research paper on renal failure

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• Published: 10 January 2022

Chronic kidney disease and its health-related factors: a case-control study

• Mousa Ghelichi-Ghojogh 1 ,
• Mohammad Fararouei 2 ,
• Mozhgan Seif 3 &
• Maryam Pakfetrat 4  

BMC Nephrology volume  23 , Article number:  24 ( 2022 ) Cite this article

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Chronic kidney disease (CKD) is a non-communicable disease that includes a range of different physiological disorders that are associated with abnormal renal function and progressive decline in glomerular filtration rate (GFR). This study aimed to investigate the associations of several behavioral and health-related factors with CKD in Iranian patients.

A hospital-based case-control study was conducted on 700 participants (350 cases and 350 controls). Logistic regression was applied to measure the association between the selected factors and CKD.

The mean age of cases and controls were 59.6 ± 12.4 and 58.9 ± 12.2 respectively ( p  = 0.827). The results of multiple logistic regression suggested that many factors including low birth weight (OR yes/no  = 4.07, 95%CI: 1.76–9.37, P  = 0.001), history of diabetes (OR yes/no  = 3.57, 95%CI: 2.36–5.40, P  = 0.001), history of kidney diseases (OR yes/no  = 3.35, 95%CI: 2.21–5.00, P  = 0.001) and history of chemotherapy (OR yes/no  = 2.18, 95%CI: 1.12–4.23, P  = 0.02) are associated with the risk of CKD.

Conclusions

The present study covered a large number of potential risk/ preventive factors altogether. The results highlighted the importance of collaborative monitoring of kidney function among patients with the above conditions.

Peer Review reports

Chronic kidney disease (CKD) is a non-communicable disease that includes a range of different physiological disorders that are associated with an abnormal renal function and progressive decline in glomerular filtration rate (GFR) [ 1 , 2 , 3 ]. Chronic kidney disease includes five stages of kidney damage, from mild kidney dysfunction to complete failure [ 4 ]. Generally, a person with stage 3 or 4 of CKD is considered as having moderate to severe kidney damage. Stage 3 is broken up into two levels of kidney damage: 3A) a level of GFR between 45 to 59 ml/min/1.73 m 2 , and 3B) a level of GFR between 30 and 44 ml/min/1.73 m 2 . In addition, GFR for stage 4 is 15–29 ml/min/1.73 m 2 [ 4 , 5 ]. It is reported that both the prevalence and burden of CKD are increasing worldwide, especially in developing countries [ 6 ]. The worldwide prevalence of CKD (all stages) is estimated to be between 8 to 16%, a figure that may indicate millions of deaths annually [ 7 ]. According to a meta-analysis, the prevalence of stage 3 to 5 CKD in South Africa, Senegal, and Congo is about 7.6%. In China, Taiwan, and Mongolia the rate of CKD is about 10.06% and in Japan, South Korea, and Oceania the rate is about 11.73%. In Europe the prevalence of CKD is about 11.86% [ 8 ], and finally, about 14.44% in the United States and Canada. The prevalence of CKD is estimated to be about 11.68% among the Iranian adult population and about 2.9% of Iranian women and 1.3% of Iranian men are expected to develop CKD annually [ 9 ]. Patients with stages 3 or 4 CKD are at much higher risk of progressing to either end-stage renal disease (ESRD) or death even prior to the development of ESRD [ 10 , 11 ].

In general, a large number of risk factors including age, sex, family history of kidney disease, primary kidney disease, urinary tract infections, cardiovascular disease, diabetes mellitus, and nephrotoxins (non-steroidal anti-inflammatory drugs, antibiotics) are known as predisposing and initiating factors of CKD [ 12 , 13 , 14 ]. However, the existing studies are suffering from a small sample size of individuals with kidney disease, particularly those with ESRD [ 15 ].

Despite the fact that the prevalence of CKD in the world, including Iran, is increasing, the factors associated with CKD are explored very little. The present case-control study aimed to investigate the association of several behavioral and health-related factors with CKD in the Iranian population.

Materials and methods

In this study, participants were selected among individuals who were registered or were visiting Faghihi and Motahari hospitals (two largest referral centers in the South of Iran located in Shiraz (the capital of Fars province). Cases and controls were frequency-matched by sex and age. The GFR values were calculated using the CKD-EPI formula [ 16 , 17 ].

Data collection

An interview-administered questionnaire and the participant’s medical records were used to obtain the required data. The questionnaire and interview procedure were designed, evaluated, and revised by three experts via conducting a pilot study including 50 cases and 50 controls. The reliability of the questionnaire was measured using the test-retest method (Cronbach’s alpha was 0.75). The interview was conducted by a trained public health‌ nurse at the time of visiting the clinics.

Avoiding concurrent conditions that their association may interpreted as reverse causation; the questionnaire was designed to define factors preceding at least a year before experiencing CKD first symptoms. Accordingly participants reported their social and demographic characteristics (age, sex, marital status, educational level, place of residency), history of chronic diseases (diabetes, cardiovascular diseases, hypertension, kidney diseases, family history of kidney diseases, autoimmune diseases and thyroid diseases [ 18 ]). Also history of other conditions namely (smoking, urinary tract infection (UTI), surgery due to illness or accident, low birth weight, burns, kidney pain (flank pain), chemotherapy, taking drugs for weight loss or obesity, taking non-steroidal anti-inflammatory drugs, and taking antibiotic) before their current condition was started. Many researchers reported recalling birth weight to be reliable for research purposes [ 19 ]. Moreover, we asked the participants to report their birth weight as a categorical variable (< 2500 g or low, 2500- < 3500 g or normal, and > 3500 g or overweight). Medical records of the participants were used to confirm/complete the reported data. In the case of contradiction between the self-reported and recorded data, we used the recorded information for our study.

Verbal informed consent was obtained from patients because the majority of the participants were illiterate. The study protocol was reviewed and approved by the ethical committee of Shiraz University of Medical Sciences (approval number: 1399.865).

Sample size

The sample size was calculated to detect an association‌ between the history of using antibiotics (one of our main study variables) and CKD as small as OR = 1.5 [ 20 ]. With an alpha value of 0.05 (2-sided) and a power of 80%, the required sample size was estimated as large as n  = 312 participants for each group.

Selection of cases

The selected clinics deliver medical care to patients from the southern part of the country. In this study, patients with CKD who were registered with the above centers from June to December 2020 were studied. A case was a patient with a GFR < 60 (ml/min/1.73 m 2 ) at least twice in 3 months. According to the latest version of the International Classification of Diseases (2010), Codes N18.3 and N18.4 are assigned to patients who have (GFR = 30–59 (ml/min/1.73 m 2 ) and GFR = 15–29 (ml/min/1.73 m 2 ) respectively [ 21 ]. In total, 350 patients who were diagnosed with CKD by a nephrologist during the study period.

Selection of the controls

We used hospital controls to avoid recall-bias. The control participants were selected from patients who were admitted to the general surgery (due to hernia, appendicitis, intestinal obstruction, hemorrhoids, and varicose veins), and orthopedic wards‌ from June to December 2020. Using the level of creatinine in the participants’ serum samples, GFR was calculated and the individuals with normal GFR (ml/min/1.73 m 2 ) GFR > 60) and those who reported no history of CKD were included ( n  = 350).

Inclusion criteria

Patients were included if they were ≥ 20 years old and had a definitive diagnosis of CKD by a nephrologist.

Exclusion criteria

Participants were excluded if they were critically ill, had acute kidney injury, those undergone renal transplantation, and those with cognitive impairment.

Statistical analysis

The Chi-square test was used to measure the unadjusted associations between categorical variables and CKD. Multiple logistic regression was applied to measure the adjusted associations for the study variables and CKD. The backward variable selection strategy was used to include variables in the regression model. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. All p -values were two-sided and the results were considered statistically significant at p  < 0.05. All analyses were conducted using Stata version 14.0 (Stata Corporation, College Station, TX, USA).

In total, 350 cases and 350 age and sex-matched controls were included in the analysis. The mean age of cases and controls were 59.6 ± 12.4 and 58.9 ± 12.2 respectively ( p  = 0.83). Overall, 208 patients (59.4%) and 200 controls (57.1%) were male ( p  = 0.54). Also, 149 patients (42.6%) and 133 controls (38.0%) were illiterate or had elementary education ( p  = 0.001). Most cases (96.9%) and controls (95.7%) were married ( p  = 0.42). The mean GFR for CKD and control groups were 38.6 ± 11.4 and 78.3 ± 10.2 (ml/min/1.73 m2) respectively.

Result of univariate analysis

Table  1 illustrates the unadjusted associations of demographic and health-related variables with CKD. Accordingly, significant (unadjusted) associations were found between the risk of CKD and several study variables including education, history of chronic diseases (diabetes, cardiovascular, hypertension, kidney diseases, autoimmune diseases, and hypothyroidism), family history of kidney diseases, smoking, UTI, surgery due to illness or accident, low birth weight, burns, kidney pain, chemotherapy, taking non-steroidal anti-inflammatory drugs, and taking antibiotics) ( P  < 0.05 for all).

Results of multivariable analysis

Table  2 illustrates the adjusted associations between the study variables and the risk of CKD. Most noticeably, low birth weight (OR yes/no  = 4.07, 95%CI: 1.76–9.37, P  = 0.001), history of surgery (OR yes/no  = 1.74, 95%CI: 1.18–2.54, P  = 0.004), family history of kidney diseases (OR yes/no  = 1.97, 95%CI: 1.20–3.23, P  = 0.007), and history of chemotherapy (OR yes/no  = 2.18, 95%CI: 1.12–4.23, P  = 0.02) were significantly associated with a higher risk of CKD. On the other hand, education (OR college/illiterate or primary  = 0.54, 95%CI: 0.31–0.92, P  = 0.025) was found to be inversely associated with CKD.

The results of the present study suggested that several variables including, education, history of diabetes, history of hypertension, history of kidney diseases or a family history of kidney diseases, history of surgery due to illness or accident, low birth weight, history of chemotherapy, history of taking non-steroidal anti-inflammatory drugs, and history of taking antibiotics may affect the risk of CKD.

In our study, the level of education was inversely associated with the risk of CKD. This finding is in accordance with the results of a study conducted by K Lambert et.al, who suggested that illiteracy or elementary education may raise the risk of CKD [ 22 ]. The fact that education level is associated with health literacy, may partly explain our results that lower education and inadequate health literacy in individuals with CKD is associated with worse health outcomes including poorer control of biochemical parameters, higher risk of cardiovascular diseases (CVDs); a higher rate of hospitalization, and a higher rate of infections [ 23 ].

In the current study, the history of diabetes was associated with a higher risk of CKD. This finding is consistent with the results of other studies on the same subject [ 20 , 21 , 24 , 25 , 26 , 27 ]. It is not surprising that people with diabetes have an increased risk of CKD as diabetes is an important detrimental factor for kidney functioning as approximately, 40% of patients with diabetes develop CKD [ 27 ].

The other variable that was associated with an increased risk of CKD was a history of hypertension. Our result is consistent with the results of several other studies [ 20 , 24 , 25 , 28 ]. It is reported that hypertension is both a cause and effect of CKD and accelerates the progression of the CKD to ESRD [ 29 ].

After controlling for other variables, a significant association was observed between family history of kidney diseases and risk of CKD. Published studies suggested the same pattern [ 24 ]. Inherited kidney diseases (IKDs) are considered as the foremost reasons for the initiation of CKD and are accounted for about 10–15% of kidney replacement therapies (KRT) in adults [ 30 ].

The importance of the history of surgery due to illness or accident in this study is rarely investigated by other researchers who reported the effect of surgery in patients with acute kidney injury (AKI), and major abdominal and cardiac surgeries [ 31 , 32 ] on the risk of CKD. Also, AKI is associated with an increased risk of CKD with progression in various clinical settings [ 33 , 34 , 35 ]. In a study by Mizota et.al, although most AKI cases recovered completely within 7 days after major abdominal surgery, they were at higher risk of 1-year mortality and chronic kidney disease compared to those without AKI [ 31 ].

The present study also showed that low birth weight is a significant risk factor for CKD. This finding is consistent with the results of some other studies. However, the results of very few studies on the association between birth weight and risk of CKD are controversial as some suggested a significant association [ 19 , 36 , 37 ] whereas others suggested otherwise [ 36 ]. This may be explained by the relatively smaller size and volume of kidneys in LBW infants compared to infants that are normally grown [ 38 ]. This can lead to long-term complications in adolescence and adulthood including hypertension, decreased glomerular filtration, albuminuria, and cardiovascular diseases. Eventually, these long-term complications can also cause CKD [ 39 ].

Another important result of the current study is the association between chemotherapy for treating cancers and the risk of CKD. According to a study on chemotherapy for testicular cancer by Inai et al., 1 year after chemotherapy 23% of the patients showed CKD [ 40 ]. Another study suggested that the prevalence of stage 3 CKD among patients with cancer was 12, and < 1% of patients had stage 4 CKD [ 41 , 42 ]. Other studies have shown an even higher prevalence of CKD among cancer patients. For instance, only 38.6% of patients with breast cancer, 38.9% of patients with lung cancer, 38.3% of patients with prostate cancer, 27.5% of patients with gynecologic cancer, and 27.2% of patients with colorectal cancer had a GFR ≥90 (ml/min/1.73 m 2 ) at the time of therapy initiation [ 43 , 44 ]. The overall prevalence of CKD ranges from 12 to 25% across many cancer patients [ 45 , 46 , 47 ]. These results clearly demonstrate that, when patients with cancer develop acute or chronic kidney disease, outcomes are inferior, and the promise of curative therapeutic regimens is lessened.

In our study, the history of taking nephrotoxic agents (antibiotics or NSAIDs drugs) was associated with a higher risk of CKD. Our result is following the results reported by other studies [ 48 , 49 ]. Common agents that are associated with AKI include NSAIDs are different drugs including antibiotics, iodinated contrast media, and chemotherapeutic drugs [ 50 ].

Strengths and limitations of our study

Our study used a reasonably large sample size. In addition, a considerably large number of study variables was included in the study. With a very high participation rate, trained nurses conducted the interviews with the case and control participants in the same setting. However, histories of exposures are prone to recall error (bias), a common issue in the case-control studies. It is to be mentioned that the method of selecting controls (hospital controls) should have reduced the risk of recall bias when reporting the required information. In addition, we used the participants’ medical records to complete/ confirm the reported data. Although the design of the present study was not able to confirm a causal association between the associated variables and CKD, the potential importance and modifiable nature of the associated factors makes the results potentially valuable and easily applicable in the prevention of CKD.

Given that, chemotherapy is an important risk factor for CKD, we suggest the imperative for collaborative care between oncologists and nephrologists in the early diagnosis and treatment of kidney diseases in patients with cancer. Training clinicians and patients are important to reduce the risk of nephrotoxicity. Electronic medical records can simultaneously be used to monitor prescription practices, responsiveness to alerts and prompts, the incidence of CKD, and detecting barriers to the effective implementation of preventive measures [ 51 ]. Routine follow-up and management of diabetic patients is also important for the prevention of CKD. We suggest a tight collaboration between endocrinologists and nephrologists to take care of diabetic patients with kidney problems. In addition, surgeons in major operations should refer patients, especially patients with AKI, to a nephrologist for proper care related to their kidney function. Treatment of hypertension is among the most important interventions to slow down the progression of CKD [ 12 ]. Moreover, all patients with newly diagnosed hypertension should be screened for CKD. We suggest all patients with diabetes have their GFR and urine albumin-to-creatinine ratio (UACR) checked annually. Finally, the aging population and obesity cause the absolute numbers of people with diabetes and kidney diseases to raise significantly. This will require a more integrated approach between dialectologists/nephrologists and the primary care teams (55).

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to their being the intellectual property of Shiraz University of Medical Sciences but are available from the corresponding author on reasonable request.

Abbreviations

• Chronic kidney disease

End-stage renal disease

Glomerular filtration rate

Renal replacement treatment

Urinary tract infection

Odds ratios

Confidence intervals

Hypertension

Acute kidney injury

Ghelichi Ghojogh M, Salarilak S, Taghezadeh Afshari A, Khalkhali HR, Mohammadi-Fallah MR, Mkhdoomi K. The effect of body mass index on patient and graft survival rate in kidney transplanted patients in Iran. Nephrourol Monthly. 2017;9(4):e14386.

Zeba Z, Fatema K, Sumit AF, Zinnat R, Ali L. Early screening of chronic kidney disease patients among the asymptomatic adult population in Bangladesh. J Prev Epidemiol. 2020;5(1):e10–e.

Article   Google Scholar  

Mahajan C, Tiwari V, Divyaveer SS, Patil MR, Banerjee A, Bagur V, et al. Spectrum of renal biopsies; a three-year data from a tertiary care Centre of eastern India. J Nephropharmacol. 2020;9(2):e20–e.

Article   CAS   Google Scholar  

Kimura K, Hosoya T, Uchida S, Inaba M, Makino H, Maruyama S, et al. Febuxostat therapy for patients with stage 3 CKD and asymptomatic Hyperuricemia: a randomized trial. Am J Kidney Dis. 2018;72(6):798–810.

Article   CAS   PubMed   Google Scholar  

Foster MC, Hwang S-J, Larson MG, Lichtman JH, Parikh NI, Vasan RS, et al. Overweight, obesity, and the development of stage 3 CKD: the Framingham heart study. Am J Kidney Dis. 2008;52(1):39–48.

Article   PubMed   PubMed Central   Google Scholar  

Rachmi CN, Agho KE, Li M, Baur LA. Stunting, underweight and overweight in children aged 2.0–4.9 years in Indonesia: prevalence trends and associated risk factors. PLoS One. 2016;11(5):e0154756.

Asghari G, Momenan M, Yuzbashian E, Mirmiran P, Azizi F. Dietary pattern and incidence of chronic kidney disease among adults: a population-based study. Nutr Metab. 2018;15(1):1–11.

Ruggles DR, Freyman RL, Oxenham AJ. Influence of musical training on understanding voiced and whispered speech in noise. PLoS One. 2014;9(1):e86980.

Moazzeni SS, Arani RH, Hasheminia M, Tohidi M, Azizi F, Hadaegh F. High incidence of chronic kidney disease among Iranian diabetic adults: using CKD-EPI and MDRD equations for estimated glomerular filtration rate. Korean Diabetes J. 2021;45(5):684-97.

Salam SN, Eastell R, Khwaja A. Fragility fractures and osteoporosis in CKD: pathophysiology and diagnostic methods. Am J Kidney Dis. 2014;63(6):1049–59.

Zahmatkesh M, Tamadon MR. World kidney day 2018; chronic kidney disease in women. J Nephropathol. 2017;7(1):4–6.

Noble R, Taal MW. Epidemiology and causes of chronic kidney disease. Medicine. 2019;47(9):562–6.

Lopez-Garcia E, Schulze MB, Fung TT, Meigs JB, Rifai N, Manson JE, et al. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr. 2004;80(4):1029–35.

Sepahi MA, Niknafs B. Multifaceted role of apolipoprotein L1 risk variants and nephropathy. J Nephropathol. 2020;9(1):1-3.

Cohen JB, Tewksbury CM, Landa ST, Williams NN, Dumon KR. National postoperative bariatric surgery outcomes in patients with chronic kidney disease and end-stage kidney disease. Obes Surg. 2019;29(3):975–82.

Levey AS, Andreoli SP, DuBose T, Provenzano R, Collins AJ. Chronic kidney disease: common, harmful and treatable–world kidney day 2007. Am J Nephrol. 2007;27(1):108–12.

Article   PubMed   Google Scholar  

Argulian E, Sherrid MV, Messerli FH. Misconceptions and facts about hypertrophic cardiomyopathy. Am J Med. 2016;129(2):148–52.

Rhee CM. The interaction between thyroid and kidney disease: an overview of the evidence. Curr Opin Endocrinol Diabetes Obes. 2016;23(5):407.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Al Salmi I, Hoy WE, Kondalsamy-Chennakes S, Wang Z, Healy H, Shaw JE. Birth weight and stages of CKD: a case-control study in an Australian population. Am J Kidney Dis. 2008;52(6):1070–8.

Yacoub R, Habib H, Lahdo A, Al Ali R, Varjabedian L, Atalla G, et al. Association between smoking and chronic kidney disease: a case control study. BMC Public Health. 2010;10(1):1–6.

Saucier NA, Sinha MK, Liang KV, Krambeck AE, Weaver AL, Bergstralh EJ, et al. Risk factors for CKD in persons with kidney stones: a case-control study in Olmsted County, Minnesota. Am J Kidney Dis. 2010;55(1):61–8.

Lambert K, Mullan J, Mansfield K, Lonergan M. A cross-sectional comparison of health literacy deficits among patients with chronic kidney disease. J Health Commun. 2015;20(sup2):16–23.

Fraser SD, Roderick PJ, Casey M, Taal MW, Yuen HM, Nutbeam D. Prevalence and associations of limited health literacy in chronic kidney disease: a systematic review. Nephrol Dial Transplant. 2013;28(1):129–37.

Ji MY, Park YS, Yi SE. A case-control study to identify the risk factors of school accidents. Korean J Epidemiol. 2005;27(2):80–94.

Google Scholar  

Khajehdehi P, Malekmakan L, Pakfetrat M, Roozbeh J, Sayadi M. Prevalence of chronic kidney disease and its contributing risk factors in southern Iran a cross-sectional adult population-based study; 2014.

Li H, Lu W, Wang A, Jiang H, Lyu J. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: estimates from global burden of disease 2017. J Diabetes Investig. 2021;12(3):346.

Xu Y, Surapaneni A, Alkas J, Evans M, Shin J-I, Selvin E, et al. Glycemic control and the risk of acute kidney injury in patients with type 2 diabetes and chronic kidney disease: parallel population-based cohort studies in US and Swedish routine care. Diabetes Care. 2020;43(12):2975–82.

Sepanlou SG, Barahimi H, Najafi I, Kamangar F, Poustchi H, Shakeri R, et al. Prevalence and determinants of chronic kidney disease in northeast of Iran: results of the Golestan cohort study. PLoS One. 2017;12(5):e0176540.

Pugh D, Gallacher PJ, Dhaun N. Management of hypertension in chronic kidney disease. Drugs. 2019;79(4):365–79.

Torra R, Furlano M, Ortiz A, Ars E. Genetic kidney diseases as an underecognized cause of chronic kidney disease: the key role of international registry reports. Clin Kidney J. 2021;14(8):1879-85.

Mizota T, Dong L, Takeda C, Shiraki A, Matsukawa S, Shimizu S, et al. Transient acute kidney injury after major abdominal surgery increases chronic kidney disease risk and 1-year mortality. J Crit Care. 2019;50:17–22.

Madsen NL, Goldstein SL, Frøslev T, Christiansen CF, Olsen M. Cardiac surgery in patients with congenital heart disease is associated with acute kidney injury and the risk of chronic kidney disease. Kidney Int. 2017;92(3):751–6.

Newsome BB, Warnock DG, McClellan WM, Herzog CA, Kiefe CI, Eggers PW, et al. Long-term risk of mortality and end-stage renal disease among the elderly after small increases in serum creatinine level during hospitalization for acute myocardial infarction. Arch Intern Med. 2008;168(6):609–16.

Ishani A, Nelson D, Clothier B, Schult T, Nugent S, Greer N, et al. The magnitude of acute serum creatinine increase after cardiac surgery and the risk of chronic kidney disease, progression of kidney disease, and death. Arch Intern Med. 2011;171(3):226–33.

James MT, Ghali WA, Knudtson ML, Ravani P, Tonelli M, Faris P, et al. Associations between acute kidney injury and cardiovascular and renal outcomes after coronary angiography. Circulation. 2011;123(4):409–16.

Esmeijer K, de Vries AP, Mook-Kanamori DO, de Fijter JW, Rosendaal FR, Rabelink TJ, et al. Low birth weight and kidney function in middle-aged men and women: the Netherlands epidemiology of obesity study. Am J Kidney Dis. 2019;74(6):751–60.

White SL, Perkovic V, Cass A, Chang CL, Poulter NR, Spector T, et al. Is low birth weight an antecedent of CKD in later life? A systematic review of observational studies. Am J Kidney Dis. 2009;54(2):248–61.

Harer MW, Charlton JR, Tipple TE, Reidy KJ. Preterm birth and neonatal acute kidney injury: implications on adolescent and adult outcomes. J Perinatol. 2020;40(9):1286–95.

Al Salmi I, Hannawi S. Birth weight and susceptibility to chronic kidney disease. Saudi J Kidney Dis Transplant. 2020;31(4):717.

Inai H, Kawai K, Ikeda A, Ando S, Kimura T, Oikawa T, et al. Risk factors for chronic kidney disease after chemotherapy for testicular cancer. Int J Urol. 2013;20(7):716–22.

Launay-Vacher V, Oudard S, Janus N, Gligorov J, Pourrat X, Rixe O, et al. Prevalence of renal insufficiency in cancer patients and implications for anticancer drug management: the renal insufficiency and anticancer medications (IRMA) study. Cancer. 2007;110(6):1376–84.

Launay-Vacher V. Epidemiology of chronic kidney disease in cancer patients: lessons from the IRMA study group. Semin Nephrol. 2010;30(6):548–56.

Launay-Vacher V, Janus N, Deray G. Renal insufficiency and cancer treatments. ESMO Open. 2016;1(4):e000091.

Janus N, Launay-Vacher V, Byloos E, Machiels JP, Duck L, Kerger J, et al. Cancer and renal insufficiency results of the BIRMA study. Br J Cancer. 2010;103(12):1815–21.

