The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis
Open accessAbstract
IMPORTANCE: Identifying patients at risk of chronic kidney disease (CKD) progression may facilitate more optimal nephrology care. Kidney failure risk equations, including such factors as age, sex, estimated glomerular filtration rate, and calcium and phosphate concentrations, were previously developed and validated in 2 Canadian cohorts. Validation in other regions and in CKD populations not under the care of a nephrologist is needed. OBJECTIVE: To evaluate the accuracy of the risk equations across different geographic regions and patient populations through individual participant data meta-analysis. DATA SOURCES: Thirty-one cohorts, including 721,357 participants with CKD stages 3 to 5 in more than 30 countries spanning 4 continents, were studied. These cohorts collected data from 1982 through 2014. STUDY SELECTION: Cohorts participating in the CKD Prognosis Consortium with data on end-stage renal disease. DATA EXTRACTION AND SYNTHESIS: Data were obtained and statistical analyses were performed between July 2012 and June 2015. Using the risk factors from the original risk equations, cohort-specific hazard ratios were estimated and combined using random-effects meta-analysis to form new pooled kidney failure risk equations. Original and pooled kidney failure risk equation performance was compared, and the need for regional calibration factors was assessed. MAIN OUTCOMES AND MEASURES: Kidney failure (treatment by dialysis or kidney transplant). RESULTS: During a median follow-up of 4 years of 721,357 participants with CKD, 23,829 cases kidney failure were observed. The original risk equations achieved excellent discrimination (ability to differentiate those who developed kidney failure from those who did not) across all cohorts (overall C statistic, 0.90; 95% CI, 0.89-0.92 at 2 years; C statistic at 5 years, 0.88; 95% CI, 0.86-0.90); discrimination in subgroups by age, race, and diabetes status was similar. There was no improvement with the pooled equations. Calibration (the difference between observed and predicted risk) was adequate in North American cohorts, but the original risk equations overestimated risk in some non-North American cohorts. Addition of a calibration factor that lowered the baseline risk by 32.9% at 2 years and 16.5% at 5 years improved the calibration in 12 of 15 and 10 of 13 non-North American cohorts at 2 and 5 years, respectively (P = .04 and P = .02). CONCLUSIONS AND RELEVANCE: Kidney failure risk equations developed in a Canadian population showed high discrimination and adequate calibration when validated in 31 multinational cohorts. However, in some regions the addition of a calibration factor may be necessary.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“The uACR may also be useful in determining the rate of progression of disease and thus support planning for care of end-stage renal disease using the Kidney Failure Risk Equation (474).”
From Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus (2023) · cites this paper as [474]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Toprak, Ö., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,772
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Self-citationAhmed, A., Jorna, T., & Bhandari, S. (2016) · The Nephron journals/Nephron journals · doi:10.1159/000447068 · Cited by 11,782
Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization
Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E., & Hsu, C.-Y. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041031 · Cited by 11,369
Sontrop, J. M., Dixon, S. N., Garg, A. X., Jiménez, I. B., Dohein, O., Huang, S. S., & Clark, W. F. (2013) · American Journal of Nephrology · doi:10.1159/000350377 · Cited by 5,903
Self-citationRosa, S. D., Samoni, S., Villa, G., & Ronco, C. (2017) · Blood Purification · doi:10.1159/000452650 · Cited by 5,040
Prevalence of Chronic Kidney Disease in the United States
Coresh, J., Selvin, E., Stevens, L. A., Manzi, J., Kusek, J. W., Eggers, P., Van Lente, F., & Levey, A. S. (2007) · JAMA · doi:10.1001/jama.298.17.2038 · Cited by 4,895
Self-citationThe effect of irbesartan on the development of diabetic nephropathy in patients with type 2 diabetes
Parving, H.-H., Lehnert, H., Bröchner-Mortensen, J., Gomis, R., Andersen, S., & Arner, P. (2001) · New England Journal of Medicine · doi:10.1056/nejmoa011489 · Cited by 2,873
[Effect of irbesartan on the development of diabetic nephropathy in patients with type 2 diabetes].
