fusion strategies [ 34 ]. Therefore, it is essential to develop novel interpretable methods for identifying robust multimodal network biomarkers across diverse data types to advance our understanding of the complex factors that influence drug responses.

In this study, we introduce MOMLIN, a multi-modal and -omics ML integration framework to enhance the prediction of anticancer drug responses. MOMLIN integrates weighted multi-class SCCA (WMSCCA) that identifies interpretable components and enables effective feature selection across multi-modal and -omics datasets. Our method contributes in three keyways: (i) innovates a class-specific feature selection strategy with SCCA methods for associating multimodal biomarkers, (ii) includes an adaptive weighting scheme into multiple pairwise SCCA models to balance the influence of different data modalities, preventing dominance during training process and (iii) ensures robust feature selection by employing a combined constraint mechanism that integrate lasso and GraphNet constraints to select both the individual features and subset of co-expressed features, thereby preventing overfitting to high-dimensional data.

We applied MOMLIN to a multimodal breast cancer (BC) dataset of 147 patients comprising clinical features, DNA mutation, RNA expression, tumor microenvironment and molecular pathway data [ 9 ], to predict drug-response classes, specifically distinguishing responders and non-responders. Our results demonstrate MOMLIN’s superiority in terms of outperforming state-of-the-art methods and interpretability of the underlying biological mechanisms driving these distinct response classes.

Overview of our proposed method for treatment response prediction

The workflow of our proposed method MOMLIN for identifying class- or task-specific biomarkers from multimodal data is shown in Fig. 1 . The core of this pipeline involves three stages: (i) identification of response-specific sparse components, in terms of input features and patients, (ii) development of drug-response predictor using latent components of patients and (iii) interpretation of sparse components and multi-modal and -omics biomarker discovery.

Schematic representation of the proposed framework. In stage 1, multimodal datasets from cancer patients (e.g. BC) were sourced from a published study [ 9 ]. This dataset comprises clinical features, DNA mutations, and gene expression from pre-treatment tumors, alongside post-treatment response classes (pCR, RCB-I to III). TiME and pathway activity were derived from transcriptomic data using statistical algorithms. For identifying class-specific correlated biomarkers, class binarization and oversampling were used to balance between classes. WMSCCA models the multimodal associations across different biomarkers and identifies response-specific sparse components on diverse input features and patients. In stage 2, a binary LR classifier then utilizes these patient latent components for predicting response to therapies, evaluated by AUROC. Next in stage 3, class–specific sparse components are shown in a heatmap, highlighting key signatures (non-zero loading) in colors. Finally, the identified multi-modal and -omics signatures then formed a correlation network, revealing pathways associations with multi-modal and -omics biomarkers for each response class. Nodes with colors in the network indicate multimodal features.

The rationales underpinned of this approach is that effective biomarkers are: (i) response–related multimodal features including genes, cell types and pathways, and (ii) features that demonstrate prediction capabilities on unseen patients. The first stage, a ‘feature selection step’ that selects multimodal features on the generated sparse components based on their relevance to drug-response categories (pCR and RCB-I to III). Features with high loading identified are considered as potential biomarker candidates. The second stage, a ‘classification step’, validates these biomarkers by assessing their predictive power in distinguishing responders from non-responders to anticancer therapy; any predictions indicating chemo-resistant tumors should be considered for enrolment in clinical trials for novel therapies. The third stage, an ‘interpretation step,’ analyzes the candidate biomarkers in a multi-modal and-omics network associated with relevant biological pathways. This step aims to elucidate the underlying biological processes differentiating between drug–response phenotypes.

Stage 1. Identification of response-associated sparse components in terms of input features and patients

Multi-modal and -omics data overview and preparation.

This study utilized clinical attributes, DNA mutation and gene expression (transcriptome) data from147 matched samples of early and locally advanced BC patients (categorized as pCR, n  = 38, RCB-I, n  = 23, or RCB-II, n  = 61, or RCB-III, n  = 25), obtained from the TransNEO cohort at Cambridge University Hospitals NHS Foundation [ 9 ]. The dataset includes clinical attributes (8 features, summary attributes are available in Supplementary Table S1 available online at http://bib.oxfordjournals.org/ ), genomic features (31 DNA mutation genes, applying a strict criterion of genes mutated in at least 10 patients) and RNA-sequencing (RNA-Seq) features (18 393 genes), covering major BC subtypes-normal-like, basal-like, Her2, luminalA and luminalB. Although DNA mutation genes typically represent binary data, we used mutation frequencies to construct a mutation count matrix. Initial data pre-processing involved a log2 transformation on the RNA-Seq features after filtering out less informative features at 25th percentile (in terms of mean and standard deviation) using interquartile range. For integrative modeling, we used the top 40% of variable genes (3748 genes, based on median absolute deviation ranking) from the RNA-Seq datasets. Finally, each feature was normalized dividing by its Frobenius norm, adjusting the offset between high and low intensities across different data modalities.

To characterize TiME and pathway markers, we applied various statistical algorithms on the RNA-Seq data. The GSVA algorithm [ 35 ] calculated (i) the GGI gene sets [ 36 ] and (ii) STAT1 immune signature scores [ 37 ]. For immune cell enrichment, three methods were used: (i) MCPcounter [ 37 ] with voom-normalized RNA-Seq counts; (ii) enrichment over 14 cell types using 60 gene markers, employing log2-transformed geometric mean of transcript per million (TPM) expression [ 38 ]; and (iii) z -score scaling of cancer immunity parameters [ 39 ] to classify four immune processes (major histocompatibility complex molecules, immunomodulators, effector cells and suppressor cells). Additionally, the TIDE algorithm [ 40 ] computed T-cell dysfunction and exclusion metrics for each tumor sample using log2-transformed TPM matrix of counts, which can serve as a surrogate biomarker to predict the response to immune checkpoint blockade. Pathway activity scores for each tumor sample were computed using the GSVA algorithm with input gene sets from Reactome [ 41 ], PIP [ 42 ] and BioCarta databases within the MSigDB C2 pathway database [ 43 ].

Sparse multiset canonical correlation analysis

In this study, lowercase letters denote a vector, and uppercase ones denote matrices, respectively. The term |${\left\Vert .\right\Vert}_{1,1}$| denotes the matrix |${l}_1$| -norm, and |${\left\Vert .\right\Vert}_{gn}$| denotes the GraphNet regularization. The sparse multiset canonical correlation analysis (SMCCA) is an extension of dual-view SCCA, proposed to model associations among multiple types of datasets [ 31 ]. Given the multiple types of datasets, let |$X\in{\mathcal{R}}^{n\times p}$| represent gene expression data with |$p$| features, and |${Y}_k\in{\mathcal{R}}^{n\times{q}_k}$| represent the |$k$| -th data modality (e.g. clinical, DNA mutation and tumors microenvironment) with |${q}_k$| features. Both |$X$| and |${Y}_k$| have |$n$| samples, and |$k=\left(1,\dots, K\right)$|⁠ , where |$K$| denotes the number of different data modalities. The objective function of SMCCA is defined as follows:

where |$u$| and |${v}_k$| are the canonical weight vectors corresponding to |$X$| and |${Y}_k$|⁠ , indicating the importance of each respective biomarkers. The term |${\left\Vert .\right\Vert}_1$| represents the |${l}_1$| regularization to detect small subset of discriminative biomarkers and prevent model overfitting. |${\lambda}_u,{\lambda}_{vk}$| are non-negative tuning parameters balancing between the loss function and regularization terms. The term |${\left\Vert .\right\Vert}_2^2$| denotes the squared Euclidean norm to constraint weight vectors |$u$| and as unit length |${v}_k$|⁠ , respectively.

However, SMCCA has limitations: (i) it is naturally unsupervised, meaning SMCCA cannot leverage phenotypic information (e.g. disease status and drug-response classes); (ii) pairwise association among multiple data types can vary significantly and can lead to gradient dominance issues during optimization; and (iii) SMCCA mines a common subset of biomarkers for classifying different tasks, which diminishes its relevance, as each task might require distinct features sets.

Weighted multi-class sparse canonical correlation analysis

To address the above limitations, here we propose weighted multi-class SCCA (WMSCCA), a formal model for class/tasks-specific feature selection, different from the conventional SMCCA. Throughout this study, we used the terms tasks/classes/drug-response classes interchangeably. WMSCCA includes phenotypic information as an additional data type, employs a weighting scheme to resolve the gradient dominance issue and innovates traditional class–specific feature selection strategies through the one-versus-all strategies into its core objective function. In this study, the underlying motivation is WMSCCA can jointly identify drug-response class–specific multimodal biomarkers to improve drug-response prediction. For ease of presentation, we consider |$n$| patients with data matrices |${X}_c\in{\mathcal{R}}^{n\times p},{Y}_{ck}\in{\mathcal{R}}^{n\times{q}_k}$|⁠ , and |$Z\in{\mathcal{R}}^{n\times C}$| from C different drug-response classes. Here, |${X}_c$| denotes |$p$| features from gene expression datasets, |${Y}_{ck}$| denotes |${q}_k$| features from |$k$| -th data modality (e.g. mutation, clinical features, TiME and pathway activity), |${Z}_c$| denotes |$c$| response class, and |$k=\left(1,\dots, K\right)$|⁠ , |$K$| denotes the number of data modalities. The WMSCCA optimization problem can be formulated as follows:

where |$U\in{\mathcal{R}}^{p\times C},{V}_k\in{\mathcal{R}}^{q_k\times C}$| are canonical loading matrices correspond to |$X$| and |${Y}_k$|⁠ , representing the importance of candidate biomarkers for each class |$C$|⁠ , respectively. In this equation, the first term models associations among |$X$|⁠ , and |${Y}_k$| datasets; the second- and third terms correlate class labels |${Z}_c$| with |$X$| and |${Y}_k$| data modalities for each |${C}^{th}$| class, aiming to identify class-specific features and their relationships; |$\psi (U)$| and |$\psi \left({V}_k\right)$| represent sparsity constraints on |$U$| and |${V}_k$|⁠ , to select a subset of discriminative feature. As mentioned in Equation ( 1 ), to address gradient dominance, the adjusting weight parameter |${\sigma}_{xy}$|⁠ , |${\sigma}_{xz}$| and |${\sigma}_{yz}$| can be defined as:

where |$k=\left(1,\dots, K\right)$|⁠ , |$K$| denotes the number of data modalities. |${\sigma}_{..}$| adjusts a larger weight if the non-squared loss (denominator term) between datasets is small and vice versa.

Given high-dimensional datasets, the model in Equation ( 2 ) encounters an overfitting problem. Therefore, the use of a sparsity constraint is appropriate to address this issue. We hypothesized that gene expression biomarkers can be either single genes or co-expressed sets; thus, a combined penalty is designed for the |$X$| dataset. Therefore, |$\psi (U)$| for |$X$| takes the following form:

where, |${\mathrm{\alpha}}_u,\beta$| are nonnegative tuning parameters. |$\beta$| balances between the effect of co-expressed and individual feature selection. The first sparsity constraint is matrix |${l}_{1,1}$| -norm, which is defined as follows:

This penalty promotes class-specific features on |$U$|⁠ . The second sparsity constraint GraphNet regularization, defined as follows:

where |${L}_c$| represents the Laplacian matrices of the connectivity in |$\boldsymbol{X}$| matrices. The Laplacian matrix is defined as |$L=D-A$|⁠ , where |$D$| is the degree matrix of connectivity matrix |$A$| (e.g. gene co-expression or correlation network). This penalty term promotes a subset of connected features to discriminate each response on |$U$|⁠ .

Besides, neither every mutation marker nor every clinical/TiME/pathways involves in predicting response classes, therefore, the |${l}_{1,1}$| -norm is used on the |${Y}_k$| datasets to select individual markers, i.e. |$\psi \left({V}_k\right)$| for the |${\boldsymbol{Y}}_k$| data modalities take the following form:

where |${\mathrm{\alpha}}_{vk}$| is non-negative tuning parameter.

Finally, we obtained C pairs of canonical weight matrices |$\big({U}_c{V}_{ck}\big)\left(c=1,\dots, C;k=1,\dots, K\right)$| using an iterative alternative algorithm by solving Equation ( 2 ) [ 44 , 45 ]. Detected features with non-zero weights in each class in the weight vectors were extracted as correlated sets.

The WMSCCA method involves parameters |${\mathrm{\alpha}}_u,\mathrm{\beta}, and\ {\mathrm{\alpha}}_{vk}$| |$\left(k=1, \dots, K\right)$|⁠ . Given the limited number of samples, we applied a nested cross-validation (CV) strategy on training sets and evaluated the maximum correlation on the test datasets. Optimal values for the regularization parameters were determined within each training set via internal five-fold CV.

Stage 2. Drug-response prediction using latent components of patients

To predict drug-response categories, we trained LR classifier using the latent components of patients (or raw multimodal features) generated by MOMLIN in Fig. 1 : stages 1 and 2. We used a binary classification scheme, distinguishing pCR versus non-pCR, RCB-I versus non-RCB-I, RCB-II versus non-RCB-II and RCB-III versus non-RCB-III, to evaluate model performance. In addition, we performed analyses with existing multi-omics methods, including SMCCA+LR, MOFA+LR, DIABLO and latent principal component analysis (PCA) features, with LR classifiers. To assess prediction performance for the response to treatment in an unbiased manner, we used five-fold cross-validated performance and repeated the process over 100 runs. The partitioning of data was kept consistent across all models for fair comparisons. The accuracy of response prediction was evaluated using area under the receiver operating characteristic curve (AUROC).

Stage 3. Interpretation of sparse components and multi-omics biomarker discovery and their networks

After learning sparse latent components of features across different data modalities using MOMLIN, we identify the most relevant feature based on the loading weight of genes, TiME and pathways, which reveal underlying interactions for discriminating response classes. The larger the loading weight, the more important the pair of features in discriminating response categories. We then use these selected features to construct a sample correlation network, or a relationship matrix based on their canonical weights [ 46 ]. In this network, nodes represent selected features, and the edge weights between two interconnected features indicate correlation or relatedness. The generated network is visualized using the ggraph package in R ( https://cran.r-project.org ). Finally, we prioritize multi-omics biomarkers based on their degree centrality within the interconnected correlation network.

Derivation of response-associated latent components from BC data with MOMLIN

We applied MOMLIN to analyze a breast cancer (BC) dataset to predict treatment response and gain molecular insights. The dataset comprised 147 BC patients with early and locally advanced pretherapy tumors [ 9 ], categorized as follows: pCR with 38 patients, RCB-I (good response) with 23 patients, RCB-II (moderate response) with 61 patients and RCB-III (resistance) with 25 patients. After preprocessing and filtering least informative features, the final dataset comprised 3748 RNA genes (top 40% out of 9371 genes), 31 mutation genes, 8 clinical attributes, 64 TiME and 178 pathways activities ( Fig. 1 : stage 1). Supplementary Table S1 available online at http://bib.oxfordjournals.org/ summarizes overall clinical characteristics by patients’ response classes.

While our proposed framework offers general applicability for identifying context-specific multi-omics biomarkers, this study specifically focused on discovering drug-response–specific biomarkers to enhance the prediction of pCR and RCB resistance. MOMLIN decomposed the input multimodal data into response-associated sparse latent components of input-features and patients. These sparse components reveal patterns of how various features (e.g. genes and mutations) and clinical attributes related to treatment outcomes ( Fig. 1 : stage 1–3), and their effectiveness was evaluated by measuring prediction performance. We assessed the predictive ability of MOMLIN through five-fold CV repeated 100 times. In each iteration, the dataset is divided into five-folds, with one random fold assigned as the held-out test set, and the remaining folds used as the training set. MOMLIN was trained using the training dataset, including detection of predictive marker candidates, and its performance was evaluated on the ‘unseen’ test set. This process was repeated for all five-folds to ensure robust evaluation of MOMLIN’s generalizability. Performance was measured by the AUROC matrices ( Fig. 1 : stage 2).

Performance comparison with existing methods for drug-response prediction

To evaluate the prediction capability of MOMLIN, we modeled each response category as a binary classification problem and compared its prediction accuracy to existing multi-omics integration algorithms. For comparison, we randomly split the dataset into a training set (70%) and a test set (30% unseen data), with balanced inclusion of response classes. We employed LR as the classifier to assess predictive performance of multimodal biomarkers. We compared MOMLIN with four other classification algorithms for omics data: (i) SMCCA, which integrates multi-omics data by projecting it onto latent components for discriminant analysis; (ii) MOFA, which decomposes multi-omics data into common factors for discriminant analysis; (iii) sparse PCA; and (iv) DIABLO, a supervised integrative analysis method, represent the state-of-the-art in classification. All methods were trained on the same preprocessed data.

The classification results showed that MOMLIN outperformed the compared multi-omics integration methods in most classification tasks on unseen test samples ( Fig. 2A ). Notably, DIABLO, the next best performer, was 10 to 15% less effective than our MOMLIN. Additionally, we compared the performance of component-based LR models against raw feature-based LR models to predict RCB response classes. Although raw feature-based models showed improved prediction, their performance was notably dropped compared to component-based models ( Fig. 2B ). This indicates the superior adaptability and effectiveness of component-based models in leveraging multi-omics data for predictive purposes.

Performance comparison with existing methods and detection of informative data combination. All results in the plots depict test AUROC over five-fold CV obtained from 100 runs. (A) Box plots comparing response prediction performance of MOMLIN against existing state-of-the-art multi-omics methods. (B) Performance comparison between predictors based on latent components and those utilizing a selected subset of multimodal features. (C) Comparing AUROCs for the models with different data subset combinations (clinical, clinical + DNA, clinical + RNA and clinical + DNA + RNA) using MOMLIN.