Kitchlu A, McArthur E, Amir E, Booth CM, Sutradhar R, Majeed H, et al. Acute kidney injury in patients receiving systemic treatment for Cancer: a population-based cohort study. J Natl Cancer Inst. 2019;111(7):727–36.

Kidney Disease Improving Global Outcomes (KDIGO). KDIGO clinical practice guideline for acute kidney injury. kdigo.org/wpcontent/uploads/2016/10/KDIGO-2012-AKI-Guide line-Engli sh.pdf . Accessed 23 Mar 2020.

Königsbrügge O, Lötsch F, Zielinski C, Pabinger I, Ay C. Chronic kidney disease in patients with cancer and its association with occurrence of venous thromboembolism and mortality. Thromb Res. 2014;134(1):44–9.

Goldstein SL, Mottes T, Simpson K, Barclay C, Muething S, Haslam DB, et al. A sustained quality improvement program reduces nephrotoxic medication-associated acute kidney injury. Kidney Int. 2016;90(1):212–21.

Menon S, Kirkendall ES, Nguyen H, Goldstein SL. Acute kidney injury associated with high nephrotoxic medication exposure leads to chronic kidney disease after 6 months. J Pediatr. 2014;165(3):522–7.e2.

Perazella MA, Izzedine H. New drug toxicities in the onco-nephrology world. Kidney Int. 2015;87(5):909–17.

Luyckx VA, Tuttle KR, Garcia-Garcia G, Gharbi MB, Heerspink HJL, Johnson DW, et al. Reducing major risk factors for chronic kidney disease. Kidney Int Suppl. 2017;7(2):71–87.

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Acknowledgments

This paper is part of a thesis conducted by Mousa Ghelichi-Ghojogh, Ph.D. student of epidemiology, and a research project conducted at the Shiraz University of Medical sciences (99-01-04-22719). We would like to thank Dr. Bahram Shahryari and all nephrologists of Shiraz‌ University of medical sciences, interviewers, and CKD patients in Shiraz for their voluntary participation in the study and for providing data for the study.

Shiraz University of Medical Sciences financially supported this study. (Grant number: 99–01–04-22719).

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Candidate in Epidemiology, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

Mousa Ghelichi-Ghojogh

HIV/AIDS research center, School of Health, Shiraz University of Medical Sciences, P.O.Box: 71645-111, Shiraz, Iran

Mohammad Fararouei

Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

Mozhgan Seif

Nephrologist, Shiraz Nephro-Urology Research Center, Department of Internal Medicine, Emergency Medicine Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Maryam Pakfetrat

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Contributions

MGG: Conceptualization, Methodology, Statistical analysis, Investigation, and writing the draft of the manuscript. MP: were involved in methodology, writing the draft of the manuscript, and clinical consultation. MS: was involved in the methodology and statistical analysis. MF: was involved in conceptualization, methodology, supervision, writing, and reviewing the manuscript. The authors read and approved the final manuscript.

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Correspondence to Mohammad Fararouei .

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The study protocol was reviewed and approved by the ethical committee of Shiraz University of Medical Sciences (approval number: 1399.865). All methods were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. The participants were assured that their information is used for research purposes only. Because of the illiteracy of a considerable number of the patients, verbal informed consent was obtained from the participants. Using verbal informed consent was also granted by the ethical committee of Shiraz University of Medical Sciences.

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• Published: 19 May 2022

Machine learning to predict end stage kidney disease in chronic kidney disease

• Qiong Bai 1 ,
• Chunyan Su 1 ,
• Wen Tang 1 &
• Yike Li 2  

Scientific Reports volume  12 , Article number:  8377 ( 2022 ) Cite this article

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• Chronic kidney disease
• End-stage renal disease
• Kidney diseases

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients’ baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

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Introduction.

Chronic kidney disease (CKD) is a significant healthcare burden that affects billions of individuals worldwide 1 , 2 and makes a profound impact on global morbidity and mortality 3 , 4 , 5 . In the United States, approximately 11% of the population or 37 million people suffer from CKD that results in an annual Medicare cost of $84 billion 6 . The prevalence of this disease is estimated at 10.8% in China, affecting about 119.5 million people 7 .

Gradual loss of the kidney function can lead to end stage kidney disease (ESKD) in CKD patients, precipitating the need for kidney replacement therapy (KRT). Timely intervention in those CKD patients who have a high risk of ESKD may not only improve these patients’ quality of life by delaying the disease progression, but also reduce the morbidity, mortality and healthcare costs resulting from KRT 8 , 9 . Because the disease progression is typically silent 10 , a reliable prediction model for risk of ESKD at the early stage of CKD can be clinically essential. Such a model is expected to facilitate physicians in making personalized treatment decisions for high-risk patients, thereby improving the overall prognosis and reducing the economic burden of this disease.

A few statistical models were developed to predict the likelihood of ESKD based on certain variables, including age, gender, lab results, and most commonly, the estimated glomerular filtration rate (eGFR) and albuminuria 11 , 12 . Although some of these models demonstrated adequate predictability in patients of a specific race, typically Caucasians 13 , 14 , 15 , literature on their generalizability in other ethnic groups, such as Chinese, remains scarce 13 , 16 . In addition, models based on non-urine variables, such as patients’ baseline characteristics and routine blood tests, have reportedly yield sufficient performance 17 , 18 . Therefore, it may be feasible to predict ESKD without urine tests, leading to a simplified model with equivalent reliability.

With the advent of the big data era, new methods became available in developing a predictive model that used to rely on traditional statistics. Machine learning (ML) is a subset of artificial intelligence (AI) that allows the computer to perform a specific task without explicit instructions. When used in predictive modeling, ML algorithm can be trained to capture the underlying patterns of the sample data and make predictions about the new data based on the acquired information 19 . Compared to traditional statistics, ML represents more sophisticated math functions and usually results in better performance in predicting an outcome that is determined by a large set of variables with non-linear, complex interactions 20 . ML has recently been applied in numerous studies and demonstrated high level of performance that surpassed traditional statistics and even humans 20 , 21 , 22 , 23 .

This article presents a proof-of-concept study with the major goal to establish ML models for predicting the risk of ESKD on a Chinese CKD dataset. The ML models were trained and tested based on easily obtainable variables, including the baseline characteristics and routine blood tests. Results obtained from this study suggest not only the feasibility of ML models in performing this clinically critical task, but also the potential in facilitating personalized medicine.

Materials and methods

Study population.

The data used for this retrospective work were obtained from a longitudinal cohort previously enrolled in an observational study 24 , 25 . The major inclusion criteria for the cohort were adult CKD patients (≥ 18 years old) with stable kidney functions for at least three months prior to recruitment. Patients were excluded if they had one or more of the following situations: (1) history of KRT in any form, including hemodialysis, peritoneal dialysis or kidney transplantation; (2) any other existing condition deemed physically unstable, including life expectancy < 6 months, acute heart failure, and advanced liver disease; (3) any pre-existing malignancy. All patients were recruited from the CKD management clinic of Peking University Third Hospital between April 2006 and March 2008. Written informed consent was obtained from all patients. They were treated according to routine clinical practice determined by the experienced nephrologists and observed until December 31 st , 2015. Detailed information regarding patient recruitment and management protocol has been described in a previous publication 24 .

Data acquisition

Patient characteristics included age, gender, education level, marriage status, and insurance status. Medical history comprised history of smoking, history of alcohol consumption, presence of each comorbid condition—diabetes, cardiovascular disease and hypertension. Clinical parameters contained body mass index (BMI), systolic pressure and diastolic pressure. Blood tests consisted of serum creatinine, uric acid, blood urea nitrogen, white blood cell count, hemoglobin, platelets count, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein, albumin, alkaline phosphatase (ALP), high-density lipoprotein, low-density lipoprotein, triglycerides, total cholesterol, calcium, phosphorus, potassium, sodium, chloride, and bicarbonate. The estimated glomerular filtration rate and type of primary kidney disease were also used as predictors.

All baseline variables were obtained at the time of subject enrollment. The primary study end point was kidney failure which necessitated the use of any KRT. Subjects with the outcome of kidney failure were labeled as ESKD+, and the rest ESKD−. Patients who died before reaching the study end point or lost to follow up were discarded. Patients who developed ESKD after five years were labeled as ESKD−.

Data preprocessing

All categorical variables, such as insurance status, education, and primary disease, were encoded using the one-hot approach. Any variable was removed from model development if the missing values were greater than 50%. Missing data were handled using multiple imputation with five times of repetition, leading to five slightly different imputed datasets where each of the missing values was randomly sampled from their predictive distribution based on the observed data. On each imputed set, all models were trained and tested using a fivefold cross validation method. To minimize selection bias, subject assignment to train/test folds was kept consistent across all imputed sets. Data were split in a stratified fashion to ensure the same distribution of the outcome classes (ESKD+ vs. ESKD−) in each subset as the entire set.

Model development

The model was trained to perform a binary classification task with the goal of generating the probability of ESKD+ based on the given features. Five ML algorithms were employed in this study, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors. Grid search was performed to obtain the best hyperparameter combination for each algorithm.

Assessment of model performance

The performance of a classifiers was measured using accuracy, precision, recall, specificity, F1 score and area under the curve (AUC), as recommended by guidelines for results reporting of clinical prediction models 26 . All classifiers developed in this study were further compared with the Kidney Failure Risk Equation (KFRE), which estimates the 5-year risk of ESKD based on patient’s age, gender, and eGFR 12 . The KFRE is currently the most widely used model in predicting CKD progression to ESKD. The reported outcome of a model represented the average performance of 5 test folds over all imputed sets.

Statistical analysis

Basic descriptive statistics were applied as deemed appropriate. Results are expressed as frequencies and percentages for categorical variables; the mean ± standard deviation for continuous, normally distributed variables; and the median (interquartile range) for continuous variables that were not normally distributed. Patient characteristics were compared between the original dataset and the imputed sets using one-way analysis of variance (ANOVA). The AUC of each model was measured using the predicted probability. The optimal threshold of a classifier was determined based on the receiver operating characteristic (ROC) curve at the point with minimal distance to the upper left corner. For each ML model, this threshold was obtained during the training process and applied unchangeably to the test set. For the KFRE, the threshold was set at a default value of 0.5. Model development, performance evaluation and data analyses were all performed using Python 27 . The alpha level was set at 0.05.

Ethical approval

This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The study protocol has been approved by the Peking University Third Hospital Medical Science Research Ethics Committee on human research (No. M2020132).

Cohort characteristics

The dataset contained a total of 748 subjects with the follow-up duration of 6.3 ± 2.3 years. The baseline characteristics are summarized in Table 1 . Most patients were in stage 2 (24.5%) or 3 (47.1%) CKD at baseline. ESKD was observed in 70 patients (9.4%), all of whom subsequently received KRT, including hemodialysis in 49 patients, peritoneal dialysis in 17 and kidney transplantation in 4.

Model performance

Details of the five imputed sets are provided in the supplemental materials . There was no significant difference between the imputed sets and the original dataset in each variable where missing data were replaced by imputed values. The hyperparameter settings for each classifier are displayed in Table 2 . The best overall performance, as measured by the AUC score, was achieved by the random forest algorithm (0.81, see Table 3 ). Nonetheless, this score and its 95% confidence interval had overlap with those of the other three models, including the logistic regression, naïve Bayes, and the KFRE (Fig.  1 ). Interestingly, the KFRE model that was based on 3 simple variables, demonstrated not only a comparable AUC score but also the highest accuracy, specificity, and precision. At the default threshold, however, the KFRE was one of the least sensitive models (47%).

ROC curves of the random forest algorithm and the KFRE model.

With extensive utilization of electronic health record and recent progress in ML research, AI is expanding its impact on healthcare and has gradually changed the way clinicians pursue for problem-solving 28 . Instead of adopting a theory-driven strategy that requires a preformed hypothesis from prior knowledge, training an ML model typically follows a data-driven approach that allows the model to learn from experience alone. Specifically, the model improves its performance iteratively on a training set by comparing the predictions to the ground truths and adjusting model parameters so as to minimize the distance between the predictions and the truths. In nephrology, ML has demonstrated promising performances in predicting acute kidney injury or time to allograft loss from clinical features 29 , 30 , recognizing specific patterns in pathology slides 31 , 32 , choosing an optimal dialysis prescription 33 , or mining text in the electronic health record to find specific cases 34 , 35 . Additionally, a few recent studies were performed to predict the progression of CKD using ML methods. These models were developed to estimate the risk of short-term mortality following dialysis 36 , calculate the future eGFR values 37 , or assess the 24-h urinary protein levels 18 . To our best knowledge, there hasn’t been any attempt to apply ML methods to predict the occurrence of ESKD in CKD patients.

In the present study, a prediction model for ESKD in CKD patients was explored using ML techniques. Most classifiers demonstrated adequate performance based on easily accessible patient information that is convenient for clinical translation. In general, three ML models, including the logistic regression, naïve Bayes and random forest, showed non-inferior performance to the KFRE in this study. These findings imply ML as a feasible approach for predicting disease progression in CKD, which could potentially guide physicians in establishing personalized treatment plans for this condition at an early stage. These ML models with higher sensitivity scores may also be practically favored in patient screening over the KFRE.

To our best understanding, this study was also the first to validate the KFRE in CKD patients of Mainland China. The KFRE was initially developed and validated using North American patients with CKD stage 3–5 12 . There were seven KFRE models that consisted of different combinations of predictor variables. The most commonly used KFRE included a 4-variable model (age, gender, eGFR and urine ACR) or an 8-variable model (age, gender, eGFR, urine ACR, serum calcium, phosphorous, bicarbonate, and albumin). Besides, there was a 3-variable model (age, gender, and eGFR) that required no urine ACR and still showed comparable performance to the other models in the original article. Despite its favorable performance in prediction for ESKD in patients of Western countries 14 , 15 , 38 , 39 , the generalizability of KFRE in Asian population remained arguable following the suboptimal results revealed by some recent papers 13 , 40 , 41 . In the current study, the KFRE was validated in a Chinese cohort with CKD stage 1–5 and showed an AUC of 0.80. This result indicated the KFRE was adequately applicable to the Chinese CKD patients and even earlier disease stages. In particular, the high specificity score (0.95) may favor the use of this equation in ruling in patients who require close monitoring of disease progression. On the other hand, a low sensitivity (0.47) at the default threshold may suggest it may be less desirable than the other models for ruling out patients.

Urine test is a critical diagnostic approach for CKD. The level of albuminuria (i.e. ACR) has also been regarded as a major predictor for disease progression and therefore used by most prognostic models. However, quantitative testing for albuminuria is not always available in China especially in rural areas, which precludes clinicians from using most urine-based models for screening patients. In this regard, several simplified models were developed to predict CKD progression without the need of albuminuria. These models were based on patient characteristics (e.g. age, gender, BMI, comorbidity) and/or blood work (e.g. creatinine/eGFR, BUN), and still able to achieve an AUC of 0.87–0.89 12 , 18 or a sensitivity of 0.88 37 . Such performance was largely consistent with the findings of this study and comparable or even superior to some models incorporating urine tests 16 , 42 . Altogether, it suggested a reliable prediction for CKD progression may be obtained from routine clinical variables without urine measures. These models are expected to provide a more convenient screening tool for CKD patients in developing regions.

Missing data are such a common problem in ML research that they can potentially lead to a biased model and undermine the validity of study outcomes. Traditional methods to handle missing data include complete case analysis, missing indicator, single value imputation, sensitivity analyses, and model-based methods (e.g. mixed models or generalized estimating equations) 43 , 44 , 45 . In most scenarios, complete case analysis and single value imputation are favored by researchers primarily due to the ease of implementation 45 , 46 , 47 . However, these methods may be associated with significant drawbacks. For example, by excluding samples with missing data from analyses, complete case analysis can result in reduction of model power, overestimation of benefit and underestimation of harm 43 , 46 ; Single value imputation replaces the missing data by a single value—typically the mean or mode of the complete cases, thereby increasing the homogeneity of data and overestimating the precision 43 , 48 . In this regard, multiple imputation solves these problems by generating several different plausible imputed datasets, which account for the uncertainty about the missing data and provide unbiased estimates of the true effect 49 , 50 . It is deemed effective regardless of the pattern of missingness 43 , 51 . Multiple imputation is now widely recognized as the standard method to deal with missing data in many areas of research 43 , 45 . In the current study, a 5-set multiple imputation method was employed to obtain reasonable variability of the imputed data. The performance of each model was analyzed on each imputed set and pooled for the final result. These procedures ensured that the model bias resulting from missing data was minimized. In the future, multiple imputation is expected to become a routine method for missing data handling in ML research, as the extra amount of computation associated with multiple imputation over those traditional methods can simply be fulfilled by the high level of computational power required by ML.

Although ML has been shown to outperform traditional statistics in a variety of tasks by virtue of the model complexity, some studies demonstrated no gain or even declination of performance compared to traditional regression methods 52 , 53 . In this study, the simple logistic regression model also yielded a comparable or even superior predictability for ESKD to other ML algorithms. The most likely explanation is that the current dataset only had a small sample size and limited numbers of predictor variables, and the ESKD+ cases were relatively rare. The lack of big data and imbalanced class distribution may have negative impact on the performance of complex ML algorithms, as they are typically data hungry 54 . On the other hand, this finding could imply simple interactions among the predictor variables. In other words, the risk of ESKD may be largely influenced by only a limited number of factors in an uncomplicated fashion, which is consistent with some previous findings 12 , 18 , 55 . The fact that the 3-variable KFRE, which is also a regression model, yielded equivalent outcomes to the best ML models in this study may further support this implication. It is therefore indicated that traditional regression models may continue to play a key role in disease risk prediction, especially when a small sample size, limited predictor variables, or an imbalanced dataset is encountered. The fact that some of the complex ML models are subject to the risk of overfitting and the lack of interpretability further favors the use of simple regression models, which can be translated to explainable equations.

Several limitations should be noted. First, this cohort consisted of less than 1000 subjects and ESKD only occurred in a small portion of them, both of which might have affected model performance as discussed earlier. Second, although this study aimed to assess the feasibility of a prediction model for ESKD without any urine variables, this was partially due to the lack of quantitative urine tests at our institute when this cohort was established. As spot urine tests become increasingly popular, urine features such as ACR will be as accessible and convenient as other lab tests. They are expected to play a critical role in more predictive models. Third, the KFRE was previously established on stages 3–5 CKD patients while the current cohort contained stages 1–5. This discrepancy may have affected the KFRE performance. Forth, the generalizability of this model has not been tested on any external data due to the lack of such resource in this early feasibility study. Therefore, additional efforts are required to improve and validate this model before any clinical translation. Finally, although a simple model without urine variables is feasible and convenient, model predictability may benefit from a greater variety of clinical features, such as urine tests, imaging, or biopsy. Future works should include training ML models with additional features using a large dataset, and validating them on external patients.

In conclusion, this study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Logistic regression, naïve Bayes and random forest demonstrated comparable predictability to the KFRE in this study. These ML models also had greater sensitivity scores that were potentially advantageous for patient screenings. Future studies include performing external validation and improving the model with additional predictor variables.

Zhang, L. et al. Trends in chronic kidney disease in China. N. Engl. J. Med. 375 , 905–906. https://doi.org/10.1056/NEJMc1602469 (2016).

Article   PubMed   Google Scholar  

Bello, A. K. et al. Effective CKD care in European countries: Challenges and opportunities for health policy. Am. J. Kidney Dis. 65 , 15–25. https://doi.org/10.1053/j.ajkd.2014.07.033 (2015).

Subbiah, A. K., Chhabra, Y. K. & Mahajan, S. Cardiovascular disease in patients with chronic kidney disease: A neglected subgroup. Heart Asia 8 , 56–61. https://doi.org/10.1136/heartasia-2016-010809 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Pecoits-Filho, R. et al. Interactions between kidney disease and diabetes: Dangerous liaisons. Diabetol. Metab. Syndr. 8 , 50. https://doi.org/10.1186/s13098-016-0159-z (2016).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Weiner, D. E. et al. Chronic kidney disease as a risk factor for cardiovascular disease and all-cause mortality: A pooled analysis of community-based studies. J. Am. Soc. Nephrol. 15 , 1307–1315. https://doi.org/10.1097/01.asn.0000123691.46138.e2 (2004).

Saran, R., Robinson, B., Abbott, K. C. et al. US Renal Data System 2019 Annual Data Report: Epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 75 , A6–A7. https://doi.org/10.1053/j.ajkd.2019.09.003 (2020).

Zhang, L. et al. Prevalence of chronic kidney disease in China: A cross-sectional survey. Lancet (London, England) 379 , 815–822. https://doi.org/10.1016/S0140-6736(12)60033-6 (2012).

Article   Google Scholar  

Johns, T. S., Yee, J., Smith-Jules, T., Campbell, R. C. & Bauer, C. Interdisciplinary care clinics in chronic kidney disease. BMC Nephrol. 16 , 161. https://doi.org/10.1186/s12882-015-0158-6 (2015).

Lin, E., Chertow, G. M., Yan, B., Malcolm, E. & Goldhaber-Fiebert, J. D. Cost-effectiveness of multidisciplinary care in mild to moderate chronic kidney disease in the United States: A modeling study. PLoS Med. 15 , e1002532. https://doi.org/10.1371/journal.pmed.1002532 (2018).

Zhong, J., Yang, H. C. & Fogo, A. B. A perspective on chronic kidney disease progression. Am. J. Physiol. Ren. Physiol. 312 , F375–F384. https://doi.org/10.1152/ajprenal.00266.2016 (2017).

Article   CAS   Google Scholar  

Tangri, N. et al. A dynamic predictive model for progression of CKD. Am. J. Kidney Dis. 69 , 514–520. https://doi.org/10.1053/j.ajkd.2016.07.030 (2017).

Tangri, N. et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 305 , 1553–1559. https://doi.org/10.1001/jama.2011.451 (2011).

Article   CAS   PubMed   Google Scholar  

Tangri, N. et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: A meta-analysis. JAMA 315 , 164–174. https://doi.org/10.1001/jama.2015.18202 (2016).

Major, R. W. et al. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study. PLoS Med. 16 , e1002955. https://doi.org/10.1371/journal.pmed.1002955 (2019).

Peeters, M. J. et al. Validation of the kidney failure risk equation in European CKD patients. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc. 28 , 1773–1779. https://doi.org/10.1093/ndt/gft063 (2013).

Echouffo-Tcheugui, J. B. & Kengne, A. P. Risk models to predict chronic kidney disease and its progression: A systematic review. PLoS Med. 9 , e1001344. https://doi.org/10.1371/journal.pmed.1001344 (2012).

Chang, H. L. et al. A predictive model for progression of CKD. Medicine (Baltimore) 98 , e16186. https://doi.org/10.1097/MD.0000000000016186 (2019).

Xiao, J. et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J. Transl. Med. 17 , 119. https://doi.org/10.1186/s12967-019-1860-0 (2019).

Song, H., Triguero, I. & Özcan, E. A review on the self and dual interactions between machine learning and optimisation. Prog. Artif. Intell. 8 , 143–165. https://doi.org/10.1007/s13748-019-00185-z (2019).

Mortazavi, B. J. et al. Analysis of machine learning techniques for heart failure readmissions. Circ. Cardiovasc. Qual. Outcomes 9 , 629–640. https://doi.org/10.1161/CIRCOUTCOMES.116.003039 (2016).

Deo, R. C. Machine learning in medicine. Circulation 132 , 1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593 (2015).

Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M. & Qureshi, N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE 12 , e0174944. https://doi.org/10.1371/journal.pone.0174944 (2017).

Meiring, C. et al. Optimal intensive care outcome prediction over time using machine learning. PLoS ONE 13 , e0206862. https://doi.org/10.1371/journal.pone.0206862 (2018).

Lai, X. et al. Outcomes of stage 1–5 chronic kidney disease in Mainland China. Ren. Fail. 36 , 520–525. https://doi.org/10.3109/0886022X.2013.875859 (2014).

Bai, Q., Su, C. Y., Zhang, A. H., Wang, T. & Tang, W. Loss of the normal gradient in arterial compliance and outcomes of chronic kidney disease patients. Cardiorenal Med. 9 , 297–307. https://doi.org/10.1159/000500479 (2019).

Steyerberg, E. W. & Vergouwe, Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur. Heart J. 35 , 1925–1931. https://doi.org/10.1093/eurheartj/ehu207 (2014).

Python: A dynamic, open source programming language. https://www.python.org/ (2019).

Yu, K., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2 , 719–731. https://doi.org/10.1038/s41551-018-0305-z (2018).

Mohamadlou, H. et al. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Can. J. Kidney Health Dis. 5 , 2054358118776326. https://doi.org/10.1177/2054358118776326 (2018).

Lee, H. C. et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J. Clin. Med. https://doi.org/10.3390/jcm7100322 (2018).

Hermsen, M. et al. Deep learning-based histopathologic assessment of kidney tissue. J. Am. Soc. Nephrol. 30 , 1968–1979. https://doi.org/10.1681/ASN.2019020144 (2019).

Boor, P. Artificial intelligence in nephropathology. Nat. Rev. Nephrol. 16 , 4–6. https://doi.org/10.1038/s41581-019-0220-x (2020).

Barbieri, C. et al. Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: Proof of concept and first clinical assessment. Kidney Dis. (Basel) 5 , 28–33. https://doi.org/10.1159/000493479 (2019).

Garcelon, N., Burgun, A., Salomon, R. & Neuraz, A. Electronic health records for the diagnosis of rare diseases. Kidney Int. 97 , 676–686. https://doi.org/10.1016/j.kint.2019.11.037 (2020).

Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1 , 18. https://doi.org/10.1038/s41746-018-0029-1 (2018).

Akbilgic, O. et al. Machine learning to identify dialysis patients at high death risk. Kidney Int. Rep. 4 , 1219–1229. https://doi.org/10.1016/j.ekir.2019.06.009 (2019).