Parving, H., Lehnert, H., Brøchner‐Mortensen, J., Gomis, R., Andersen, S., & Arner, P. (2001) · PubMed · Cited by 2,527
Flagged first, then by citations
Research works printed but not linked: 1
- [37]Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease Kidney International Supplements 2013 3 1 1 150 30681074 PMC6341011
Cited by
Sorted by citations
Items 101–200 of 749
Al‐Wahsh, H., Tangri, N., Quinn, R., Liu, P., Ferguson, T. W., Fiocco, M. F., Lam, N. N., Tonelli, M. A., & Ravani, P. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.9225 · Cited by 54
Self-citationProvenzano, M., Andreucci, M., Nicola, L. D., Garofalo, C., Battaglia, Y., Borrelli, S., Gagliardi, I., Faga, T., Michael, A., Mastroroberto, P., et al. (2020) · BioMed Research International · doi:10.1155/2020/2314128 · Cited by 50
Lamprea-Montealegre, J. A., Madden, E., Tummalapalli, S. L., Chu, C. D., Peralta, C. A., Du, Y., Singh, R., Kong, S. X., Tuot, D. S., Shlipak, M. G., et al. (2022) · Diabetes Care · doi:10.2337/dc22-0614 · Cited by 50
Tangri, N., Ferguson, T. W., & Komenda, P. V. J. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfx067 · Cited by 50
Self-citationDuggal, V., Thomas, I., Montez‐Rath, M. E., Chertow, G. M., & Tamura, M. K. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020121780 · Cited by 50
Supportive Care: Time to Change Our Prognostic Tools and Their Use in CKD
Couchoud, C., Hemmelgarn, B. R., Kotanko, P., Germain, M. J., Moranne, O. P., & Davison, S. N. (2016) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.12631115 · Cited by 47
Urinary Matrix Metalloproteinase 7 and Prediction of IgA Nephropathy Progression
Yang, X., Ou, J., Zhang, H., Xu, X. P., Zhu, L., Li, Q., Li, J., Xie, D., Sun, J., Zha, Y., et al. (2019) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2019.07.018 · Cited by 46
Ramspek, C. L., Teece, L., Snell, K. I. E., Evans, M., Riley, R. D., van Smeden, M., van Geloven, N., & van Diepen, M. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab256 · Cited by 45
Self-citationMendu, M. L., Ahmed, S., Maron, J. K., Rao, S. K., Chaguturu, S. K., May, M. F., Mutter, W. P., Burdge, K. A., Steele, D., Mount, D. B., et al. (2019) · BMC Nephrology · doi:10.1186/s12882-019-1260-y · Cited by 45
Potok, O., Nguyen, H. A., Abdelmalek, J. A., Beben, T., Woodell, T. B., & Rifkin, D. E. (2019) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.07970718 · Cited by 44
Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD
Hundemer, G. L., Tangri, N., Sood, M. M., Ramsay, T., Bugeja, A. L., Brown, P. A., Clark, E. G., Biyani, M., White, C. A., & Akbari, A. (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03940320 · Cited by 44
Self-citationLerner, B., Desrochers, S., & Tangri, N. (2017) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2016.12.004 · Cited by 43
Self-citationTime-Centered Approach to Understanding Risk Factors for the Progression of CKD
Ku, E., Johansen, K. L., & McCulloch, C. E. (2018) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.10360917 · Cited by 43
Ali, I., Donne, R. L., & Kalra, P. A. (2021) · BMC Nephrology · doi:10.1186/s12882-021-02402-1 · Cited by 42
Bhachu, H. K., Cockwell, P., Subramanian, A., Adderley, N. J., Gokhale, K. M., Fenton, A., Kyte, D. G., Nirantharakumar, K., & Calvert, M. (2021) · Kidney International Reports · doi:10.1016/j.ekir.2021.05.031 · Cited by 42
Tangri, N., Ferguson, T. W., Bamforth, R. J., Leon, S. J., Arnott, C. G., Mahaffey, K. W., Kotwal, S. S., Heerspink, H. J. L., Perkovic, V., Fletcher, R. A., et al. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15678 · Cited by 41
Self-citationValidation of the Kidney Failure Risk Equation in Manitoba
Whitlock, R. H., Chartier, M., Komenda, P. V. J., Hingwala, J. P., Rigatto, C., Walld, R., Dart, A. B., & Tangri, N. (2017) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358117705372 · Cited by 41
Self-citationAli, I., Chinnadurai, R., Ibrahim, S. T., Green, D., & Kalra, P. A. (2020) · BMC Nephrology · doi:10.1186/s12882-020-01982-8 · Cited by 41
Hockham, C., Schanschieff, F., & Woodward, M. (2022) · Kidney Medicine · doi:10.1016/j.xkme.2022.100535 · Cited by 40
Self-citationCombining robust urine biomarkers to assess chronic kidney disease progression
Bienaimé, F., Muorah, M., Metzger, M., Broueilh, M., Houiller, P., Flamant, M., Haymann, J., Vonderscher, J., Mizrahi, J. L., Friedlander, G., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104635 · Cited by 40
Self-citationShared decision-making in advanced kidney disease: a scoping review
Engels, N., de Graav, G. N., van der Nat, P. B., van den Dorpel, M., Stiggelbout, A. M., & Bos, W. J. W. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-055248 · Cited by 38
“Should the definition of CKD be changed to include age-adapted GFR criteria?”