Moreover, to test and demonstrate generalizability of this framework, we applied MOMLIN to a preprocessed multi-omics dataset of colorectal adenocarcinoma (COAD) with 256 patients [ 47 ]. This dataset included gene expression, copy number variations and micro-RNA expression data, which we used to classify COAD subtypes such as chromosomal instability (CIN, n  = 174), genomically stable (GS, n  = 34) and microsatellite instability (MSI, n  = 48). The performance results shown in Supplementary Table S2 available online at http://bib.oxfordjournals.org/ and Supplementary Figure S1 available online at http://bib.oxfordjournals.org/ , indicate that MOMLIN outperformed all state-of-the-art methods tested in classifying COAD subtypes. Moreover, when comparing the raw feature-based accuracies with sparse components-based (features derived from MOMLIN) accuracies, we found that raw feature-based classifier was superior against existing methods ( Figure S1A and B ), but lower than the components-based classifier. This consistent observation supports our findings with BC drug-response performances.

Importance of different omics data for treatment response prediction

To assess the added value of integrating multimodal data for predicting treatment response, we trained four prediction models with different feature combinations: (i) clinical features only, plus adding (ii) DNA, (iii) RNA and (iv) both DNA and RNA. We found that adding different data modalities improved prediction performance across all response classes ( Fig. 2C ). Notably, the models that combined clinical data with either RNA or both DNA and RNA demonstrated superior and comparable performance with an average AUROC of 0.978. In contrast, the model based on clinical features alone had much lower AUROC, ranging from 0.51 to 0.82. These results suggest that RNA transcriptome is the most informative data modality in this dataset. Thus, integrating gene expression with clinical features could significantly improve our ability to predict treatment outcomes in BC.

Interpretation of response-associated sparse components identified by MOMLIN

To understand the molecular landscape of treatment response in BC, we used MOMLIN to model response–specific bi-multivariate associations across multiple data modalities. We observed stronger correlations between RNA gene expression and both TiME ( r  = 0.701) and pathway activity ( r  = 0.868), indicating greater overlap or explained information between them. Conversely, moderate correlations were found between RNA gene expression and DNA mutations ( r  = 0.526), or clinical features ( r  = 0.488), indicating partially overlapping or independent information. These results suggest that multimodal biological features provide complementary information in a combinatorial manner.

When investigating the importance of each feature to predict response classes, MOMLIN identified four distinct loading vectors corresponding to pCR and RCB response classes, highlighting distinct weight patterns for pCR versus non-pCR and RCB versus non-RCB classes ( Fig. 3 ). For example, in the pCR (complete response) components—taking the top five molecular features across different modalities revealed distinct molecular patterns. Specifically, gene expression analysis showed that downregulation of FBXO2 and RPS28P7 inhibits tumor cell proliferation, and potentially may enhance treatment efficacy, and the upregulation of C2CD4D-AS1, CSF3R, and SMPDL3B genes may promote immune response, increasing tumor cell vulnerability and therapeutic effect ( Fig. 3A ). Mutational analysis revealed negative associations of marker genes HMCN1 and GATA3, but a positive association for COL5A1 ( Fig. 3C ). Additionally, tumor mutation burden (TMB), and homologous recombination deficiency (HRD)-Telomeric AI signatures were higher in pCR patients, suggesting high genomic instability compared to RCB patients [ 9 ]. TiME analysis showed reduced immunosuppressive mast cells and extracellular matrix (ECM), along with increased infiltration of neutrophils, TIM-3 and CD8+ T-cells ( Fig. 3D ). Subsequently, the pathway analysis further revealed potential downregulation of the PDGFRB pathway, involved in stromal cell activity and associated with improved patient response [ 49 ], while upregulation of pathways for antimicrobial peptides, FLT3 signaling, ephrin B reverse signaling and potential therapeutics for SARS ( Fig. 3E ), suggesting enhanced immune surveillance and interaction with tumor cells. In summary, MOMLIN reveals distinct genomic landscape with higher immune activity and genomic instability in pCR that characterizes its favorable treatment response.

Heatmaps illustrate the features importance on response-associated components identified by MOMLIN. Each row in the heatmap represents a drug-response class, pCR, RCB-I , RCB-II and RCB-III, with columns representing features across different data modalities. The color gradient indicates feature loading or importance, representing the strength of association with response classes. The sign (negative or positive) of gradient denotes the association directions to response classes. All results in the heatmaps depict an average over 100 runs of five-fold CV. (A–E) represents the response-associated candidate biomarkers detected in latent components in (A) gene expression data (highlighting DE genes), (B) clinical features, (C) DNA mutations (highlighting mutated genes), (D) TiME cells and (E) functional pathway profiles (highlighting altered pathways).

Similarly, in the RCB-I (good response) components—RNA expression analysis revealed that lower expression of genes GPX1P1 and HBB are linked to less aggressive tumors [ 48 ], while those of thiosulfate sulfurtransferase (TST), NPIPA5 and GSDMB were overexpressed, linked to enhanced immune response and therapeutic effectiveness [ 49 , 50 ]. Mutational analysis showed positive association for therapeutic targets signatures TP53, MUC16 and RYR2 [ 51 , 52 ], but a negative in NEB, and CIN scores. TiME analysis demonstrated increased infiltration of Tregs, cancer-associated fibroblast (CAF), monocytic lineage and natural killer (NK) cells, indicating more active of immune environment [ 9 ], with reduced TEM CD4 cells. Pathway analysis further identified downregulation of NOD1/2 signaling, EPHA-mediated growth cone collapse and toll-like receptor (TLR1, TLR2) pathways, involved in inflammation and immune response, with the upregulation of allograft rejection, and G0 and early G1 pathways. In summary, tumors that achieve RCB-I is marked by distinct genomics marker, active immune response, and lower CIN.

In RCB-II (moderate response) components: RNA expression analysis revealed overexpression of RPLP0P9, FTH1P20, RNF5P1 pseudogenes, following accumulation of overexpressed ERVMER34-1, and PON3 genes play an oncogenic role in BC [ 53 ]. Mutation analysis revealed positive association of HRD-LOH, RYR1 and MT-ND4, but negative association of MACF1 and neoantigen loads, in line with previous reports [ 54 , 55 ]. Analysis of TiME features demonstrated increased infiltration of IDO1 and TAP2, with reduced CTLA 4, NK cells and PD-L2 cells, indicating a less suppressive immune environment. Pathways analysis further revealed downregulation pathways of G1/S DNA damage checkpoints and TP53 regulation, highlighting DNA repair issues, with the upregulation of PDGFRB pathway, E2F targets and signaling by Hedgehog associated with cell proliferation. In summary, RCB-II patients display distinct genomics markers including pseudogenes, lack of suppressive immune environment and active proliferation.

In RCB-III (resistant) components: RNA gene expression analysis revealed lower expression of therapeutic target PON3, and FGFR4 [ 56 ], and flowed accumulation of lower expressed lncRNAc ENSG00000225489, ENSG00000261116 and RNF5P1. Mutation signature analysis identified a positive association of MT-ND1, but a negative association in therapeutic targets TP53, and MT-ND4 [ 7 , 52 ]. Neoantigen loads were higher following lower TMB indicate reduced tumor suppressor activity. TiME analysis revealed reduced activity of T-cell exclusion, and HLA-E, with increased ECM, HLA DPA1 and LAG3, suggesting an immune suppressive tumor environment. Pathway analysis revealed upregulation of pathways involved in neurotransmitter release, cell-cycle progression (RB-1) and immune system diseases, suggesting active cell signaling and proliferation, with downregulation of EPHB FWD pathway and nucleotide catabolism. In summary, patients that attained RCB-III, characterized by low mutational burden and an immune suppressive environment, leading to treatment resistance.

Linking biology to treatment response through biomarker network analysis

To further extract multimodal network biomarkers and understand the complex biological interactions in patients with pCR and RCB, we performed cross-interaction network analysis using candidate signatures identified by MOMLIN across different modalities. This analysis included clinical features, DNA mutations, gene expression, TiME cells and enriched pathways, aiming to elucidate the underlying biology associated with specific treatment responses. Figure 4 shows the interaction networks of selected multimodal features for each RCB class. To identify potential biomarkers associated with pCR and RCB response, we specifically focused on the top ten multimodal features based on network edge connections. For example, tumors that attained in pCR, the network analysis revealed co-enrichment of mutations in HMCN1 and COL5A1 genes, particularly in estrogen receptor (ER)-negative patients. HMCN1 and COL5A1 therapeutic targets like molecules encode proteins for ECM structure, and mutations of these genes regulate tumor architecture and cell adhesion, potentially facilitating immune cell infiltration [ 52 ]. We also observed elevated expressions of FBXO2, CSF3R, C2CD4D-AS1 and RPS28P7 genes, alongside increased infiltration of CD8+ T-cells [ 9 , 57 ]. FBXO2 is a component of the ubiquitin-proteasome system, which regulates protein degradation and influences cell cycle and apoptosis [ 58 ], while CSF3R plays a vital role in granulocyte production and immune response [ 59 ]. These gene expression patterns, coupled with increased CD8+ T-cell infiltration, suggest a robust anti-tumor immune response. Furthermore, these molecular perturbations may be linked to antimicrobial peptide pathways and FLT3 signaling, potentially contributing to the favorable outcome in achieving pCR [ 60 , 61 ]. Future work could specifically search for these complex interactions across different molecules to gain more clinically relevant insights into pCR tumors. Supplementary Table S3 available online at http://bib.oxfordjournals.org/ presents the more detailed list (top 30) of the multi-modal and -omics biomarkers identified using the MOMLIN pipeline.

Multimodal network biomarkers explain drug-response classes. The multimodal networks detail the candidate biomarkers and their interactions for each response class, (A) the pCR patients (B) the RCB-I patients (good response), (C) the RCB-II patients (moderate response) and (D) the RCB-III resistance patients. Nodes in the network represent candidate biomarkers derived from clinical features, DNA mutations, gene expression, enriched cell-types and pathways, each indicated in different colors in the figure legend. Negative edges are light green; positive edges are in light magenta. Edge width reflects the strength of the interaction between features. Node size corresponds to the number of connections (degree), and the font size of node labels scales with degree centrality, highlighting the most interconnected biomarkers.

Similarly, RCB-I tumors exhibited co-enriched mutations in MUC16 and TP53, particularly in HER2+ cases [ 14 ]. MUC16 (CA125) is therapeutic molecule associated with immune evasion and tumor growth [ 51 ], while TP53 mutations can lead to loss of cell cycle control and genomic instability [ 62 ]. We also observed elevated expression of TST involved in the detoxification processes and GPX1P1 [long non-coding RNA (lncRNA)] involved in oxidative stress response. The immune landscape of these tumors showed increased infiltration of TEM CD4 cells (adaptive immunity), monocytic lineage cells (phagocytosis and antigen presentation) and NK cells (innate immunity), as well as CAFs. This immune landscape, coupled with potential perturbations in the allograft rejection pathway, suggests an active but potentially incomplete immune response against the tumor, resulting in minimal residual disease.

RCB-II tumors had lower neoantigen loads compared to pCR, both in ER-negative and HER2+ patients. This reduced neoantigen load might contribute to a weaker immune response. Gene expression analysis showed elevated levels of specific lncRNAs, including FTH1P20 (associated with iron metabolism), RNF5P1 (potentially affecting protein degradation) and RPLP0P9 (involved in protein synthesis), along with ERVMER34-1, which can influence gene expression and immune response in BC patients. Numerous studies have underscored the key regulatory roles of lncRNAs in tumors and the immune system. Notably, increased expression of the immune checkpoint protein IDO1 negatively regulates the expression of CTLA-4, both known to modulate antitumor immune responses [ 63 ]. The combined effect of these molecular alterations suggests potential tumor survival mechanisms, including immune evasion and dysregulation of G1/S DNA damage [ 64 ] contributing to moderate residual disease.

In RCB-III tumors, we observed the reduced prevalence of TP53 and MT-ND4 mutations, typically associated with genomic instability and aggressive tumor behavior [ 51 ], coupled with a higher neoantigen load, suggesting an alternative mechanism (pathways) that drives tumor progression. Despite the higher neoantigen loads, increased expression of HLA-E immune checkpoints and T-cell exclusion in the tumor microenvironment hindered effective anti-tumor immune responses. Additionally, the low-expressed genes PON3, ENSG00000261116 (lncRNA) and RNF5P1 are involved in detoxification, gene regulation and protein degradation, respectively, represents an adaptive response to cellular stress in these tumors. Clinical markers indicating lymph node involvement suggest a more advanced disease state [ 9 ]. These findings, along with potential perturbations in the neurotransmitter release cycle pathway, collectively portray RCB-III tumors as genetically unstable, yet effectively evading immune surveillance, contributing to their significant treatment resistance. Overall, further investigation of these interactive molecular networks, comprising both positive and negative interactions offers a more depth understudying of these potential candidate biomarkers for distinguishing treatment-sensitive pCR and resistant RCB tumors.

The advent of multi-omics technologies has revolutionized our understanding of cancer biology, offering unprecedented insights into the complex molecular interactions that shape tumor behavior and treatment response. In this study, we presented MOMLIN (multi-modal and -omics ML integration), a novel method to enhance cancer drug-response prediction by integrating multi-omics data. MOMLIN specifically utilizes class-specific feature learning and sparse correlation algorithms to model multi-omics associations, enables the detection of class-specific multimodal biomarkers from different omics datasets. Applied to a BC multimodal dataset of 147 patients (comprising RNA expression, DNA mutation, tumor microenvironment, clinical features and pathway functional profiles), MOMLIN was highly predictive of responses to anticancer therapies and identified cohesive multi-modal and -omics network biomarkers associated with responder (pCR) and various levels of RCB (RCB-I: good response, RCB-II: moderate response and RCB-III: resistance).

Using MOMLIN, we identified that pCR is determined by an interactive set of multimodal network biomarkers driven by distinct genetic alterations, such as HMCN1 and COL5A1, particularly in ER-negative tumors [ 9 , 65 ]. Gene expression signatures, including FBXO2 and CSF3R were associated with the immune cell infiltration (CD8+ T-cells), which has been previously reported as a key determinant of response [ 57 ]. The association of these biomarkers with antimicrobial peptide and FLT3 signaling pathways suggests a robust immune response [ 61 ] as a critical driver of complete response. Additionally, C2CD4D-AS1, an lncRNA was identified, and its exact role with these complex molecular interactions in BC remains to be elucidated. Future work could specifically search for these complex interactions across different molecules to gain more clinically relevant insights into pCR tumors.

RCB-I tumors, despite responding well to response, were associated with a distinct multimodal molecular signature. These tumors were enriched for mutations in the therapeutic target MUC16 (CA125), known for its role in immune evasion [ 51 ], and the tumor suppressor gene TP53, particularly in HER2+ cases [ 14 ]. Elevated expression of TST and GPX1P1 (lncRNA involved in oxidative stress response) were associated with increased infiltration of diverse immune cells, including Tem CD4+ cells, monocytes and NK cells [ 10 ]. This active immune landscape and the intricate interactions of these signature with the potential perturbations in the allograft rejection pathway, suggests a robust yet potentially incomplete anti-tumor immune response, contributing to the minimal residual disease observed in this subtype.

RCB-II tumors showed lower neoantigen loads compared to pCR, which could contribute to a weaker immune response, particularly in ER-negative and HER2+ subtypes. Increased expression of lncRNAs, such as FTH1P20, RNF5P1, RPLP0P9 and ERVMER34–1, were associated with the immune checkpoint protein IDO1, and negatively regulate the CTLA-4 protein expression, suggests immune evasion and alterations in tumor cell metabolism and proliferation. These molecules altered intricate interactions implicate dysregulation of G1/S DNA damage as a possible mechanism for moderate treatment response [ 64 ].

RCB-III tumors, classified as resistant, were associated with a distinct multimodal molecular landscape driven by reduced TP53 and MT-ND4 mutations [ 52 ], accompanied with higher neoantigen loads compared to other response groups. This suggests an alternative mechanism driving tumor progression and immune evasion. Despite the high neoantigen load which could potentially trigger immune response, these tumors exhibited immune evasion through increased HLA-E immune checkpoints and T-cell exclusion [ 40 , 55 ]. Also, the downregulation of genes like PON3 and the lncRNA ENSG00000261116, along with lymph node involvement, pointed to advanced disease and cellular stress adaptation [ 9 ]. The presence of these complex interactions, including potential perturbations in the neurotransmitter release cycle pathway, could contribute to treatment resistance in RCB-III tumors. Future studies targeting these immunosuppressive mechanisms and exploring novel pathways could offer promising avenues to overcome resistance in this aggressive subtype.

These findings above emphasize the potential of MOMLIN to enable deeper understanding of complex biological mechanism correspondence to each response class, ultimately paving the way for personalized treatment strategies in cancer. MOMLIN also demonstrated the best prediction performance for unseen patients by utilizing these identified sets of network biomarkers. By identifying response-associated biomarkers, researchers can stratify patients based on their likelihood of achieving pCR or experiencing RCB to anticancer treatments, facilitating more informed treatment decisions and potentially improving patient outcomes. Moreover, the identified biomarkers could serve as valuable targets for the development of novel therapeutic interventions and new biological hypothesis generation. However, the clinical translation of multimodal biomarkers necessitates addressing the potential economic burden associated with multi-omics testing. Developing targeted biomarker panels and prioritizing key hub molecules from the large-scale candidate multimodal network biomarkers identified by MOMLIN could be a viable strategy for reducing costs while maintaining predictive accuracy. Furthermore, ongoing advancements in sequencing and diagnostic technologies are expected to make multi-omics testing more accessible and affordable over time.

In conclusion, our study demonstrates MOMLIN’s capacity to uncover nuanced molecular signatures associated with different drug-response classes in BC. By integrating multi-modal and -omics datasets, we have highlighted the complex interplay between genetic alterations, gene expression, immune infiltration and cellular pathways that contribute to treatment response and resistance. Future research in this direction holds promise for refining risk stratification, optimizing treatment selection and ultimately improving patient outcomes.

While MOMLIN demonstrates promising results as shown, a key limitation lies in its reliance on correlation-based algorithms for multi-omics data integration. These algorithms are great at identifying associations, but they can fall short when it comes to inferring causality between different omics layers. This is a challenge faced by most current state-of-the-art methods [ 28 , 30 ]. In the future iterations of MOMLIN, we aim to incorporate causal inference methodologies alongside sparse correlation algorithms to better understand the complex causal relationships within multi-omics datasets.