Zhao, J., Gu, S. & McDermaid, A. Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression. Math. Biosci. 310 , 24–30. https://doi.org/10.1016/j.mbs.2019.02.001 (2019).

Article   MathSciNet   PubMed   PubMed Central   MATH   Google Scholar  

Tangri, N., Ferguson, T. & Komenda, P. Pro: Risk scores for chronic kidney disease progression are robust, powerful and ready for implementation. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc. 32 , 748–751. https://doi.org/10.1093/ndt/gfx067 (2017).

Marks, A. et al. Looking to the future: Predicting renal replacement outcomes in a large community cohort with chronic kidney disease. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc. 30 , 1507–1517. https://doi.org/10.1093/ndt/gfv089 (2015).

Wang, Y. et al. Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia. BMC Nephrol. 20 , 451. https://doi.org/10.1186/s12882-019-1643-0 (2019).

Yamanouchi, M. et al. Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy. PLoS ONE 13 , e0190930. https://doi.org/10.1371/journal.pone.0190930 (2018).

Lin, C. C. et al. Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes. Sci. Rep. 7 , 10177. https://doi.org/10.1038/s41598-017-09243-9 (2017).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Pedersen, A. B. et al. Missing data and multiple imputation in clinical epidemiological research. Clin. Epidemiol. 9 , 157–166. https://doi.org/10.2147/CLEP.S129785 (2017).

Sterne, J. A. et al. Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ (Clin. Res. Ed.) 338 , b2393. https://doi.org/10.1136/bmj.b2393 (2009).

Jakobsen, J. C., Gluud, C., Wetterslev, J. & Winkel, P. When and how should multiple imputation be used for handling missing data in randomised clinical trials: A practical guide with flowcharts. BMC Med. Res. Methodol. 17 , 162. https://doi.org/10.1186/s12874-017-0442-1 (2017).

Karadaghy, O. A., Shew, M., New, J. & Bur, A. M. Development and assessment of a machine learning model to help predict survival among patients with oral squamous cell carcinoma. JAMA Otolaryngol. Head Neck Surg. 145 , 1115–1120. https://doi.org/10.1001/jamaoto.2019.0981 (2019).

Bur, A. M. et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral Oncol. 92 , 20–25. https://doi.org/10.1016/j.oraloncology.2019.03.011 (2019).

Zhang, Z. Missing data imputation: Focusing on single imputation. Ann. Transl. Med. 4 , 9. https://doi.org/10.3978/j.issn.2305-5839.2015.12.38 (2016).

Graham, J. W. Missing data analysis: Making it work in the real world. Annu. Rev. Psychol. 60 , 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530 (2009).

Klebanoff, M. A. & Cole, S. R. Use of multiple imputation in the epidemiologic literature. Am. J. Epidemiol. 168 , 355–357. https://doi.org/10.1093/aje/kwn071%JAmericanJournalofEpidemiology (2008).

Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. Multiple imputation by chained equations: What is it and how does it work?. Int. J. Methods Psychiatr. Res. 20 , 40–49. https://doi.org/10.1002/mpr.329 (2011).

Desai, R. J., Wang, S. V., Vaduganathan, M., Evers, T. & Schneeweiss, S. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw. Open 3 , e1918962. https://doi.org/10.1001/jamanetworkopen.2019.18962 (2020).

Christodoulou, E. et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110 , 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004 (2019).

Nusinovici, S. et al. Logistic regression was as good as machine learning for predicting major chronic diseases. J. Clin. Epidemiol. 122 , 56–69. https://doi.org/10.1016/j.jclinepi.2020.03.002 (2020).

Nusinovici, S. et al. Logistic regression was as good as machine learning for predicting major chronic diseases. J. Clin. Epidemiol. https://doi.org/10.1016/j.jclinepi.2020.03.002 (2020).

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This work was supported by PKU-Baidu Fund (2020BD030 to Wen Tang), and by fund from China International Medical Foundation (Z-2017-24-2037 to Wen Tang). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Bai, Q., Su, C., Tang, W. et al. Machine learning to predict end stage kidney disease in chronic kidney disease. Sci Rep 12 , 8377 (2022). https://doi.org/10.1038/s41598-022-12316-z

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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review

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A Correction to this article was published on 06 March 2023

This article has been updated

In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management.

We included English language studies retrieved from PubMed. The review is therefore to be classified as a “rapid review”, since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate.

From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria.

Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context.

Conclusions

Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.

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Introduction

Chronic Kidney Disease (CKD) is a state of progressive loss of kidney function ultimately resulting in the need for renal replacement therapy (dialysis or transplantation) [ 1 ]. It is defined as the presence of kidney damage or an estimated glomerular filtration rate less than 60 ml/min per 1.73 m 2 , persisting for 3 months or more [ 2 ]. CKD prevalence is growing worldwide, along with demographic and epidemiological transitions [ 3 ]. The implications of this disease are enormous for our society in terms of quality of life and the overall sustainability of national health systems. Worldwide, CKD accounted for 2,968,600 (1%) disability-adjusted life-years and 2,546,700 (1% to 3%) life-years lost in 2012 [ 4 ]. Therefore, it is of the utmost importance to assess how to promptly and adequately diagnose and treat patients with CKD.

The causes of CKD vary globally. The most common primary diseases causing CKD and ultimately kidney failure are diabetes mellitus, hypertension, and primary glomerulonephritis, representing 70–90% of the total primary causes [ 1 , 2 , 4 ]. Although these three causes are at the top of the CKD etiology charts, other features are involved in CKD pathophysiology (e.g., pollution, infections and autoimmune diseases) [ 5 , 6 , 7 , 8 , 9 ]. Similarly, there are numerous factors that play a role in CKD progression, namely non-modifiable risk factors (e.g., age, gender, ethnicity) and modifiable ones (e.g., systolic and diastolic blood pressure, proteinuria) [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 ].

Given how dauntingly vast the number of factors that can play a significant role in the etiology and progression of CKD is, it can be difficult to correctly assess the individual risk of CKD and its progression. Naturally, as with any complex problem, humans seek simplification, and therefore the question shifts to what to take into account when assessing CKD risk. Thanks to new methodological techniques, we now have the ability to improve our diagnostic and predictive capabilities.

Artificial Intelligence (AI) is the capacity of human-built machines to manifest complex decision-making or data analysis in a similar or augmented fashion in comparison to human intelligence [ 10 ]. Machine Learning (ML) is the collection of algorithms that empower models to learn from data, and therefore to undertake complex tasks through complex calculations [ 11 , 12 , 13 , 14 , 15 ]. In recent years AI and ML have offered enticing solutions to clinical problems, such as how to perform a diagnosis from sparse and seemingly contrasting data, or how to predict a prognosis [ 16 ]. Given the enormous potential of ML, and its capacity to learn from data, researchers have tried to apply its capacities to resolve complex problems, such as predicting CKD diagnosis and prognosis, and managing its treatment.

In this complex scenario, we aimed to systematically review the published studies that applied machine learning in the diagnosis and prediction, prognosis, and treatment of CKD patients. In doing so, the primary objective is to describe how ML models and variables have been used to predict, diagnose and treat CKD, as well as what results have been achieved in this field.

Search strategy and selection criteria

We conducted a systematic literature review, following the Preferred Reporting Items for Systematic Reviews (PRISMA) approach [ 17 ], including studies that applied ML algorithms to CKD forecasting, diagnosis, prognosis, and treatment. This systematic review’s outcomes of interest are machine learning models, features used, performances and uses regarding diagnosis, prognosis and treatment of CKD. The review itself and its protocol were not registered.

The initial search was implemented on October 20, 2021. The search query consisted of terms considered pertinent by the authors.

We searched for publications on PubMed using the following search string: “((artificial intelligence[Title/Abstract]) OR (machine learning[Title/Abstract]) OR (computational*[Title/Abstract]) OR (deep learning[Title/Abstract])) AND ((ckd) OR (chronic kidney disease) OR (chronic kidney injury) OR (chronic kidney) OR (chronic renal) OR (end stage renal) OR (end stage kidney) OR (ESKD) OR (ESRD) OR (CKJ) OR (CKI) OR (((renal) OR (kidney)) AND (failure)))” .

We included articles for review if they were in vivo studies (human-based), which applied AI & ML techniques in order to assess the diagnosis, prognosis, or therapy of CKD patients and reported original data. We did not limit our inclusion criteria to any specific study design, nor to any outcome of interest, as our main goal was to be as inclusive as possible, and we wanted to capture all available evidence from any study design and any outcome of interest.

We excluded studies that were not in English, those focusing on animals, reviews, systematic reviews, opinions, editorials, and case reports. We decided to exclude in vitro studies (conducted on cellular substrates) and studies focusing on animals, in order to summarize the current evidence on the application of ML models on humans.

Data extraction

Data were extracted by two independent reviewers (AC and FS). Disagreement on extracted data was discussed with an independent arbiter (DGol).

The following data were extracted from each included article (main text and/or supplementary material): author(s) name, date of publication, first author affiliation (country and region), main study objective, objective category (risk, diagnosis, prognosis, and treatment), prognosis category, study population, data source, sample size, problem type (regression, classification), machine learning algorithms examined in the study, predictor categories, number of predictors used, predictor list, performance metrics, final conclusions, use in clinical context and the 5 most important model features. When more than one model was considered in the study, the one the authors deemed best was extracted. Performance metrics always refer to the models’ performance on test sets.

Quality and risk assessment

Evaluation of the included studies was performed using both PROBAST [ 18 ] and the Guidelines for developing and reporting machine learning predictive models in biomedical research developed by Luo and colleagues [ 19 ].

Included studies

Of the 648 articles retrieved from PubMed, 421 were ruled out after title screening, and 140 were excluded after abstract screening; a total of 87 articles were selected for full-text screening (Fig.  1 ). Of these 87 studies, 68 were included in the final set of articles (Table 1 ) [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 ].

PRISMA flow-chart

Most of the included articles ( n  = 51) were published from 2019 to 2021. Among the 68 articles selected for data extraction, the majority were published by authors from organizations based in Asia ( n  = 33; 48.5%). The remaining articles were published by authors from Europe ( n  = 17; 25%), North America ( n  = 12; 17.6%), Africa ( n  = 5; 7.35%) and South America ( n  = 1; 1.47%). The analyzed studies were classified as observational.

A total of 28 studies focused on the use of ML algorithms in disease prognosis analysis, 21 investigated the use of ML techniques on diagnosis (4 evaluated both), 12 evaluated the risk of developing the disease, and 3 investigated the use of ML in CKD treatment. Among the articles focusing on prognosis, the majority studied the application of ML in evaluating CKD progression ( n  = 13) and mortality ( n  = 8).

Study populations and sample size

The most commonly investigated study population consisted of patients with CKD and healthy subjects ( n  = 26; 38.2%), followed by patients with CKD only ( n  = 16; 23.5%) and patients with CKD treated with hemodialysis ( n  = 12; 17.6%). The sample size investigated in the selected articles varied from a minimum of 30 individuals to a maximum of 550,000 (median = 776; IQR 400–12,020).

Data sources

The majority of the included articles analyzed data obtained from single-hospital registries ( n  = 33; 48.5%), datasets provided by universities ( n  = 15; 22.1%), and datasets collected in multi-center studies ( n  = 12, 17.6%). Five studies analyzed health insurance data (7.35%) and 3 studies used data provided by national health services (4.41%).

The most commonly used data were various combinations of demographic data along with individual clinical characteristics and laboratory data ( n  = 60; 82.24%), followed by data obtained by medical imaging technologies ( n  = 5; 7.35%) and genomic data ( n  = 3; 4.41%).

The number of models tested and reported in each article varied from a minimum of 1 model to a maximum of 10 (mean = 3). The most frequently tested model class was tree algorithms ( n  = 58, 33.53%), such as random forest ( n  = 27, 15.61%), decision trees ( n  = 10, 5.78%) and extreme gradient boosting ( n  = 9, 5.20). Subsequently, neural networks (NNs) were often inspected ( n  = 44, 16.18%), especially the multilayer perceptron (MLP) ( n  = 28, 16.18%). Another popular choice of machine learning model class was Support Vector Machines ( n  = 25, 14.45%) and logistic regression ( n  = 18, 10.45%) with various regularizations. Another popular method that we did not classify into a larger model class was the non-parametric k-Nearest Neighbors algorithm ( n  = 8, 2.31%). The complete list of models can be found in Table 2 .

All the articles implemented supervised learning algorithms, 57 (83.8%) of them addressed classification tasks and 11 (16.2%) regression tasks.

The majority of the included articles ( n  = 52) specified the total number of features used to train the models. These models used a highly variable number of features, ranging from 4 to 6624 (median = 24; IQR = 17—46). Of the 68 included studies, 55 specified the variables used in the models ( n  = 130). The most frequently used features are reported in Fig.  2 .

Occurrence of variables in the selected articles, divided per aim

Performance metrics

The most common performance metrics were accuracy ( n  = 30, 17.05%) and the area under the receiver operating characteristic curve (often also referred to as ROC-AUC, AUROC, AUC, or C-statistic) ( n  = 30, 17.05%). Subsequently, other classification metrics, such as sensitivity ( n  = 29, 16.48%), specificity ( n  = 24, 13.64%), precision ( n  = 16, 9.09%), and F1-score ( n  = 14, 7.95%) were often used to compare the machine learning models. Note that all the aforementioned metrics, except ROC AUC, were used for classification and required establishing a risk threshold as a decision boundary. ROC AUC conversely did not require setting a decision threshold as it was calculated by iterating over all the decision thresholds. In terms of regression, the most used metrics for comparison were mean absolute error ( n  = 6, 3.41%) and root mean squared error ( n  = 5, 2.84%). The full list of the metrics and how often they occurred can be found in Table 3 .

Best performing models, and their performances

In the included articles, neural networks were the models that commonly performed best ( n  = 28, 41.18%) compared to the median performance of other models, such as MLP ( n  = 18, 26.47%) and convolutional neural networks ( n  = 7, 24.53%). Tree-based algorithms performed best ( n  = 24, 35.29%); these algorithms included Random Forest ( n  = 16, 23.53%) and Extreme Gradient Boosting ( n  = 5, 7.35%). The results for Support Vector Machines ( n  = 5, 7.35%) were also noteworthy. A complete list of the best performing models in the selected papers can be found in Table 4 .

In terms of performance, we compared the metrics of prediction models, diagnostic models and risk prediction models separately. Of the 25 (36.76%) machine learning models for diagnosis, 19 papers reported accuracy. Three models reported the highest accuracy of 1.00 while the lowest reported accuracy is 0.80 (mean = 0.95, median = 0.98). Sensitivity was reported 15 times, with a maximum of 1.00, a minimum of 0.56, a mean of 0.95 and a median of 0.99. In addition, specificity was reported in 13 cases (max = 1.00, min = 0.79, mean = 0.96, median = 0.99). The ROC-AUC was reported in 6 papers (max = 0.99, min = 0.91, mean = 0.941, median = 0.94).

For the prediction models ( n  = 32, 47.06%), 15 papers reported the ROC-AUC with a maximum of 0.96 and a minimum of 0.69 (mean = 0.82, median = 0.82). Ten papers reported accuracy, ranging from 0.54 to 0.99, with a mean of 0.85 and a median of 0.87. Sensitivity was reported 8 times, ranging from 0.54 to 0.93 (mean = 0.765, median = 0.76), and specificity was reported 5 times (max = 0.99, min = 0.78, mean = 0.917, median = 0.96).

Next, the risk prediction models ( n  = 12, 17.65%) showed ROC-AUC 9 times (max = 0.96, min = 0.76, mean = 0.864, median = 0.86) and accuracy 4 times (max = 0.99, min = 0.82, mean = 0.901, median = 0.91).

Finally, 3 (4.41%) papers focused on therapy, one of which reported an accuracy of 0.95, while the other two focused on outcome differences ( p -values).

Most common variables and most important ones

The total number of variables used in the included studies was 813. The five most common ones were: Blood Pressure ( n  = 62, 7.63%), Age ( n  = 45, 5.54%), Hemoglobin ( n  = 37, 4.55%), Creatinine (serum) ( n  = 31, 3.81%) and Sex ( n  = 31, 3.81%).

Nonetheless, to better capture how variables were used in the selected papers, we classified the variables into 4 subsets (CKD Prognosis, CKD Diagnosis, Risk of Developing CKD, CKD Treatment) based on the primary aim the authors stated their model would have attempted to achieve.

Regarding CKD Prognosis, 342 variables were used out of 813 total (42%). The most common ones were: Blood Pressure ( n  = 24, 7%), Age ( n  = 19, 5,56%), Cholesterol (serum) ( n  = 18, 5.26%), Sex ( n  = 14, 4%) and Hemoglobin (blood) ( n  = 13, 3.8%), with the most important variables being: Age, Hemoglobin and Proteinuria.

Concerning CKD Diagnosis, 311 variables were used out of 813 total (38.25%). The most common ones were: Blood Pressure ( n  = 22, 7%), Hemoglobin (blood) ( n  = 19, 6.1%), Pus Cell General—used to indicate the number of dead white cells in urine—( n  = 18, 5.79%), Age ( n  = 14, 4.50%) and Glucose (serum) ( n  = 14, 4.50%). The most important variables in this case were Albumin, Creatinine, and Hemoglobin.

With regard to Risk of Developing CKD, 137 variables were used out of 813 total (16.85%). The most common ones were: Blood Pressure ( n  = 12, 8.75%), Age ( n  = 9, 6.57%), Sex ( n  = 7, 5.11%), History of Cardiovascular Disease ( n  = 6, 4.38%) and estimated Glomerular Filtration Rate (eGFR) ( n  = 6, 4.38%). The most important variables were Age, GFR and Blood Pressure.

Finally, regarding CKD Treatment, 23 variables were used out of 813 total (2.83%). The most common ones were: Blood Iron ( n  = 5, 21.74%), Hemoglobin ( n  = 3, 13%), Drugs Used ( n  = 2, 8.70%), MCV ( n  = 2, 8.70%) and White Blood Cells (blood) ( n  = 2, 8.70%). Regarding this aim, no weights were listed in the examined articles.

The complete spreadsheet with all variables and percentages can be found in Supplemental Material, together with the most important variables, divided per aim.

Other than using PROBAST to assess risk of bias, we also assessed fairness based on how the authors explicitly used variables. In some studies, variables were not fully listed, and in such cases, if the variable (sex, or race/ethnicity) was not indexed, we considered the feature as not included in the general model.

Out of 68 studies, 43 included gender in the model and 12 included race/ethnicity. When Non-Hispanic Whites were part of the assessed cohort, they were the majority group, ranging from 87 to 31%. Ten out of 68 studies addressed both gender and race/ethnicity, and included these variables in the model.

Race/ethnicity was included in 4 out of 12 studies predicting risk, in 5 out of 28 studies predicting prognosis, and in 3 out of 21 studies classifying diagnosis. It was never included in models investigating prognosis and diagnosis combined, and therapeutics.

Clinical Deployment

Regarding Diagnosis, just one model was actually deployed in a clinical environment [ 60 ]. The authors applied a lasso regression with metabolites as features, achieving an accuracy of 99%; the authors used data from a real clinical context, and therefore they deployed and evaluated their model performance on a clinical context, nevertheless, they did not validate their model. Regarding Prognosis, just 3 studies were conducted in a clinical setting [ 49 , 50 , 62 ]. Komaru et al. [ 49 ] predicted 1-year mortality following the start of hemodialysis through hierarchical clustering and achieved an AUC of 0.8; the authors used data from a clinical prospective study to deploy and evaluate their model. Furthermore, they validated the used clusters. Kanda et al. [ 50 ] applied a support vector machine model onto a real population in an observational study to deploy and evaluate their model. The authors achieved an accuracy of 89% through 13 variables; unfortunately, they did not disclose the weights of the variables nor did they validate the model, and therefore we do not know which variables were the most important. Akbilgic et al. [ 62 ] used a model based on a Random Forest algorithm, and achieved an AUC of 0.69; the most important features were eGFR, Spontaneous Bacterial Peritonitis, Age, Diastolic Blood Pressure and BUN. The authors used data from a real clinical context to deploy and evaluate their model; furthermore, they validated their results and model internally. Regarding Risk of developing CKD, one study’s model was used in a clinical context [ 42 ]. The authors used a NN, achieving an AUC of 0.89, using retinal images as features from a clinical context to deploy, evaluate and validate their model. Finally, regarding CKD Treatment, one study’s model was used in a clinical environment [ 26 ]; they presented their results through differences in achieved values by their algorithms, and the best performance was achieved by a NN. They evaluated the model with clinical data, but did not validate it.

Quality assessment

According to the PROBAST assessment tool [ 18 ], most of the included articles showed an overall low risk of bias ( n  = 48; 67.6%), and 65 (91.5%) of the included articles showed low applicability. Moreover, only 8.5% of the included studies scored less than 70% in the reporting guidelines for machine learning predictive models in biomedical research developed by Luo and colleagues [ 19 ]. The complete quality assessment can be found in Supplemental Material.

This systematic review describes how machine learning has been used for CKD. Six overarching themes were found, each of which underlines the need for further consideration by the scientific community.

First, despite the ever-growing number of studies focusing on the topic, a staggeringly low amount are being considered for actual clinical implementation. In this review, just 5 out of 68 articles tried to deploy their model in a real clinical setting. This might indicate either that the technology is not ready yet, or, considering 4 of these 5 articles were published in the last 3 years, that the technology is just starting to creep into real clinical settings. Recent evidence suggests that it is paramount to test newly developed algorithms in clinical settings before trying to deploy them [ 88 ]. Despite promising laboratory results, clinical translation is not always guaranteed. As an example, when studying the feasibility of providing an automated electronic alarm for acute kidney injury in different clinical settings, substantial heterogeneity in the findings among hospitals was described, with the worrying result of a significantly increased risk of death for some hospitals [ 89 ].

Second, as expected, the most important features were profoundly related to the main aim the authors were pursuing. In this regard, there were no surprises in the studied topics as the most important features were related to conditions known to lead to CKD diagnosis, worsening of prognosis and risk of developing CKD (e.g., age, comorbidities, systolic and diastolic blood pressure and eGFR values).

Third, a lack of consistency in reporting results was found. Most of the studies chose to report accuracy, but this was not the norm. Furthermore, while accuracy provides information on model performance, it fails to consider class imbalance and data representation. This is extremely important as accuracy in highly unbalanced datasets can be very high by always predicting the same binary outcome because of a flawed model. For instance, considering a low prevalence disease, if the algorithm is flawed for it always predicts a negative event, the accuracy will be high, but the veracity of the model will not [ 90 ]. As a result, AUCs and ROCs better measure the model precision without requiring the definition of a risk threshold. Twenty-nine authors chose to express their results including AUCs and ROCs: the minimum value was 0.69 and the maximum was 0.99 (mean: 0.83, median: 0.84). These results best express how precise the algorithms were and confirm the overall high performance of the assessed models.

Fourth, a common conundrum regarding feature selection and output was found in studies assessing CKD diagnosis. The definition of CKD requires certain variables to be present in order to make a diagnosis, thus including those variables in the model might be considered mandatory. Nonetheless, including those variables forces the model to streamline its decision process to a simple match in altered values, effectively transforming a complex machine learning model into a linear decision flow-chart, the performance of which will always be stellar.

This phenomenon is especially clear in four of the studies this systematic review assessed [ 36 , 39 , 46 , 47 ]. In these studies, the same database [ 91 ] is used, and accuracy, sensitivity, specificity, and ROC-AUC are never below 98%. We believe researchers should carefully assess the variables used in their machine learning models to make sure that no data leakage is present between features and results.

Fifth, model bias and fairness were almost never considered. This is critical, as both biased and unfair models will not achieve the same results in different demographics, and their societal impact could exasperate disparities in certain populations. These issues need to be further explored before any model can be implemented at point of care.

Finally, among the included studies, only 6 evaluated their models in a clinical setting [ 26 , 42 , 49 , 50 , 60 , 62 ], and only 3 were validated [ 42 , 49 , 62 ]. These studies showed promising results and did not report any unintended consequences after evaluation and/or validation. Notwithstanding the robust results described by the authors, as discussed before, recent evidence suggests that it is paramount to test newly developed algorithms in clinical settings to avoid adverse or unintended consequences [ 88 , 89 ]. Taking into account the pinnacle of importance of validating ones’ results in real clinical contexts and not just “in lab”, in reading their results, their generalizability has to be questioned, especially since no multi-center validations were described among the validated models.

This systematic review presents a few limitations: first, only one database (PubMed) was used to collect studies of interest. It should be noted that systematic reviews are usually exhorted to use at least two databases as stated by the PRISMA statement. Nonetheless, as PubMed has grown to be one of the most used search engines for medical sciences this limitation should be self-amending. Secondly, this systematic review assessed only papers written in English since English is the most widely adopted and commonly used language for the publication of medical papers.

In addition to these limitations, due to this review’s design, all in vitro studies (on cellular substrates) were excluded. Consequently, the evidence presented in this review is not to be interpreted as definitive for all things concerning CKD, since in vitro studies (on cellular substrates), the insight of which is critical in understanding pathogenetic as well as therapeutic mechanisms, were not assessed.

Lastly, the majority of included studies did not evaluate the integration of ML models in daily clinical practice, therefore the results and discussion have to be considered largely from an academic standpoint. Despite these limitations, we feel this review advances the knowledge on the current state of data-driven algorithms to advance CKD diagnosis, prognosis and treatment.