Levey, A. S., Inker, L. A., & Coresh, J. (2019) · Kidney International · doi:10.1016/j.kint.2019.08.032 · Cited by 38
Self-citationChytilova, E., Jemcov, T., Malík, J., Pajek, J., Fila, B., & Kaván, J. (2021) · The Journal of Vascular Access · doi:10.1177/1129729820965064 · Cited by 37
Srivastava, A., Schmidt, I. M., Pálsson, R., Weins, A., Bonventre, J. V., Sabbisetti, V. S., Stillman, I. E., Rennke, H. G., & Waikar, S. S. (2021) · Kidney International Reports · doi:10.1016/j.ekir.2020.12.025 · Cited by 38
Chronic kidney disease measures for cardiovascular risk prediction
Mok, Y., Ballew, S. H., & Matsushita, K. (2021) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2021.09.007 · Cited by 37
OMICS in Chronic Kidney Disease: Focus on Prognosis and Prediction
Provenzano, M., Serra, R., Garofalo, C., Michael, A., Crugliano, G., Battaglia, Y., Ielapi, N., Bracale, U. M., Faga, T., Capitoli, G., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms23010336 · Cited by 36
Hoshino, J., Furuichi, K., Yamanouchi, M., Mise, K., Sekine, A., Kawada, M., Sumida, K., Hiramatsu, R., Hasegawa, E., Hayami, N., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190923 · Cited by 36
Yohanna, S., Naylor, K. L., Mucsi, I., McKenzie, S. Q., Belenko, D., Blake, P. G., Coghlan, C., Dixon, S. N., Elliott, L., Getchell, L. E., et al. (2021) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358121997266 · Cited by 36
Self-citationApplication of artificial intelligence in renal disease
Yao, L., Zhang, H., Zhang, M., Chen, X., Zhang, J., Huang, J., & Zhang, L. (2021) · Clinical eHealth · doi:10.1016/j.ceh.2021.11.003 · Cited by 36
Research Priorities for Palliative Care for Older Adults with Advanced Chronic Kidney Disease
O’Hare, A. M., Song, M., Tamura, M. K., & Moss, A. H. (2017) · Journal of Palliative Medicine · doi:10.1089/jpm.2016.0571 · Cited by 36
Foti, K. E., Wang, D., Chang, A. R., Selvin, E., Sarnak, M. J., Chang, T. I., Muntner, P., & Coresh, J. (2021) · Kidney International · doi:10.1016/j.kint.2020.12.019 · Cited by 35
Self-citationThe Effect of Age on Performance of the Kidney Failure Risk Equation in Advanced CKD
Hundemer, G. L., Tangri, N., Sood, M. M., Clark, E. G., Canney, M., Edwards, C., White, C. A., Oliver, M. J., Ramsay, T., & Akbari, A. (2021) · Kidney International Reports · doi:10.1016/j.ekir.2021.09.006 · Cited by 35
Self-citationHelping More Patients Receive a Living Donor Kidney Transplant
Garg, A. X. (2018) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.00760118 · Cited by 34
Self-citationHallan, S. I., Rifkin, D. E., Potok, O., Katz, R., Langlo, K. A. R., Bansal, N., & Ix, J. H. (2019) · Kidney International · doi:10.1016/j.kint.2019.04.022 · Cited by 35
Self-citationMendu, M. L., Waikar, S. S., & Rao, S. K. (2017) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2016.11.013 · Cited by 33
Green, J. A., Ephraim, P. L., Hill‐Briggs, F., Browne, T. A., Strigo, T. S., Hauer, C., Stametz, R. A., Darer, J. D., Patel, U. D., Lang‐Lindsey, K., et al. (2018) · Contemporary Clinical Trials · doi:10.1016/j.cct.2018.09.004 · Cited by 36
Self-citationHigh Unawareness of Chronic Kidney Disease in Germany
Stolpe, S., Kowall, B., Scholz, C., Stang, A., & Blume, C. A. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182211752 · Cited by 33
Selby, N. M., & Taal, M. W. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15683 · Cited by 32