We proposed MOMLIN, a novel framework designed to integrate multimodal data and identify response-associated network biomarkers, to understand biological mechanisms and regulatory roles.

MOMLIN employed an adaptive weighting for different data modalities and employs innovative regularization constraint to ensure robust feature selection to analyze high-dimensional omics data.

MOMLIN demonstrates significantly improved performance compared to current state-of-the-art methods.

MOMLIN identifies interpretable and phenotype-specific components, providing insights into the molecular mechanisms driving treatment response and resistance.

We thank Dr Yoshihiro Yamnishi and Mr Chen Yuzhou for their technical help.

This work was supported by the core research budget of Bioinformatics Institute, ASTAR.

Supplemental information and software are available at the Bib website. Our algorithm’s software is available for free download at https://github.com/mamun41/MOMLIN_softwar/tree/main

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• Published: 27 June 2024

Clinical application of transcranial neuroendoscopy combined with supraorbital keyhole approach in minimally invasive surgery of the anterior skull base

• Long Zhou 1   na1 ,
• Xiongfei Jing 2   na1 ,
• Chang Wang 3   na1 ,
• Huikai Zhang 1 ,
• Pan Lei 1 ,
• Ping Song 1 ,
• Zhiyang Li 1 ,
• Lun Gao 1 ,
• Minghui Lu 1 ,
• Qianxue Chen 1 &
• Qiang Cai 1  

Scientific Reports volume  14 , Article number:  14886 ( 2024 ) Cite this article

Metrics details

• Diseases of the nervous system

To explore the techniques, safety, and feasibility of minimally invasive neurosurgery through the supraorbital eyebrow arch keyhole approach by neuroendoscopy. Retrospective analysis of clinical data of patients with various cranial diseases treated by transcranial neuroendoscopic supraorbital eyebrow keyhole approach in our hospital from March 2021 to October 2023. A total of 39 complete cases were collected, including 21 cases of intracranial aneurysms, 9 cases of intracranial space occupying lesions, 5 cases of brain trauma, 3 cases of cerebrospinal fluid rhinorrhea, and 1 case of cerebral hemorrhage. All patients’ surgeries were successful. The good prognosis rate of intracranial aneurysms was 17/21 (81%), and the symptom improvement rate of intracranial space occupying lesions was 8/9 (88.9%). Among them, the initial symptoms of one patient with no improvement were not related to space occupying, while the total effective rate of the other three types of patients was 9/9 (100%). The average length of the craniotomy bone window of the supraorbital eyebrow arch keyhole is 3.77 ± 0.31 cm, and the average width is 2.53 ± 0.23 cm. The average postoperative hospital stay was 14.77 ± 6.59 days. The average clearance rate of hematoma by neuroendoscopy is 95.00% ± 1.51%. Our results indicate that endoscopic surgery through the supraorbital eyebrow arch keyhole approach is safe and effective for the treatment of anterior skull base lesions and cerebral hemorrhage. However, this retrospective study is a single center, small sample study, and the good surgical results do not exclude the subjective screening of suitable patients by clinical surgeons, which may have some bias. Although the clinical characteristics such as indications and contraindications of this surgical method still require further prospective and multicenter clinical research validation, our study still provides a new approach and choice for minimally invasive surgical treatment of anterior skull base lesions.

Introduction

With the advancement of medical imaging technology, neuronavigation technology, surgical optical equipment, and surgical techniques, the pursuit of neurosurgeons to reduce surgical treatment related side injuries seems to have become a trend. This has led neurosurgeons to design a “minimally invasive” surgical approach that minimizes surgical trauma while also considering cosmetic effects. The concept of “minimally invasive” surgery is to minimize ineffective exposure of the surgical area and minimize harassment of non-target brain tissue during the operation process. Based on the above concepts, neurosurgeons have developed a supraorbital eyebrow arch keyhole approach to enter the surgical area of the anterior and middle cranial fossa. With the assistance of neuroendoscopy, surgeons can obtain the maximum surgical area through the smallest surgical channel, while optimizing the surgical effect to maximize patient benefits 1 . With the development of surgical optical equipment and the demand for high-quality lighting in the surgical area by surgeons, neuroendoscopic technology is increasingly favored by neurosurgeons. Neuroendoscopy significantly enhances the high-quality lighting of the surgical area, providing a wider surgical field of view and higher magnification, as well as its ability to “look around”, clearly and concisely displaying the local structural details of the surgical area and its relationship with surrounding tissues 2 . The combination of supraorbital keyhole approach and neuroendoscopy perfectly embodies the concept of minimally invasive neurosurgery, fully leveraging the advantages of minimal surgical trauma, clear surgical field of view, good surgical results, short hospitalization time, low total hospitalization costs, and good cosmetic effects 3 , 4 , 5 .

Materials and methods

General information.

This retrospective study collected clinical data of patients with various cranial diseases who underwent surgery through the eyebrow arch keyhole approach under transcranial neuroendoscopy in our hospital from March 2021 to October 2023. A total of 39 complete cases (aged 29–76 years) were collected, including 21 cases of intracranial aneurysms, 9 cases of intracranial space occupying lesions, 5 cases of brain trauma, 3 cases of cerebrospinal fluid rhinorrhea, and 1 case of cerebral hemorrhage. All patients’ surgeries were successful.

Typical cases

A 49 years old male patient admitted the hospital for sudden severe headache. After admission, complete cranial CT showed subarachnoid hemorrhage, intraventricular hemorrhage, and diffuse brain swelling. Head and neck CTA shows an aneurysm of the anterior communicating artery and an aneurysm at the beginning of the P1 segment of the right posterior cerebral artery. Considering the size of the patient's intracranial aneurysm and the extent of subarachnoid hemorrhage, it is considered that the anterior communicating artery is a ruptured aneurysm with a high risk of rebleeding, and emergency surgical treatment is needed. After fully evaluating the surgical plan and risks, the right supraorbital eyebrow arch keyhole approach was chosen for surgical treatment. During the operation, severe swelling of brain tissue was observed, and puncture was performed to release cerebrospinal fluid for decompression in the lateral ventricle of the surgical field. Then, routine surgical treatment was performed, and the surgery was successful with good results. And interventional embolization was performed again at the beginning of the P1 segment aneurysm of the right posterior cerebral artery 24 days after surgery. One week after surgery, a follow-up head and neck CTA showed no significant residue of the anterior communicating artery aneurysm and the P1 segment aneurysm of the right posterior cerebral artery, and no delayed bleeding or stenosis of the parent artery. After one month of outpatient follow-up, the patient had clear consciousness and no positive signs of the nervous system (Fig.  1 A–P).

( A ) Preoperative CT shows subarachnoid hemorrhage. ( B – C ) Preoperative CTA suggests anterior communicating artery aneurysm. ( D ) Intraoperative puncture of the lateral ventricle to release cerebrospinal fluid for decompression. ( E – I ) Separation and clipping of aneurysms by neuroendoscopy. ( J ) Postoperative CT shows aneurysm clip. ( K ) Postoperative CT scan revealed ventricular drainage tube. ( L , M ) Postoperative 3D Slicer reconstruction of virtual reality images of lateral ventricles and extraventricular drainage tubes. ( N , O ) Postoperative CTA indicates complete occlusion of the anterior communicating artery aneurysm, with no stenosis in the parent artery and branches. ( P ) Surgical incision and bone window.

A 60 years female patient admitted the hospital due to 10 days of intracranial aneurysm detected during cerebral infarction examination. She had a history of hypertension for 8 years and a history of 2 ischemic attacks. After admission, head and neck CTA showed bilateral bifurcation aneurysms in the middle cerebral artery. Based on the patient's history of two episodes of numbness on the right side of the face, as well as the presence of a large and irregular aneurysm on the left side, it is considered a high-risk aneurysm and has a priority level of treatment. After a thorough evaluation of the surgical plan and risks, the feasibility of the surgery was evaluated using 3D Slicer reconstruction virtual reality technology. Finally, the left supraorbital eyebrow keyhole approach was chosen for surgical treatment, and the aneurysm was successfully clamped during the operation with good results. One week after surgery, a follow-up CTA of the head and neck showed good occlusion of the aneurysm in the left bifurcation of the brain, without delayed bleeding or stenosis of the parent artery and branches. After one month of outpatient follow-up, the patient had clear consciousness and no positive signs of the nervous system (Fig.  2 A–O).

( A – C ) Preoperative CTA showed bilateral middle cerebral artery bifurcation aneurysms and left middle cerebral bifurcation aneurysms that require priority treatment (red arrow). ( D , E ) Feasibility evaluation of supraorbital eyebrow arch keyhole surgery using 3D Slicer reconstruction virtual reality technology (white arrow). ( F – I ) Neuroendoscopic clipping of left middle cerebral artery bifurcation aneurysm. ( J , K ) Postoperative CT showed aneurysm clipped. ( L – N ) After clipping the aneurysm at the bifurcation of the left middle cerebral artery in postoperative CTA, no stenosis of the parent artery or branch was observed, and an untreated aneurysm at the bifurcation of the right middle cerebral artery was found (red arrow). ( O ) Surgical incision and bone window.

A 36 years female patient admitted the hospital due to 2 years of headache with decreased vision and worsening for 1 month. After admission, MRI enhancement of the sellar region showed a mass in the sellar region, indicating a high possibility of meningioma. The right internal carotid artery cavernous sinus segment and bilateral anterior cerebral artery A1 segment were enveloped. The patient had a space occupying lesion in the anterior skull base and underwent minimally invasive surgery through the supraorbital eyebrow arch keyhole approach by neuroendoscopy, with good prognosis. Pathological examination diagnosed meningioma. Postoperative CT and enhanced MRI of the sellar region at 8 months confirmed complete resection without delayed bleeding, tumor residue or recurrence. After 8 months of outpatient follow-up, the patient had clear consciousness and no positive signs of the nervous system (Fig.  3 A–L).

( A – C ) Preoperative MRI shows mass lesions in the sellar region and suprasellar region. ( D ) Tumor (Black arrow), olfactory nerve (White arrow), and optic nerve visible (Red arrow) by neuroendoscopy. ( E ) Detachment of tumor base. ( F ) The right optic nerve is well preserved (Black arrow). ( G ) The pituitary stalk (White arrow) and left optic nerve (Black arrow) are well preserved. ( H ) Postoperative CT shows tumor resection with no bleeding in the surgical area. ( I ) Pathological examination diagnosed meningioma. ( J – L ) Follow up MRI at 8 months post-surgery showed no recurrence of the tumor.

A 46 years male patient admitted to the hospital due to being found to have a consciousness disorder for more than 10 h. The patient's family informed the patient that they had a head injury 2 days before admission, and there were no other special symptoms except for mild headache, so they did not seek medical treatment. Upon admission, emergency cranial CT showed hematoma formation in the left temporal lobe and bilateral frontal lobe, subarachnoid hemorrhage, left frontal epidural hematoma, and occipital bone fracture. The patient suffered from bilateral frontal lobe brain contusion and hematoma formation. The 3D Slicer accurately calculated the hematoma volume to be 81.43 ml, indicating a consciousness disorder and clear surgical indications. However, the patient did not have any brain herniation. A comprehensive evaluation of the surgical plan was conducted, and no skull removal decompression was required. The 3D Slicer reconstructed a virtual reality image of the hematoma, and the feasibility of the eyebrow arch keyhole surgery under simulation endoscopy was preliminarily evaluated. After communicating the surgical plan with the patient's family, they chose to undergo eyebrow arch keyhole surgery. By neuroendoscopy, the left supraorbital eyebrow arch keyhole approach was performed to remove bilateral frontal lobe brain contusions and intracerebral hematoma. Postoperative CT confirmed that the hematoma was completely cleared, and 3D Slicer was used to accurately calculate the hematoma volume of approximately 4.42 ml, with a clearance rate of 94.57%. After one month of outpatient follow-up, the patient had clear consciousness and no positive signs of the nervous system (Fig.  4 A–L).

( A ) Preoperative CT shows bilateral frontal lobe contusion and hematoma formation. ( B ) Postoperative CT shows bilateral frontal lobe brain contusions and hematoma cleared completely. ( C , D ) 3D Slicer reconstruction of virtual reality images of hematoma for preoperative evaluation. E: Surgical incision and bone window. ( F , G ) Clearing ipsilateral brain contusion and hematoma by neuroendoscopy. ( H , I ) Neuroendoscopic incision of cerebral falx and removal of contralateral brain contusion and hematoma. ( J ) 20 days post-surgery, CT scan showed good recovery. ( K – L ) 3D Slicer accurately calculates preoperative and postoperative hematoma volume.

A 56 years male patient admitted to the hospital due to cerebrospinal fluid rhinorrhea after head injury for over a month. Upon admission, the brain CT revealed multiple intracranial gas accumulation, with a gas containing cavity formed in the right frontal lobe, and multiple skull fractures in the craniofacial bone and anterior cranial fossa floor. We used 3D Slicer reconstruction for preoperative planning and still used the eyebrow keyhole approach for minimally invasive surgery. During the surgery, multiple fistulas were visible by neuroendoscopy, and bubbles emerged at the fistulas. The dura mater and artificial meninges of the fistulas were sutured and repaired with biological glue. Postoperative follow-up of brain CT and 3-month outpatient follow-up showed no recurrence of cerebrospinal fluid leakage or other complications (Fig.  5 A–O).

( A – D ) Preoperative brain CT findings of intracranial gas accumulation and comminuted skull fractures. ( E , F ) Preoperative 3D Slicer reconstruction and simulation of surgical approach to evaluate the feasibility of this surgical approach. ( G , H ) Further evaluation of the location of bone fistula using preoperative 3D Slicer reconstruction. ( I ) Intraoperative neuroendoscopic findings of fistula and brain tissue swelling. ( J ) After thorough cleaning, obvious fistula can be seen (white arrow). ( K , L ) Multilayer artificial meninges and biological glue repair (black arrow). ( M , N ) Postoperative brain CT reexamination. ( O ) Postoperative 3D Slicer reconstruction of actual bone window.

Ethical approval

All patients or family members have signed informed consent forms for study participation. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study was approved by the ethics committee of Clinical Research, Our Hospital (WDRY2023-K139).

The good prognosis rate of intracranial aneurysms was 17/21 (81%), and the symptom improvement rate of intracranial space occupying lesions was 8/9 (88.9%). Among them, the initial symptoms of one patient with no improvement were not related to space occupying, while the total effective rate of the other three types of patients was 9/9 (100%). The length of the skin incision for all patients is about 4 cm. All patients were statistically analyzed for bone window size, length of hospital stay and postoperative length of hospital stay, preoperative neurological symptoms, and neurological symptoms obtained from outpatient or telephone follow-up one month after surgery. Further analysis of the location of intracranial aneurysms, preoperative Hunt Hess score, and modified Fisher score in patients with intracranial aneurysms; Further analysis of intracranial space occupying lesions, including tumor pathological type and grading, lesion size and location; Further analysis of the location of bleeding, presence of brain herniation, preoperative and postoperative hematoma volume (hematoma clearance rate) in patients with traumatic brain injury and cerebral hemorrhage. The average length of the craniotomy bone window of the supraorbital eyebrow arch keyhole is 3.77 ± 0.31 cm, and the average width is 2.53 ± 0.23 cm. The average hospital stay for all patients was 18.77 ± 7.00 days, and the average postoperative hospital stay was 14.77 ± 6.59 days. The average clearance rate of hematoma by neuroendoscopy is 95.00% ± 1.51%. The detailed information of all patients mentioned above is shown in Tables 1 , 2 , and 3 .

With the development of micro neurosurgery and neuroendoscopy technology, people's understanding of side injuries in craniotomy surgery is also increasing. Large surgical incisions, damage to normal brain tissue structures, brain tissue atrophy, damage to neurovascular structures, and longer postoperative recovery time are considered urgent changes that need to be made. Over the past few decades, neurosurgeons have been dedicated to the research of “minimally invasive” surgery. The term “minimally invasive” surgery is a conceptualization of surgical techniques, which not only requires minimizing additional damage to the target area, but also requires attention to avoid unnecessary damage to the intracranial and extracranial tissues during the surgical approach process. Hippocrates emphasized the importance of minimizing iatrogenic trauma during intervention over 2000 years ago. From a philosophical perspective, the less tissue is destroyed, the less tissue needs to be healed; The less exposed the brain, the less damaged the brain; The smaller the surgical pathway, the fewer tissues and functional structures at risk 1 . Based on the above requirements, with the unremitting efforts of neurosurgeons, the concept of “keyhole” has been proposed, which utilizes modern surgical technology to develop the minimum traumatic approach that allows for normal surgical operations, while achieving maximum surgical results while ensuring safety. The concept of “keyhole approach” surgery has greatly promoted the development of this field, improved surgical efficiency and safety, reduced the incidence of various postoperative complications, and accelerated the rehabilitation process. At present, the main keyhole surgical approaches include supraorbital eyebrow arch keyhole craniotomy, supraorbital lateral craniotomy, intraorbital keyhole approach, lesser pterygoid keyhole craniotomy, mini orbitozygomatic craniotomy, and lesser anterior longitudinal fissure approach 1 , 6 . However, these minimally invasive surgeries have inherent defects such as narrow field of view and low deep illumination, and the emergence of neuroendoscopy can precisely compensate for the shortcomings in such surgeries. Neuroendoscopy has the advantages of good illumination, flexible degrees of freedom, and close observation 7 . It can provide high-intensity illumination and a broad surgical field for the deep surgical area of minimally invasive surgery. It can reach the target area through narrow surgical channels and display deeper pathological tissues with high resolution. Research 8 has shown that a combination of neuroendoscopy, image-guided systems, and intraoperative assistive devices (such as ultrasound and magnetic resonance imaging) can aid in precise surgical localization and complete lesion resection through a keyhole approach. The supraorbital eyebrow arch keyhole approach under transcranial neuroendoscopy has gradually become one of the popular surgical treatments for anterior and middle skull base diseases. The advantage of this method is that it is minimally invasive and utilizes gravity to naturally sag the frontal lobe brain tissue, directly reaching the deep target area of the skull base through the subdural space and avoiding secondary damage caused by brain tissue fistula. With the flexible degrees of freedom of transcranial neuroendoscopy and the assistance of multiple angle working lenses, a deep surgical area several times wider than the surgical entrance can be obtained.