Despite the potential benefits, the application of machine learning for CKD diagnosis, prognosis, and treatment presents several issues, namely fairness, model and result interpretability [ 90 ], and the lack of validated models. Result interpretability concerns reflect the inability to explain which aspects of the dataset used in the training phase led to a predicted result in a particular case [ 92 , 93 ]. Therefore, as the trend in machine learning techniques moves from traditional algorithms (e.g., lasso regressions, support vector machine, and decision trees), to more complex ones (e.g., ensemble algorithms and deep learning), the interpretability concerns become more pronounced [ 90 ]. Notably, researchers highlighted the need for explainability and for models that could have a significant impact on patients' health [ 94 , 95 ]. These models should be reported using best practice reporting guidelines such as the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) [ 94 ] or MINimum Information for Medical AI Reporting (MINIMAR) [ 97 ]. Transparent and accurate reports are also fundamental in advancing multi-center validations of the applied models, which in turn is an essential step to ensure that only safe and sound models are applied on a large scale.

Most of the studies failed to report on the ethical issues revolving around their model development; the impact on the patient's well-being can also be affected by algorithmic bias [ 98 , 99 ] and this can be worse in certain underrepresented populations. This concern is closely related to the generalizability of the developed model [ 100 , 101 , 102 ]. Specifically, retrospective data that are usually used during the training phase often have significant biases towards subgroups of individuals that have been defined by factors such as age, gender, educational level, socioeconomic status, and location [ 98 ]. The issues of fairness and bias in algorithms should be evaluated by investigating the models’ performance within population subgroups.

This systematic review underlines the potential benefits and pitfalls of ML in the diagnosis, prognosis, and management of CKD. We found that most of the studies included in this systematic review reported that ML offers invaluable help to clinicians allowing them to make informed decisions and provide better care to their patients; nonetheless most of those articles were not actually piloted in real life settings, and therefore, notwithstanding the excellent model performance results reported by authors, the technology might not be ready for mass real-time adoption or implementation.

Although future work is needed to address the viability, interpretability, generalizability, and fairness issues, to allow a safer translation of these models for use in daily clinical practice, the implementation of these techniques could further enhance the effective management of hospital resources in a timely and efficient manner by potentially identifying patients at high risk for adverse events and the need for additional resources.

We hope the summarized evidence from this article will facilitate implementation of ML approaches in the clinical practice.

Data availability Statement

Data that support the findings of this study are available upon reasonable request from the corresponding author, AC.

Change history

06 march 2023.

A Correction to this paper has been published: https://doi.org/10.1007/s40620-023-01609-9

Webster AC, Nagler EV, Morton RL et al (2017) Chronic kidney disease. Lancet Lond Engl 389(10075):1238–1252. https://doi.org/10.1016/S0140-6736(16)32064-5

Article   Google Scholar  

Chen TK, Knicely DH, Grams ME (2019) Chronic kidney disease diagnosis and management. JAMA 322(13):1294–1304. https://doi.org/10.1001/jama.2019.14745

Article   CAS   PubMed   PubMed Central   Google Scholar  

Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Lond Engl. 2020;395(10225):709–733. doi: https://doi.org/10.1016/S0140-6736(20)30045-3

Vaidya SR, Aeddula NR. Chronic Renal Failure. In: StatPearls . StatPearls Publishing; 2022. Accessed July 28, 2022. http://www.ncbi.nlm.nih.gov/books/NBK535404/

Romagnani P, Remuzzi G, Glassock R et al (2017) Chronic kidney disease. Nat Rev Dis Primer 3:17088. https://doi.org/10.1038/nrdp.2017.88

Thomas R, Kanso A, Sedor JR (2008) Chronic kidney disease and its complications. Prim Care 35(2):329–vii. https://doi.org/10.1016/j.pop.2008.01.008

Article   PubMed   PubMed Central   Google Scholar  

Fraser SD, Blakeman T (2016) Chronic kidney disease: identification and management in primary care. Pragmatic Obs Res 7:21–32. https://doi.org/10.2147/POR.S97310

Chronic Kidney Disease: Overview . Institute for Quality and Efficiency in Health Care (IQWiG); 2018. Accessed July 28, 2022. https://www.ncbi.nlm.nih.gov/books/NBK492977/

Kazancioğlu R (2013) Risk factors for chronic kidney disease: an update. Kidney Int Suppl 3(4):368–371. https://doi.org/10.1038/kisup.2013.79

Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7

Article   CAS   PubMed   Google Scholar  

Nichols JA, Herbert Chan HW, Baker MAB (2018) Machine learning: applications of artificial intelligence to imaging and diagnosis. Biophys Rev 11(1):111–118. https://doi.org/10.1007/s12551-018-0449-9

Sidey-Gibbons JAM, Sidey-Gibbons CJ (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19:64. https://doi.org/10.1186/s12874-019-0681-4

Peterson DJ, Ostberg NP, Blayney DW et al (2021) Machine learning applied to electronic health records: identification of chemotherapy patients at high risk for preventable emergency department visits and hospital admissions. JCO Clin Cancer Inform 5:1106–1126. https://doi.org/10.1200/CCI.21.00116

Article   PubMed   Google Scholar  

Lenain R, Seneviratne MG, Bozkurt S et al (2019) Machine learning approaches for extracting stage from pathology reports in prostate cancer. Stud Health Technol Inform 264:1522–1523. https://doi.org/10.3233/SHTI190515

Cahan EM, Hernandez-Boussard T, Thadaney-Israni S et al (2019) Putting the data before the algorithm in big data addressing personalized healthcare. NPJ Digit Med 2:78. https://doi.org/10.1038/s41746-019-0157-2

Rajpurkar P, Chen E, Banerjee O et al (2022) AI in health and medicine. Nat Med 28(1):31–38. https://doi.org/10.1038/s41591-021-01614-0

Page MJ, McKenzie JE, Bossuyt PM et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:71. https://doi.org/10.1136/bmj.n71

Wolff RF, Moons KGM, Riley RD et al (2019) PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med 170(1):51–58. https://doi.org/10.7326/M18-1376

Luo W, Phung D, Tran T et al (2016) Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res 18(12):e323. https://doi.org/10.2196/jmir.5870

Goldstein BA, Pomann GM, Winkelmayer WC et al (2017) A comparison of risk prediction methods using repeated observations: an application to electronic health records for hemodialysis. Stat Med 36(17):2750–2763. https://doi.org/10.1002/sim.7308

Sabanayagam C, Xu D, Ting DSW et al (2020) A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Health 2(6):e295–e302. https://doi.org/10.1016/S2589-7500(20)30063-7

Rodriguez M, Salmeron MD, Martin-Malo A et al (2016) A new data analysis system to quantify associations between biochemical parameters of chronic kidney disease-mineral bone disease. PLoS ONE 11(1):e0146801. https://doi.org/10.1371/journal.pone.0146801

Barbieri C, Mari F, Stopper A et al (2015) A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Comput Biol Med 61:56–61. https://doi.org/10.1016/j.compbiomed.2015.03.019

Kumar A, Sinha N, Bhardwaj A (2020) A novel fitness function in genetic programming for medical data classification. J Biomed Inform 112:103623. https://doi.org/10.1016/j.jbi.2020.103623

Peng H, Zhu H, Ieong CWA et al (2021) A two-stage neural network prediction of chronic kidney disease. IET Syst Biol 15(5):163–171. https://doi.org/10.1049/syb2.12031

Barbieri C, Molina M, Ponce P et al (2016) An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int 90(2):422–429. https://doi.org/10.1016/j.kint.2016.03.036

Kanda E, Epureanu BI, Adachi T et al (2020) Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan. PLoS ONE 15(5):e0233491. https://doi.org/10.1371/journal.pone.0233491

Yu H, Samuels DC, Zhao YY, Guo Y (2019) Architectures and accuracy of artificial neural network for disease classification from omics data. BMC Genom 20(1):167. https://doi.org/10.1186/s12864-019-5546-z

Lin SY, Hsieh MH, Lin CL et al (2019) Artificial intelligence prediction model for the cost and mortality of renal replacement therapy in aged and super-aged populations in Taiwan. J Clin Med. https://doi.org/10.3390/jcm8070995

Ohara T, Ikeda H, Sugitani Y et al (2021) Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients. Int J Med Sci 18(8):1831–1839. https://doi.org/10.7150/ijms.53298

Akl AI, Sobh MA, Enab YM et al (2001) Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy. Am J Kidney Dis Off J Natl Kidney Found 38(6):1277–1283. https://doi.org/10.1053/ajkd.2001.29225

Article   CAS   Google Scholar  

Kolachalama VB, Singh P, Lin CQ et al (2018) Association of pathological fibrosis with renal survival using deep neural networks. Kidney Int Rep 3(2):464–475. https://doi.org/10.1016/j.ekir.2017.11.002

Daniel AJ, Buchanan CE, Allcock T et al (2021) Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network. Magn Reson Med 86(2):1125–1136. https://doi.org/10.1002/mrm.28768

Kuo CC, Chang CM, Liu KT et al (2019) Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. NPJ Digit Med 2:29. https://doi.org/10.1038/s41746-019-0104-2

Parab J, Sequeira M, Lanjewar M et al (2021) Backpropagation neural network-based machine learning model for prediction of blood urea and glucose in CKD patients. IEEE J Transl Eng Health Med 9:4900608. https://doi.org/10.1109/JTEHM.2021.3079714

Chen Z, Zhang X, Zhang Z (2016) Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models. Int Urol Nephrol 48(12):2069–2075. https://doi.org/10.1007/s11255-016-1346-4

Rashed-Al-Mahfuz M, Haque A, Azad A et al (2021) Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for use in low-cost diagnostic screening. IEEE J Transl Eng Health Med 9:4900511. https://doi.org/10.1109/JTEHM.2021.3073629

Roth JA, Radevski G, Marzolini C et al (2021) Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with human immunodeficiency virus: a prospective multicenter cohort study. J Infect Dis 224(7):1198–1208. https://doi.org/10.1093/infdis/jiaa236

Huang ML, Chou YC (2019) Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network. Comput Methods Programs Biomed. 180:105016. https://doi.org/10.1016/j.cmpb.2019.105016

Jeong B, Cho H, Kim J et al (2020) Comparison between statistical models and machine learning methods on classification for highly imbalanced multiclass kidney data. Diagn Basel Switz. https://doi.org/10.3390/diagnostics10060415

Xin G, Zhou G, Zhang W et al (2020) Construction and validation of predictive model to identify critical genes associated with advanced kidney disease. Int J Genomics 2020:7524057. https://doi.org/10.1155/2020/7524057

Zhang K, Liu X, Xu J et al (2021) Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images. Nat Biomed Eng 5(6):533–545. https://doi.org/10.1038/s41551-021-00745-6

Schena FP, Anelli VW, Trotta J et al (2021) Development and testing of an artificial intelligence tool for predicting end-stage kidney disease in patients with immunoglobulin A nephropathy. Kidney Int 99(5):1179–1188. https://doi.org/10.1016/j.kint.2020.07.046

Galloway CD, Valys AV, Shreibati JB et al (2019) Development and validation of a deep-learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiol 4(5):428–436. https://doi.org/10.1001/jamacardio.2019.0640

Yuan Q, Zhang H, Xie Y et al (2020) Development of prognostic model for patients at CKD stage 3a and 3b in South Central China using computational intelligence. Clin Exp Nephrol 24(10):865–875. https://doi.org/10.1007/s10157-020-01909-5

Polat H, Danaei Mehr H, Cetin A (2017) Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. J Med Syst 41(4):55. https://doi.org/10.1007/s10916-017-0703-x

Senan EM, Al-Adhaileh MH, Alsaade FW et al (2021) Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques. J Healthc Eng 2021:1004767. https://doi.org/10.1155/2021/1004767

Pellicer-Valero OJ, Cattinelli I, Neri L et al (2020) Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks. Artif Intell Med. 107:101898. https://doi.org/10.1016/j.artmed.2020.101898

Komaru Y, Yoshida T, Hamasaki Y et al (2020) Hierarchical clustering analysis for predicting 1-year mortality after starting hemodialysis. Kidney Int Rep 5(8):1188–1195. https://doi.org/10.1016/j.ekir.2020.05.007

Kanda E, Kanno Y, Katsukawa F (2019) Identifying progressive CKD from healthy population using Bayesian network and artificial intelligence: a worksite-based cohort study. Sci Rep 9(1):5082. https://doi.org/10.1038/s41598-019-41663-7

Singh A, Nadkarni G, Gottesman O et al (2015) Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration. J Biomed Inform 53:220–228. https://doi.org/10.1016/j.jbi.2014.11.005

Inaguma D, Kitagawa A, Yanagiya R et al (2020) Increasing tendency of urine protein is a risk factor for rapid eGFR decline in patients with CKD: a machine learning-based prediction model by using a big database. PLoS ONE 15(9):e0239262. https://doi.org/10.1371/journal.pone.0239262

Elhoseny M, Shankar K, Uthayakumar J (2019) Intelligent diagnostic prediction and classification system for chronic kidney disease. Sci Rep 9(1):9583. https://doi.org/10.1038/s41598-019-46074-2

Nusinovici S, Tham YC, Chak Yan MY et al (2020) Logistic regression was as good as machine learning for predicting major chronic diseases. J Clin Epidemiol 122:56–69. https://doi.org/10.1016/j.jclinepi.2020.03.002

Song X, Waitman LR, Yu AS et al (2020) Longitudinal risk prediction of chronic kidney disease in diabetic patients using a temporal-enhanced gradient boosting machine: retrospective cohort study. JMIR Med Inform 8(1):e15510. https://doi.org/10.2196/15510

Tang Y, Zhang W, Zhu M et al (2018) Lupus nephritis pathology prediction with clinical indices. Sci Rep 8(1):10231. https://doi.org/10.1038/s41598-018-28611-7

Segal Z, Kalifa D, Radinsky K et al (2020) Machine learning algorithm for early detection of end-stage renal disease. BMC Nephrol 21(1):518. https://doi.org/10.1186/s12882-020-02093-0

Forné C, Cambray S, Bermudez-Lopez M et al (2020) Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease. Clin Kidney J 13(4):631–639. https://doi.org/10.1093/ckj/sfz094

Huang J, Huth C, Covic M et al (2020) Machine learning approaches reveal metabolic signatures of incident chronic kidney disease in individuals with prediabetes and type 2 diabetes. Diabetes 69(12):2756–2765. https://doi.org/10.2337/db20-0586

Guo Y, Yu H, Chen D et al (2019) Machine learning distilled metabolite biomarkers for early stage renal injury. Metabolomics Off J Metabolomic Soc 16(1):4. https://doi.org/10.1007/s11306-019-1624-0

Krishnamurthy S, Ks K, Dovgan E et al (2021) Machine learning prediction models for chronic kidney disease using national health insurance claim data in Taiwan. Healthc Basel Switz. https://doi.org/10.3390/healthcare9050546

Akbilgic O, Obi Y, Potukuchi PK et al (2019) Machine learning to identify dialysis patients at high death risk. Kidney Int Rep 4(9):1219–1229. https://doi.org/10.1016/j.ekir.2019.06.009

Belur Nagaraj S, Pena MJ, Ju W et al (2020) Machine-learning-based early prediction of end-stage renal disease in patients with diabetic kidney disease using clinical trials data. Diabetes Obes Metab 22(12):2479–2486. https://doi.org/10.1111/dom.14178

Vitsios D, Petrovski S (2020) Mantis-ml: disease-agnostic gene prioritization from high-throughput genomic screens by stochastic semi-supervised learning. Am J Hum Genet 106(5):659–678. https://doi.org/10.1016/j.ajhg.2020.03.012

Shang N, Khan A, Polubriaginof F et al (2021) Medical records-based chronic kidney disease phenotype for clinical care and “big data” observational and genetic studies. NPJ Digit Med 4(1):70. https://doi.org/10.1038/s41746-021-00428-1

Luo L, Small D, Stewart WF et al (2013) Methods for estimating kidney disease stage transition probabilities using electronic medical records. EGEMS Wash DC 1(3):1040. https://doi.org/10.13063/2327-9214.1040

Almansour NA, Syed HF, Khayat NR et al (2019) Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med 109:101–111. https://doi.org/10.1016/j.compbiomed.2019.04.017

Chen CA, Li YC, Lin YF et al (2007) Neuro-fuzzy technology as a predictor of parathyroid hormone level in hemodialysis patients. Tohoku J Exp Med 211(1):81–87. https://doi.org/10.1620/tjem.211.81

Escandell-Montero P, Chermisi M, Martínez-Martínez JM et al (2014) Optimization of anemia treatment in hemodialysis patients via reinforcement learning. Artif Intell Med 62(1):47–60. https://doi.org/10.1016/j.artmed.2014.07.004

Weber C, Röschke L, Modersohn L et al (2020) Optimized identification of advanced chronic kidney disease and absence of kidney disease by combining different electronic health data resources and by applying machine learning strategies. J Clin Med. https://doi.org/10.3390/jcm9092955

Garcia-Montemayor V, Martin-Malo A, Barbieri C et al (2021) Predicting mortality in hemodialysis patients using machine learning analysis. Clin Kidney J 14(5):1388–1395. https://doi.org/10.1093/ckj/sfaa126

Norouzi J, Yadollahpour A, Mirbagheri SA et al (2016) Predicting renal failure progression in chronic kidney disease using integrated intelligent fuzzy expert system. Comput Math Methods Med 2016:6080814. https://doi.org/10.1155/2016/6080814

Kusiak A, Dixon B, Shah S (2005) Predicting survival time for kidney dialysis patients: a data mining approach. Comput Biol Med 35(4):311–327. https://doi.org/10.1016/j.compbiomed.2004.02.004

Jeong YS, Kim J, Kim D et al (2021) Prediction of postoperative complications for patients of end stage renal disease. Sensors. https://doi.org/10.3390/s21020544

Martínez-Martínez JM, Escandell-Montero P, Barbieri C et al (2014) Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques. Comput Methods Programs Biomed 117(2):208–217. https://doi.org/10.1016/j.cmpb.2014.07.001

Noh J, Yoo KD, Bae W et al (2020) Prediction of the mortality risk in peritoneal dialysis patients using machine learning models: a nation-wide prospective cohort in Korea. Sci Rep 10(1):7470. https://doi.org/10.1038/s41598-020-64184-0

Glazyrin YE, Veprintsev DV, Ler IA et al (2020) Proteomics-based machine learning approach as an alternative to conventional biomarkers for differential diagnosis of chronic kidney diseases. Int J Mol Sci. https://doi.org/10.3390/ijms21134802

Navaneeth B, Suchetha M (2019) PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications. Comput Biol Med 108:85–92. https://doi.org/10.1016/j.compbiomed.2019.03.017

Chen C, Yang L, Li H et al (2020) Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure. Photodiagnosis Photodyn Ther. 30:101792. https://doi.org/10.1016/j.pdpdt.2020.101792

Han X, Zheng X, Wang Y et al (2019) Random forest can accurately predict the development of end-stage renal disease in immunoglobulin a nephropathy patients. Ann Transl Med 7(11):234. https://doi.org/10.21037/atm.2018.12.11

Shih CC, Lu CJ, Chen GD et al (2020) Risk prediction for early chronic kidney disease: results from an adult health examination program of 19,270 individuals. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph17144973

Kannan S, Morgan LA, Liang B et al (2019) Segmentation of glomeruli within trichrome images using deep learning. Kidney Int Rep 4(7):955–962. https://doi.org/10.1016/j.ekir.2019.04.008

Aldhyani THH, Alshebami AS, Alzahrani MY (2020) Soft clustering for enhancing the diagnosis of chronic diseases over machine learning algorithms. J Healthc Eng 2020:4984967. https://doi.org/10.1155/2020/4984967

Kleiman RS, LaRose ER, Badger JC et al (2018) Using machine learning algorithms to predict risk for development of calciphylaxis in patients with chronic kidney disease. AMIA Jt Summits Transl Sci Proc AMIA Jt Summits Transl Sci 2017:139–146

Google Scholar  

Dovgan E, Gradišek A, Luštrek M et al (2020) Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients. PLoS ONE 15(6):e0233976. https://doi.org/10.1371/journal.pone.0233976

Wu X, Yuan X, Wang W et al (2020) Value of a machine learning approach for predicting clinical outcomes in young patients with hypertension. Hypertens Dallas Tex. 75(5):1271–1278. https://doi.org/10.1161/HYPERTENSIONAHA.119.13404

Ogunleye A, Wang QG (2020) XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans Comput Biol Bioinform 17(6):2131–2140. https://doi.org/10.1109/TCBB.2019.2911071

Connell A, Black G, Montgomery H et al (2019) Implementation of a digitally enabled care pathway (part 2): qualitative analysis of experiences of health care professionals. J Med Internet Res 21(7):e13143. https://doi.org/10.2196/13143

Wilson FP, Martin M, Yamamoto Y et al (2021) Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial. BMJ 372:4786. https://doi.org/10.1136/bmj.m4786

Röösli E, Bozkurt S, Hernandez-Boussard T (2022) Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model. Sci Data 9(1):24. https://doi.org/10.1038/s41597-021-01110-7

UCI Machine Learning Repository. Accessed July 28, 2022. https://archive.ics.uci.edu/ml/index.php

Linardatos P, Papastefanopoulos V, Kotsiantis S (2020) Explainable AI: a review of machine learning interpretability methods. Entropy 23(1):18. https://doi.org/10.3390/e23010018

Murdoch WJ, Singh C, Kumbier K et al (2019) Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci U S A 116(44):22071–22080. https://doi.org/10.1073/pnas.1900654116

Amann J, Blasimme A, Vayena E et al (2020) Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 20:310. https://doi.org/10.1186/s12911-020-01332-6

Payrovnaziri SN, Chen Z, Rengifo-Moreno P et al (2020) Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. J Am Med Inform Assoc JAMIA 27(7):1173–1185. https://doi.org/10.1093/jamia/ocaa053

Collins GS, Reitsma JB, Altman DG et al (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 13(1):1. https://doi.org/10.1186/s12916-014-0241-z

Hernandez-Boussard T, Bozkurt S, Ioannidis JPA et al (2020) MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc JAMIA 27(12):2011–2015. https://doi.org/10.1093/jamia/ocaa088

Gianfrancesco MA, Tamang S, Yazdany J et al (2018) Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 178(11):1544–1547. https://doi.org/10.1001/jamainternmed.2018.3763

Panch T, Mattie H, Atun R (2002) Artificial intelligence and algorithmic bias: implications for health systems. J Glob Health 9(2):20318. https://doi.org/10.7189/jogh.09.020318

Ramspek CL, Jager KJ, Dekker FW et al (2020) External validation of prognostic models: what, why, how, when and where? Clin Kidney J 14(1):49–58. https://doi.org/10.1093/ckj/sfaa188

Steyerberg EW, Bleeker SE, Moll HA et al (2003) Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J Clin Epidemiol 56(5):441–447. https://doi.org/10.1016/s0895-4356(03)00047-7

Riley RD, Ensor J, Snell KIE et al (2016) External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges. BMJ 353:3140. https://doi.org/10.1136/bmj.i3140

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FS and AC had the idea, extracted, and analyzed the data and wrote the manuscript. CF analyzed the data and wrote the manuscript. DGol, DGor, helped in results interpretation. THB revised the manuscript and helped in results interpretation. AC supervised the entire process.