Bundy, J. D., Mills, K. T., Anderson, A. H., Yang, W., Chen, J., He, J., & CRIC Study Investigators* (2022) · Annals of Internal Medicine · doi:10.7326/m21-2928 · Cited by 32
Risk-based versus GFR threshold criteria for nephrology referral in chronic kidney disease
Oliva-Dámaso, N., Delanaye, P., Oliva-Dámaso, E., Payán, J., & Glassock, R. J. (2022) · Clinical Kidney Journal · doi:10.1093/ckj/sfac104 · Cited by 32
Taal, M. W., Lucas, B. J., Roderick, P. J., Cockwell, P., Wheeler, D. C., Saleem, M. A., Fraser, S. D., Banks, R. E., Johnson, T. S., Hale, L. J., et al. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad110 · Cited by 33
The transition clinic in chronic kidney disease care
Evans, M., & Lopau, K. (2020) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaa022 · Cited by 32
Self-citationCKD for Primary Care Practitioners: Can We Cut to the Chase Without Too Many Shortcuts?
Thavarajah, S., Knicely, D. H., & Choi, M. J. (2016) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2016.02.043 · Cited by 32
Risk Prediction in Chronic Kidney Disease
Samuels, J., Connelly, K., & Tangri, N. (2022) · Wiley eBooks · doi:10.1002/9781119105954.ch5 · Cited by 32
Self-citationRisk factor analysis for a rapid progression of chronic kidney disease
Vestergaard, A. H. S., Jensen, S. K., Heide‐Jørgensen, U., Frederiksen, L. E., Birn, H., Jarbøl, D. E., Søndergaard, J., Persson, F., Thomsen, R. W., & Christiansen, C. F. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad271 · Cited by 31
Cheungpasitporn, W., Athavale, A. M., Ghazi, L., KASHANI, K. B., Colicchio, T. K., Koyner, J. L., Chen, J., Ix, J. H., Nadkarni, G. N., & Neyra, J. A. (2026) · Clinical Kidney Journal · doi:10.1093/ckj/sfag004 · Cited by 31
Self-citationCon: Most clinical risk scores are useless
Dekker, F. W., Ramspek, C. L., & van Diepen, M. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfx073 · Cited by 34
Wang, J., Bao, B., Shen, P., Kong, G., Yang, Y., Sun, X., Ding, G., Gao, B., Yang, C., Zhao, M., et al. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-030102 · Cited by 31
Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia
Wang, Y., Nguyen, F. N. H. L., Allen, J. C., Lew, J., Tan, N. C., & Jafar, T. H. (2019) · BMC Nephrology · doi:10.1186/s12882-019-1643-0 · Cited by 31
Rubin, S., Orieux, A., Clouzeau, B., Rigothier, C., Combe, C., Gruson, D., & Boyer, A. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8122215 · Cited by 31
CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model
Aoki, J., Kaya, C., Khalid, O. M., Kothari, T., Silberman, M. A., Skordis, C., Hughes, J. A., Hussong, J. W., & Salama, M. E. (2023) · Kidney Medicine · doi:10.1016/j.xkme.2023.100692 · Cited by 30
on behalf of the CKD-JAC Study Investigators, Hasegawa, T., Sakamaki, K., Koiwa, F., Akizawa, T., & Hishida, A. (2018) · Clinical and Experimental Nephrology · doi:10.1007/s10157-018-1621-z · Cited by 30
Validation of the Kidney Failure Risk Equation in Kidney Transplant Recipients
Tangri, N., Ferguson, T. W., Wiebe, C., Eng, F., Nash, M. M., Astor, B. C., Lam, N. N., Feng, Y., Shin, J. I., Whitlock, R. H., et al. (2020) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358120922627 · Cited by 30
Self-citationYang, Y., Li, Y., Chen, R., Zheng, J., Cai, Y., & Fortino, G. (2021) · Big Data Research · doi:10.1016/j.bdr.2021.100234 · Cited by 31
Shared decision making in elderly patients with kidney failure
Kanbay, M., Basile, C., Battaglia, Y., Mantovani, A., Yavuz, F., Pizzarelli, F., Luyckx, V. A., Covic, A. C., Liakopoulos, V., & Mitra, S. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad211 · Cited by 29