The supraorbital eyebrow arch keyhole approach, due to its minimally invasive surgical opening and cone-shaped enlarged surgical area, directly reaches the skull base and brain pools such as the optic chiasm pool, carotid artery pool, and vertebral plate through the natural space under the dura mater of the anterior skull base to release cerebrospinal fluid drainage, thereby obtaining sufficient surgical space. It is widely popular in craniotomy surgeries for anterior circulation aneurysms, anterior skull base tumors, and suprasellar tumors. A cadaveric autopsy study 9 showed that under 0° endoscopy, the olfactory bulb, olfactory tract, bilateral optic nerve, optic chiasm, bilateral anterior cerebral artery segments A1 and A2, anterior communicating artery, internal carotid artery, middle cerebral artery, origin of posterior communicating artery, and anterior choroid artery can be exposed. Under 30° endoscopy, the funnel, sellar septum, and upper and lower trunk branches of the M2 segment of the middle cerebral artery can be exposed. Endoscopy is inserted into the interspinous cistern between the bifurcation of the internal carotid artery and the oculomotor nerve, which is sufficient to expose structures such as the basal artery and some posterior cerebral arteries. However, due to severe atrophy of brain tissue after formalin fixation in autopsy, there may be significant differences in the actual exposure range compared to live anatomy, especially in cases of brain tissue swelling or large intracranial masses after arterial aneurysm hemorrhage, the exposure of the surgical area will be more difficult. The advantage of the neuroendoscopic approach lies in the instrument itself, as it is movable and can be equipped with lenses from different angles, providing a panoramic view for the supraorbital microsurgical approach. In order to utilize these attributes and be able to perform dual handed microsurgical operations simultaneously, it may be necessary for another surgeon to assist in controlling the neuroendoscopy, who needs to ensure the best field of view for the surgeon while avoiding collisions with other instruments 10 . But not all surgeries are suitable for the supraorbital eyebrow arch keyhole approach by neuroendoscopy. Only by selecting the appropriate case for the above surgery under the premise of clear surgical field and sufficient surgical space can the safety of patients be guaranteed. Therefore, some scholars advocate the use of neuroendoscopy for microsurgical neurosurgery via the supraorbital eyebrow arch keyhole approach only in unruptured intracranial aneurysms or aneurysms that rupture after absorption of subarachnoid hemorrhage 1 .

Our team completed 21 cases of anterior communicating artery aneurysm, anterior cerebral artery aneurysm, middle cerebral artery aneurysm, and posterior communicating artery aneurysm through the eyebrow arch keyhole approach by neuroendoscopy. All aneurysm surgeries require dissection of the optic chiasmal pool, carotid artery pool, and opening of the endplate to release cerebrospinal fluid for decompression, in order to obtain sufficient surgical space. For cases of ruptured and bleeding aneurysms with severe brain tissue swelling that cannot enter the anterior skull base through the subdural space, we puncture the lateral ventricle in the surgical area and place a tube to release cerebrospinal fluid for decompression before entering the anterior skull base, which can fully expose the surgical field of view. Our clinical research results on the feasibility of using this surgical approach to treat intracranial aneurysms are similar to those of Van Lindert et al. 11 . Meanwhile, they also pointed out that clipping the contralateral internal carotid artery aneurysm is safe unless the aneurysm is located on the dorsal side or obstructed by the anterior clinoid process. For the P1 segment aneurysm of the posterior cerebral artery and the tip aneurysm of the basilar artery, they also believe that it can be theoretically reached and clipped, but considering the narrow surgical pathway and the uncontrollability of deep operations, they do not recommend this surgical procedure.

Anterior skull base tumors are mainly caused by their occupying effect, which compresses important structures such as the adjacent olfactory nerve, optic nerve, vascular structure, and frontal lobe gyrus, leading to positive neurological symptoms. Although traditional coronal incisions through the anterior skull base or pterional approach have achieved good results in removing these tumors, due to the large surgical trauma, long surgical time, and long postoperative recovery time Poor cosmetic effects and neurological complications have forced neurosurgeons to explore more minimally invasive surgical methods 12 . A large number of studies 8 , 13 , 14 , 15 , 16 , 17 have shown that the supraorbital eyebrow arch keyhole approach by neuroendoscopy is a good choice. Our team used the above method to complete 9 cases of anterior skull base tumors, including supraorbital meningioma, olfactory sulcus meningioma, supraorbital epidermoid cyst, and suprasellar mass. Except for a transient diabetes insipidus in the suprasellar mass, all patients had no significant neurological complications. Our research results show that with the assistance of different angles of neuroendoscopy, the skull base lesions and surrounding nerve and vascular structures can be clearly displayed, especially the occluded deep lesions that cannot be observed under the direct light field of the microscope. Its advantages of close observation and flexible degrees of freedom provide high-definition, close range, and almost all-round observation capabilities. Therefore, we believe that the neuroendoscopic supraorbital eyebrow keyhole approach is a promising approach for anterior skull base surgery.

The endoscopic approach through the eyebrow arch keyhole is increasingly being used in the surgical treatment of skull base diseases 16 . In addition to its application in the surgery of intracranial aneurysms and brain tumors, our team has also attempted this surgical approach to treat anterior skull base diseases such as frontal lobe brain contusion and laceration, cerebrospinal fluid rhinorrhea, and has achieved good results. The advantages of good lighting, flexible degrees of freedom, and close observation of neuroendoscopy have been well demonstrated in the process of hematoma removal. With the use of working lenses at 0° and 30° angles, even the unilateral eyebrow arch keyhole approach can reach the bilateral anterior skull base to remove hematoma. Our research shows that the supraorbital eyebrow arch keyhole approach has significant advantages in surgical time and blood loss for the removal of frontal and basal brain contusions and hematomas. For elderly patients with frontal and basal brain contusions, this may be a good choice. However, further prospective multicenter clinical research and evaluation are needed to determine the indications and timing of surgery. The current popular surgical method for anterior skull base cerebrospinal fluid rhinorrhea is endoscopic repair of cerebrospinal fluid leakage through the nasal cavity 18 . We choose the neuroendoscopic approach through the eyebrow arch keyhole, which uses gravity to make the frontal lobe brain tissue naturally sag and reach the damaged fistula site through the natural subdural space for repair surgery. Compared with transnasal surgery, it solves the problem of requiring multi-instrument surgical operations in narrow surgical channels; At the same time, the upstream repair method for intracranial repair is significantly better than the leak blocking downstream repair method for extracranial repair.

Of course, the supraorbital eyebrow keyhole approach by neuroendoscopy also has its limitations. Neuroendoscopy is most suitable for use with reasonable control of bleeding and does not require highly technically challenging manual operation, as it takes up some space, especially with the tilted field of view provided by endoscopes at a certain angle, making the operation even more difficult; Therefore, endoscopy can become a powerful tool for entering blind spots, but improper use may worsen the damage. Akçakaya et al. 9 showed that the length of skin incision through the supraorbital eyebrow arch keyhole approach was 3.4–4 cm, with an average of 3.68 ± 0.19 cm. The average length of the bone window is about 2.65 ± 0.23 cm (2.2-3 cm), and the average width is about 1.43 ± 0.12 cm (1.3–1.7 cm), which also limits the surgical operation of two people, multiple hands, and multiple instruments to a certain extent. Therefore, our bone window has an average length of 3.77 ± 0.31 cm and an average width of 2.53 ± 0.23 cm, slightly larger than the bone window area of Akçakaya et al. In addition, due to the lateral area of the endoscope not being within the surgical field of view, there may be a "black under the lamp" situation, and adjusting the angle of the endoscope may accidentally damage any key structures within the surgical channel, such as the optic nerve, perforating vessels, bridging veins, brain tissue, etc 1 . Meanwhile, due to the different development of the frontal sinus in each patient, the incidence of accidental frontal sinus opening is about 18% 15 . Improper management of frontal sinus opening can increase the risk of postoperative infection.

Neuroendoscopic minimally invasive surgery through the supraorbital eyebrow arch keyhole approach is a relatively new technique. It not only has minimal trauma and is aesthetically pleasing, but can also enter the intracranial area including the anterior cranial fossa, middle fossa, and the lateral side of the cavernous sinus. It basically covers all anterior and partial middle cranial base surgeries. Our results indicate that endoscopic surgery through the supraorbital eyebrow arch keyhole approach is safe and effective for the treatment of anterior skull base lesions and cerebral hemorrhage. However, this retrospective study is a single center, small sample study, and the good surgical results do not exclude the subjective screening of suitable patients by clinical surgeons, which may have some bias. Although the clinical characteristics such as indications and contraindications of this surgical method still require further prospective and multi neutral clinical research validation, our study still provides a new approach and choice for minimally invasive surgical treatment of anterior skull base lesions.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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This work was supported by National Natural Science Foundation of China (82271518; 81971158; 81671306); The Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University (JCRCFZ-2022-030); Guiding projects of traditional Chinese medicine in 2023–2024 by Hubei provincial administration of traditional Chinese medicine (ZY2023F038).

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These authors contributed equally: Long Zhou, Xiongfei Jing and Chang Wang.

Authors and Affiliations

Department of Neurosurgery, Renmin Hospital of Wuhan University, No. 238, Jiefang Road, Wuchang District, Wuhan City, 430060, Hubei Province, China

Long Zhou, Huikai Zhang, Pan Lei, Ping Song, Zhiyang Li, Lun Gao, Minghui Lu, Qianxue Chen & Qiang Cai

Department of Neurosurgery, Xiantao First People’s Hospital Affiliated to Yangtze University, No. 29, Middle Section of Mianzhou Avenue, Xiantao City, 433000, Hubei Province, China

Xiongfei Jing

Department of Neurosurgery, Xiaochang First People’s Hospital, No. 1, Zhanqian Road, Xiaochang County, Xiaogan City, 432900, Hubei Province, China

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Q.C. studied concept and design, critical revision of manuscript for intellectual content, acquisition of data. L.Z., X.J. and C.W. collected and analysised data, and wrote the main manuscripts. H.Z., P.L., P.S., Z.L., L.G., M.L. and Q.C. collected and interpretated of data. All authors reviewed the manuscript.

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Correspondence to Qiang Cai .

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Zhou, L., Jing, X., Wang, C. et al. Clinical application of transcranial neuroendoscopy combined with supraorbital keyhole approach in minimally invasive surgery of the anterior skull base. Sci Rep 14 , 14886 (2024). https://doi.org/10.1038/s41598-024-65758-y

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SGLT-2 inhibitors, GLP-1 receptor agonists, and DPP-4 inhibitors and risk of hyperkalemia among people with type 2 diabetes in clinical practice: population based cohort study

• Related content
• Peer review
• Deborah J Wexler , associate professor 3 4 ,
• Sara J Cromer , assistant professor 3 4 ,
• Katsiaryna Bykov , assistant professor 1 ,
• Julie M Paik , associate professor 1 5 6 ,
• 1 Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
• 2 Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
• 3 Diabetes Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
• 4 Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
• 5 Division of Renal (Kidney) Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
• 6 New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA, USA
• Correspondence to: E Patorno epatorno{at}bwh.harvard.edu
• Accepted 22 April 2024

Objectives To evaluate the comparative effectiveness of sodium-glucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and dipeptidyl peptidase-4 (DPP-4) inhibitors in preventing hyperkalemia in people with type 2 diabetes in routine clinical practice.

Design Population based cohort study with active-comparator, new user design.

Setting Claims data from Medicare and two large commercial insurance databases in the United States from April 2013 to April 2022.

Participants 1:1 propensity score matched adults with type 2 diabetes newly starting SGLT-2 inhibitors versus DPP-4 inhibitors (n=778 908), GLP-1 receptor agonists versus DPP-4 inhibitors (n=729 820), and SGLT-2 inhibitors versus GLP-1 receptor agonists (n=873 460).

Main outcome measures Hyperkalemia diagnosis in the inpatient or outpatient setting. Secondary outcomes were hyperkalemia defined as serum potassium levels ≥5.5 mmol/L and hyperkalemia diagnosis in the inpatient or emergency department setting.

Results Starting SGLT-2 inhibitor treatment was associated with a lower rate of hyperkalemia than DPP-4 inhibitor treatment (hazard ratio 0.75, 95% confidence interval (CI) 0.73 to 0.78) and a slight reduction in rate compared with GLP-1 receptor agonists (0.92, 0.89 to 0.95). Use of GLP-1 receptor agonists was associated with a lower rate of hyperkalemia than DPP-4 inhibitors (0.79, 0.77 to 0.82). The three year absolute risk was 2.4% (95% CI 2.1% to 2.7%) lower for SGLT-2 inhibitors than DPP-4 inhibitors (4.6% v 7.0%), 1.8% (1.4% to 2.1%) lower for GLP-1 receptor agonists than DPP-4 inhibitors (5.7% v 7.5%), and 1.2% (0.9% to 1.5%) lower for SGLT-2 inhibitors than GLP-1 receptor agonists (4.7% v 6.0%). Findings were consistent for the secondary outcomes and among subgroups defined by age, sex, race, medical conditions, other drug use, and hemoglobin A1c levels on the relative scale. Benefits for SGLT-2 inhibitors and GLP-1 receptor agonists on the absolute scale were largest for those with heart failure, chronic kidney disease, or those using mineralocorticoid receptor antagonists. Compared with DPP-4 inhibitors, the lower rate of hyperkalemia was consistently observed across individual agents in the SGLT-2 inhibitor (canagliflozin, dapagliflozin, empagliflozin) and GLP-1 receptor agonist (dulaglutide, exenatide, liraglutide, semaglutide) classes.

Conclusions In people with type 2 diabetes, SGLT-2 inhibitors and GLP-1 receptor agonists were associated with a lower risk of hyperkalemia than DPP-4 inhibitors in the overall population and across relevant subgroups. The consistency of associations among individual agents in the SGLT-2 inhibitor and GLP-1 receptor agonist classes suggests a class effect. These ancillary benefits of SGLT-2 inhibitors and GLP-1 receptor agonists further support their use in people with type 2 diabetes, especially in those at risk of hyperkalemia.

Introduction

People with type 2 diabetes are prone to developing hyperkalemia, especially those with comorbid conditions such as heart failure and chronic kidney disease. 1 2 3 However, several drugs that improve clinical outcomes in people with type 2 diabetes and related comorbidities increase serum potassium levels, such as inhibitors of the renin-angiotensin-aldosterone system. 4 5 6 7 8 9 Hyperkalemia is associated with a risk of life threatening cardiac arrhythmias and increased mortality, 10 and the occurrence of hyperkalemia frequently leads to dose reduction or discontinuation of cardiorenal protective drugs. Stopping these drugs is associated with increased risk of adverse cardiovascular outcomes. 11 12 13 14 Therefore, strategies that reduce the risk of hyperkalemia in this population are urgently needed.

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors and glucagon-like peptide-1 (GLP-1) receptor agonists have become cornerstone drug classes in the treatment of type 2 diabetes 15 16 owing to their cardiovascular and kidney benefits. 17 18 19 20 Post hoc analyses of randomized trials have recently shown that SGLT-2 inhibitors also lower the risk of hyperkalemia compared with placebo, an outcome that was not defined as primary or secondary in those trials. 21 22 23 However, we do not know whether these benefits are also observed outside the highly controlled setting of randomized trials, and whether all agents within the SGLT-2 inhibitor class similarly reduce the risk of hyperkalemia. Furthermore, large scale epidemiological studies are needed that investigate the effects of GLP-1 receptor agonists on the risk of hyperkalemia in people with type 2 diabetes, with only a few small clinical studies suggesting plausible mechanisms for increased potassium excretion. 24 25 GLP-1 receptor agonists might lead to increased potassium secretion owing to enhancement in sodium delivery to the cortical collecting duct and altered tubular electronegativity. 25 26 Additionally, long term kidney preservation by SGLT-2 inhibitors or GLP-1 receptor agonists might contribute to reduced hyperkalemia risks. Notably, a recent study found that GLP-1 receptor agonist use was associated with lower hyperkalemia risk in patients with chronic kidney disease, but whether these benefits extend to the broader population with type 2 diabetes is unknown. 27 The aim of this study was to investigate the comparative effectiveness of SGLT-2 inhibitors, GLP-1 receptor agonists, and dipeptidyl peptidase-4 (DPP-4) inhibitors in lowering the risk of hyperkalemia among adults with type 2 diabetes.

Data sources

We used data from Medicare fee-for-service (parts A, B, and D) and two commercial insurance databases: Optum’s deidentified Clinformatics Data Mart Database (CDM) and MarketScan. All three databases contain deidentified longitudinal information on patient demographics, healthcare use, inpatient and outpatient medical diagnoses and procedures, prescription dispensing records, and outpatient laboratory test results (available for approximately 45% of the population in CDM and 5-10% of patients in MarketScan). This study was approved by the Mass General Brigham institutional review board and granted waiver of informed consent because only deidentified claims data were used. Data use agreements were in place.