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Sanmarchi, F., Fanconi, C., Golinelli, D. et al. Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 36 , 1101–1117 (2023). https://doi.org/10.1007/s40620-023-01573-4

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Received : 06 August 2022

Accepted : 01 January 2023

Published : 14 February 2023

Issue Date : May 2023

DOI : https://doi.org/10.1007/s40620-023-01573-4

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1. Introduction

2. oxidative stress, 3. inflammation, 3.1. interleukins, 3.2. macrophages, 3.3. nod-like receptor protein 3, 4. neutrophil gelatinase-associated lipocalin, 5. matrix metalloproteinases, 6. gut–kidney axis, 7. new targets of treatment, 8. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

• Ammirati, A.L. Chronic Kidney Disease. Rev. Assoc. Med. Bras. 2020 , 66 (Suppl. S1), s03–s09. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ene-Iordache, B.; Perico, N.; Bikbov, B.; Carminati, S.; Remuzzi, A.; Perna, A.; Islam, N.; Bravo, R.F.; Aleckovic-Halilovic, M.; Zou, H.; et al. Chronic Kidney Disease and Cardiovascular Risk in Six Regions of the World (ISN-KDDC): A Cross-Sectional Study. Lancet Glob. Health 2016 , 4 , e307–e319. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kim, K.M.; Oh, H.J.; Choi, H.Y.; Lee, H.; Ryu, D.-R. Impact of Chronic Kidney Disease on Mortality: A Nationwide Cohort Study. Kidney Res. Clin. Pract. 2019 , 38 , 382–390. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. Suppl. 2013 , 3 , 1–150. [ Google Scholar ]
• Chapter 1: Definition and Classification of CKD. Kidney Int. Suppl. 2013 , 3 , 19–62. [ CrossRef ] [ Green Version ]
• Charles, C.; Ferris, A.H. Chronic Kidney Disease. Prim. Care 2020 , 47 , 585–595. [ Google Scholar ] [ CrossRef ]
• Vaidya, S.R.; Aeddula, N.R. Chronic Renal Failure. In StatPearls ; StatPearls Publishing: Treasure Island, FL, USA, 2022. [ Google Scholar ]
• Kalantar-Zadeh, K.; Jafar, T.H.; Nitsch, D.; Neuen, B.L.; Perkovic, V. Chronic Kidney Disease. Lancet 2021 , 398 , 786–802. [ Google Scholar ] [ CrossRef ]
• Kućmierz, J.; Frąk, W.; Młynarska, E.; Franczyk, B.; Rysz, J. Molecular Interactions of Arterial Hypertension in Its Target Organs. Int. J. Mol. Sci. 2021 , 22 , 9669. [ Google Scholar ] [ CrossRef ]
• Rapa, S.F.; Di Iorio, B.R.; Campiglia, P.; Heidland, A.; Marzocco, S. Inflammation and Oxidative Stress in Chronic Kidney Disease-Potential Therapeutic Role of Minerals, Vitamins and Plant-Derived Metabolites. Int. J. Mol. Sci. 2019 , 21 , 263. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Duni, A.; Liakopoulos, V.; Roumeliotis, S.; Peschos, D.; Dounousi, E. Oxidative Stress in the Pathogenesis and Evolution of Chronic Kidney Disease: Untangling Ariadne’s Thread. Int. J. Mol. Sci. 2019 , 20 , 3711. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Niwa, T. Indoxyl Sulfate Is a Nephro-Vascular Toxin. J. Ren. Nutr. 2010 , 20 (Suppl. S5), S2–S6. [ Google Scholar ] [ CrossRef ]
• Schei, J.; Fuskevåg, O.-M.; Stefansson, V.T.N.; Solbu, M.D.; Jenssen, T.G.; Eriksen, B.O.; Melsom, T. Urinary Markers of Oxidative Stress Are Associated With Albuminuria But Not GFR Decline. Kidney Int. Rep. 2018 , 3 , 573–582. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Roberts, L.; Morrow, J.D. Measurement of F(2)-isoprostanes as an index of oxidative stress in vivo. Free Radic. Biol. Med. 2000 , 28 , 505–513. [ Google Scholar ] [ CrossRef ]
• Colombo, G.; Reggiani, F.; Angelini, C.; Finazzi, S.; Astori, E.; Garavaglia, M.L.; Landoni, L.; Portinaro, N.M.; Giustarini, D.; Rossi, R.; et al. Plasma Protein Carbonyls as Biomarkers of Oxidative Stress in Chronic Kidney Disease, Dialysis, and Transplantation. Oxidative Med. Cell. Longev. 2020 , 2020 , 2975256. [ Google Scholar ] [ CrossRef ]
• Daenen, K.; Andries, A.; Mekahli, D.; Van Schepdael, A.; Jouret, F.; Bammens, B. Oxidative Stress in Chronic Kidney Disease. Pediatr. Nephrol. 2019 , 34 , 975–991. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Metodiewa, D.; Kośka, C. Reactive oxygen species and reactive nitrogen species: Relevance to cyto(neuro)toxic events and neurologic disorders. An overview. Neurotox. Res. 2000 , 1 , 197–233. [ Google Scholar ] [ CrossRef ]
• Nagata, M. Podocyte Injury and Its Consequences. Kidney Int. 2016 , 89 , 1221–1230. [ Google Scholar ] [ CrossRef ]
• Wang, H.; Chen, X.; Su, Y.; Paueksakon, P.; Hu, W.; Zhang, M.-Z.; Harris, R.C.; Blackwell, T.S.; Zent, R.; Pozzi, A. P47(Phox) Contributes to Albuminuria and Kidney Fibrosis in Mice. Kidney Int. 2015 , 87 , 948–962. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zeisberg, M.; Neilson, E.G. Mechanisms of tubulointerstitial fibrosis. J. Am. Soc. Nephrol. 2010 , 21 , 1819–1834. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mack, M.; Yanagita, M. Origin of Myofibroblasts and Cellular Events Triggering Fibrosis. Kidney Int. 2015 , 87 , 297–307. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bondi, C.D.; Manickam, N.; Lee, D.Y.; Block, K.; Gorin, Y.; Abboud, H.E.; Barnes, J.L. NAD(P)H oxidase mediates TGF-beta1 induced activation of kidney myofibroblasts. J. Am. Soc. Nephrol. 2010 , 21 , 93–102. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Modlinger, P.S.; Wilcox, C.S.; Aslam, S. Nitric Oxide, Oxidative Stress, and Progression of Chronic Renal Failure. Semin. Nephrol. 2004 , 24 , 354–365. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Justiz Vaillant, A.A.; Qurie, A. Interleukin. In StatPearls ; StatPearls Publishing: Treasure Island, FL, USA, 2022. [ Google Scholar ]
• Mertowska, P.; Mertowski, S.; Smarz-Widelska, I.; Grywalska, E. Biological Role, Mechanism of Action and the Importance of Interleukins in Kidney Diseases. Int. J. Mol. Sci. 2022 , 23 , 647. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Barreto, D.V.; Barreto, F.C.; Liabeuf, S.; Temmar, M.; Lemke, H.-D.; Tribouilloy, C.; Choukroun, G.; Vanholder, R.; Massy, Z.A.; European Uremic Toxin Work Group (EUTox). Plasma Interleukin-6 Is Independently Associated with Mortality in Both Hemodialysis and Pre-Dialysis Patients with Chronic Kidney Disease. Kidney Int. 2010 , 77 , 550–556. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mima, T.; Nishimoto, N. Clinical Value of Blocking IL-6 Receptor. Curr. Opin. Rheumatol. 2009 , 21 , 224–230. [ Google Scholar ] [ CrossRef ]
• Kreiner, F.F.; Kraaijenhof, J.M.; von Herrath, M.; Hovingh, G.K.K.; von Scholten, B.J. Interleukin 6 in Diabetes, Chronic Kidney Disease, and Cardiovascular Disease: Mechanisms and Therapeutic Perspectives. Expert Rev. Clin. Immunol. 2022 , 18 , 377–389. [ Google Scholar ] [ CrossRef ]
• Jones, S.A.; Fraser, D.J.; Fielding, C.A.; Jones, G.W. Interleukin-6 in Renal Disease and Therapy. Nephrol. Dial. Transplant. 2015 , 30 , 564–574. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mantovani, A.; Dinarello, C.A.; Molgora, M.; Garlanda, C. Interleukin-1 and Related Cytokines in Innate and Adaptive Immunity in Health and Disease. Immunity 2019 , 50 , 778–795. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Bandach, I.; Segev, Y.; Landau, D. Experimental Modulation of Interleukin 1 Shows Its Key Role in Chronic Kidney Disease Progression and Anemia. Sci. Rep. 2021 , 11 , 6288. [ Google Scholar ] [ CrossRef ]
• Schunk, S.J.; Triem, S.; Schmit, D.; Zewinger, S.; Sarakpi, T.; Becker, E.; Hütter, G.; Wrublewsky, S.; Küting, F.; Hohl, M.; et al. Interleukin-1α Is a Central Regulator of Leukocyte-Endothelial Adhesion in Myocardial Infarction and in Chronic Kidney Disease. Circulation 2021 , 144 , 893–908. [ Google Scholar ] [ CrossRef ]
• Lemos, D.R.; McMurdo, M.; Karaca, G.; Wilflingseder, J.; Leaf, I.A.; Gupta, N.; Miyoshi, T.; Susa, K.; Johnson, B.G.; Soliman, K.; et al. Interleukin-1β Activates a MYC-Dependent Metabolic Switch in Kidney Stromal Cells Necessary for Progressive Tubulointerstitial Fibrosis. J. Am. Soc. Nephrol. 2018 , 29 , 1690–1705. [ Google Scholar ] [ CrossRef ]
• Romanova, Y.; Laikov, A.; Markelova, M.; Khadiullina, R.; Makseev, A.; Hasanova, M.; Rizvanov, A.; Khaiboullina, S.; Salafutdinov, I. Proteomic Analysis of Human Serum from Patients with Chronic Kidney Disease. Biomolecules 2020 , 10 , 257. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wei, C.-C.; Li, H.-H.; Hsu, Y.-H.; Hsing, C.-H.; Sung, J.-M.; Chang, M.-S. Interleukin-20 Targets Renal Cells and Is Associated with Chronic Kidney Disease. Biochem. Biophys. Res. Commun. 2008 , 374 , 448–453. [ Google Scholar ] [ CrossRef ]
• Chang, M.S.; Hsu, Y.H. The Role of IL-20 in Chronic Kidney Disease and Diabetic Nephropathy: Pathogenic and Therapeutic Implications. J. Leukoc. Biol. 2018 , 104 , 919–923. [ Google Scholar ] [ CrossRef ]
• Hsu, Y.; Wu, C.; Chiu, C.; Chen, W.; Chang, Y.; Wabitsch, M.; Chang, M. IL-20 Is Involved in Obesity by Modulation of Adipogenesis and Macrophage Dysregulation. Immunology 2021 , 164 , 817–833. [ Google Scholar ] [ CrossRef ]
• Chiu, S.; Bharat, A. Role of Monocytes and Macrophages in Regulating Immune Response Following Lung Transplantation. Curr. Opin. Organ Transplant. 2016 , 21 , 239–245. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Geissmann, F.; Manz, M.G.; Jung, S.; Sieweke, M.H.; Merad, M.; Ley, K. Development of Monocytes, Macrophages and Dendritic Cells. Science 2010 , 327 , 656–661. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kim, S.Y.; Nair, M.G. Macrophages in Wound Healing: Activation and Plasticity. Immunol. Cell. Biol. 2019 , 97 , 258–267. [ Google Scholar ] [ CrossRef ]
• Stout, R.D.; Suttles, J. Immunosenescence and Macrophage Functional Plasticity: Dysregulation of Macrophage Function by Age-Associated Microenvironmental Changes. Immunol. Rev. 2005 , 205 , 60–71. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Caillon, A.; Paradis, P.; Schiffrin, E.L. Role of Immune Cells in Hypertension. Br. J. Pharmacol. 2019 , 176 , 1818–1828. [ Google Scholar ] [ CrossRef ]
• Duffield, J.S. Macrophages and Immunologic Inflammation of the Kidney. Semin. Nephrol. 2010 , 30 , 234–254. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Abais, J.M.; Xia, M.; Li, G.; Chen, Y.; Conley, S.M.; Gehr, T.W.; Boini, K.M.; Li, P.-L. Nod-like Receptor Protein 3 (NLRP3) Inflammasome Activation and Podocyte Injury via Thioredoxin-Interacting Protein (TXNIP) during Hyperhomocysteinemia. J. Biol. Chem. 2014 , 289 , 27159–27168. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Latz, E.; Xiao, T.; Stutz, A. Activation and regulation of the inflammasomes. Nat. Rev. Immunol. 2013 , 13 , 397–411. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Cantero-Navarro, E.; Rayego-Mateos, S.; Orejudo, M.; Tejedor-Santamaria, L.; Tejera-Muñoz, A.; Sanz, A.B.; Marquez-Exposito, L.; Marchant, V.; Santos-Sanchez, L.; Egido, J.; et al. Role of Macrophages and Related Cytokines in Kidney Disease. Front. Med. 2021 , 8 , 688060. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kelley, N.; Jeltema, D.; Duan, Y.; He, Y. The NLRP3 Inflammasome: An Overview of Mechanisms of Activation and Regulation. Int. J. Mol. Sci. 2019 , 20 , 3328. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Nazir, S.; Gadi, I.; Al-Dabet, M.M.; Elwakiel, A.; Kohli, S.; Ghosh, S.; Manoharan, J.; Ranjan, S.; Bock, F.; Braun-Dullaeus, R.C.; et al. Cytoprotective Activated Protein C Averts Nlrp3 Inflammasome-Induced Ischemia-Reperfusion Injury via MTORC1 Inhibition. Blood 2017 , 130 , 2664–2677. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zheng, Z.; Xu, K.; Li, C.; Qi, C.; Fang, Y.; Zhu, N.; Bao, J.; Zhao, Z.; Yu, Q.; Wu, H.; et al. NLRP3 Associated with Chronic Kidney Disease Progression after Ischemia/Reperfusion-Induced Acute Kidney Injury. Cell Death Discov. 2021 , 7 , 324. [ Google Scholar ] [ CrossRef ]
• Kim, H.-J.; Lee, D.W.; Ravichandran, K.; Keys, D.O.; Akcay, A.; Nguyen, Q.; He, Z.; Jani, A.; Ljubanovic, D.; Edelstein, C.L. NLRP3 Inflammasome Knockout Mice Are Protected against Ischemic but Not Cisplatin-Induced Acute Kidney Injury. J. Pharmacol. Exp. Ther. 2013 , 346 , 465–472. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Faubel, S.; Edelstein, C.L. Caspases as Drug Targets in Ischemic Organ Injury. Curr. Drug Targets Immune Endocr. Metabol. Disord. 2005 , 5 , 269–287. [ Google Scholar ] [ CrossRef ]
• Jang, H.N.; Kim, J.H.; Jung, M.H.; Tak, T.; Jung, J.H.; Lee, S.; Jung, S.; Chang, S.-H.; Kim, H.-J. Human Endothelial Progenitor Cells Protect the Kidney against Ischemia-Reperfusion Injury via the NLRP3 Inflammasome in Mice. Int. J. Mol. Sci. 2022 , 23 , 1546. [ Google Scholar ] [ CrossRef ]
• Anders, H.-J.; Suarez-Alvarez, B.; Grigorescu, M.; Foresto-Neto, O.; Steiger, S.; Desai, J.; Marschner, J.A.; Honarpisheh, M.; Shi, C.; Jordan, J.; et al. The macrophage phenotype and inflammasome component NLRP3 contributes to nephrocalcinosis-related chronic kidney disease independent from IL-1–mediated tissue injury. Kidney Int. 2017 , 93 , 656–669. [ Google Scholar ] [ CrossRef ]
• Liu, D.; Xu, M.; Ding, L.-H.; Lv, L.-L.; Liu, H.; Ma, K.-L.; Zhang, A.-H.; Crowley, S.D.; Liu, B.-C. Activation of the Nlrp3 Inflammasome by Mitochondrial Reactive Oxygen Species: A Novel Mechanism of Albumin-Induced Tubulointerstitial Inflammation. Int. J. Biochem. Cell Biol. 2014 , 57 , 7–19. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Glassford, N.; Schneider, A.; Eastwood, G.; Peck, L.; Young, H.; Bellomo, R. Neutrophil gelatinase-associated lipocalin as a marker of tubular damage appears to be unrelated to fractional excretion of sodium as a marker of tubular function in septic patients, with or without AKI. Crit. Care 2011 , 15 (Suppl. S3), P10. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Devarajan, P. Neutrophil gelatinase-associated lipocalin—An emerging troponin for kidney injury. Nephrol. Dial. Transplant. 2008 , 23 , 3737–3743. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Banai, A.; Rozenfeld, K.-L.; Levit, D.; Merdler, I.; Loewenstein, I.; Banai, S.; Shacham, Y. Neutrophil gelatinase-associated lipocalin (NGAL) for the prediction of acute kidney injury in chronic kidney disease patients treated with primary percutaneous coronary intervention. Int. J. Cardiol. Heart Vasc. 2020 , 32 , 100695. [ Google Scholar ] [ CrossRef ]
• Sise, M.E.; Barasch, J.; Devarajan, P.; Nickolas, T.L. Elevated urine neutrophil gelatinase-associated lipocalin can diagnose acute kidney injury in patients with chronic kidney diseases. Kidney Int. 2009 , 75 , 115–116. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Viau, A.; El Karoui, K.; Laouari, D.; Burtin, M.; Nguyen, C.; Mori, K.; Pillebout, E.; Berger, T.; Mak, T.W.; Knebelmann, B.; et al. Lipocalin 2 is essential for chronic kidney disease progression in mice and humans. J. Clin. Investig. 2010 , 120 , 4065–4076. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhou, F.; Luo, Q.; Wang, L.; Han, L. Diagnostic value of neutrophil gelatinase-associated lipocalin for early diagnosis of cardiac surgery-associated acute kidney injury: A meta-analysis. Eur. J. Cardiothorac. Surg. 2016 , 49 , 746–755. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, A.; Cai, Y.; Wang, P.-F.; Qu, J.-N.; Luo, Z.-C.; Chen, X.-D.; Huang, B.; Liu, Y.; Huang, W.-Q.; Wu, J.; et al. Diagnosis and prognosis of neutrophil gelatinase-associated lipocalin for acute kidney injury with sepsis: A systematic review and meta-analysis. Crit. Care 2016 , 20 , 41. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tecson, K.M.; Erhardtsen, E.; Eriksen, P.M.; Gaber, A.O.; Germain, M.; Golestaneh, L.; Lavoria, M.D.L.A.; Moore, L.W.; McCullough, P.A. Optimal cut points of plasma and urine neutrophil gelatinase-associated lipocalin for the prediction of acute kidney injury among critically ill adults: Retrospective determination and clinical validation of a prospective multicentre study. BMJ Open 2017 , 7 , e016028. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Albert, C.; Zapf, A.; Haase, M.; Röver, C.; Pickering, J.W.; Albert, A.; Bellomo, R.; Breidthardt, T.; Camou, F.; Chen, Z.; et al. Neutrophil Gelatinase-Associated Lipocalin Measured on Clinical Laboratory Platforms for the Prediction of Acute Kidney Injury and the Associated Need for Dialysis Therapy: A Systematic Review and Meta-analysis. Am. J. Kidney Dis. 2020 , 76 , 826–841.e1. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schmidt-Ott, K.; Mori, K.; Kalandadze, A.; Li, J.-Y.; Paragas, N.; Nicholas, T.; Devarajan, P.; Barasch, J. Neutrophil gelatinase-associated lipocalin-mediated iron traffic in kidney epithelia. Curr. Opin. Nephrol. Hypertens. 2006 , 15 , 442–449. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Marouf, R.; Adekile, A.D.; El-Muzaini, H.; Abdulla, R.; Mojiminiyi, O.A. Neutrophil gelatinase-associated lipocalin as a biomarker of nephropathy in sickle cell disease. Ann. Hematol. 2021 , 100 , 1401–1409. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Anderson, A.H.; Xie, D.; Wang, X.; Baudier, R.L.; Orlandi, P.; Appel, L.J.; Dember, L.M.; He, J.; Kusek, J.W.; Lash, J.P.; et al. Novel Risk Factors for Progression of Diabetic and Nondiabetic CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study. Am. J. Kidney Dis. 2020 , 77 , 56–73.e1. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Zeng, F.; Singh, A.B.; Harris, R.C. The Role of the EGF Family of Ligands and Receptors in Renal Development, Physiology and Pathophysiology. Exp. Cell Res. 2009 , 315 , 602–610. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Provenzano, M.; Andreucci, M.; Garofalo, C.; Faga, T.; Michael, A.; Ielapi, N.; Grande, R.; Sapienza, P.; de Franciscis, S.; Mastroroberto, P.; et al. The Association of Matrix Metalloproteinases with Chronic Kidney Disease and Peripheral Vascular Disease: A Light at the End of the Tunnel? Biomolecules 2020 , 10 , 154. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Thrailkill, K.M.; Clay Bunn, R.; Fowlkes, J.L. Matrix Metalloproteinases: Their Potential Role in the Pathogenesis of Diabetic Nephropathy. Endocrine 2009 , 35 , 1–10. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zheng, C.M.; Lu, K.C.; Chen, Y.J.; Li, C.Y.; Lee, Y.H.; Chiu, H.W. Matrix metalloproteinase-7 promotes chronic kidney disease progression via the induction of inflammasomes and the suppression of autophagy. Biomed. Pharmacother. 2022 , 154 , 113565. [ Google Scholar ] [ CrossRef ]
• Tan, R.J.; Li, Y.; Rush, B.M.; Cerqueira, D.M.; Zhou, D.; Fu, H.; Ho, J.; Beer Stolz, Y.D.; Liu, Y. Tubular injury triggers podocyte dysfunction by β-catenin-driven release of MMP-7. JCI Insight 2019 , 4 , e122399. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Liu, Z.; Tan, R.J.; Liu, Y. The Many Faces of Matrix Metalloproteinase-7 in Kidney Diseases. Biomolecules 2020 , 10 , 960. [ Google Scholar ] [ CrossRef ]
• Rysz, J.; Gluba-Brzózka, A.; Franczyk, B.; Jabłonowski, Z.; Ciałkowska-Rysz, A. Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome. Int. J. Mol. Sci. 2017 , 18 , 1702. [ Google Scholar ] [ CrossRef ]
• Yang, X.; Chen, C.; Teng, S.; Fu, X.; Zha, Y.; Liu, H.; Wang, L.; Tian, J.; Zhang, X.; Liu, Y.; et al. Urinary matrix metalloproteinase-7 predicts severe AKI and poor outcomes after cardiac surgery. J. Am. Soc. Nephrol. 2017 , 28 , 3373–3382. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhou, D.; Tian, Y.; Sun, L.; Zhou, L.; Xiao, L.; Tan, R.J.; Tian, J.; Fu, F.; Hou, F.F.; Liu, Y. Matrix metalloproteinase-7 is a urinary biomarker and pathogenic mediator of kidney fibrosis. J. Am. Soc. Nephrol. 2017 , 28 , 598–611. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Andreucci, M.; Provenzano, M.; Faga, T.; Michael, A.; Patella, G.; Mastroroberto, P.; Serraino, G.F.; Bracale, U.M.; Ielapi, N.; Serra, R. Aortic Aneurysms, Chronic Kidney Disease and Metalloproteinases. Biomolecules 2021 , 11 , 194. [ Google Scholar ] [ CrossRef ]
• Sampieri, C.L.; Orozco-Ortega, R.A. Matrix Metalloproteinases and Tissue Inhibitors of Metalloproteinases in Chronic Kidney Disease and Acute Kidney Injury: A Systematic Review of the Literature. Hippokratia 2018 , 22 , 99–104. [ Google Scholar ]
• Peiskerová, M.; Kalousová, M.; Kratochvílová, M.; Dusilová-Sulková, S.; Uhrová, J.; Bandúr, S.; Malbohan, I.M.; Zima, T.; Tesar, V. Fibroblast Growth Factor 23 and Matrix-Metalloproteinases in Patients with Chronic Kidney Disease: Are They Associated with Cardiovascular Disease? Kidney Blood Press. Res. 2009 , 32 , 276–283. [ Google Scholar ] [ CrossRef ]
• Cheng, S.; Pollock, A.S.; Mahimkar, R.; Olson, J.L.; Lovett, D.H. Matrix Metalloproteinase 2 and Basement Membrane Integrity: A Unifying Mechanism for Progressive Renal Injury. FASEB J. 2006 , 20 , 1898–1900. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hu, Y.; Ivashkiv, L.B. Costimulation of Chemokine Receptor Signaling by Matrix Metalloproteinase-9 Mediates Enhanced Migration of IFN-α Dendritic Cells. J. Immunol. 2006 , 176 , 6022–6033. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wang, H.-X.; Wang, Y.-P. Gut Microbiota-brain Axis. Chin. Med. J. 2016 , 129 , 2373–2380. [ Google Scholar ] [ CrossRef ]
• Lozupone, C.A.; Stombaugh, J.I.; Gordon, J.I.; Jansson, J.K.; Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 2012 , 489 , 220–230. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Tokarek, J.; Gadzinowska, J.; Młynarska, E.; Franczyk, B.; Rysz, J. What Is the Role of Gut Microbiota in Obesity Prevalence? A Few Words about Gut Microbiota and Its Association with Obesity and Related Diseases. Microorganisms 2021 , 10 , 52. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vaziri, N.D.; Wong, J.; Pahl, M.; Piceno, Y.M.; Yuan, J.; DeSantis, T.Z.; Ni, Z.; Nguyen, T.H.; Andersen, G.L. Chronic kidney disease alters intestinal microbial flora. Kidney Int. 2013 , 83 , 308–315. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rysz, J.; Franczyk, B.; Ławiński, J.; Olszewski, R.; Ciałkowska-Rysz, A.; Gluba-Brzózka, A. The Impact of CKD on Uremic Toxins and Gut Microbiota. Toxins 2021 , 13 , 252. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mishima, E.; Fukuda, S.; Mukawa, C.; Yuri, A.; Kanemitsu, Y.; Matsumoto, Y.; Akiyama, Y.; Fukuda, N.N.; Tsukamoto, H.; Asaji, K.; et al. Evaluation of the impact of gut microbiota on uremic solute accumulation by a CE-TOFMS-based metabolomics approach. Kidney Int. 2017 , 92 , 634–645. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Gonçalves, S.; Pecoits-Filho, R.; Perreto, S.; Barberato, S.H.; Stinghen, A.E.; Lima, E.G.; Fuerbringer, R.; Sauthier, S.M.; Riella, M.C. Associations between renal function, volume status and endotoxaemia in chronic kidney disease patients. Nephrol. Dial. Transplant. 2006 , 21 , 2788–2794. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wang, F.; Jiang, H.; Shi, K.; Ren, Y.; Zhang, P.; Cheng, S. Gut bacterial translocation is associated with microinflammation in end-stage renal disease patients. Nephrology 2012 , 17 , 733–738. [ Google Scholar ] [ CrossRef ]
• Hida, M.; Aiba, Y.; Sawamura, S.; Suzuki, N.; Satoh, T.; Koga, Y. Inhibition of the accumulation of uremic toxins in the blood and their precursors in the feces after oral administration of Lebenin, a lactic acid bacteria preparation, to uremic patients undergoing hemodialysis. Nephron 1996 , 74 , 349–355. [ Google Scholar ] [ CrossRef ]
• Vaziri, N.D.; Dure-Smith, B.; Miller, R.; Mirahmadi, M.K. Pathology of gastrointestinal tract in chronic hemodialysis patients: An autopsy study of 78 cases. Am. J. Gastroenterol. 1985 , 80 , 608–611. [ Google Scholar ]
• Wong, J.; Piceno, Y.M.; DeSantis, T.Z.; Pahl, M.; Andersen, G.L.; Vaziri, N.D. Expansion of urease- and uricase-containing, indole- and p-cresol-forming and contraction of short-chain fatty acid-producing intestinal microbiota in ESRD. Am. J. Nephrol. 2014 , 39 , 230–237. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Sumida, K.; Kovesdy, C.P. The gut-kidney-heart axis in chronic kidney disease. Physiol. Int. 2019 , 106 , 195–206. [ Google Scholar ] [ CrossRef ]
• Hsu, Y.H.; Li, H.H.; Sung, J.M.; Chen, W.Y.; Hou, Y.C.; Weng, Y.H.; Lai, W.T.; Wu, C.H.; Chang, M.S. Interleukin-20 targets podocytes and is upregulated in experimental murine diabetic nephropathy. Exp. Mol. Med. 2017 , 49 , e310. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Headley, S.A.; Chapman, D.J.; Germain, M.J.; Evans, E.E.; Hutchinson, J.; Madsen, K.L.; Ikizler, T.A.; Miele, E.M.; Kirton, K.; O’Neill, E.; et al. The effects of 16-weeks of prebiotic supplementation and aerobic exercise training on inflammatory markers, oxidative stress, uremic toxins, and the microbiota in pre-dialysis kidney patients: A randomized controlled trial-protocol paper. BMC Nephrol. 2020 , 21 , 517. [ Google Scholar ] [ CrossRef ]
• Lee, S.B.; Kalluri, R. Mechanistic connection between inflammation and fibrosis. Kidney Int. 2010 , 78 (Suppl. S119), S22–S26. [ Google Scholar ] [ CrossRef ]
• Moon, J.A.; Kim, H.T.; Cho, I.S.; Sheen, Y.Y.; Kim, D.K. IN-1130, a novel transforming growth factor-beta type I receptor kinase (ALK5) inhibitor, suppresses renal fibrosis in obstructive nephropathy. Kidney Int. 2006 , 70 , 1234–1243. [ Google Scholar ] [ CrossRef ]

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Frąk, W.; Kućmierz, J.; Szlagor, M.; Młynarska, E.; Rysz, J.; Franczyk, B. New Insights into Molecular Mechanisms of Chronic Kidney Disease. Biomedicines 2022 , 10 , 2846. https://doi.org/10.3390/biomedicines10112846

Frąk W, Kućmierz J, Szlagor M, Młynarska E, Rysz J, Franczyk B. New Insights into Molecular Mechanisms of Chronic Kidney Disease. Biomedicines . 2022; 10(11):2846. https://doi.org/10.3390/biomedicines10112846

Frąk, Weronika, Joanna Kućmierz, Magdalena Szlagor, Ewelina Młynarska, Jacek Rysz, and Beata Franczyk. 2022. "New Insights into Molecular Mechanisms of Chronic Kidney Disease" Biomedicines 10, no. 11: 2846. https://doi.org/10.3390/biomedicines10112846

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Article Contents

The global burden of non-communicable diseases, the case of chronic kidney disease, causes of ckd vary in developed and developing nations, ckd is a major risk factor for cardiovascular disease, the need to raise awareness about early ckd and implement prevention programs, conclusions.