Risk-directed management of chronic kidney disease
Blum, M. F., Neuen, B. L., & Grams, M. E. (2025) · Nature Reviews Nephrology · doi:10.1038/s41581-025-00931-8 · Cited by 30
Self-citationPlasma biomarkers are associated with renal outcomes in individuals with APOL1 risk variants
Nadkarni, G. N., Chauhan, K., Verghese, D. A., Parikh, C. R., Do, R., Horowitz, C. R., Böttinger, E. P., & Coca, S. G. (2018) · Kidney International · doi:10.1016/j.kint.2018.01.026 · Cited by 29
Self-citationRenoprotection: focus on TRPV 1, TRPV 4, TRPC 6 and TRPM 2
Markó, L., Mannaa, M., Haschler, T. N., Krämer, S., & Gollasch, M. (2016) · Acta Physiologica · doi:10.1111/apha.12828 · Cited by 29
Metabolic Surgery Reduces the Risk of Progression From Chronic Kidney Disease to Kidney Failure
Funes, D. R., Blanco, D. G., Gómez, C. O., Frieder, J. S., Menzo, E. L., Szomstein, S., White, K. P., & Rosenthal, R. J. (2019) · Annals of Surgery · doi:10.1097/sla.0000000000003456 · Cited by 28
Green, J. A., Ephraim, P. L., Hill‐Briggs, F., Browne, T. A., Strigo, T. S., Hauer, C. L., Yule, C., Stametz, R. A., Littlewood, D., Pendergast, J., et al. (2021) · Kidney Medicine · doi:10.1016/j.xkme.2021.03.009 · Cited by 28
Self-citationBellocchio, F., Lonati, C., Titapiccolo, J. I., Nadal, J., Meiselbach, H., Schmid, M. C., Bärthlein, B., Tschulena, U., Schneider, M. P., Schultheiß, U. T., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182312649 · Cited by 31
The public health dimension of chronic kidney disease: what we have learnt over the past decade
Hu, J., & Coresh, J. (2016) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfw416 · Cited by 29
Self-citationFiltration Markers as Predictors of ESRD and Mortality: Individual Participant Data Meta-Analysis
Inker, L. A., Coresh, J., Sang, Y., Hsu, C., Foster, M. C., Eckfeldt, J. H., Karger, A. B., Nelson, R. G., Liu, X., Sarnak, M. J., et al. (2016) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03660316 · Cited by 27
Self-citationLong-term outcomes after AKI—a major unmet clinical need
Selby, N. M., & Taal, M. W. (2018) · Kidney International · doi:10.1016/j.kint.2018.09.005 · Cited by 27
Provenzano, M., Nicola, L. D., Pena, M. J., Capitoli, G., Garofalo, C., Borrelli, S., Gagliardi, I., Antolini, L., & Andreucci, M. (2020) · The Nephron journals/Nephron journals · doi:10.1159/000508983 · Cited by 27
A Validation Study Comparing Risk Prediction Models of IgA Nephropathy
Ouyang, Y., Zhao, Z., Li, G., Luo, H., Xu, F., Shao, L., Chen, Z., Yu, S., Jin, Y., Xu, J., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.753901 · Cited by 27
Patient Education and Support During CKD Transitions: When the Possible Becomes Probable
Green, J. A., & Boulware, L. E. (2016) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2016.04.004 · Cited by 27
Grams, M. E., & Melamed, M. L. (2025) · Annals of Internal Medicine · doi:10.7326/annals-25-02684 · Cited by 27
Self-citationSerum albumin concentration and risk of end-stage renal disease: the REGARDS study
Walther, C. P., Gutiérrez, O. M., Cushman, M., Judd, S. E., Lang, J. M., McClellan, W., Muntner, P., Sarnak, M. J., Shlipak, M. G., Warnock, D. G., et al. (2017) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfx331 · Cited by 26
Self-citationBarriers to conservative care from patients’ and nephrologists’ perspectives: the CKD-REIN study