Study design and study population

We identified three study cohorts of patients who started SGLT-2 inhibitors versus DPP-4 inhibitors (cohort 1), GLP-1 receptor agonists versus DPP-4 inhibitors (cohort 2), and SGLT-2 inhibitors versus GLP-1 receptor agonists (cohort 3) from April 2013 to the end of available data (December 2019 in Medicare, December 2020 in MarketScan, and April 2022 in CDM). Cohort entry was the date of a newly filled prescription of SGLT-2 inhibitors, GLP-1 receptor agonists, or DPP-4 inhibitors. We chose DPP-4 inhibitors as comparator because they were commonly used as second or third line diabetes drugs during our study period, similar to SGLT-2 inhibitors or GLP-1 receptor agonists. In contrast, patients using metformin or insulin probably have less or more advanced diabetes, which would increase the risk of unmeasured confounding by diabetes severity and baseline risk of hyperkalemia. We restricted the study cohorts to patients with a diagnosis of type 2 diabetes and without use of any of the two drug classes being compared for the past 365 days, aged ≥18 years (≥65 years for Medicare), and with at least 12 months of continuous insurance enrollment before cohort entry. We excluded patients who had a history of type 1 diabetes, secondary or gestational diabetes, chronic kidney disease stage 5 or end stage kidney disease, nursing home admission, or a history of organ transplantation, pancreatitis, cirrhosis, acute hepatitis, or multiple endocrine neoplasia type 2 within 365 days before cohort entry. To decrease the risk of reverse causation bias (ie, that early outcomes would be related to a previous hyperkalemia diagnosis before starting the drug and therefore not related to the treatments under study), we further excluded people who had a hyperkalemia diagnosis in the inpatient or outpatient setting or potassium binder use in the 90 days before cohort entry. Supplemental table 1 provides definitions for inclusion and exclusion criteria and supplemental figure 1 gives an overview of the longitudinal design.

Outcomes and follow-up

The primary outcome was the occurrence of a diagnosis code for hyperkalemia in the inpatient or outpatient setting (supplemental table 2 gives definitions). Secondary outcomes were the occurrence of serum potassium ≥5.5 mmol/L during follow-up in the outpatient setting, and hyperkalemia diagnosis in the inpatient or emergency department setting. The laboratory based hyperkalemia outcome definition (serum potassium ≥5.5 mmol/L) was only assessed in CDM because Medicare and MarketScan contain no or too few laboratory test results. For this analysis, we restricted the study population to people who had at least two serum potassium measurements in the 365 days before cohort entry.

To test the specificity and sensitivity of the claims based hyperkalemia definitions, an internal validation study was performed in CDM. Briefly, we included all 12.3 million adults with serum potassium measurements (logical observation identifiers names and codes (LOINC) 6298-4, 77142-8, 12812-4, 12813-2, 42569-4). Then, we assessed whether there was a hyperkalemia diagnosis in the three months after the serum potassium test. For the primary outcome definition (ie, hyperkalemia diagnosis in inpatient or outpatient setting), specificity was 99.5% and sensitivity was 22.3% when we used serum potassium ≥5.5 mmol/L to define hyperkalemia; specificity was 99.3% and sensitivity was 37.1% when serum potassium ≥6.0 mmol/L was used as the gold standard. Relative risk estimates will be unbiased when specificity is high and non-differential, even if sensitivity is low. 28 However, absolute rate differences will be biased towards the null when sensitivity is low.

We started follow-up on the day after cohort entry and continued until outcome occurrence or until any of the following occurred: treatment discontinuation or starting a drug in the comparator class, death, end of continuous health plan enrollment, or end of available data. We did not censor participants when they started other diabetes drugs (eg, sulfonylureas) during follow-up. We defined discontinuation as no prescription refill for the index exposure in the 30 days after the end of the days’ supply for the most recent prescription.

Confounders

We measured potential confounders during the 365 days before and including cohort entry date. We identified covariates that were confounders, confounder proxies or predictors for the outcome based on subject matter knowledge and previous studies that evaluated outcomes associated with drug use in people with type 2 diabetes. 29 These included age, sex, race (race was only available in CDM and Medicare), and geographical region; comorbidities, such as heart failure and chronic kidney disease; diabetes specific complications, such as diabetic nephropathy, neuropathy, and retinopathy; use of drugs used to treat diabetes and cardiovascular disease, for example, insulin and renin-angiotensin system inhibitors; use of other drugs; measures of healthcare use, such as number of emergency department visits, hospital admissions, endocrinologist and internist visits, and laboratory tests; healthy behavior markers, such as screening and vaccinations; and calendar year. We also adjusted for a claims based frailty index 30 to address potential confounding by frailty and for a claims based combined comorbidity score. 31 Comorbidities and drug use were assessed in the 365 days before and including the cohort entry date and based on international classification of diseases (version 9 and 10) diagnosis and procedure codes, and generic drug names, respectively. In the subset of patients who had creatinine measurements available, we calculated estimated glomerular filtration rate using the race-free 2021 CKD-EPI (chronic kidney disease epidemiology collaboration) equation. 32

Statistical analysis

To adjust for confounding, we used 1:1 propensity score matching with the nearest neighbor method and a caliper of 0.01 of the propensity score. 33 We used multivariable logistic regression models to estimate the propensity scores. These models were fitted separately for each of the data sources (ie, CDM, MarketScan, and Medicare) and for each drug comparison (SGLT-2 inhibitors v DPP-4 inhibitors, GLP-1 receptor agonists v DPP-4 inhibitors, and SGLT-2 inhibitors v GLP-1 receptor agonists), for a total of nine propensity score models. All covariates listed in supplemental table 3 were included in the propensity score models, except for the laboratory test results, which were only available for a subset of patients. Because race was only available in CDM and Medicare, it was only used in the six propensity scores developed in the CDM and Medicare cohorts. Continuous covariates (eg, age) were entered as main terms and quadratic terms. We assessed covariate balance before and after propensity score matching with standardized mean differences, with a standardized mean difference <0.10 indicating sufficient balance. 34 35 Because laboratory test results were not included in the propensity score, we considered their balance after propensity score matching to reflect residual unmeasured confounding. Hazard ratios were estimated with Cox regression models, and incidence rate differences were estimated with generalized linear regression models using an identity link function and normal error distribution. 36 Effect estimates and their standard errors were estimated separately in each of the three data sources, and then pooled with fixed effects meta-analysis. Cumulative incidence curves were estimated with the Aalen-Johansen estimator in the propensity score matched cohort, which accounts for the competing risk of death. 37 Absolute risks and risk differences at six month intervals were obtained from the cumulative incidences. There were no missing data for covariates other than the laboratory measurements. Analyses were performed using R version 3.6.2 and the Aetion Evidence Platform version 4.53. 38

Subgroup and sensitivity analyses

To investigate potential treatment effect modification, we performed a number of subgroup analyses in the following prespecified strata: age (<65 years v ≥65 years), sex, race (white v black, based on Medicare data only, where the race variable has been validated against self-reported race 39 ), heart failure, cardiovascular disease, chronic kidney disease, use of renin-angiotensin-aldosterone system inhibitors, mineralocorticoid receptor antagonists, loop diuretics and insulin on the cohort entry date, and by baseline hemoglobin A1c level (<7.5% v 7.5-9.0% v ≥9.0%). We re-estimated propensity scores and reperformed matching for each subgroup stratum. 40

To examine the robustness of our findings, we performed the following sensitivity analyses: treatment discontinuation was defined as no prescription refill for the index drug within 60 days rather than 30 days; to investigate the potential influence of informative censoring, we followed patients for a maximum of 180 and 365 days, regardless of treatment discontinuation or starting a drug in the comparator class; finally, we excluded patients with a history of hyperkalemia or potassium binder use in the previous 365 days.

Individual agents in SGLT-2 inhibitor and GLP-1 receptor agonist classes

We investigated potential differences in the risk of hyperkalemia for individual agents in the SGLT-2 inhibitor or GLP-1 receptor agonist classes by constructing separate cohorts for empagliflozin, canagliflozin, dapagliflozin, liraglutide, dulaglutide, exenatide, and semaglutide versus DPP-4 inhibitors, re-estimated the propensity scores and reperformed the matching, and calculated effect estimates for the primary outcome. The SGLT-2 inhibitor cohorts were restricted to the dates when both drugs under comparison were on the market (April 2013 for canagliflozin v DPP-4 inhibitors, January 2014 for dapagliflozin v DPP-4 inhibitors, and August 2014 for empagliflozin v DPP-4 inhibitors).

Patient and public involvement

There were no funds or time allocated for patient and public involvement, so we were unable to involve patients. Nevertheless, this study was inspired by conversations with patients in clinical practice. We also asked a member of the public to provide feedback on the article before resubmission. To be compliant with our data use agreements, we are not allowed to reidentify and contact patients who were included in the study dataset to share the results of this research.

Baseline characteristics of study populations

Figure 1 reports patient inclusion flowcharts. After 1:1 propensity score matching, there were 389 454 propensity score matched pairs in the SGLT-2 inhibitor versus DPP-4 inhibitor cohort, 364 910 pairs in the GLP-1 receptor agonist versus DPP-4 inhibitor cohort, and 436 730 matched pairs in the SGLT-2 inhibitor versus GLP-1 receptor agonist cohort. After matching, all baseline characteristics in the three cohorts were well balanced, with standardized mean differences <0.10. Laboratory test results, including potassium, were also balanced, despite not being included in propensity score models ( table 1 , supplemental tables 3-5).

Patient flowchart. *<65 years for Medicare. DPP-4=dipeptidyl peptidase-4; GLP-1=glucagon-like peptide-1; SGLT-2=sodium-glucose cotransporter-2

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Selected baseline characteristics of people with type 2 diabetes starting SGLT-2 inhibitors versus DPP-4 inhibitors, GLP-1 receptor agonists versus DPP-4 inhibitors, and SGLT-2 inhibitors versus GLP-1 receptor agonists after 1:1 propensity score matching

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In the SGLT-2 inhibitor versus DPP-4 inhibitor cohort, the mean age was 63 years, 54% were male, and 30% had a history of cardiovascular disease. Commonly used drugs included metformin (81%), angiotensin converting enzyme inhibitors or angiotensin II receptor blockers (72%), statins (71%), and β blockers (35%). Mean estimated glomerular filtration rate was 79 mL/min/1.73 m 2 and mean serum potassium level was 4.4 mmol/L among the subset with available laboratory test results. Baseline characteristics were comparable in the GLP-1 receptor agonist versus DPP-4 inhibitor cohort, and the SGLT-2 inhibitor versus GLP-1 receptor agonist cohort. In the SGLT-2 inhibitor versus DPP-4 inhibitor cohort, 40.7% started empagliflozin, 38.7% started canagliflozin, and 20.3% started dapagliflozin (supplemental table 6). The most commonly used GLP-1 receptor agonists were liraglutide (37.2%), dulaglutide (31.8%), exenatide (15.7%), and semaglutide (13.0%).

Risk of hyperkalemia after starting SGLT-2 inhibitors, GLP-1 receptor agonists, and DPP-4 inhibitors

Mean on-treatment follow-up ranged between 8.1 and 8.8 months, reflecting the large rate of discontinuation in routine clinical practice (supplemental table 7). Use of SGLT-2 inhibitors versus DPP-4 inhibitors was associated with a lower rate of hyperkalemia in the propensity score matched cohort, with an adjusted hazard ratio of 0.75 (95% confidence interval (CI) 0.73 to 0.78). Incidence rates were 25.3 versus 18.5 events per 1000 person years, corresponding to an incidence rate difference of −6.88 (95% CI −7.65 to −6.11) events per 1000 person years ( table 2 ). Similarly, use of GLP-1 receptor agonists versus DPP-4 inhibitors was associated with a lower rate of hyperkalemia, with an adjusted hazard ratio of 0.79 (0.77 to 0.82). Incidence rates were 28.5 versus 22.1 events per 1000 person years, corresponding to an incidence rate difference of −6.36 (−7.24 to −5.48) per 1000 person years. The adjusted hazard ratio for SGLT-2 inhibitors versus GLP-1 receptor agonists was 0.92 (0.89 to 0.95). Incidence rates were 22.1 versus 19.8 events per 1000 person years, corresponding to an incidence rate difference of −2.31 (−3.05 to −1.57). Figure 2 shows cumulative incidence curves for all three cohorts and supplemental table 8 reports corresponding absolute risks and risk differences at six month intervals. The lower risk of hyperkalemia for SGLT-2 inhibitors and GLP-1 receptor agonists versus DPP-4 inhibitors appeared within six months of follow-up. At three years of follow-up, the absolute risk was 2.4% (95% CI 2.1% to 2.7%) lower for SGLT-2 inhibitors than DPP-4 inhibitors (4.6% v 7.0%), and 1.8% (1.4% to 2.1%) lower for GLP-1 receptor agonists than DPP-4 inhibitors (5.7% v 7.5%).

Comparative effectiveness of SGLT-2 inhibitors versus DPP-4 inhibitors, GLP-1 receptor agonists versus DPP-4 inhibitors, and SGLT-2 inhibitors versus GLP-1 receptor agonists in reducing risk of hyperkalemia in inpatient or outpatient setting after 1:1 propensity score matching

Cumulative incidence curves for SGLT-2 inhibitors versus DPP-4 inhibitors (upper panel), GLP-1 receptor agonists versus DPP-4 inhibitors (middle panel), and SGLT-2 inhibitors versus GLP-1 receptor agonists (lower panel) for primary outcome of risk of hyperkalemia diagnosis in inpatient or outpatient setting after 1:1 propensity score matching. DPP-4=dipeptidyl peptidase-4; GLP-1=glucagon-like peptide-1; SGLT-2=sodium-glucose cotransporter-2

When using serum potassium ≥5.5 mmol/L as the outcome definition, hazard ratios were 0.86 (0.78 to 0.95) for SGLT-2 inhibitors versus DPP-4 inhibitors, 0.82 (0.73 to 0.91) for GLP-1 receptor agonists versus DPP-4 inhibitors, and 1.01 (0.91 to 1.12) for SGLT-2 inhibitors versus GLP-1 receptor agonists (supplemental table 9). Furthermore, when using hyperkalemia diagnosis in the inpatient or emergency department setting, adjusted hazard ratios were 0.77 (0.69 to 0.85), 0.65 (0.59 to 0.72), and 0.96 (0.86 to 1.06), respectively (supplemental table 10).

SGLT-2 inhibitors and GLP-1 receptor agonists showed protective associations for hyperkalemia across all subgroups compared with DPP-4 inhibitors ( fig 3 , fig 4 ). Benefits for SGLT-2 inhibitors and GLP-1 receptor agonists on the absolute scale were largest for those with heart failure, chronic kidney disease, or those using mineralocorticoid receptor antagonists. Findings for the SGLT-2 inhibitor versus GLP-1 receptor agonist cohort were consistent, with absence of large differences in hyperkalemia rate between the two drug classes across subgroups ( fig 5 ). Findings were also consistent across sensitivity analyses (supplemental table 11).

Comparative effectiveness of SGLT-2 inhibitors versus DPP-4 inhibitors for primary outcome of risk of hyperkalemia diagnosis in inpatient or outpatient setting among subgroups after 1:1 propensity score matching. Number of propensity score matched patients in subgroups do not exactly add up to overall number of propensity score matched patients in main analysis because propensity score matching was performed within each subgroup; therefore, it is possible that more patients are matched within subgroups. ACE=angiotensin converting enzyme inhibitor; ARB=angiotensin II receptor blocker; ARNI=angiotensin receptor/neprilysin inhibitor; CI=confidence interval; CVD=cardiovascular disease; CKD=chronic kidney disease; DPP-4=dipeptidyl peptidase-4; IR=incidence rate; MRA=mineralocorticoid receptor antagonist; PY=person years; SGLT-2=sodium-glucose cotransporter-2. *Only data from Medicare; †only data from Optum’s deidentified Clinformatics Data Mart Database

Comparative effectiveness of GLP-1 receptor agonists versus DPP-4 inhibitors for primary outcome of risk of hyperkalemia diagnosis in inpatient or outpatient setting among subgroups after 1:1 propensity score matching. Number of propensity score matched patients in subgroups do not exactly add up to overall number of propensity score matched patients in main analysis because propensity score matching was performed within each subgroup; therefore, it is possible that more patients are matched within subgroups. ACE=angiotensin converting enzyme inhibitor; ARB=angiotensin II receptor blocker; ARNI=angiotensin receptor/neprilysin inhibitor; CI=confidence interval; CVD=cardiovascular disease; CKD=chronic kidney disease; DPP-4=dipeptidyl peptidase-4; GLP-1=glucagon-like peptide-1; IR=incidence rate; MRA=mineralocorticoid receptor antagonist; PY=person years. *Only data from Medicare; †only data from Optum’s deidentified Clinformatics Data Mart Database

Comparative effectiveness of SGLT-2 inhibitors versus GLP-1 receptor agonists for primary outcome of risk of hyperkalemia diagnosis in inpatient or outpatient setting among subgroups after 1:1 propensity score matching. Number of propensity score matched patients in subgroups do not exactly add up to overall number of propensity score matched patients in main analysis because propensity score matching was performed within each subgroup; therefore, it is possible that more patients are matched within subgroups. ACE=angiotensin converting enzyme inhibitor; ARB=angiotensin II receptor blocker; ARNI=angiotensin receptor/neprilysin inhibitor; CI=confidence interval; CVD=cardiovascular disease; CKD=chronic kidney disease; GLP-1=glucagon-like peptide-1; IR=incidence rate; MRA=mineralocorticoid receptor antagonist; PY=person years; SGLT-2=sodium-glucose cotransporter-2. *Only data from Medicare; †only data from Optum’s deidentified Clinformatics Data Mart Database

Effectiveness of individual agents in SGLT-2 inhibitor and GLP-1 receptor agonist classes compared with DPP-4 inhibitors

Compared with DPP-4 inhibitors, the lower rate of hyperkalemia was consistent for single agents within the SGLT-2 inhibitor class: hazard ratios were 0.76 (0.72 to 0.80) for canagliflozin, 0.85 (0.79 to 0.91) for dapagliflozin, and 0.75 (0.71 to 0.78) for empagliflozin ( table 3 ). Hazard ratios were consistent among individual GLP-1 receptor agonist agents compared with DPP-4 inhibitors, with hazard ratios of 0.80 (0.76 to 0.84) for dulaglutide, 0.78 (0.73 to 0.84) for exenatide, 0.79 (0.75 to 0.83) for liraglutide, and 0.74 (0.68 to 0.80) for semaglutide ( table 4 ).