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Chronic kidney disease: a research and public health priority

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Norberto Perico, Giuseppe Remuzzi, Chronic kidney disease: a research and public health priority, Nephrology Dialysis Transplantation , Volume 27, Issue suppl_3, October 2012, Pages iii19–iii26, https://doi.org/10.1093/ndt/gfs284

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The growing global burden of non-communicable diseases (NCDs) worldwide has been disregarded until recently by policy makers, major aid donors and academics. However, NCDs are the leading cause of death in the world [ 1–3 ]. In 2008, there were 57 million deaths globally, of which 63% were due to NCDs. These chronic diseases are the largest cause of death, led by cardiovascular disease (17 million deaths, mainly from ischaemic heart disease and stroke) followed by cancer (7.6 million), chronic lung disease (4.2 million, including asthma and chronic obstructive pulmonary disease) and diabetes mellitus (1.3 million deaths) [ 4 ]. They share key risk factors: tobacco use, unhealthy diets, lack of physical activity and alcohol abuse [ 4 ]. The current burden of chronic diseases reflects past exposure to these risk factors, and the future burden will be largely determined by the current exposure. Actually, worldwide the prevalence of these chronic diseases is projected to increase substantially over the next decades [ 5 ]. According to WHO, the global number of individuals with diabetes in 2000 was estimated to be 171 million (2.8% of the world's population), a figure anticipated to increase in 2030 to 366 million (6.5%), 298 million of whom will live in developing countries [ 6 ].

As a consequence, predictions for the next two decades show a near 3-fold increase in the ischaemic heart disease and stroke mortality rate in Latin America, Sub-Saharan Africa and the Middle East [ 4 ]. Countries in transition in the South-East and East Asia have also witnessed a rapid deterioration of their chronic disease risk and mortality profile [ 7 ]. India, the second most populous country, has the highest number of diabetics in the world, and in 2008, the estimates for age-standardized deaths per 100 000 population due to diabetes and cardiovascular disease were 386.3 and 283.0 in males and females, respectively [ 7 ]. In China, age-specific death rates from cardiovascular disease increased between 200 and 300% in those aged 35 through 44 years between 1986 and 1999, and by more than 100% in those aged 45–54 years [ 8 ]. Of note, the 2011 WHO report on CKD Country Profiles [ 7 ] shows that globally low- and lower-middle income countries have the highest proportion of deaths under 60 years of age from NCDs. In 2008, the proportion of these premature NCD deaths was 41% in low-income and 28% in lower-middle income countries, respectively, threefold and more than twofold as compared with the proportion in the high-income countries (13%).

Risk factors for chronic diseases are also escalating. Smoking prevalence and obesity levels among adolescents have risen considerably worldwide over the past decade and portend a rapid increase in chronic diseases [ 9 , 10 ].

In all countries, the increased burden of NCDs is also leading to growing economic costs. For example, it has been anticipated that in the United States, cardiovascular diseases and diabetes together cost $750 billion annually [ 11 ]. In the next 10 years the United Kingdom will lose $33 billion in national income as a result of largely preventable heart disease, stroke and diabetes [ 12 , 13 ]. Over the same period, the national income loss for NCDs in India and China will account for $237 and $558 billion, respectively [ 12 , 13 ].

Thus, NCDs are among the most severe threats to global economic development, probably more detrimental than fiscal crisis, as underlined by the World Economic Forum's 2009 report.

Chronic kidney disease (CKD) is a key determinant of the poor health outcomes for major NCDs [ 14 ]. CKD is a worldwide threat to public health, but the size of the problem is probably not fully appreciated. Estimates of the global burden of the diseases report that diseases of the kidney and urinary tract contribute with ∼830 000 deaths annually and 18 867 000 disability-adjusted life years (DALY), making them the 12th highest cause of death (1.4% of all deaths) and the 17th cause of disability (1% of all DALY). This ranking is similar across World Bank regions, but, among developing areas, East Asia and Pacific regions have the highest annual rate of death due to diseases of the genitourinary system [ 15 ].

National and international renal registries offer an important source of information on several aspects of CKD. In particular, they are useful in characterizing the population on renal replacement therapy (RRT) due to end-stage renal disease (ESRD), describing the prevalence and incidence of ESRD and trends in mortality and disease rates. One of the most comprehensive sources of information about the prevalence of ESRD worldwide is the United States Renal Data System (USRDS). We have implemented the USRDS dataset with ESRD data from renal registries identified after searches of web resources for registry databases, annual reports and published literature. According to this analysis, the most recent available data indicate that the prevalence of ESRD ranges from 2447 pmp in Taiwan to 10 pmp in Nigeria (Figure  1 ). However, there is paucity of renal registries globally with an international standard for registry data collection, especially in low- and middle-income countries, where, in addition, the use of RRT is scarce or non-existent, eventually making it difficult to compare ESRD results [ 16 ]. For these reasons, the reported prevalence rate of ESRD varies widely among countries, especially in the emerging world, which may be related more to the capacity of the health system to provide the costly RRT treatment than true difference in epidemiology of renal disease. Thus, in Latin America, the ESRD prevalence ranges from 1019 pmp in Uruguay to 34 pmp in Honduras, a difference that may also reflect the relationship with the gross national product [ 17 ]. Much less is known in Africa, with the highest ESRD prevalence in Tunisia (713 pmp) and Egypt (669 pmp) [ 18 ]. In relatively developed regions of China, especially in major cities, the prevalence of ESRD has been reported to be 102 pmp [ 19 ], whereas in Japan, it is more than 2200 pmp, one of the highest rates worldwide.

Prevalence of ESRD (dialysis and transplantation) worldwide. Data are from the 2011 USRDS Annual Report and from national registry database and published literature. All rates are unadjusted and presented as prevalence rate per million population.

Therefore, overall there are ∼1.8 million people in the world who are alive simply because they have access to one form or another of RRT [ 20 ]. Ninety per cent of those live in industrialized countries, where the average gross income is in excess of US $10 000 per capita [ 21 ]. The size of this population has been expanding at a rate of 7% per year. As an example, over the last decade, the number of those requiring dialysis has increased annually by 6.1% in Canada [ 22 ], 11% in Japan [ 23 ] and 9% in Australia [ 24 ]. However, <10% of all patients with ESRD receive any form of RRT in countries such as India and Pakistan. In India, ∼100 000 patients develop ESRD each year [ 25 ]. Of these, 90% never see a nephrologist. Of the 10 000 patients who do consult a nephrologist, RRT is initiated in 90%; the remaining 10% are unable to afford any form of RRT. Of the 8900 patients who start haemodialysis, 60% are lost to follow-up within 3 months. These patients drop out of therapy, because they realize that dialysis is not a cure and has to be performed over the long-term, ultimately causing impoverishment of their families.

Patients on RRT can be regarded as the tip of the iceberg, whereas the number of those with CKD not yet in need of RRT is much greater. However, the exact prevalence of pre-dialysis CKD is not known and only rough estimates exist. In industrialized countries such as the USA, the Third National Health and Nutrition Evaluation Survey (NHANES III, 1999–2006) has shown a prevalence of CKD in the adult population of 11.5% (∼23.2 million people) [ 26 ]. A sizeable proportion of these people will experience the progression of their disease to ESRD. In Europe, the Prevention of End-Stage Renal and Vascular End-points (PREVEND) study undertaken in the city of Groningen (the Netherlands) evaluated almost 40 000 individuals in a cross-sectional cohort study [ 27 ]. It was found that no less than 16.6% had high normal albuminuria and ∼7% of those screened had microalbuminuria. If these data were to be extrapolated to the world population, the number of people with CKD could be estimated as hundreds of millions.

Although data concerning the prevalence of pre-dialysis CKD in developing countries are scarce, we would expect that there are comparable numbers of patients with CKD in poor countries as in industrialized nations. To this, the International Society of Nephrology (ISN) Global Outreach (GO) funded the Kidney Disease Data Center database to house data from sponsored programmes aimed at preventing CKD and its complications in developing nations. Some examples indicate that the overall prevalence of CKD, diagnosed based on a urinary albumin/creatinine ratio ≥30 or glomerular filtration rate (GFR) ≤60 L/min/1.73 m 2 (as Modification of Diet in Renal Disease, four variables), is 11 and 10.6% in urban areas, respectively, of Moldova [ 28 ] and Nepal [ 29 ]. Moreover, in the attempt to compare the burden of illness among centres in Nepal, China and Mongolia, in 11 394 adult subjects, it has been found that decreased estimated GFR (<60 L/min/1.73 m 2 ) was present in 7.3–14% of participants across centres; proteinuria (≥1+) on dipstick (2.4–10%) was also common [ 30 ]. By a recent cross-sectional survey of a nationally representative sample of Chinese adults, the overall prevalence of CKD was 10.8% [ 31 ].

Data from India also suggest that in a developing country, the prevalence rate of CKD could vary almost 5-fold between the rural and city population [ 32 , 33 ]. These observations imply that CKD would affect not only very many people in the developing world, but preferentially the poor within these countries who usually have no information about disease and risk factors, and cannot have access to healthcare. Interestingly, low socioeconomic status is associated with CKD also in developed nations, as shown in Unites States by the NHANES survey, which reported people with lower income being disproportionately afflicted with a higher burden of CKD risk factors [ 34 ]. Similarly, in Sweden [ 35 ] and the UK [ 36 ], lower income and social deprivation are associated with micro- or macro-albuminuria, reduced GFR and progressive kidney function loss.

Diabetes and hypertension

Diabetes and hypertension are the major causes of CKD leading to kidney failure in the USA, accounting for 153 and 99 pmp, respectively [ 37 ], of incident causes of ESRD. Definitely lower is the contribution of glomerulonephritis (23.7 pmp) [ 37 ]. The proportion of people with CKD not explained by diabetes and hypertension is substantially lower in the USA (28% of stage 3–4 CKD) than in developing countries [ 37 , 38 ]. Indeed, in a recent study analysing screening programs in Nepal, China and Mongolia, 43% of people with CKD did not have diabetes or hypertension [ 30 ].

Infectious diseases

There is also increasing evidence that infectious diseases, still a major health problem in low-income countries, may substantially contribute to the burden of chronic nephropathies. This mainly relates to poor environmental conditions, unsafe life habit and malnutrition. Urinary tract infections, occurring in the entire population, but with particular impact on females of all ages, especially during pregnancy, may have long-term consequences over and above the direct infectious disease morbidity and mortality these infections cause. They include chronic injury of the kidney which eventually may lead to loss of renal function, development of secondary hypertension and, for pregnant women, increased risk of maternal toxaemia, neonatal prematurity and low birth weight which is usually associated with lower-than-normal nephron number anticipating the high risk for hypertension and chronic renal injury during the life time [ 39 ]. Moreover, in several regions worldwide, tuberculosis is still an endemic infection with many cases of renal tuberculosis remaining clinically silent for years while irreversible renal destruction takes place [ 40 ]. Glomerular involvement with parasitic diseases, including malaria [ 41 ], schistosomiasis [ 42 ] and leishmaniasis [ 43 ], may also pave the way to progressive renal disease. A variety of glomerular lesions, and in particular a unique form of glomerular damage, HIV-associated nephropathy, have emerged as significant forms of renal disease in HIV-infected patients [ 44 ]. With the increasing rate of this viral infection, kidney failure in HIV-infected patients will progressively become a major public health problem, particularly in Sub-Saharan Africa. Therefore, in developing countries, infectious diseases add substantial burden to non-communicable risk factors, in enhancing the global prevalence of CKDs.

Malnutrition

There are also factors that link early malnutrition with being overweight in adulthood, ultimately developing into diabetes and diabetic nephropathy [ 45 ]. A number of observational epidemiological studies have postulated that early (intrauterine or early postnatal) malnutrition causes an irreversible differentiation of the metabolic system, which may, in turn, increase the risk of certain chronic diseases in adulthood. For example, a fetus of an undernourished mother will respond to a reduced energy supply by switching on genes that optimize energy conservation. This survival strategy means a permanent differentiation of regulatory systems that result in an excess accumulation of energy (and consequently body fat) when the adult is exposed to an unrestricted dietary energy supply [ 45 ]. Because intrauterine growth retardation and low birth weight are common in developing countries or within minority groups, this mechanism may result in the establishment of a population in which many adults are particularly susceptible to developing obesity and CKD. These observations further imply that CKD would affect preferentially the poor within these countries.

Acute kidney injury

CKD is also linked to acute kidney injury (AKI). Thus, both the rate of progression to ESRD and all-cause mortality are increased in patients with CKD after transient increases in serum creatinine when compared with patients without CKD [ 46 ]. Moreover, up to 28% of the patients with no pre-existing kidney disease who recover from AKI develop de novo CKD [ 47 ]. Non-steroidal anti-inflammatory medications, several cardiovascular and diabetes drugs, as well as traditional medicines used in the primary-care setting in developing countries, may lead to the development of transient episodes of AKI. These findings emphasize the relevance of CKD detection and appropriate adjustments in management to optimal outcome in major NCDs.

It is increasingly recognized that the burden of CKD is not limited to its implication on demands for RRT but has a major impact on the health of the overall population. Indeed, patients with reduced kidney function represent a population not only at risk for the progression of kidney disease and development of ESRD, but also at even greater risk for cardiovascular diseases. CKD is a major risk factor for cardiovascular mortality, and kidney disease is a major complication of diabetes. In ∼400 000 Medicare patients with diabetes and CKD, in USA over 2 years of follow-up, the risk of death for cardiovascular diseases (32.3%) far exceeded that of the development of ESRD (6:1) [ 48 ]. Moreover, CKD has been documented as an independent risk factor for angina, myocardial infarction, heart failure, stroke, peripheral vascular disease and arrhythmias [ 49 , 50 ]. The increased risk of cardiovascular disease associated with CKD has been shown in both general [ 37 , 51 , 52 ] and high-risk [ 52 ] populations, in young and elderly people [ 53 ], as well as in Caucasians [ 49 ], African blacks [ 54 ] and in Asian people [ 55 ].

There is also evidence that the increased cardiovascular risk in CKD patients does not just coexist with diabetes or hypertension. Indeed, an independent and progressive association between GFR and risk of cardiovascular events and death has been found in a community-based study in more than 1 million adult subjects in the USA [ 56 ]. Similarly, a recent study in more than 6000 people followed on average 7 years has shown that the risk of cardiovascular death was increased 46% in subjects with a mild-to-moderate reduction in GFR (30–60 L/min), independent of conventional risk factors such as diabetes and hypertension [ 57 ].

The reason why CKD is a risk factor for cardiovascular outcomes is not entirely clear, but it seems largely related to the excess prevalence of traditional cardiovascular risk factors, including hypertension, diabetes and dyslipidaemia associated with the renal disease. In addition, other factors such as hyperhomocystinaemia, abnormalities of mineral metabolism and parathyroid function may become more prevalent and have pathogenetic relevance as CKD progresses [ 58 , 59 ]. Even patients with microalbuminuria and proteinuria, but still normal renal function, are at increased risk of cardiovascular morbidity and mortality [ 60 ]. Large studies in the general population showed that the presence of microalbuminuria or proteinuria is associated with enhanced risk of all-cause mortality at all levels of baseline kidney function [ 27 , 49 , 61–63 ].

Thus, through its impact on cardiovascular morbidity, CKD may directly contribute to the increasing global burden of death caused by cardiovascular disease. Therefore, these are the patients in whom efforts should be focused.

The major societal effect of CKD is the enormous financial cost and loss of productivity with associated advanced or ESRD. In many developed countries, treatment for ESRD accounts for more than 2–3% of their annual health-care budget, while the population with ESRD represents ∼0.02–0.03% of the total population [ 64 ]. This situation is even worse in most developing countries, where RRT is often unavailable or unaffordable, and ∼1 million people die with ESRD each year [ 65 ]. On the other hand, awareness of early and advanced CKD is low, even in developed nations, being <20% [ 38 ]. For example, in a recent survey in almost 500 000 people in Taiwan, as a part of medical screening programme, <4% of those with CKD (12%) were aware of their condition [ 66 ]. Moreover, it should be considered that CKD, even at more advanced stages, is treatable. Ample evidence from clinical trials has shown that control of hypertension and of proteinuria, especially with inhibitors of the renin–angiotensin system, are highly effective interventions for slowing the progression of diabetic and non-diabetic CKD [ 67 , 68 ]. Studies have also documented that even sustained remission or regression of proteinuric CKD is achievable especially in a large proportion of non-diabetic patients [ 69 ].

Together, these observations underline the urgent need for strategies to enhance awareness about CKD, especially in developing countries, where the low awareness may serve as a barrier to accessing appropriate care even when available [ 70 ] (Table  1 ). To this purpose, recently, the International Society of Nephrology and the International Federation of Kidney Foundation joined efforts to raise awareness regarding CKD by promoting the annual World Kidney Day (WKD). On this particular day, public activities such as free screening for CKD and its risk factors and meeting with the community population and leaders are planned and performed in numerous centres worldwide [ 71 ]. Nevertheless, the resources to implement effective early awareness, detection and prevention programmes for CKD should ultimately come from government health programmes as part of global strategy to improve public health. Some examples are the National Health Programme in Uruguay that has already incorporated CKD into their NCD prevention programmes, and the Strategic Network of Health Services against Chronic Kidney Disease in Mexico.

Public health initiatives targeting CKD

These programmes will help to decrease the costs of managing ESRD and cardiovascular disease and respond to public health demand. However, before these surveillance and intervention efforts are expanded, information on their sustainability and affordability to the public sector, especially in low-income countries, should be collected.

Medicine is developing evidence for the importance of CKD to public health and its contribution to the global burden of major NCDs, but has no equity plan [ 14 , 72 ]. A more concerted, strategic and multisectorial approach, underpinned by solid research, is essential to help reverse the negative trends in the incidence of CKD and its risk factors, not just for a few beneficiaries but on a global health equity programme. Thus, a pragmatic approach to reduce the global burden of renal and cardiovascular diseases has to be adopted. For that, well-defined screening of community or high-risk populations followed by intervention programmes have to be initiated, especially in developing countries.

In recognition of the increasing burden and importance of chronic diseases, a high-level United Nations meeting with heads of governments of member states was organized last September in New York to discuss a global NCD Action Plan prepared by WHO. Although this document did provide the unique opportunity to bring attention to the pandemic of NCDs, it prioritized four chronic diseases, namely cardiovascular disease, cancer, diabetes and chronic respiratory disease [ 73 ]. Nevertheless, through intensive lobbying also by ISN, CKD has gained recognition in the final Political Declaration [ 73 ]. Indeed, a paragraph of the NCD Action Plan stated that the members of States of the UN General Assembly ‘recognize that renal, oral and eye disease pose a major health burden for many countries and that diseases share common risk factors and can benefit from common responses to non-communicable diseases’ [ 73 ]. However, NCD advocacy groups, such as ISN [ 74 ], as well as the editors of The Lancet and The British Medical Journal have underlined their disappointment over the insufficient emphasis on action to be taken by governments [ 75 , 76 ]. In addition, they pointed out that a major opportunity to advance global health was in danger of being lost since the Political Declaration did not set substantive targets or timelines in the need for member states to activate policies in their public health programmes to address NCD issues [ 74–77 ].

In developing nations, there must also be a commitment to create in-country capacity, notably a human capacity that can determine for itself locally specific problems dealing with kidney diseases to be addressed through clinical research programmes. However, this implies greater efforts by the developed nations to limit the brain drain of scientists and health personnel from low- and middle-income countries [ 78 ]. The North-South capacity gap in health science, including nephrology, continues to narrow, but it has by no means disappeared. At the same time, a new gap in capacity has emerged between scientifically proficient and scientifically lagging developing countries, the so-called South–South gap. This divide has surfaced because the number of developing countries making significant strides in building scientific capacity remains small (Brazil, Argentina, Mexico, Chile, South Africa, India, China and Malaysia). There are examples of increasing South–South cooperation that are helping to close this gap. However, even developing countries that have successfully strengthened their scientific capacity have proven more adept at building their knowledge base than applying the know-how, scientists/physicians acquire to address societal concerns. Along these lines, ISN through its Global Outreach programmes, especially the Research and Prevention programme, has developed several initiatives for emerging countries that can be implemented according to the peculiar needs and organization facilities of the given nation [ 79 ]. Overall, the emphasis is on models to promote and foster autonomous programmes in regions where they are most needed.

The hope is that all these efforts will assist to make a major advance in addressing the neglected aspect of the renal health of people worldwide.

Conflict of interest statement . None declared.

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Ozempic Cuts Risk of Chronic Kidney Disease Complications, Study Finds

A major clinical trial showed such promising results that the drug’s maker halted it early.

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By Dani Blum

Dani Blum has reported on Ozempic and similar drugs since 2022.

Semaglutide, the compound in the blockbuster drugs Ozempic and Wegovy , dramatically reduced the risk of kidney complications, heart issues and death in people with Type 2 diabetes and chronic kidney disease in a major clinical trial, the results of which were published on Friday. The findings could transform how doctors treat some of the sickest patients with chronic kidney disease, which affects more than one in seven adults in the United States but has no cure.

“Those of us who really care about kidney patients spent our whole careers wanting something better,” said Dr. Katherine Tuttle, a professor of medicine at the University of Washington School of Medicine and an author of the study. “And this is as good as it gets.” The research was presented at a European Renal Association meeting in Stockholm on Friday and simultaneously published in The New England Journal of Medicine .

The trial, funded by Ozempic maker Novo Nordisk, was so successful that the company stopped it early . Dr. Martin Holst Lange, Novo Nordisk’s executive vice president of development, said that the company would ask the Food and Drug Administration to update Ozempic’s label to say it can also be used to reduce the progression of chronic kidney disease or complications in people with Type 2 diabetes.

Diabetes is a leading cause of chronic kidney disease, which occurs when the kidneys don’t function as well as they should. In advanced stages, the kidneys are so damaged that they cannot properly filter blood. This can cause fluid and waste to build up in the blood, which can exacerbate high blood pressure and raise the risk of heart disease and stroke, said Dr. Subramaniam Pennathur, the chief of the nephrology division at Michigan Medicine.

The study included 3,533 people with kidney disease and Type 2 diabetes, about half of whom took a weekly injection of semaglutide, and half of whom took a weekly placebo shot.

Researchers followed up with participants after a median period of around three and a half years and found that those who took semaglutide had a 24 percent lower likelihood of having a major kidney disease event, like losing at least half of their kidney function, or needing dialysis or a kidney transplant. There were 331 such events among the semaglutide group, compared with 410 in the placebo group.

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A Systematic Review of Screening Tests for Chronic Kidney Disease: An Accuracy Analysis

Fatemeh keshvari-shad.