Comparative effectiveness of individual SGLT-2 inhibitor agents versus DPP-4 inhibitors in reducing risk of hyperkalemia in inpatient or outpatient setting after 1:1 propensity score matching

Comparative effectiveness of individual GLP-1 receptor agonist agents versus DPP-4 inhibitors in reducing risk of hyperkalemia in inpatient or outpatient setting after 1:1 propensity score matching

Statement of principal findings

In this cohort study using three nationwide administrative claims databases in the United States, we found a lower rate of hyperkalemia in people with type 2 diabetes who started SGLT-2 inhibitors or GLP-1 receptor agonists compared with DPP-4 inhibitors. These observations were consistent in subgroups and several sensitivity analyses, and across comparisons of single agents within the SGLT-2 inhibitor and GLP-1 receptor agonist classes.

Novelty and comparison with previous studies

Our study provides several new findings and builds upon current evidence. An individual participant meta-analysis using data from six randomized clinical trials and comprising 49 875 patients found that SGLT-2 inhibitors reduced the risk of hyperkalemia compared with placebo. 21 Our study provides additional evidence by extending these results to a broader group of >750 000 people with type 2 diabetes in routine clinical practice. Additionally, our study provides evidence of the association between GLP-1 receptor agonists and hyperkalemia, which has been lacking in large scale epidemiological studies or trial analyses. The relative rate reduction observed for GLP-1 receptor agonists versus DPP-4 inhibitors (21% reduction) was similar to the reduction observed for SGLT-2 inhibitors versus DPP-4 inhibitors (25% relative reduction in hazard). In head-to-head comparisons of SGLT-2 inhibitors versus GLP-1 receptor agonists, we only observed small differences (hazard ratio 0.92 in the primary analysis), and in several secondary and sensitivity analyses we observed no association. We interpret these findings to indicate that no large differences exist in the rate of hyperkalemia between SGLT-2 inhibitors and GLP-1 receptor agonists, although the subgroup with chronic kidney disease showed a larger effect size on the relative scale. However, these subgroup findings should be considered hypothesis generating and interpreted with caution because many subgroup analyses were performed. Finally, our large study population allowed us to investigate associations with a precision sufficient to exclude the presence of clinically meaningful treatment effect heterogeneity by relevant patient subgroups. We were also able to exclude the presence of large differences in the reduction of hyperkalemia risk across individual SGLT-2 inhibitor and GLP-1 receptor agonist agents compared with DPP-4 inhibitors.

Possible explanations and clinical implications

There are several potential mechanisms by which SGLT-2 inhibitors and GLP-1 receptor agonists might lower the risk of hyperkalemia. SGLT-2 inhibitors and GLP-1 receptor agonists could increase the delivery of sodium and water to the cortical collecting duct of the kidney. Increased absorption of sodium by the principal cells might increase the electronegative charge, leading to increased potassium secretion. 25 26 41 42 A small randomized trial of 35 participants with type 2 diabetes showed increased fractional and absolute excretion of potassium after eight weeks of treatment with the GLP-1 receptor agonist lixisenatide. 24 Furthermore, both drug classes have been shown to slow progression of kidney function decline and albuminuria, and the preserved kidney function might contribute to the prevention of hyperkalemia in the long term. 43 44 45 46 47 48

Our findings have important clinical implications. Hyperkalemia is a common electrolyte disorder among patients with type 2 diabetes, especially in those with concurrent heart failure or decreased kidney function, and who use guideline recommended treatments that increase potassium levels, such as angiotensin converting enzyme inhibitors, angiotensin II receptor blockers, or mineralocorticoid receptor antagonists. 10 The occurrence of hyperkalemia frequently leads to dose reduction or discontinuation of these drugs, and this discontinuation is associated with adverse cardiovascular and kidney outcomes. 11 12 13 Although newer potassium binders such as patiromer and sodium zirconium cyclosilicate might allow the use of renin-angiotensin system inhibitors, 49 50 51 they add to the pill burden, and their benefits on hard clinical outcomes are unknown. Identifying additional strategies that prevent hyperkalemia is therefore a key priority. Our findings suggest that SGLT-2 inhibitors and GLP-1 receptor agonists are associated with lower risk of hyperkalemia. This ancillary benefit further supports the use of SGLT-2 inhibitors and GLP-1 receptor agonists in people with type 2 diabetes.

Unanswered questions and future research

In our analyses, we focused on hyperkalemia as an outcome. A recent post hoc analysis of the CREDENCE (canagliflozin and renal events in diabetes with established nephropathy clinical evaluation) and DAPA-CKD (dapagliflozin and prevention of adverse outcomes in chronic kidney disease) trials found that SGLT-2 inhibitor use was associated with a lower rate of discontinuation of renin-angiotensin-aldosterone system inhibitors compared with placebo during follow-up in patients with albuminuric chronic kidney disease. Future studies should investigate whether these effects are also observed for GLP-1 receptor agonists, and whether this is mediated by a lower risk of hyperkalemia. Similarly, studies could investigate whether SGLT-2 inhibitors or GLP-1 receptor agonists have an effect on the use of loop diuretics.

Strengths and weaknesses of the study

The strengths of our study include its large sample size, more than 15-fold larger than the individual participant meta-analysis of randomized trials previously discussed, 21 which allowed investigation of important subgroups and individual agents, and rich adjustment for >140 potential confounders. Furthermore, we applied rigorous methods, including the use of an active comparator and new user cohort design, which reduces confounding and mitigates time related and selection bias caused by prevalent users. 52 53

Our study has several limitations. We cannot rule out the presence of unmeasured confounding. However, our analysis accounted for a wide set of confounders, 53 and balance was achieved even among the laboratory test results that were not included in the adjustment. Furthermore, confounding by indication is less likely because hyperkalemia is an unintended effect of glucose lowering drugs and currently not an indication that would drive a choice of one of the three investigated drug classes. 54 55 We also used a claims based definition for our primary outcome, with excellent specificity (>99%), but low sensitivity (22.3%). Therefore, although relative risk estimates are not expected to be biased under the assumption of non-differential measurement error, differences on the absolute scale are probably an underestimate of the true benefit of SGLT-2 inhibitors and GLP-1 receptor agonists. We believe non-differential measurement error might be a plausible assumption in our study because hyperkalemia has not been a safety concern for either of these drug classes. Furthermore, we adjusted for a wide number of measures of healthcare use (eg, number of outpatient visits and number of laboratory tests) to ensure patients were comparable at baseline with respect to healthcare surveillance and would have a similar opportunity for potassium monitoring during follow-up.

Mean follow-up in our study was relatively short (around eight to nine months) owing to high rates of treatment discontinuation. Nevertheless, this represents the reality of routine clinical practice in which many patients discontinue their treatment during follow-up. Therefore, our results reflect the outcomes that could be expected in patients from clinical practice after starting these drugs. We believe this timeframe should be sufficient to show the effects of GLP-1 receptor agonists and SGLT-2 inhibitors because mechanistic studies have found rapid effects of GLP-1 receptor agonists on potassium handling, 24 25 and post hoc analyses of randomized trials of SGLT-2 inhibitors have shown separation of survival curves within one year for hyperkalemia. 22 23 Finally, our findings are representative of the insured population in the United States, but might not be generalizable to uninsured patients.

In this analysis of three nationwide US databases, use of SGLT-2 inhibitors and GLP-1 receptor agonists was associated with a lower rate of hyperkalemia compared with DPP-4 inhibitors. This study further supports the use of these agents in a broad range of people with type 2 diabetes.

What is already known on this topic

Hyperkalemia is associated with increased mortality and limits the use of guideline recommended drugs such as renin-angiotensin system inhibitors among people with type 2 diabetes

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and dipeptidyl peptidase-4 (DPP-4) inhibitors are increasingly being used in the treatment of type 2 diabetes

The comparative effectiveness of these drugs in preventing hyperkalemia in routine clinical practice is unclear

What this study adds

In this population based cohort study of people with type 2 diabetes in the United States, starting SGLT-2 inhibitors or GLP-1 receptor agonists was associated with a lower risk of hyperkalemia compared with DPP-4 inhibitors

Benefits were consistent among demographic and clinical subgroups, and among single agents within the SGLT-2 inhibitor and GLP-1 receptor agonist classes

In addition to improving cardiovascular and kidney outcomes, the potential benefit of preventing hyperkalemia further solidifies the use of SGLT-2 inhibitors and GLP-1 receptor agonists in people with type 2 diabetes

Ethics statements

Ethical approval.

This study was approved by the Mass General Brigham institutional review board and granted waiver of informed consent since only deidentified claims data were used.

Data availability statement

A data use agreement is required for each of these data sources. These data use agreements do not permit the authors to share patient level source data or data derivatives with individuals and institutions not covered under the data use agreements. The databases used in this study are accessible to other researchers by contacting the data providers and acquiring data use agreements or licenses.

Acknowledgments

The authors thank Julianna Mastrorilli for help with Aetion and administrative support.

Contributors: Concept and design: ELF, EP. Acquisition, analysis of interpretation of data: all authors. Drafting of the manuscript: ELF. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: ELF. Administrative, technical or material support: all authors. Supervision: EP. EP is the guarantor.

Funding: This study was funded by the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA. ELF is supported by a Rubicon grant from the Netherlands Organization for Scientific Research. EP is supported by research grants from the Patient-Centered Outcomes Research Institute (DB-2020C2-20326) and the Food and Drug Administration (5U01FD007213). JMP is supported by a National Institute of Health grant AR 075117. KB is supported by a grant from the National Institute on Aging (K01AG068365). SJC is supported by the American Diabetes Association (grant No 7-21-JDFM-005). The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare the following: support from Brigham and Women’s Hospital, Harvard Medical School, the Netherlands Organization for Scientific Research, Patient-Centered Outcomes Research Institute, Food and Drug Administration, National Institutes of Health, National Institute on Aging, American Diabetes Association for the submitted work. EP is principal investigator of an investigator initiated grant to the Brigham and Women’s Hospital from Boehringer Ingelheim, not directly related to the topic of the submitted work; DJW reports serving on data monitoring committees for Novo Nordisk not related to the topic of this work; SJC reports serving on advisory boards for Alexion Pharmaceuticals and employment of a family member by a Johnson and Johnson company. No other potential conflicts of interest relevant to this article were reported.

Transparency: The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The authors intend to disseminate these results through social media, press releases, and their website (bwhpromise.org).

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• Nilsson E ,
• Gasparini A ,
• Humphrey T ,
• Davids MR ,
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• Conference Participants
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• Harris ST ,
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• Caramori ML ,
• ElSayed NA ,
• on behalf of the American Diabetes Association
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• Ferreira JP ,
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• Blijdorp CJ ,
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• Wexler DJ ,
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• Eneanya ND ,
• Chronic Kidney Disease Epidemiology Collaboration
• Groenwold RHH ,
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• van Diepen M ,
• Cheung YB ,
• ↵ Evidence A. Platform® (2022). Software for real-world data analysis. Aetion, Inc. http://aetion.com .
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Guidelines To Writing A Clinical Case Report

What is a clinical case report.

A case report is a detailed report of the symptoms, signs, diagnosis, treatment, and follow-up of an individual patient. Case reports usually describe an unusual or novel occurrence and as such, remain one of the cornerstones of medical progress and provide many new ideas in medicine. Some reports contain an extensive review of the relevant literature on the topic. The case report is a rapid short communication between busy clinicians who may not have time or resources to conduct large scale research.

WHAT ARE THE REASONS FOR PUBLISHING A CASE REPORT?

The most common reasons for publishing a case are the following: 1) an unexpected association between diseases or symptoms; 2) an unexpected event in the course observing or treating a patient; 3) findings that shed new light on the possible pathogenesis of a disease or an adverse effect; 4) unique or rare features of a disease; 5) unique therapeutic approaches; variation of anatomical structures.

Most journals publish case reports that deal with one or more of the following:

• Unusual observations
• Adverse response to therapies
• Unusual combination of conditions leading to confusion
• Illustration of a new theory
• Question regarding a current theory
• Personal impact.

STRUCTURE OF A CASE REPORT[ 1 , 2 ]

Different journals have slightly different formats for case reports. It is always a good idea to read some of the target jiurnals case reports to get a general idea of the sequence and format.

In general, all case reports include the following components: an abstract, an introduction, a case, and a discussion. Some journals might require literature review.

The abstract should summarize the case, the problem it addresses, and the message it conveys. Abstracts of case studies are usually very short, preferably not more than 150 words.

Introduction

The introduction gives a brief overview of the problem that the case addresses, citing relevant literature where necessary. The introduction generally ends with a single sentence describing the patient and the basic condition that he or she is suffering from.

This section provides the details of the case in the following order:

• Patient description
• Case history
• Physical examination results
• Results of pathological tests and other investigations
• Treatment plan
• Expected outcome of the treatment plan
• Actual outcome.

The author should ensure that all the relevant details are included and unnecessary ones excluded.

This is the most important part of the case report; the part that will convince the journal that the case is publication worthy. This section should start by expanding on what has been said in the introduction, focusing on why the case is noteworthy and the problem that it addresses.

This is followed by a summary of the existing literature on the topic. (If the journal specifies a separate section on literature review, it should be added before the Discussion). This part describes the existing theories and research findings on the key issue in the patient's condition. The review should narrow down to the source of confusion or the main challenge in the case.

Finally, the case report should be connected to the existing literature, mentioning the message that the case conveys. The author should explain whether this corroborates with or detracts from current beliefs about the problem and how this evidence can add value to future clinical practice.

A case report ends with a conclusion or with summary points, depending on the journal's specified format. This section should briefly give readers the key points covered in the case report. Here, the author can give suggestions and recommendations to clinicians, teachers, or researchers. Some journals do not want a separate section for the conclusion: it can then be the concluding paragraph of the Discussion section.

Notes on patient consent

Informed consent in an ethical requirement for most studies involving humans, so before you start writing your case report, take a written consent from the patient as all journals require that you provide it at the time of manuscript submission. In case the patient is a minor, parental consent is required. For adults who are unable to consent to investigation or treatment, consent of closest family members is required.

Patient anonymity is also an important requirement. Remember not to disclose any information that might reveal the identity of the patient. You need to be particularly careful with pictures, and ensure that pictures of the affected area do not reveal the identity of the patient.

• Open access
• Published: 22 June 2024

Correlation between the incidence of inguinal hernia and risk factors after radical prostatic cancer surgery: a case control study

• An-Ping Xiang 1 , 2 ,
• Yue-Fan Shen 1 ,
• Xu-Feng Shen 1 &
• Si-Hai Shao 1  

BMC Urology volume  24 , Article number:  131 ( 2024 ) Cite this article

120 Accesses

Metrics details

The incidence of recurrent hernia after radical resection of prostate cancer is high, so this article discusses the incidence and risk factors of inguinal hernia after radical resection of prostate cancer.

This case control study was conducted in The First People’s Hospital of Huzhou clinical data of 251 cases underwent radical resection of prostate cancer in this hospital from March 2019 to May 2021 were retrospectively analyzed. According to the occurrence of inguinal hernia, the subjects were divided into study group and control group, and the clinical data of each group were statistically analyzed, Multivariate Logistic analysis was performed to find independent influencing factors for predicting the occurrence of inguinal hernia. The Kaplan-Meier survival curve was drawn according to the occurrence and time of inguinal hernia.

The overall incidence of inguinal hernia after prostate cancer surgery was 14.7% (37/251), and the mean time was 8.58 ± 4.12 months. The average time of inguinal hernia in patients who received lymph node dissection was 7.61 ± 4.05 (month), and that in patients who did not receive lymph node dissection was 9.16 ± 4.15 (month), and there was no significant difference between them ( P  > 0.05). There were no statistically significant differences in the incidence of inguinal hernia with age, BMI, hypertension, diabetes, PSA, previous abdominal operations and operative approach ( P  > 0.05), but there were statistically significant differences with surgical method and pelvic lymph node dissection ( P  < 0.05). The incidence of pelvic lymph node dissection in the inguinal hernia group was 24.3% (14/57), which was significantly higher than that in the control group 11.8% (23/194). Logistic regression analysis showed that pelvic lymph node dissection was a risk factor for inguinal hernia after prostate cancer surgery (OR = 0.413, 95%Cl: 0.196–0.869, P  = 0.02). Kaplan-Meier survival curve showed that the rate of inguinal hernia in the group receiving pelvic lymph node dissection was significantly higher than that in the control group ( P  < 0.05).

Pelvic lymph node dissection is a risk factor for inguinal hernia after radical resection of prostate cancer.

Peer Review reports

Prostate cancer is a common malignant tumor in urology, which occurs in the prostate epithelial tissue, There are an average of 190,000 new cases of prostate cancer each year and about 80,000 deaths worldwide each year [ 1 , 2 ]. In recent years, the incidence of prostate cancer has increased year by year, seriously affecting the health and quality of life of patients [ 3 ]. Worldwide, the incidence of prostate cancer is second only to lung cancer, and its death rate ranks 7th among male cancer causes [ 4 ]. Radical resection of prostate cancer (RP) is the main means for the treatment of prostate cancer, and the surgical methods are generally divided into open radical resection of prostate cancer (RRP) and minimally invasive radical resection of prostate cancer, the latter including laparoscopic radical resection of prostate cancer (LRP) and robot-assisted laparoscopic radical resection of prostate cancer (RALP) [ 5 , 6 , 7 ].

Inguinal hernia (IH) is a relatively common disease in clinic, which is caused by increased abdominal pressure, thinning of abdominal wall, and bulging of abdominal organs. Inguinal hernias include direct hernias, oblique hernias and femoral hernias [ 8 ]. At the onset, lumps protruding outward from the inguinal region can be seen. If the intestines cannot return to the abdominal cavity in time, it is easy to cause intestinal necrosis, intestinal obstruction, intestinal perforation and other complications, which may endanger the life safety of patients in severe cases [ 9 , 10 ].