1 Department of Health Economics, School of Management and Medical Informatics, Tabriz University of Medical Sceinecs, Tabriz, Iran

Sakineh Hajebrahimi

2 Research Center for Evidence Based Medicine, Faculty of Medicine, Urology Department, Tabriz University of Medical Sciences, Tabriz, Iran

Maria Pilar Laguna Pes

3 Department of Urology Istanbul Medipol University Istanbul, Turkey

Alireza Mahboub-Ahari

4 Department of Health Economics, Iranian Evidence-Based Medicine Center of Excellence, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran

Mohammad Nouri

5 Department of Biochemistry and Clinical Laboratories, Tabriz University of Medical Sciences, Tabriz, Iran

Farshad Seyednejad

6 Department of Radiation Oncology, Madani Hospital, Tabriz Medical University, Tabriz, Iran

Mahmood Yousefi

7 Department of Health Economics, Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran

This systematic review was conducted to assess the diagnostic accuracy of chronic kidney disease screening tests in the general population. MEDLINE, EMBASE, Web of Science, Scopus, The Cochrane Library and ProQuest databases were searched for English-language publications up to November 2016. Two reviewers independently screened studies and extracted study data in standardized tables. Methodological quality was assessed using the QUADAS-2 tool. Sensitivity and specificity of all available screening methods were identified through included studies. Ten out of 1349 screened records included for final analysis. Sensitivities of the dipstick test with a cutoff value of trace were ranged from 37.1% to 69.4% and specificities from 93.7% to 97.3% for the detection of ACR>30 mg/g. The diagnostic sensitivities of the UAC>10 mg/dL testing was shown to vary from 40% to 87%, and specificities ranged from 75% to 96%. While the sensitivities of ACR were fluctuating between 74% and 90%, likewise the specificities were between 77% and 88%. Sensitivities for C-G, Grubb and Larsson equations were 98.9%, 86.2%, and 70.1% respectively. In the meantime the study showed specificities of 84.8%, 84.2% and 90.5% respectively for these equations. Individual studies were highly heterogeneous in terms of target populations, type of screening tests, thresholds used to detect CKD and variations in design. Results pointed to the superiority of UAC and dipstick over the other tests in terms of all parameters involved. The diversity of methods and thresholds for detection of CKD, necessitate considering the cost parameter along with the effectiveness of tests to scale-up an efficient strategy.

Introduction

Chronic Kidney Disease (CKD) is one of the leading causes of mortality and morbidity throughout the world. The prevalence of CKD (stages 1-5) has been estimated around 13.4% worldwide [ 1 ]. CKD annually imposes a significant economic burden on health systems and societies [ 2 , 3 ]. In 2002, the National Kidney Foundation-Kidney Disease Outcomes Quality Initiative (NKF-KDOQI) published the first guideline and defined the CKD as kidney damage or kidney dysfunction (estimated glomerular filtration rate [eGFR]<60 mL/min/1.73 m2) that lasts for at least three months [ 4 ]. The CKD often, until its late stages, is silent and asymptomatic. Evidence shows that the early detection of CKD based on the presence of proteinuria or reduced eGFR can prevent or delay the progression of the disease to advanced stages [ 5 ]. The considerable burden of the CKD, along with the availability and effectiveness of diagnostic tests, and treatments for early detected CKD patients, makes the condition as an appropriate candidate for the screening [ 6 ]. By realizing the fact that both the general and high-risk population will theoretically benefit from the undergoing of CKD screening programs [ 7 ], different strategies of CKD screening for detecting patients with CKD have been developed. The most common tests for the diagnosis of CKD include GFR, which is estimated through the serum creatinine concentration (eGFR) and albuminuria, which is measured by the urinary albumin to creatinine ratio (ACR) [ 8 - 11 ]. The diversity of existing diagnostic strategies necessitates the understanding of the strengths and limitations of each diagnostic approach to go through efficient decision making [ 12 ]. Since screening targets people with apparently healthy conditions, the test should be applied to a large proportion of the population [ 13 - 15 ]. Thus it can be argued that the initiation of a screening program requires a significant amount of society’s resources should be allocated to the program [ 16 - 18 ]. In other words, any decision about CKD screening in favor of society requires examining all the available options [ 19 ]. Accordingly, the decision-makers need high-quality data to support decisions about a diagnostic test in the screening program. Understanding the accuracy of each screening intervention in terms of sensitivity and specificity is essential for reaching a rigorous conclusion on the decisions made [ 20 ], such that the uncertainty in each of these parameters will affect the final outcome. Addressing the abovementioned issues, the aim of this systematic review is to find and extract information on sensitivity and specificity of CKD screening tests in the general population in a way that makes the application of results in screening programs feasible.

Search Strategy

Study selection.

We followed the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for conducting and reporting systematic reviews [ 21 ]. We performed a comprehensive search of MEDLINE (PubMed), EMBASE, Web of Science, Scopus, the Cochrane Library, and ProQuest databases up to November 2016 and updated later to the end of 2017. The search strategy included three major key terms: screening, CKD, and screening tests for CKD. Furthermore, a combination of words such as “screening,” “albuminuria,” “proteinuria,” “glomerular filtration rate,” “creatinine,” “Chronic kidney disease,” “Chronic renal disease,” “Chronic renal insufficiencies,” “Chronic renal failure,” “Chronic Kidney Failure” were searched using each individual databases. We also used the Medical Subject Headings (MeSH) terms in the search strategy, and the search was limited to the English language. Using the EndNote X7.4, a pool of retrieved literature was constructed. By removing the duplicates, the title and abstract of the remained studies screened by two independent reviewers (F.K and M.Y). In the cases where relevant studies might have been missed due to the improper search strategy, a list of the article references as well as the related systematic reviews were also checked in full-text by the reviewers. Any disagreement was resolved through consensus. It is worth mentioning that different study designs were incorporated into this review including those with one or more index tests and with any reference method (gold standard) that investigated the CKD screening in the general population. Eligible studies had to report sensitivity and specificity or the data that could be used to calculate those values, involve an asymptomatic population, included adult populations, and be published as full-length articles. Studies that reported outcomes from diabetic or hypertension groups were excluded.

Data Extraction and Quality Assessment

Two reviewers (F.K and M.Y) independently extracted the relevant data using a created data extraction form. The following data was captured from studies; characteristics of the studies such as publication date and location, study sample, the type of study, age-range and mean age, index test, reference test, threshold level, and outcome measures such as sensitivity, specificity and likelihood ratios (LRs). The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool by two independent reviewers [ 22 ]. It consists of four key domains, including patient selection, index test, reference standard, and flow of patients and timing of the index test and reference standard. The risk of bias and applicability concerns were assessed using a number of signaling questions for each study. Disagreements about the risk of bias and applicability concerns in each domain were resolved with the arbitration of the third and fourth investigator (S.H and A.M).

Sensitivity, specificity, and LRs were descriptively analyzed for the included studies. Sensitivity is defined as the percentage of individuals with the disease that correctly identified, and specificity as the percentage of the individual without disease that correctly identified [ 23 ]. For studies in which positive and negative LR (PLR and NLR) had not been reported, these values were calculated as follows: PLR=sensitivity/ (1-specificity); and NLR=(1-sensitivity)/specificity. The LR specify how many times more likely, it is that to receive a particular test result in people with target condition than without [ 24 ]. Given that the study aimed at finding all available strategies of CKD screening then there was a great heterogeneity in the target populations, types of tests, thresholds used and variations in the design of included studies this made doing the meta-analysis of effect size inappropriate.

Study Selection and Characteristics

A total of 3042 citations were initially identified. After removing duplicates, 1349 results were screened based on title and abstract, out of which 28 full texts were identified to be examined ( Figure-1 ). Finally, nine studies met the review criteria, and 19 studies were excluded due to not meeting the inclusion criteria. One further study was identified by the updated search in MEDLINE (PubMed) and included in this review [ 25 ]. In total, ten articles were included in this review. Eight out of ten selected studies had a cross-sectional design [ 25 - 32 ]. One was a cohort study [ 33 ] and one study was a cross-sectional cohort [ 34 ]. These studies had been published from 2005 to 2017 with worldwide distribution, including china, Australia, Netherlands, Japan, Pakistan, Taiwan, Italy, Iceland, and South Korea. General characteristics of the selected studies are summarized in Table-1 . Briefly, these studies have included population samples ranging from 557 to 43,516 participants. The mean age of the subjects was between 43 to 59.7 years. Except for two studies [ 29 , 30 ], gender distribution was described in all studies [ 25 - 28 , 32 - 34 ]. Nine studies had been conducted on general the population, and one study included diabetic patients as well [ 30 ]. It was demonstrated that age is an indispensable part of all studies and had been considered as inclusion criteria.

GP : General population; DP : Diabetic patient

Flowchart of the article selection process.

Index and Reference Tests

In order to detect CKD, different studies had utilized various screening tests. The eGFR was evaluated in one study [ 32 ]. Three studies used the dipstick test for detection of albuminuria [ 25 , 26 , 34 ]. Strip test was used as an index test for measuring the ACR in one study [ 30 ]. Three of the ten included studies evaluated the urine albumin concentration (UAC) [ 27 , 28 , 33 ], two of which also made a comparison of the UAC and ACR [ 27 , 28 ]. One article provided separate assessments of semi-quantitative urine protein-to-creatinine (P/C) ratios, quantitative protein concentrations, and dipstick protein [ 29 ]. One study assessed routine urinalysis [ 31 ]. The ACR was used as the reference standard in three studies [ 25 , 26 , 34 ]. GFR was used in one study [ 31 ]. Three studies considered the 24-hour urine collection UAE ≥30 mg as the reference test [ 27 , 28 , 33 ]; and the rest of the studies used quantitative P/C ratio and laboratory method in urine as the reference standard [ 29 , 30 ]. Except for one study [ 32 ], the reference standard and the procedures were adequately described in most of the included articles.

Study Quality

In general, the data showed a satisfactory level of quality for the selected studies. Nine studies exhibited a low or unclear risk of bias as well as applicability concerns. Moreover, most of the studies demonstrated a clear description of the subjects, index and the reference tests, and diagnostic criteria ( Figure-2 ). Due to the ambiguous methods of patient selection, four studies were identified to have presented an unclear risk of bias in patient selection [ 25 , 26 , 29 , 31 ]. The risk of bias primarily arose from insufficient blinding between the index and reference tests [ 25 , 26 , 28 , 29 , 31 ]. Also, high risk of bias was observed in one study [ 32 ] in which no standard test was specified. Three studies also failed to demonstrate a clear interval between the index and reference tests [ 26 , 27 , 30 ].

Bar charts for QUADAS-2 analysis. Risk of bias and applicability concerns graph review investigators’ judgments about each domain presented as percentages across included studies.

Diagnostic Accuracy

A high degree of heterogeneity was found between studies in terms of reported sensitivity and specificity of included index tests. The sensitivity, specificity, and LRs for each study have been summarized in Table-2 . The accuracy of dipstick testing was evaluated across the general population in three studies [ 25 , 26 , 34 ]. For the detection of ACR>30 mg/g, the sensitivities of the dipstick with a cut-off point of trace were ranged from 37.1-69.4% and specificities from 93.7-97.3%. We have also obtained 23.3% to 98.9% sensitivities and 92.6% to 98.9% specificities for the dipstick test result of >1 and identified ACR of >300 mg/g (massive proteinuria). The study by Graziani et al . [ 30 ], was the only study that evaluated the test accuracy of a strip test for measuring ACR, where they used a cut-off of 3.4 mg/mmol to define microalbuminuria in the general population and to compare it with those found in a diabetic population. The test results of this study demonstrated a sensitivity and specificity of 92 % and 95 %, respectively. Furthermore, in the diabetic group, the sensitivity and specificity of the test was 92 % and 95 %, respectively. The UAC was examined in three selected studies [ 27 , 28 , 33 ]. The diagnostic sensitivities of the UAC>10 mg/dL testing were shown to range from 40% to 87%, whereas the specificities ranged from 75% to 96%. Two studies demonstrated that the sensitivities of ACR varied between 74% and 90%, and the specificities ranged between 77% and 88% [ 27 , 28 ]. One study examined the performance of routine urinalysis for the diagnosis of eGFR<60 ml/min/1.73 m2 [ 31 ]. The sensitivity and specificity of urinalysis were 11% and 92/8% respectively. Wetmore et al . compared the performance of “C-G,” “Grubb” and “Larsson” equations with the “Modification of Diet in Renal Disease (MDRD)” equation to eGFR, with a cut-off point of 60 ml/min/1.73 m2. The sensitivity for C-G, Grubb and Larsson equations was 98.9%, 86.2%, and 70.1%, respectively. The study also showed the specificities of 84.8%, 84.2%, and 90.5% for these equations, respectively. The C-G equation had better performance in terms of sensitivity and specificity. Semi-quantitative P/C ratio, dipstick protein, and quantitative protein tests were compared in one study for detecting proteinuria [ 29 ]. For Semi-Quantitative P/C ratio sensitivities were 70-75.6%, and specificity was 95.9% to both of them. Sensitivity and specificity for dipstick protein were 45.0% and 98.3%, respectively. Also, the study reported the accuracy of the quantitative protein test, for which a sensitivity of 50.1% and a specificity of 98.2% was reported.

ACR : Albumin- creatinine ratio; UAC : Urinary albumin concentration; AUC : Area under the curve; DV : Discriminator value; CV : Cardiovascular; UAE : Urinary albumin excretion; eGFR : Estimated glomerular filtration rate; C-G equation : Cockroft–Gault equation; P/C : Protein- to- creatinine

In the current study, we systematically reviewed the literature to evaluate the accuracy of different tests for screening CKD among the general population without risk factors for CKD. Although little evidence exists on the recommendation of routine screening [ 7 , 14 , 35 ], guidelines propose the detecting of urine protein (micr- or macro albuminuria) as well as measuring the serum creatinine to estimate GFR for the screening of CKD [ 8 , 36 , 37 ]. Despite the availability of a wide range of screening tests, selecting a single method, and defining the specific criteria for further implications remain to be major consideration [ 7 , 38 , 39 ]. The present study is one of the pioneering systematic reviews, which compares the diagnostic accuracy of various tests for CKD screening in the general population. To obtain more insights into the accuracy of the tests for CKD, ten studies were included in our review. Overall, a broad range of sensitivity and specificity was reported for the various tests. The variations in index and reference tests, threshold, participants, and study designs among the studies do not allow for performing a meta-analysis of the data. Our findings highlighted that the UAC test, with high sensitivity and specificity, can indeed compete with the ACR to accurately detect microalbuminuria across the general population in 24-hour timed urine collections as the gold standard. Sensitivities above 74% and specificities above 81% were reported for the ACR and the UAC. However, no significant difference was observed in the ability of the UAC and the ACR to detect microalbuminuria [ 27 , 28 ]. Generally, the ACR has been accepted to offer a slightly better diagnostic accuracy than measuring solely the concentration of urine albumin to detect albuminuria in many populations. This can be due to the composition variability in the standardization of the methods used for quantifying total protein in urine samples. However, in terms of the cost, this method is more expensive in comparison with methods used for total urine protein measurement and decisions on the recommendation of this strategy needs other criteria to be taken into account [ 8 , 40 ]. In this systematic review, when the estimation of the accuracy of urine dipstick by comparing its characteristics to spot ACR as the gold standard is considered, three studies showed poor sensitivity and high specificity [ 25 , 26 , 34 ]. Due to its unclear clinical significance, the result of trace protein reading on urinalysis on the general population is mostly disregarded by the clinicians [ 41 , 42 ]. However, proteinuria is considered as an independent risk factor to develop end-stage renal disease [ 43 ]. Despite this, two studies have supported the concomitant occurrence of trace proteinuria and microalbuminuria in a large proportion of individuals, especially men, the elderly, diabetic patients, and patients with hypertension. As well, these studies revealed that using the trace as a cut-off value led to recovery both in terms of sensitivity and specificity [ 26 , 34 ]. A high sensitivity and specificity was shown by Graziani et al . in which the strip test was used to measure the ACR in the general population [ 30 ]. The current review has several strength points that include presenting the methods used for the identification and recruitment of the available literature, as well as using the most up to date guidelines for diagnostic reviews. We performed a comprehensive systematic review of six electronic data bases and continuously adapted the review during the writing process. We exclusively considered studies that performed on the general population. Selected studies incorporate a wide spectrum of demographic characteristics from Asia, Europe, and Australia supporting the generalizability of their results. In this review, the details of the index test, reference test, and population characteristics were deemed to have been adequately reported. The overall quality of original studies was also assessed, pointing to minimal risk of bias and applicability concerns. There are several limitations in our study. First, this review only includes studies published in English that may cause language bias. Second, the attempt to have the advantage of accessing to all available options led to an increase in heterogeneity between different screening methods, which in turn prevented conducting a meta-analysis. The weak points mostly rooted in the methodological constraints of the included studies, especially the blinding of operators when conducting and interpreting the index and reference tests. Differences in gender, race, and prevalence of CKD between studies could also contribute to some of the variability in the study results. In this review, the female participants of the included studies were mostly older adults fluctuating on a wide range from 36-63.8%. The selected studies had also compared various tests available in local laboratory methods. In most of the cases, large biases occur in the existing laboratory methods. For instance, although testing the total protein using 24-hours urine collections is the gold standard for comparing proteinuria assays, it has several limitations such as being time consuming, cumbersome, inconvenience for patients. Furthermore, errors such as incomplete collection may lead to inaccuracies [ 44 , 45 ]. To the best of our knowledge, no systematic review has been previously conducted to assess the diagnostic performance of various screening tests for CKD risk in the general population. A recent review on diabetic patients reported that either UAC or ACR can yield a similar sensitivity and specificity to detect microalbuminuria. The findings of the aforementioned study concluded that the UAC and ACR can offer rational rule out results to detecting significant proteinuria in diabetic patients [ 46 ]. There are also still issues ahead of using CKD screening in settings where limited resources are available [ 7 , 47 ]. Nevertheless, depending on the availability of resources and the level of risks (e.g., diabetic patients and the general population) different results are expected in terms of cost effectiveness of CKD screening [ 48 , 49 ]. In addition, there is still a lack of strong guidelines specifically addressing the CKD screening in general population and resource-limited settings [ 50 ]. In a systematic review published by Fink et al . studying the RCT of CKD screening, no direct evidence was found to confirm the advantages or disadvantages of CKD screening or monitoring of patients with stages 1-3 of CKD progression [ 51 ]. While indirect evidence proposed that targeting CKD screening or monitoring may be possible but the potential benefit of these interventions was not ensured. A major standard for an accurate screening test is the acceptable sensitivity, specificity, and high predictive values [ 52 - 54 ]. The better the performance of the test, the higher is the chance of detecting disease. This reduces the burden of false positive results, which can lead to additional detriment and costs [ 7 , 55 ]. The screening tests usually burden various levels of false positive results, and thus may dramatically influence the results taken from subjects where the prevalence of disease is very low [ 56 ]. The dipstick screening method has numerous well-known potential benefits including feasibility and potential to be used as a test for CKD screening in resource-limited settings [ 57 ]. However, urine dipstick testing fails to meet the whole criteria of an ideal screening test [ 52 ] and it may burden many false positive results when conduction on the general population (between 53.1% and 72.8% of positive tests for detection of ACR>30 mg/g), leading to over-diagnosis of many CKD high-risk group when the diagnostic tests are not repeated [ 34 ]. This also poses an economic concern, since it increases the unnecessary therapeutic interventions or further diagnostic investigations where the resources are almost inadequate. In conclusion, we conducted a systematic review to assess the diagnostic accuracy of CKD screening tests in the general population. According to our results, the UAC and ACR yielded high sensitivity and specificity in the general population and the diagnostic performance of the UAC is similar to ACR for accurate detection of microalbuminuria in general population, but less expensive. Therefore, the UAC may become the screening tool of choice for the general population. Regarding sensitivity and specificity of urine dipsticks in this review, dipstick proteinuria has been suggested as a CKD screening test in resource-limited settings.

Further studies are needed to evaluate the accuracy of CKD screening tests in the general population. The choice of an effective screening tool for detection of CKD requires a comprehensive evaluation of all possible strategies in terms of accuracy measures, threshold levels and the quality of conducted studies. Given the diversity of the screening methods as well as the availability of various thresholds for detection of CKD, requires considering the cost parameter along with the effectiveness of tests to scale-up an efficient strategy. UAC and dipstick revealed superiority over the others when it comes to considering all parameters together. But for choosing between these two tests in population-scale, it needs the affordability issue to be taken into account and cost of implementing each strategy be compared in terms of the cost-effectiveness.

Acknowledgment

This study was part of a master degree thesis supported by the Tabriz University of Medical Sceinecs (IR.TBZMED.REC.1396.135).

Conflict of Interest

The authors declare that they have no Conflict of interest.

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Opioid analgesic use among patients with kidney disease: a systematic review

• PMID: 38824925
• DOI: 10.1159/000538258

Introduction: Opioid analgesics are often used to manage moderate to severe pain. A significant proportion of patients taking opioids have compromised kidney function. This systematic review aimed to examine the available evidence on the safety and analgesic effect of opioid use in adults with kidney disease.

Methods: We searched eight electronic databases from inception to 26th January 2023. Published original research articles in English reporting on opioid use and pharmacokinetic data among adults with reduced renal function were included. Article screening, data extraction, and quality assessment were conducted by at least two investigators independently. This review was registered prospectively on PROSPERO (ID: CRD42020159091).

Results: There were 32 observational studies included, 14 of which reported on morphine use, three involved fentanyl use, two involved hydromorphone use and 13 articles reported on other opioids including codeine, dihydrocodeine, and buprenorphine.

Conclusion: There is limited and low-quality evidence to inform the safety and analgesic effect of opioid use in reduced renal function. Morphine remains the opioid for which there is the most evidence available on safety and analgesic effect in the context of renal disease. Greater caution and consideration of potential risks and benefits should be applied when using other opioids. Further high-quality studies examining clinical outcomes associated with the use of different opioids and opioid doses in renal disease are warranted.

The Author(s). Published by S. Karger AG, Basel.

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• Patient Care & Health Information
• Diseases & Conditions
• Chronic kidney disease
• What is kidney disease? An expert explains

Learn more from kidney doctor Andrew Bentall, M.D.

I'm Dr. Andrew Bentall, a kidney doctor at Mayo Clinic. I look after patients with kidney disease, either in the early stages, or with more advanced kidney disease considering dialysis and transplantation as treatment options. In this video, we'll cover the basics of chronic kidney disease. What is it? Who gets it? The symptoms, diagnosis and treatment. Whether you are looking for answers for yourself or for someone you love, we're here to give you the best information available.

Chronic kidney disease is a disease characterized by progressive damage and loss of function in the kidneys. It's estimated that chronic kidney disease affects about one in seven American adults. And most of those don't know they have it. Before we get into the disease itself, let's talk a little bit about the kidneys and what they do. Our kidneys play many important roles keeping our bodies in balance. They remove waste and toxins, excess water from the bloodstream, which is carried out of the body in urine. They helped to make hormones to produce red blood cells, and they turn vitamin D into its active form, so it's usable in the body.

There are quite a few things that can cause or put you at higher risk for chronic kidney disease. Some of them are not things that can be avoided. Your risk is simply higher if you have a family history of certain genetic conditions like polycystic kidney disease or some autoimmune diseases like lupus or IgA nephropathy. Defects in the kidney structure can also cause your kidneys to fail, and you have an increased risk as you get older. Sometimes, other common medical conditions can increase your risk. Diabetes is the most common cause of kidney disease. Both type 1 and type 2 diabetes. But also heart disease and obesity can contribute to the damage that causes kidneys to fail. Urinary tract issues and inflammation in different parts of the kidney can also lead to long-term functional decline. There are things that are more under our control: Heavy or long-term use of certain medications, even those that are common over-the-counter. Smoking can also be a contributing factor to chronic kidney disease.

Often there are no outward signs in the earlier stages of chronic kidney disease, which is grouped into stages 1 through 5. Generally, earlier stages are known as 1 to 3. And as kidney disease progresses, you may notice the following symptoms. Nausea and vomiting, muscle cramps, loss of appetite, swelling via feet and ankles, dry, itchy skin, shortness of breath, trouble sleeping, urinating either too much or too little. However, these are usually in the later stages, but they can also happen in other disorders. So don't automatically interpret this as having kidney disease. But if you're experiencing anything that concerns you, you should make an appointment with your doctor.

Even before any symptoms appear, routine blood work can indicate that you might be in the early stages of chronic kidney disease. And the earlier it's detected, the easier it is to treat. This is why regular checkups with your doctor are important. If your doctor suspects the onset of chronic kidney disease, they may schedule a variety of other tests. They may also refer you to a kidney specialist, a nephrologist like myself. Urine tests can reveal abnormalities and give clues to the underlying cause of the chronic kidney disease. And this can also help to determine the underlying issues. Various imaging tests like ultrasounds or CT scans can be done to help your doctor assess the size, the structure, as well as evaluate the visible damage, inflammation or stones of your kidneys. And in some cases, a kidney biopsy may be necessary. And a small amount of tissue is taken with a needle and sent to the pathologist for further analysis.

Treatment is determined by what is causing your kidneys to not function normally. Treating the cause is key, leading to reduced complications and slowing progression of kidney disease. For example, getting better blood pressure control, improved sugar control and diabetes, and reducing weight are often key interventions. However, existing damage is not usually reversible. In some conditions, treatment can reverse the cause of the disease. So seeking medical review is really important. Individual complications vary, but treatment might include high blood pressure medication, diuretics to reduce fluid and swelling, supplements to relieve anemia, statins to lower cholesterol, or medications to protect your bones and prevent blood vessel calcification. A lower-protein diet may also be recommended. It reduces the amount of waste your kidneys need to filter from your blood. These can not only slow the damage of kidney disease, but make you feel better as well. When the damage has progressed to the point that 85 to 90 percent of your kidney function is gone, and they no longer work well enough to keep you alive, it's called end-stage kidney failure. But there are still options. There's dialysis, which uses a machine to filter the toxins and remove water from your body as your kidneys are no longer able to do this. Where possible, the preferred therapy is a kidney transplant. While an organ transplant can sound daunting, it's actually often the better alternative, and the closest thing to a cure, if you qualify for a kidney transplant.

If you have kidney disease, there are lifestyle choices. Namely quit smoking. Consuming alcohol in moderation. If you're overweight or obese, then try to lose weight. Staying active and getting exercise can help not only with your weight, but fatigue and stress. If your condition allows, keep up with your routine, whether that's working, hobbies, social activities, or other things you enjoy. It can be helpful to talk to someone you trust, a friend or relative who's good at listening. Or your doctor could also refer you to a therapist or social worker. It can also be helpful to find a support group and connect with people going through the same thing. Learning you have chronic kidney disease and learning how to live with it can be a challenge. But there are lots of ways to help you to be more comfortable for longer before more drastic measures are needed. And even then, there is plenty of hope. If you'd like to learn even more about chronic kidney disease, watch our other related videos or visit mayoclinic.org. We wish you well.

Chronic kidney disease, also called chronic kidney failure, involves a gradual loss of kidney function. Your kidneys filter wastes and excess fluids from your blood, which are then removed in your urine. Advanced chronic kidney disease can cause dangerous levels of fluid, electrolytes and wastes to build up in your body.

In the early stages of chronic kidney disease, you might have few signs or symptoms. You might not realize that you have kidney disease until the condition is advanced.

Treatment for chronic kidney disease focuses on slowing the progression of kidney damage, usually by controlling the cause. But, even controlling the cause might not keep kidney damage from progressing. Chronic kidney disease can progress to end-stage kidney failure, which is fatal without artificial filtering (dialysis) or a kidney transplant.

• How kidneys work

One of the important jobs of the kidneys is to clean the blood. As blood moves through the body, it picks up extra fluid, chemicals and waste. The kidneys separate this material from the blood. It's carried out of the body in urine. If the kidneys are unable to do this and the condition is untreated, serious health problems result, with eventual loss of life.

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Signs and symptoms of chronic kidney disease develop over time if kidney damage progresses slowly. Loss of kidney function can cause a buildup of fluid or body waste or electrolyte problems. Depending on how severe it is, loss of kidney function can cause:

• Loss of appetite
• Fatigue and weakness
• Sleep problems
• Urinating more or less
• Decreased mental sharpness
• Muscle cramps
• Swelling of feet and ankles
• Dry, itchy skin
• High blood pressure (hypertension) that's difficult to control
• Shortness of breath, if fluid builds up in the lungs
• Chest pain, if fluid builds up around the lining of the heart

Signs and symptoms of kidney disease are often nonspecific. This means they can also be caused by other illnesses. Because your kidneys are able to make up for lost function, you might not develop signs and symptoms until irreversible damage has occurred.

When to see a doctor

Make an appointment with your doctor if you have signs or symptoms of kidney disease. Early detection might help prevent kidney disease from progressing to kidney failure.

If you have a medical condition that increases your risk of kidney disease, your doctor may monitor your blood pressure and kidney function with urine and blood tests during office visits. Ask your doctor whether these tests are necessary for you.

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• Healthy kidney vs. diseased kidney

A typical kidney has about 1 million filtering units. Each unit, called a glomerulus, joins a tubule. The tubule collects urine. Conditions such as high blood pressure and diabetes harm kidney function by damaging these filtering units and tubules. The damage causes scarring.

• Polycystic kidney

A healthy kidney (left) eliminates waste from the blood and maintains the body's chemical balance. With polycystic kidney disease (right), fluid-filled sacs called cysts develop in the kidneys. The kidneys grow larger and gradually lose the ability to function as they should.

Chronic kidney disease occurs when a disease or condition impairs kidney function, causing kidney damage to worsen over several months or years.

Diseases and conditions that cause chronic kidney disease include:

• Type 1 or type 2 diabetes
• High blood pressure
• Glomerulonephritis (gloe-mer-u-low-nuh-FRY-tis), an inflammation of the kidney's filtering units (glomeruli)
• Interstitial nephritis (in-tur-STISH-ul nuh-FRY-tis), an inflammation of the kidney's tubules and surrounding structures
• Polycystic kidney disease or other inherited kidney diseases
• Prolonged obstruction of the urinary tract, from conditions such as enlarged prostate, kidney stones and some cancers
• Vesicoureteral (ves-ih-koe-yoo-REE-tur-ul) reflux, a condition that causes urine to back up into your kidneys
• Recurrent kidney infection, also called pyelonephritis (pie-uh-low-nuh-FRY-tis)

Risk factors

Factors that can increase your risk of chronic kidney disease include:

• Heart (cardiovascular) disease
• Being Black, Native American or Asian American
• Family history of kidney disease
• Abnormal kidney structure
• Frequent use of medications that can damage the kidneys

Complications

Chronic kidney disease can affect almost every part of your body. Potential complications include:

• Fluid retention, which could lead to swelling in your arms and legs, high blood pressure, or fluid in your lungs (pulmonary edema)
• A sudden rise in potassium levels in your blood (hyperkalemia), which could impair your heart's function and can be life-threatening
• Heart disease
• Weak bones and an increased risk of bone fractures
• Decreased sex drive, erectile dysfunction or reduced fertility
• Damage to your central nervous system, which can cause difficulty concentrating, personality changes or seizures
• Decreased immune response, which makes you more vulnerable to infection
• Pericarditis, an inflammation of the saclike membrane that envelops your heart (pericardium)
• Pregnancy complications that carry risks for the mother and the developing fetus
• Irreversible damage to your kidneys (end-stage kidney disease), eventually requiring either dialysis or a kidney transplant for survival

To reduce your risk of developing kidney disease:

• Follow instructions on over-the-counter medications. When using nonprescription pain relievers, such as aspirin, ibuprofen (Advil, Motrin IB, others) and acetaminophen (Tylenol, others), follow the instructions on the package. Taking too many pain relievers for a long time could lead to kidney damage.
• Maintain a healthy weight. If you're at a healthy weight, maintain it by being physically active most days of the week. If you need to lose weight, talk with your doctor about strategies for healthy weight loss.
• Don't smoke. Cigarette smoking can damage your kidneys and make existing kidney damage worse. If you're a smoker, talk to your doctor about strategies for quitting. Support groups, counseling and medications can all help you to stop.
• Manage your medical conditions with your doctor's help. If you have diseases or conditions that increase your risk of kidney disease, work with your doctor to control them. Ask your doctor about tests to look for signs of kidney damage.

Chronic kidney disease care at Mayo Clinic

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• Goldman L, et al., eds. Chronic kidney disease. In: Goldman-Cecil Medicine. 26th ed. Elsevier; 2020. http://www.clinicalkey.com. Accessed April 27, 2021.
• Chronic kidney disease (CKD). National Institute of Diabetes and Digestive and Kidney Diseases. https://www.niddk.nih.gov/health-information/kidney-disease/chronic-kidney-disease-ckd#:~:text=Chronic kidney disease (CKD) means,family history of kidney failure. Accessed April 26, 2021.
• Rosenberg M. Overview of the management of chronic kidney disease in adults. https://www.uptodate.com/contents/search. Accessed April 26, 2021.
• Chronic kidney disease (CKD) symptoms and causes. National Kidney Foundation. https://www.kidney.org/atoz/content/about-chronic-kidney-disease. Accessed April 26, 2021.
• Chronic kidney disease. Merck Manual Professional Version. https://www.merckmanuals.com/professional/genitourinary-disorders/chronic-kidney-disease/chronic-kidney-disease?query=Chronic kidney disease. Accessed April 26, 2021.
• Ammirati AL. Chronic kidney disease. Revista da Associação Médica Brasileira. 2020; doi:10.1590/1806-9282.66.S1.3.
• Chronic kidney disease basics. Centers for Disease Control and Prevention. https://www.cdc.gov/kidneydisease/basics.html. Accessed April 26, 2021.
• Warner KJ. Allscripts EPSi. Mayo Clinic; April 21, 2021.
• Office of Patient Education. Chronic kidney disease treatment options. Mayo Clinic; 2020.
• Chronic kidney disease: Is a clinical trial right for me?
• Eating right for chronic kidney disease
• Effectively managing chronic kidney disease
• Kidney biopsy
• Kidney disease FAQs
• Low-phosphorus diet: Helpful for kidney disease?
• MRI: Is gadolinium safe for people with kidney problems?
• Renal diet for vegetarians

Associated Procedures

• Deceased-donor kidney transplant
• Hemodialysis
• Kidney transplant
• Living-donor kidney transplant
• Nondirected living donor
• Peritoneal dialysis
• Preemptive kidney transplant

News from Mayo Clinic

• Mayo Clinic Minute: Why Black Americans are at higher risk of chronic kidney disease March 05, 2024, 05:00 p.m. CDT
• Mayo Clinic Minute: Can extra salt hurt your kidneys? Feb. 16, 2024, 04:00 p.m. CDT
• Mayo Clinic Minute: Using AI to predict kidney failure in patients with polycystic kidney disease April 06, 2023, 04:00 p.m. CDT
• Mayo Clinic Q and A: Understanding chronic kidney disease March 23, 2023, 12:35 p.m. CDT
• Mayo Clinic Minute: Game-changing treatment for chronic kidney disease could slow down progression of the disease March 06, 2023, 04:01 p.m. CDT
• Science Saturday: Seeking a cellular therapy for chronic kidney disease Nov. 12, 2022, 12:00 p.m. CDT
• Science Saturday: Mayo Clinic researchers integrate genomics into kidney disease diagnosis, care Sept. 17, 2022, 11:00 a.m. CDT
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Managing Chronic Kidney Disease

If you have chronic kidney disease (CKD), you can take steps to protect your kidneys from more damage.

The sooner you know you have kidney disease, the better. The steps you take to protect your kidneys from damage also may help prevent heart disease—and improve your health overall. Making these changes when you have no symptoms may be hard, but it’s worthwhile.

Control your blood pressure

The most important step you can take to treat kidney disease is to control your blood pressure . High blood pressure can damage your kidneys. You can protect your kidneys by keeping your blood pressure at or less than the goal set by your health care provider. For most people, the blood pressure goal is less than 140/90 mm Hg.

Work with your health care provider to develop a plan to meet your blood pressure goals. Steps you can take to meet your blood pressure goals may include eating heart-healthy and low-sodium meals, quitting smoking, being active, getting enough sleep, and taking your medicines as prescribed.

Meet your blood glucose goal if you have diabetes

To reach your blood glucose goal, check your blood glucose level regularly. Use the results to guide decisions about food, physical activity, and medicines. Ask your health care provider how often you should check your blood glucose level.

Your health care provider will also test your A1C. The A1C is a blood test that measures your average blood glucose level over the past 3 months. This test is different from the blood glucose checks you do regularly. The higher your A1C number, the higher your blood glucose levels have been during the past 3 months. Stay close to your daily blood glucose numbers to help you meet your A1C goal.

The A1C goal for many people with diabetes is below 7 percent. Ask your health care provider what your goal should be. Reaching your goal numbers will help you protect your kidneys. Learn more about how to manage diabetes .

Work with your health care team to monitor your kidney health

The tests that health care providers use to test for kidney disease can also be used to track changes to kidney function and damage. Kidney disease tends to get worse over time. Each time you get checked, ask your provider how the test results compare to the last results. Your goals will be to

• keep your GFR the same
• keep your urine albumin the same or lower

Your health care provider will also check your blood pressure and, if you have diabetes, your A1C level, to make sure you are meeting your blood pressure and blood glucose goals.

Bring this document to your appointment to help keep track of your kidney test results (PDF, 262 KB) .

How can I prepare for visits with my health care provider?

The more you plan for your visits, the more you will be able to learn about your health and treatment options.

Make a list of questions It’s normal to have a lot of questions. Write down your questions as you think of them so that you can remember everything you want to ask when you see your health care provider. You may want to ask about what tests are being done, what test results mean, or the changes you need to make to your diet and medicines.

Sample questions to ask your provider for people with kidney disease

About your tests

• What is my GFR? What does that mean?
• Has my GFR changed since last time?
• What is my urine albumin? What does it mean?
• Has my urine albumin changed since the last time it was checked?
• Is my kidney disease getting worse?
• Is my blood pressure where it needs to be?

About treatment and self-care

• What can I do to keep my disease from getting worse?
• Do any of my medicines or doses need to be changed?
• What time of day should I take each of my medicines?
• Do I need to change what I eat?
• Will you refer me to a dietitian for diet counseling?
• When will I need to see a nephrologist (kidney specialist)?
• Do I need to worry about dialysis or a kidney transplant?
• What do I need to do to protect my veins?

About complications

• What other health problems may I face because of my kidney disease?
• Should I be looking for any symptoms? If so, what are they?

Bring a friend or relative with you for support A trusted friend or family member can take notes, ask questions you may not have thought of, offer support, and help remember what the provider said during the visit. Talk ahead of time about what you want to get out of the visit and the role you would like your friend or relative to play.

Who is part of my health care team?

The following health care providers may be part of the health care team involved in your treatment:

Primary care provider. Your primary care provider (PCP)—doctor, nurse practitioner, or physician assistant—is the person you see for routine medical visits. Your PCP may monitor your kidney health and help you manage your diabetes and high blood pressure. A PCP also prescribes medicines and may refer you to specialists.

Nurse. A nurse may help with your treatment and teach you about monitoring and treating kidney disease, as well as managing your health conditions. Some nurses specialize in kidney disease.

Registered dietitian. A registered dietitian is a food and nutrition expert who helps people create a healthy eating plan when they have a health condition such as kidney disease. Dietitians can help you by creating an eating plan based on how your kidneys are doing. “Renal dietitians” often work in dialysis centers and are specially trained to work with people with kidney failure.

Diabetes educator. A diabetes educator teaches people with diabetes how to manage their disease and handle diabetes-related problems.

Pharmacist. A pharmacist educates you about your medicines and fills your prescriptions. An important job for the pharmacist is to review all of your medicines, including over-the-counter (OTC) medicines, and supplements, to avoid unsafe combinations and side effects.

Social worker. When you are close to needing dialysis, you may have a chance to meet with a social worker. A dialysis social worker helps people and their families deal with the life changes and costs that come with having kidney disease and kidney failure. A dialysis social worker also can help people with kidney failure apply for help to cover treatment costs.

Nephrologist. A nephrologist is a doctor who is a kidney specialist. Your PCP may refer you to a nephrologist if you have a complicated case of kidney disease, your kidney disease is quickly getting worse, or your kidney disease is advanced.

Take medicines as prescribed

Many people with CKD take medicines prescribed to lower blood pressure, control blood glucose, and lower cholesterol .

Two types of blood pressure medicines, ACE inhibitors and ARBs , may slow kidney disease and delay kidney failure, even in people who don’t have high blood pressure. The names of these medicines end in –pril or –sartan.

Many people need to take two or more medicines for their blood pressure. You may also need to take a diuretic, sometimes called a water pill. The aim is to meet your blood pressure goal. These medicines may work better if you limit your salt intake.

Know that your medicines may change over time

Your health care provider may change your medicines as your kidney disease gets worse. Your kidneys don’t filter as well as they did in the past, and this can cause an unsafe buildup of medicines in your blood. Some medicines can also harm your kidneys. As a result, your provider may tell you to

• take a medicine less often or take a smaller dose
• stop taking a medicine or switch to a different one

Your pharmacist and health care provider need to know about all the medicines you take, including OTC medicines, vitamins, and supplements.

Be careful about the over-the-counter medicines you take

If you take OTC or prescription medicines for headaches, pain, fever, or colds, you may be taking nonsteroidal anti-inflammatory drugs (NSAIDs). NSAIDs include commonly used pain relievers and cold medicines that can damage your kidneys and lead to acute kidney injury , especially in those with kidney disease, diabetes, and high blood pressure.

Ibuprofen and naproxen are NSAIDs. NSAIDs are sold under many different brand names, so ask your pharmacist or health care provider if the medicines you take are safe to use.

You also can look for NSAIDs on Drug Facts labels like the one below:

Watch a video explaining how NSAIDs can harm your kidneys .

If you have been taking NSAIDs regularly to control chronic pain, you may want to ask your health care provider about other ways to treat pain, such as meditation or other relaxation techniques. You can read more about pain management at the NIH National Center for Complementary and Integrative Health website .

Tips for managing your medicines

The next time you pick up a prescription or buy an OTC medicine or supplement, ask your pharmacist how the product may

• affect your kidneys
• affect other medicines you take

Fill your prescriptions at only one pharmacy or pharmacy chain so your pharmacist can

• keep track of your medicines and supplements
• check for harmful interactions

Keep track of your medicines and supplements:

• Keep an up-to-date list of your medicines and supplements in your wallet. Take your list with you, or bring all of your medicine bottles, to all health care visits.

Work with a dietitian to develop a meal plan

What you eat and drink can help you

• protect your kidneys
• reach your blood pressure and blood glucose goals
• prevent or delay health problems caused by kidney disease

As your kidney disease gets worse, you may need to make more changes to what you eat and drink .

A dietitian who knows about kidney disease can work with you to create a meal plan that includes foods that are healthy for you and that you enjoy eating. Cooking and preparing your food from scratch can help you eat healthier.

Nutrition counseling from a registered dietitian to help meet your medical or health goals is called medical nutrition therapy (MNT). If you have diabetes or kidney disease and a referral from your primary care provider, your health insurance may cover MNT. If you qualify for Medicare, MNT is covered.

Your health care provider may be able to refer you to a dietitian. You can also find a registered dietitian online through the Academy of Nutrition and Dietetics. Work closely with your dietitian to learn to eat right for CKD.

Make physical activity part of your routine

Be active for 30 minutes or more on most days. Physical activity can help you reduce stress, manage your weight, and achieve your blood pressure and blood glucose goals. If you are not active now, ask your health care provider about the types and amounts of physical activity that are right for you.

View physical activity and weight-management resources to help you get and stay motivated.

Aim for a healthy weight

Being overweight makes your kidneys work harder and may damage your kidneys. The NIH Body Weight Planner is an online tool to help you tailor your calorie and physical activity plans to achieve and stay at a healthy weight.

Get enough sleep

Aim for 7 to 8 hours of sleep each night. Getting enough sleep is important to your overall physical and mental health and can help you meet your blood pressure and blood glucose goals. You can take steps to improve your sleep habits .

Stop smoking

Cigarette smoking can make kidney damage worse. Quitting smoking may help you meet your blood pressure goals, which is good for your kidneys, and can lower your chances of having a heart attack or stroke . For tips on quitting, go to Smokefree.gov .

Find healthy ways to cope with stress and depression

Long-term stress can raise your blood pressure and your blood glucose and lead to depression. Some of the steps that you are taking to manage your kidney disease are also healthy ways to cope with stress. For example, physical activity and sleep help reduce stress. Listening to your favorite music, focusing on something calm or peaceful, or meditating may also help you. Learn more about healthy ways to cope with stress .

Depression is common among people with a chronic, or long-term, illness . Depression can make it harder to manage your kidney disease. Ask for help if you feel down. Seek help from a mental health professional. Talking with a support group, clergy member, friend, or family member who will listen to your feelings may help.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

• DOI: 10.34067/KID.0000000000000402
• Corpus ID: 270148180

An Elderly Patient with Progressive Kidney Failure in the Setting of Chronic Pancreatitis

• Yan Cai , Cui Wang , Leping Shao
• Published in Kidney360 1 May 2024

Figures from this paper

4 References

Secondary oxalate nephropathy: causes and clinicopathological characteristics of a case series, etiologies, clinical features, and outcome of oxalate nephropathy, oxalate nephropathy associated with chronic pancreatitis., enteric hyperoxaluria: a hidden cause of early renal graft failure in two successive transplants: spontaneous late graft recovery., related papers.

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Masks Strongly Recommended but Not Required in Maryland, Starting Immediately

Due to the downward trend in respiratory viruses in Maryland, masking is no longer required but remains strongly recommended in Johns Hopkins Medicine clinical locations in Maryland. Read more .

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Coronavirus: Kidney Damage Caused by COVID-19

Featured Expert:

C. John Sperati, M.D., M.H.S.

Does COVID-19 affect the kidneys? It can. In addition to attacking the lungs, the coronavirus that causes COVID-19 — officially called SARS-CoV-2 — also can cause severe and lasting harm in other organs, including the  heart  and kidneys. 

C. John Sperati, M.D., M.H.S. , an expert in kidney health, discusses how the new coronavirus might affect kidney function as the illness develops and afterward as a person recovers.

COVID-19 Kidney Damage: A Known Complication

Some people suffering with severe cases of COVID-19 will show signs of kidney damage, even those who had no underlying kidney problems before they were infected with the coronavirus. Signs of kidney problems in patients with COVID-19 include high levels of protein or blood in the urine and abnormal blood work.

Studies indicate more than 30% of patients hospitalized with COVID-19 develop kidney injury, and more than 50% of patients in the intensive care unit with kidney injury may require dialysis. Sperati says early in the pandemic, some hospitals were running short on machines and sterile fluids needed to perform dialysis.

“As general treatments for patients with COVID-19 have improved, the rates of dialysis have decreased. This has helped to alleviate shortages, although intermittent supply chain disruptions remain a concern.

“Many patients with severe COVID-19 are those with co-existing, chronic conditions, including high blood pressure and diabetes. Both of these increase the risk of kidney disease,” he says.

But Sperati and other doctors are also seeing kidney damage in people who did not have kidney problems before they got infected with the virus.

How does COVID-19 damage the kidneys?

The impact of COVID-19 on the kidneys is complex. Here are some possibilities doctors and researchers are exploring:

Coronavirus might target kidney cells

The virus itself infects the cells of the kidney. Kidney cells have receptors that enable the new coronavirus to attach to them, invade, and make copies of itself, potentially damaging those tissues. Similar receptors are found on cells of the lungs and heart, where the new coronavirus has been shown to cause injury.

Too little oxygen can cause kidneys to malfunction

Another possibility is that kidney problems in patients with the coronavirus are due to abnormally low levels of oxygen in the blood, a result of the pneumonia commonly seen in severe cases of the disease.

Cytokine storms can destroy kidney tissue

The body’s reaction to the infection may be responsible as well. The immune response to the new coronavirus can be extreme in some people, leading to what is called a cytokine storm.

When that happens, the immune system sends a rush of cytokines into the body. Cytokines are small proteins that help the cells communicate as the immune system fights an infection. But this sudden, large influx of cytokines can cause severe inflammation. In trying to kill the invading virus, this inflammatory reaction can destroy healthy tissue, including that of the kidneys.

COVID-19 causes blood clots that might clog the kidneys

The kidneys are like filters that screen out toxins, extra water and waste products from the body. COVID-19 can cause tiny clots to form in the bloodstream, which can clog the smallest blood vessels in the kidney and impair its function.

Johns Hopkins Team Develops Method to Make Dialysis Fluid for Patients with COVID-19

When New York-based hospitals started running out of dialysis fluid for the type of dialysis used in intensive care, a team from Johns Hopkins answered the call.

Coronavirus Kidney Damage: A Serious Sign

Organ systems like the heart, lungs, liver, and kidneys rely on and support each other’s functions, so when the new coronavirus causes damage in one area, others might be at risk. The kidneys’ essential functions have an impact on the heart, lungs and other systems. That may be why doctors note that kidney damage arising in patients with COVID-19 is a possible warning sign of a serious, even fatal course of the disease.

Can kidneys recover after COVID-19?

Sperati says, “Patients with acute kidney injury due to COVID-19 who do not require dialysis will have better outcomes than those who need dialysis, and we have seen patients at Johns Hopkins who recover kidney function. We have even had patients in the ICU with acute kidney injury who have required dialysis, and subsequently regained their kidney function.”

He notes that patients with acute kidney injury requiring dialysis are much more likely to die than patients without acute kidney injury. In those who survive, approximately a third will not regain complete kidney function by the time of discharge from the hospital.

Should I keep taking my high blood pressure medication?

Hypertension (high blood pressure)  is a common cause of kidney problems. Hypertension damages the blood vessels of the kidneys and affects their ability to filter the blood. Kidneys also help to regulate blood pressure, so kidney damage can make hypertension worse. Over time, hypertension can cause kidney failure.

If you are living with hypertension, you might take medication for the problem. You may be reading news reports questioning the safety of taking certain prescription medicines to manage their condition: ACE inhibitors and angiotensin receptor blockers (ARBs).

Sperati says multiple studies have confirmed that ACE inhibitors and ARBs do not increase the risk for complications in patients with COVID-19. Staying the course with your prescriptions, he adds, can lower the risk of heart and kidney damage from unchecked high blood pressure.

He does recommend patients with kidney issues stay away from non-steroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen and naproxen. These can raise blood pressure and increase fluid volume in the body, which puts strain on the kidneys.

Research is revealing more about SARS-CoV-2 kidney damage

Researchers have learned a lot about kidney damage in COVID-19 since the start of the pandemic. Sperati, who also conducts research on kidney disease, says the Johns Hopkins Division of Nephrology is exploring exactly how SARS-CoV-2 — and the body’s response to it — is affecting kidney health.

He says that patients with COVID-19-related kidney damage should follow up with their doctors to ensure kidney function is returning to normal. Lasting kidney damage might require dialysis or other therapies even after recovery from COVID-19.

Mostly, Sperati stresses the importance of adhering to the basics of prevention, including staying up to date on COVID-19 vaccines and boosters , physical distancing, masking, and hand-washing. “For everyone, especially people with underlying chronic disease, avoiding infection with COVID-19 is important,” he says.

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