With the extensive development of radical resection of prostate cancer in various hospitals, the problem of postoperative inguinal hernia has gradually attracted the attention of urologists. The previously reported incidence of IH after radical prostate cancer surgery was approximately 13.7% [ 11 ]. A study by Nagatani S et al. showed that the incidence of inguinal hernia after radical prostate cancer surgery was 7-21%, most of which occurred within 2 years after surgery [ 12 ]. A study by Stranne J et al. showed that the cumulative risk of IH occurrence within 48 months in open radical resection for prostate cancer group and non-surgical group was 12.2% and 5.8%, respectively [ 13 ]. Most cases of IH require surgery due to pain, discomfort, and incarceration and are considered an advanced complication of radical resection of prostate cancer. The adhesion after radical resection of prostate cancer also increases the difficulty of hernia repair. Therefore, urologists need to be concerned not only about the risk of urinary incontinence and erectile dysfunction after radical resection of prostate cancer, but also about the occurrence of IH.

In recent 10 years, many scholars around the world have studied the risk factors of inguinal hernia after radical prostate cancer surgery. Currently, most of the studies believe that anastomotic stenosis, previous history of inguinal hernia, and patent processus vaginalis are risk factors, However there is no consensus on the risk of lymph node dissection. For example, Niitsu H et al. believed that pelvic lymph node dissection during radical prostate cancer operation might damage the pectineal foramina, thereby increasing the risk of inguinal hernia [ 14 ]. Contrary to the results of Johan Stranne’s study, the author suggested that previous incidence of inguinal hernia and advanced age increased the risk of inguinal hernia after radical prostate cancer surgery, and pelvic lymph node dissection was not a significant risk factor [ 15 ]. There is also no consistent conclusion on the influence of BMI, age and surgical method.

Therefore, in order to further investigate the risk factors of inguinal hernia after radical prostate cancer surgery, especially the correlation between pelvic lymph node dissection and inguinal hernia, this study was conducted. This study retrospectively analyzed the clinical data of 251 patients who underwent radical resection of prostate cancer in our hospital from March 2019 to May 2021, and investigated the risk factors of postoperative inguinal hernia. It is reported as follows:

Research objectives

The objective of this study was to explore the incidence and risk factors of inguinal hernia after radical resection of prostate cancer, which provides reference for further research and guide the clinician to choose the appropriate surgical method according to the patient’s condition.

Research methods

The patient was also examined by B-ultrasound every 3 months at the outpatient PSA review to verify the occurrence of inguinal hernia. The subjects were divided into the inguinal hernia group (study group) and the non-inguinal hernia group (control group), If the diagnosis of inguinal hernia occurred, the follow-up was completed, and the type and time of inguinal hernia were recorded; otherwise, the follow-up was 2 years, and the relevant clinical parameters of each group were statistically analyzed (age, BMI, hypertension, diabetes mellitus, PSA value, previous abdominal operations, operation methods, operative approach, pelvic lymph node dissection)and the correlation between these parameters and the occurrence of inguinal hernia was analyzed, and the risk factors of inguinal hernia were found by Logistic regression analysis. According to the occurrence and time of inguinal hernia, Kaplan-Meier survival curve was drawn to compare the differences between the two groups.

The content of this study has been approved by the Ethics Committee of our hospital(approval number, 2,018,137). All patients signed informed consent forms. This is the protocol was registered on the Chinese Clinical Trial Registry. The study is planned to begin in mid-March 2019 and is planned to end by May 2021.

Inclusion criteria

Patients who received radical surgery for prostate cancer in Huzhou First People’s Hospital from March 2019 to May 2021; PSA was reviewed every 3 months after surgery, and check the inguinal area for protruding masses. Complete the 2-year follow-up plan.

Exclusion criteria

Patients with inguinal hernia before operation; patients with prior inguinal hernia surgery.

Statistical methods

SPSS 21.0 statistical software was used for statistical processing, the research data followed normal distribution, and the measured data were represented by X ± S. P  < 0.05 was considered statistically significant.

From March 2019 to May 2021, 318 cases of radical prostatectomy were performed in our hospital, during the follow-up period, a total of 28 cases died of other diseases, a total of 39 cases were lost to follow-up or clinical data were incomplete, and a total of 251 cases were finally followed up. There were no significant differences in age, BMI, hypertension, diabetes, PSA, previous abdominal operations and operative approach between the two groups ( P  > 0.05), while there were significant differences in surgical method and pelvic lymph node dissection ( P  < 0.05). The incidence of pelvic lymph node dissection in the inguinal hernia group 24.3% (14/57) was significantly higher than that in the control group 11.8% (23/194). See Table  1 for details.

Multivariate Logistic regression analysis of risk factors showed that pelvic lymph node dissection was a risk factor for inguinal hernia after prostate cancer surgery (OR =0.413, 95%Cl: 0.196-0.869, P  = 0.02). There was no statistical significance in age, BMI, hypertension, diabetes, PSA value, previous abdominal operations, operation method, operative approach were not risk factors for inguinal hernia ( P  > 0.05). See Table  2 for details.

The cases of inguinal hernia were grouped according to whether or not they had received pelvic lymph node dissection. The incidence and time of inguinal hernia in the two groups were recorded, and the Kaplan-Meier survival curve was drawn. The overall incidence of inguinal hernia after radical resection of prostate cancer was 14.7% (37/251), There were 26 cases with indirect hernia, accounting for 70.2% (26/37), 21.6% (8/37) with direct hernia, 8.2% (3/37) with oblique hernia and direct hernia, and the mean time of occurrence was 8.58 ± 4.12 months. The average time of inguinal hernia was 7.61 ± 4.05 (month) for those who received lymph node dissection and 9.16 ± 4.15 (month) for those who did not receive lymph node dissection, and there was no significant difference between them ( P  > 0.05). The incidence of inguinal hernia in the group receiving pelvic lymph node dissection was significantly higher than that in the control group ( P  < 0.05). See Fig.  1 for details.

Survival curve of pelvic lymph node dissection and inguinal hernia (month)

In recent years, the incidence of prostate cancer has increased year by year, seriously affecting the health and quality of life of patients, the complications after radical prostate cancer surgery mainly include urinary incontinence and sexual dysfunction, but inguinal hernia is also one of the common complications [ 16 ]. Liu L et al. found that open radical resection for prostate cancer technique and advanced patient age, especially those over 80 years old, are associated with a higher incidence of IH. Appropriate prophylaxis during surgery should be evaluated in high-risk patients [ 17 ].In some regional studies, low BMI has been identified as a risk factor for IH, and the risk threshold for BMI has not been determined, which is about BMI < 25 kg/m2 [ 18 ]. However, a number of studies have found that low BMI does not increase the risk of postoperative IH [ 19 , 20 ]. At present, there is no uniform conclusion on the risk of IH between open radical resection for prostate cancer and laparoscopic radical prostatectomy. The study of Alder R scholars believed that the incidence of IH after laparoscopic radical prostatectomy was relatively low [ 21 ], while Otaki T’s study shows that the incidence of IH after laparoscopic radical prostatectomy is 7.3% and that of open radical resection for prostate cancer is 8.4%, showing no statistical difference between them [ 20 ]. There is no consensus on whether pelvic lymph node dissection is a risk factor for inguinal hernia [ 14 , 15 ]. In short, the specific mechanism of inguinal hernia after radical prostate cancer surgery is unclear.

This study retrospectively analyzed the clinical data of 251 cases treated in our hospital, and found that the overall incidence of inguinal hernia was 14.7% (37/251), which was consistent with most of the current research results. We also found that the average time of occurrence of inguinal hernia after surgery was 8.58 ± 4.12 months, which provided certain guidance for our postoperative follow-up time.

In this study, through Logistic multivariate analysis, it was found that pelvic lymph node dissection was a risk factor for inguinal hernia after prostate cancer surgery (OR = 0.413, 95%Cl: 0.196–0.869, P  = 0.02). There was no statistical significance in age, BMI, hypertension, diabetes, PSA value, previous abdominal operations, operation method, operative approach and the occurrence of inguinal hernia after prostate cancer surgery ( P  > 0.05),but there were statistically significant differences with surgical method and pelvic lymph node dissection ( P  < 0.05). Therefore, the advantages and disadvantages of pelvic lymph node dissection should be reasonably evaluated for low-medium-risk prostate cancer patients, so as to avoid the occurrence of inguinal hernia. By drawing Kaplan-Meier survival curve, it was found that the rate of inguinal hernia in the group receiving pelvic lymph node dissection was significantly higher than that in the control group. Some studies believe that pelvic lymph node dissection during radical resection of prostate cancer operation will cause postoperative scar contraction in the inguinal region, resulting in an increase in abdominal pressure outward and downward, resulting in an increase in the incidence of inguinal hernia. Lodding P designed a comparative study between the group of radical resection of prostate cancer plus pelvic lymph node dissection, the group of pelvic lymph node dissection and the group without operation. They found that the incidence of inguinal hernia in the three observation groups was 13.6%, 7.6% and 3.1%, respectively, and the difference between the prostatectomy group and the group without operation was statistically significant. There was no significant difference between the group and pelvic lymph node dissection group. This result implies that pelvic lymph node dissection is an important factor in the development of inguinal hernia [ 22 ]. Another Sun M study compared the incidence of inguinal hernias after radical prostate cancer surgery and pelvic lymph node dissection alone, and showed that the risk of inguinal hernias increased by 6.8% and 7.8% at 5 and 10 years, respectively, in the radical prostate cancer resection group compared with the pelvic lymph node dissection group [ 23 ]. Niitsu H et al. believed that pelvic lymph node dissection during radical resection of prostate cancer might damage the pectineal foramina, while inguinal hernia originated from the defective pectineal foramina [ 14 ].

Shimbo M et al. found that due to prostatectomy and vesicourethral anastomosis, preoperative and postoperative sagittal MRI images showed that the rectovesical excavation (RE) was moved downward by about 2 to 3 cm [ 24 ]. Accordingly, they speculated that due to the displacement of RE, the peritoneum and vas deferens after urethrovesical anastomosis were pulled, which further pulled the opening of the inner ring and caused it to shift medially, which led to the occurrence of postoperative IH. Based on this theory, many scholars have prevented the occurrence of hernia after operation by reducing the tension of peritoneum and vas deferens at the inner ring and ligation and rupture of sheathing process. Several other articles have reported the role of preserving the retropubic space (RS) in preventing IH after radical resection of prostate cancer. Chang KD et al. found that robot-assisted laparoscopic radical prostatectomywith retained Retzius space significantly reduced the incidence of postoperative IH compared with standard robot-assisted laparoscopic radical prostatectomy [ 25 ]. In addition, the study of Matsubara et al. also showed that compared with standard open radical resection for prostate cancer, the incidence of IH after transperineal radical resection of prostate cancer with retained anatomical structures such as the Retzius space was lower [ 26 ]. Therefore, urological surgeons can take some effective measures in the operation to prevent the recurrence of inguinal hernia.

In this study, we identified risk factors for inguinal hernia after pelvic lymphadenectomy for prostate cancer. Other risk factors such as age, BMI, hypertension, diabetes mellitus, PSA value, history of abdominal surgery, operative method, operative approach were not significant in multivariate analysis, which was inconsistent with the results of Iwamoto H et al [ 27 ]. They found that dilatation of the right internal inguinal ring and different manipulation of the medial peritoneal incision of the ventral femoral ring were independent risk factors for IH after laparoscopic radical prostatectomy. The reason why postoperative IH occurs more often on the right side is not known. Alder R et al. found that the incidence of IH after open radical prostate cancer treatment was significantly higher than laparoscopic radical prostate cancer treatment [ 21 ], but our study did not show a difference between the two groups, possibly due to the small number of cases included in open radical prostate surgery.

In summary, the incidence of inguinal hernia after radical prostate cancer surgery is relatively high, and the specific cause is still unclear. Our study shows that pelvic lymph node dissection is a risk factor for inguinal hernia.

Limitations

The sample size of this study is small, and it belongs to a single-center study, so the representativeness of the research conclusions may not be strong. This time, we followed up the samples for 2 years, which was not long enough and may have overlooked the real incidence of inguinal hernia. In addition, this study is a retrospective study, and the clinical parameters observed are not very comprehensive, which may ignore the influence of other factors on the IH. Because our data is derived from clinical data, some data cannot be detected. These problems need further study by more scholars.

Data availability

We cannot provide and share our datasets in publicly available repositories because of informed consent for participants as confidential patient data. Data may be obtained from the corresponding author upon reasonable request.

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This work was supported by the following funding: the grant 2019GY23 from Huzhou Science and Technology Bureau Public welfare application research project of China.

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An-Ping Xiang, Yue-Fan Shen, Xu-Feng Shen & Si-Hai Shao

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An-Ping Xiang designed the study and drafted and revised the manuscript, Yue-Fan Shen recorded the patients cases, Xu-Feng Shen participated in the follow-up. An-Ping Xiang and Si-Hai Shao analyzes the data and draw graphs.

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Xiang, AP., Shen, YF., Shen, XF. et al. Correlation between the incidence of inguinal hernia and risk factors after radical prostatic cancer surgery: a case control study. BMC Urol 24 , 131 (2024). https://doi.org/10.1186/s12894-024-01493-w

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Published on 27.6.2024 in Vol 12 (2024)

Data Flow Construction and Quality Evaluation of Electronic Source Data in Clinical Trials: Pilot Study Based on Hospital Electronic Medical Records in China

Authors of this article:

• Yannan Yuan 1 , MS ; 
• Yun Mei 2 , MS ; 
• Shuhua Zhao 1 , MS ; 
• Shenglong Dai 3 , MS ; 
• Xiaohong Liu 1 , MS ; 
• Xiaojing Sun 3 , MA ; 
• Zhiying Fu 1 , MS ; 
• Liheng Zhou 3 , MS ; 
• Jie Ai 2 , MS ; 
• Liheng Ma 3 , MD ; 
• Min Jiang 4 , MS

1 Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), National Drug Clinical Trial Center, Peking University Cancer Hospital & Institute, , Beijing, , China

2 Yidu Tech Inc, , Beijing, , China

3 Pfizer (China) Research & Development Co, , Shanghai, , China

4 State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Beijing Key Laboratory of Carcinogenesis and Translational Research, National Drug Clinical Trial Center, Peking University Cancer Hospital & Institute, , Beijing, , China

Corresponding Author:

Min Jiang, MS

Background: The traditional clinical trial data collection process requires a clinical research coordinator who is authorized by the investigators to read from the hospital’s electronic medical record. Using electronic source data opens a new path to extract patients’ data from electronic health records (EHRs) and transfer them directly to an electronic data capture (EDC) system; this method is often referred to as eSource. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness. At the same time, improved data collection efficiency reduces clinical trial costs.

Objective: This study aims to explore how to extract clinical trial–related data from hospital EHR systems, transform the data into a format required by the EDC system, and transfer it into sponsors’ environments, and to evaluate the transferred data sets to validate the availability, completeness, and accuracy of building an eSource dataflow.

Methods: A prospective clinical trial study registered on the Drug Clinical Trial Registration and Information Disclosure Platform was selected, and the following data modules were extracted from the structured data of 4 case report forms: demographics, vital signs, local laboratory data, and concomitant medications. The extracted data was mapped and transformed, deidentified, and transferred to the sponsor’s environment. Data validation was performed based on availability, completeness, and accuracy.

Results: In a secure and controlled data environment, clinical trial data was successfully transferred from a hospital EHR to the sponsor’s environment with 100% transcriptional accuracy, but the availability and completeness of the data could be improved.

Conclusions: Data availability was low due to some required fields in the EDC system not being available directly in the EHR. Some data is also still in an unstructured or paper-based format. The top-level design of the eSource technology and the construction of hospital electronic data standards should help lay a foundation for a full electronic data flow from EHRs to EDC systems in the future.

Introduction

Source data are the original records from clinical trials or all information recorded on certified copies, including clinical findings, observations, and records of other relevant activities necessary for the reconstruction and evaluation of the trial [ 1 ]. Electronic source data are data initially recorded in an electronic format (electronic source data or eSource) [ 2 , 3 ].

The traditional clinical trial data collection process requires a clinical research coordinator (CRC) who is authorized by the investigators to read from the hospital’s electronic medical record and other clinical trial–related data from the hospital information system and then manually enter the patient’s data into the electronic data capture (EDC) system. After data entry, the clinical research associate visits the site to perform source data verification and source data review. The drawbacks of collecting data by manual transcription are that data quality and timeliness cannot be guaranteed and that it is a waste of human and material resources. Using electronic source data opens a new path to extract patients’ data from electronic health records (EHRs) and transfer it directly to EDC systems (often the method is referred to as eSource) [ 4 ]. eSource technology in a clinical trial data flow can improve data quality without compromising timeliness [ 5 ]. At the same time, improved data collection efficiency reduces clinical trial costs [ 6 ].

eSource can be divided into two levels. The first level is to enable the hospital information system to obtain complete data sets; the second level is to allow direct data transfer to EDC systems based on the clinical trial patients’ electronic data in hospitals to avoid the electronic data being transcribed manually again, which is the core purpose of eSource [ 7 ]. This project will explore the use of eSource technology to extract clinical trial data from EHRs, send it to the sponsor data environment, and discuss the issues and challenges occurring in its application process.

Ethics Approval

This study was approved by the Ethics Committee and Human Genetic Resource Administration of China (2020YW135). During the ethical review process, the most significant challenges were patients’ informed consent, privacy protection, and data security. The B7461024 Informed Consent Form (Version 4) states that “ interested parties may use subjects’ personal information to improve the quality, design, and safety of this and other studies,” and “Is my personal information likely to be used in other studies? Your coded information may be used to advance scientific research and public health in other projects conducted in future.” This project is an exploration of using electronic source data technology instead of traditional manual transcription in the process of transferring data from hospital EHRs to EDC systems, which will improve the data quality of clinical trials and will improve the data flow in the future. Therefore, this project is within the scope of the informed consent form for study B7461024, which was approved by the ethics committee after clarification.

Project Information

This project was conducted from December 15, 2020, to November 19, 2021, which was before China’s personal information protection law and data security law were introduced. The data for this project were obtained from an ongoing phase 2, multicenter, open-label, dual-cohort study to evaluate the efficacy and safety of Lorlatinib (pf-06463922) monotherapy in anaplastic lymphoma kinase (ALK) inhibitor–treated locally advanced or metastatic ALK-positive non–small cell lung cancer patients in China (B7461024), registered by the sponsor on the Drug Clinical Trials Registration and Disclosure Platform (CTR20181867). The data extraction involved 4 case report form (CRF) data modules: demographics, concomitant medication, local lab, and vital signs, which were collected in the following ways:

• Demographics: Originally entered directly into the hospital EHR then manually transcribed by the CRC to the sponsor’s EDC system
• Local lab: Laboratory data collected by the hospital laboratory information management system (LIMS) and then manually transcribed by the CRC into the EDC system
• Vital signs: Hospital uses paper-based tracking form provided by the sponsor to record patients’ vital signs and investigators transcribe the vital signs data into the hospital medical record
• Concomitant medication: Similar to vital signs, hospital uses the paper tracking form provided by the sponsor to record the adverse reactions and concomitant medication; investigator might also transfer the concomitant medication data into the hospital EHR, but there was no mandatory requirement to transfer these data into patients’ medical records

All information was collected from 6 patients in a total of 29 fields ( Textbox 1 ).

Demographics

• Date of birth

Concomitant medication

• Combined drug name
• Whether for the treatment of adverse reactions
• Adverse event number
• Combined drug start date
• Combined drug end date
• Currently still in use

Vital signs

• Date of vital signs collection
• Weight unit
• Body temperature
• Height unit
• Location of temperature measurement
• Systolic blood pressure
• Diastolic blood pressure
• Laboratory inspection name
• Laboratory name and address
• Sponsor number
• Laboratory number
• Incomplete laboratory inspection
• Sample collection data
• Inspection results

Data Process Workflow

The study chosen in our project used the traditional manual data entry method to transcribe patients’ CRF data into the EDC system. This project proposes testing the acquisition of data directly from the hospital EHR, deidentification of the patients’ electronic data on the hospital medical data intelligence platform, mapping and transforming the data based on the sponsor’s EDC data standard, and transferring the data into the sponsor’s environment. The data was transferred from the hospital to the sponsor’s data environment and compared to data that was captured by traditional manual entry methods to verify the availability, completeness, and accuracy of the eSource technology.

In the network environment of this project, the technology provider accessed the hospital network through a virtual private network (VPN) and a bastion host, and processed the data of this project as a private cloud, thus ensuring the security of the hospital data.

Data Integration

The hospital information system involved in this project has reached the national standards of “Level 3 Equivalence,” “Electronic Medical Record Level 5,” and “Interoperability Level 4.” The medical data intelligence platform in this project is deployed in a hospital intranet, isolated from external networks. Integrated data from different information systems, including the hospital information system, LIMS, picture archiving and communication system, etc, were deidentified from the platform and transferred to a third-party private cloud platform for translation and data format conversion after authorization by the hospital through a VPN.

The scope of data collection in this project was limited to patients who signed Informed Consent Form (Version 4) for study B7461024. The structured data of four CRF data modules (demographic, concomitant medications, local lab, and vital signs) were extracted from the source data in hospital systems, and data processing was completed.

Three-Layer Deidentification of Data

In this project, three layers of deidentification were performed on the electronic source data to ensure data security. The first layer of deidentification was performed before the certified copy of data was loaded to the hospital’s medical data intelligence platform. The second layer of deidentification follows the Health Insurance Portability and Accountability Act (HIPAA) by deidentifying 18 data fields at the system level. A third layer of deidentification was performed when mapping and transforming third-party databases for the clinical trial data (demographics, concomitant medications, laboratory tests, and vital signs) collected for this study, as required by the project design.

Collected data did not contain any sensitive information with personal identifiers of the patients, and all deidentification processes were conducted in the internal environment of the hospital. In addition to complying with the relevant laws and regulations, we followed the requirements of Good Clinical Practice regarding patient privacy and confidentiality, and further complied with the requirements of HIPAA to deidentify the 18 basic data fields. Data fields outside the scope of HIPAA will be deidentified and processed in accordance with the TransCelerate guidelines published in April 2015 to ensure the security of patients’ personal information and to eliminate the possibility of patient information leakage [ 8 ].

The general rules for the third layer of deidentification were as follows:

• Time field: A specific time point is used as the base time, and the encrypted time value is the difference between the word time and the base time
• ID field: Categorized according to the value and only shows the category
• Age field: Categorized according to the value and only shows the category
• Low-frequency field: set to null

In addition, all data flows keep audit trails throughout and are available for audit.

Data Normalization and Information Extraction

After three layers of deidentification, the data was transferred from a hospital to a third-party private cloud platform through a VPN, where translation from Chinese to English and data format conversion were implemented. The whole transfer process was performed for the data that was collected for the clinical trial of this study. Standardization of data is a crucial task during the data preparation phase. This process involves consolidating data from different systems and structures into a consistent, comprehensible, and operable format. First, a thorough examination of data from various systems is necessary. Understanding the data structure, format, and meaning of each system is essential. The second step involves establishing a data dictionary that clearly outlines the meaning, format, and possible values of each data element. Next, selecting a data standard is necessary to ensure consistency and comparability. In this study, we adopted the Health Level 7 (HL7) standard. Additionally, data cleansing and transformation are needed to meet standard requirements, including handling missing data, resolving mismatched data formats, or performing data type conversions. Extract, transform, and load tools were used to integrate data from different systems. Data security must be ensured throughout the data integration process. This includes encrypting sensitive information and strictly managing data permissions. Data verification and validation steps were then performed by professional staff on the translated data. The data from the hospital’s medical data intelligence platform were then converted from JSON format to XML and Excel formats. The processed data was transferred back to the hospital via a VPN to a designated location for final adjudication before loading to the sponsor’s environment.

One-Time Data Push and Quality Assessment

After the hospital received the processed data, it was then pushed by the hospital to the sponsor’s secure and controlled environment ( Figure 1 ). All data deidentification processes were conducted in the hospital’s environment, and none of the data obtained by the sponsor can be traced back to patients’ personal information to ensure their privacy and information security.

The data quality of this project was assessed using industry data quality assessment rules [ 9 ], which are shown in Table 1 .

a CRF: case report form.

b EHR: electronic health record.

c Total number of data fields in the hospital’s EHR.

d Total number of data fields requested in the electronic CRF.

e Total number of data fields captured (processed and sent to the sponsor) through the eSource method.

f Total number of nonempty data fields captured (processed and sent to the sponsor) through the eSource method.

In this project, we collected patients’ demographics, vital signs information, local laboratory data, and concomitant medication data from EHRs, successfully pushed the data directly to the designated sponsor environment, and evaluated the data quality from three perspectives including availability, completeness, and accuracy ( Table 2 ).

• The eSource-CRF availability score, which is used to evaluate the ratio of fields in EHR that can be collected by eSource and used for CRF, was low for demographics, blood tests, and urine sample tests but higher for vital signs and concomitant medications.
• Data completeness, defined as the ratio of the total number of nonnull data captured by eSource to the total number of data fields required in the electronic CRF, was used to evaluate the ratio of nonnull data fields in the CRF that can be captured by eSource. In this study, the completeness score of the vital signs module was only 1.32%, and the concomitant medications and laboratory test modules also had poor performance in the data completeness evaluation.
• Data accuracy, defined as the compatibility between the data field values in the hospital EHR and the data field values that can be collected using eSource, was 100% for all modules.
• EHR-CRF availability, which is used to evaluate the ratio of fields in the EHR that can be used for the CRF, was 50%, 60%, and 66.67% for demographics, blood tests, and urine sample tests, respectively, in this study, and the rest of the data were 100% available.

c Checks were made with the relevant clinical research associates (CRAs) regarding the original data collection and CRF completion methods for the following reasons: vital signs were obtained using paper tracking forms provided by the sponsor as the original data source, and the data may not be transcribed into the hospital information system (HIS) by the researcher. Therefore, data from many visits are not available in the HIS.

d A total of 2708 blood biochemistry tests were involved.

e Concomitant medication uses tracking forms to record adverse event and ConMed (a paper source), and data may not be transcribed into the HIS. As confirmed by the CRA, the percentage of paper ConMed sources was approximately 80%.

Although EHRs have been widely used, the degree of structure of EHR data varies substantially among different data modules. In EHRs, demographics, vital signs, local lab data, and concomitant medications are more structured than patient history or progress notes and often contain unstructured text [ 10 ]. Therefore, we selected these 4 well-structured data modules for exploration in this project.

For demographics data, among the 6 required fields (subject ID, date of birth, sex, ethnicity, race, and age), subject ID (subject code number/identifier in the trial, not the patient code number/identifier in the EHR system), ethnicity, and race were not available in the EHR, so the EHR-CRF availability score was 50%. Since this was an exploratory project, the date of birth field was also deidentified and thus could not be collected based on our deidentification rule, so the eSource-CRF availability score was 33%. In the future, the availability score can reach close to 100% by bidirectional design of the EHR and CRF under the premise of obtaining compliance for industrial-level applications.

The low availability score of local laboratory data on EHR-CRFs is due to the lack of required fields in the hospital system; “Lab ID” and “Not Done” do not exist in the LIMS, and for the “Clinically Significant” field, the meaning of laboratory test results needs to be manually interpreted by an investigator, so they cannot be transcribed directly. The availability score of eSource-CRFs was further decreased because the field “Laboratory Name and Address” is not an independent structured field in the EHR. The completeness score of urine sample test data was only 37.56% because during the actual clinical trial, especially amid the COVID-19 pandemic period, patients completed study-related laboratory tests at other sites, and those test results were collected via paper-based reports, so the complete data sets cannot be extracted from the site’s system.

To improve data availability in future applications, clinical trial–specific fields need to be added to EHR designs for those data that require an investigator’s interpretation such as “Clinically Significant,” and data transfer and mapping processes for the determination of the scope of data collection also needs to be optimized. Based on these two conditions, the completeness score can be improved to over 90%.

The availability and accuracy of vital signs data are ideal. However, since not all vital signs data collection was recorded by the electronic system during the actual study visit, many vital signs data were collected in “patient diary” and other types of paper-based documents during the study, resulting in a serious limitation in data completeness. With the development of more clinical trial–related electronic hardware and enhancements in products intelligence, more vital signs data will be directly collected by electronic systems, and the completeness of vital signs data transferred from EHR to EDC will be greatly improved in the future.

In the concomitant medication module, there was a good score for availability and accuracy because the standardization and structuring of prescriptions are well done in this hospital system. However, the patient’s medication use period during hospitalization is recorded in unstructured text, so the data could not be captured for this study, resulting in a low completeness score of 18.42% for concomitant medication.

In summary, the accuracy score of eSource data in this study was high (100% for all fields). A study by Memorial Sloan Kettering Cancer Center and Yale University confirmed that the error rate of automatic transcription reduced from 6.7% to 0% compared to manual transcription [ 10 ]. However, data availability and completeness have not reached a good level. Data availability varies widely across studies, ranging from 13.4% in the Retrieving EHR Useful Data for Secondary Exploitation (REUSE) project [ 11 ] to 75% in The STARBRITE Proof-of-Concept Study [ 12 ], mainly related to the coverage and structure of the EHR.

National drug regulatory agencies (eg, US Food and Drug Administration [FDA], European Medicines Agency, Medicines and Healthcare products Regulatory Agency, and Pharmaceuticals and Medical Devices Agency) have developed guidelines to support the application of eSource to clinical trials [ 3 , 13 - 15 ]. The new Good Clinical Practice issued by the Center for Drug Evaluation in 2020 encourages investigators to use clinical trials’ electronic medical records for source data documentation [ 1 ]. Despite this, we still encountered challenges, including ethical review and data security, during this study’s implementation process. Without knowing the precedents, the project team decided to follow the requirements for clinical trials to control the quality of the study. There were no existing regulatory policies or national guidance on eSource in China at the time of this study. The project team provided explanations for inapplicable documents and communicated several times to ensure the approval of relevant institutional departments before finally becoming the first eSource technology study to be approved by the Ethics Committee and Human Genetic Resource Administration of China.

In the absence of regulatory guidelines, our eSource study, the first in China’s International Multi-center Clinical Trial, navigated challenges in data deidentification. We adopted HIPAA and TransCelerate’s guidelines [ 8 ]. Securing approval under “China International Cooperative Scientific Research Approval for Human Genetic Resources,” we answered queries and achieved unprecedented recognition. For transferring data from the hospital to the sponsor’s environment, we prioritized security and obtained necessary approvals. Iterative revisions ensured a robust data flow design. Challenges in mapping hospital EHR to EDC standards highlighted the need for a scalable mechanism. This study pioneers eSource tech integration in China, emphasizing the importance of seamless data mapping. In the process of executing data standardization, several challenges may arise, including inconsistent data definitions. Data from different systems may use different definitions due to the independent development of these systems, leading to varied interpretations of even identical concepts. To address this issue, establishing a unified data dictionary is crucial to ensure consensus on the definition of each data element. Different systems might also use distinct data formats such as text encodings. Preintegration format conversion is required, and extract, transform, and load tools or scripts can assist in standardizing these formats. During the integration of data from multiple systems, it is possible to discover data in one system that is not present in another. In the data standardization process, considerations must be made on how to handle missing data, which may involve interpolation, setting default values, etc. Quality issues like errors, duplicates, or inaccuracies may exist in data from different systems. Data cleansing, involving deduplication, error correction, logical validation, etc, is necessary to address these quality issues. Different systems may generate data based on diverse business rules and hospital use scenarios. In data standardization, unifying these rules requires collaboration with domain experts to ensure consistency.

Internationally, multiple research studies and publications have been released on regulations, guidelines, and validation of eSource. The FDA provided guidance on the use of electronic source data in clinical trials in 2013 that aims to address barriers to capturing electronic source data for clinical trials, including the lack of interoperability between EHRs and EDC systems. The European-wide Electronic Health Records for Clinical Research (EHR4CR) project was launched in 2011 to explore technical options for the direct capture of EHR data within 35 institutions, and the project was completed in 2016 [ 16 ]. The second phase of the project connected the EHRs to EDC systems [ 17 ] and aimed to realize the interoperability of EHRs and EDC systems. The US experience focuses more on improving and standardizing the existing EHRs to make them more uniform.

In Europe, the experience focuses on breaking down the technical barrier of interoperability between EHRs and EDC systems. In China, the current industry trends focus on the governance of existing EHR data in the hospital and the building of clinical data repository platforms [ 7 ]. Clinical data repository platforms focus on data integration and cleaning between EHRs and other systems in hospital environments and on unstructured data normalization and standardization by natural language processing and other AI technology [ 18 ]. At the national level, China is also actively promoting the digitization of medical big data and is committed to the formation of regional health care databases [ 19 ], which lays the foundation for the future implementation of eSource in China [ 20 ].

This study evaluates the practical application value of eSource in terms of availability, completeness, and accuracy. To improve availability, the structure of the CRF needs to be designed according to the information of the EHR data at the design stage of clinical trials. Even so, since EHRs are designed for the physicians to conduct daily health care activities, certain fields in clinical trials (eg, judgment of normal or abnormal values of laboratory tests and judgment of correlations of adverse events and combined medications) are still not available, and clinical trial–specific fields need to be added to EHR designs for those data that require investigators’ interpretation to improve data availability. Completeness could be improved by the development of hospital digitalization that ensures patients’ data is collected electronically rather than on paper. Additionally, 2708 blood test records were successfully collected from only 6 patients via eSource in this study, which indicates that laboratory tests often contain large amounts of highly structured data that are suitable for eSource. EHR-EDC end-to-end automatic data extraction by eSource is suitable for laboratory examinations and can improve the efficiency and accuracy of data extraction significantly as well as reduce redundant manual transcriptions and labor costs. Processing unstructured or even paper-based data in eSource is still a big challenge. Using machine learning tools (eg, natural language processing tools) for autostructuring can be explored in the future. The goal is to have common data standards and better top-level design to facilitate data integrity, interoperability, data security, and patient privacy protection in eSource applications. During deidentification, we processed certain data with a specific logic to protect privacy. The accuracy assessment was performed during the deidentification step to ensure that the data was still sufficiently accurate while meeting privacy requirements. Reversible methods need to be used when performing deidentification as well as providing controlled access mechanisms to the data so that the raw data can be accessed when needed. It is worth noting that different regions and industries may have different privacy regulations and compliance requirements. When deidentifying, you need to ensure that you are compliant with the relevant regulations and understand the limitations of data use. This may require working closely with a legal team.

In the future, we can consider adding performance analysis, including an assessment of data import performance. This involves evaluating the speed and efficiency of data import to ensure it is completed within a reasonable timeframe. Additionally, analyzing data query performance is crucial in practical applications to ensure that the imported data meets the expected query performance in the application. For long-term applications involving a larger size of patients, it is advisable to consider adding analyses related to maintainability and cost-effectiveness. This includes implementing detailed logging and monitoring mechanisms to promptly identify and address potential issues. Furthermore, for the imported data, establishing a version control mechanism is essential for tracing and tracking changes in the data. Simultaneously, for overall resource use, evaluating the resources required during the data import process ensures completion within a cost-effective framework. It is also important to consider the value of imported data for clinical trial operations and related decision-making, providing a comparative analysis between cost and value.

Acknowledgments

This research was supported by the Capital's Funds for Health Improvement and Research (grant No. CFH2022-2Z-2153), and the Beijing Municipal Science & Technology Commission (grant No. Z211100003521008).

Conflicts of Interest

None declared.

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Abbreviations

Edited by Christian Lovis; submitted 19.09.23; peer-reviewed by Hareesh Veldandi, Yujie Su; final revised version received 20.12.23; accepted 18.04.24; published 27.06.24.

© Yannan Yuan, Yun Mei, Shuhua Zhao, Shenglong Dai, Xiaohong Liu, Xiaojing Sun, Zhiying Fu, Liheng Zhou, Jie Ai, Liheng Ma, Min Jiang. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.6.2024.

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