Hamroun, A., Speyer, É., Ayav, C., Combe, C., Fouque, D., Jacquelinet, C., Laville, M., Liabeuf, S., Massy, Z. A., Pecoits‐Filho, R., et al. (2022) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfac009 · Cited by 30
Self-citationChu, C. D., McCulloch, C. E., Hsu, R. K., Powe, N. R., Bieber, B. A., Robinson, B. M., Raina, R., Pecoits‐Filho, R., & Tuot, D. S. (2023) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2023.03.014 · Cited by 26
Epidemiology research to foster improvement in chronic kidney disease care
Luo, S., & Grams, M. E. (2019) · Kidney International · doi:10.1016/j.kint.2019.11.010 · Cited by 28
Self-citationPrognostic models for chronic kidney disease: a systematic review and external validation
van Rijn, M. H. C., van de Luijtgaarden, M. W. M., van Zuilen, A. D., Blankestijn, P. J., Wetzels, J. F. M., Debray, T. P. A., & van den Brand, J. A. J. G. (2020) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaa155 · Cited by 26
Kerschbaum, J., Rudnicki, M., Dzien, A., Dzien‐Bischinger, C., Winner, H., Heerspink, H. J. L., Rosivall, L., Więcek, A., Mark, P. B., Eder, S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-76773-0 · Cited by 25
Self-citationA Deep Learning Approach for the Estimation of Glomerular Filtration Rate
Wang, H., Bowe, B. C., Cui, Z., Yang, H., Swamidass, S. J., Xie, Y., & Al-Aly, Z. (2022) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2022.3147957 · Cited by 25
Sullivan, M. K., Jani, B., Rutherford, E., Welsh, P. I., McConnachie, A., Major, R., McAllister, D., Nitsch, D., Mair, F. S., Mark, P. B., et al. (2022) · British Journal of General Practice · doi:10.3399/bjgp.2022.0145 · Cited by 25
Yamanouchi, M., Hoshino, J., Ubara, Y., Takaichi, K., Kinowaki, K., Fujii, T., Ohashi, K., Mise, K., Toyama, T., Hara, A., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190930 · Cited by 25
Role of serum complement C3 and C4 on kidney outcomes in IgA nephropathy
Tringali, E., Vetrano, D., Tondolo, F., Maritati, F., Fabbrizio, B., Pasquinelli, G., Provenzano, M., Manna, G. L., & Baraldi, O. (2024) · Scientific Reports · doi:10.1038/s41598-024-65857-w · Cited by 24
Zacharias, H. U., Altenbuchinger, M., Schultheiß, U. T., Samol, C., Kotsis, F., Poguntke, I., Sekula, P., Krumsiek, J., Köttgen, A., Spang, R., et al. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00983 · Cited by 24
Bhachu, H. K., Fenton, A., Cockwell, P., Aiyegbusi, O. L., Kyte, D. G., & Calvert, M. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-055572 · Cited by 24
Metabolic Acidosis is Associated With Acute Kidney Injury in Patients With CKD
Zhu, A., Whitlock, R. H., Ferguson, T. W., Nour-Mohammadi, M., Komenda, P. V. J., Rigatto, C., Collister, D., Bohm, C. J., Reaven, N. L., Funk, S. E., et al. (2022) · Kidney International Reports · doi:10.1016/j.ekir.2022.07.005 · Cited by 24
Self-citationDecisional Conflict About Kidney Failure Treatment Modalities Among Adults With Advanced CKD
DePasquale, N., Green, J. A., Ephraim, P. L., Morton, S., Peskoe, S. B., Davenport, C. A., Mohottige, D., McElroy, L. M., Strigo, T. S., Hill‐Briggs, F., et al. (2022) · Kidney Medicine · doi:10.1016/j.xkme.2022.100521 · Cited by 24
Rashid, A. M., Khan, M. S., Cherney, D. Z. I., Mehta, A. N., Rangaswami, J., Shafi, T., & Butler, J. (2025) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000752 · Cited by 23
Risk prediction in chronic kidney disease
Ali, I., & Kalra, P. A. (2019) · Current Opinion in Nephrology & Hypertension · doi:10.1097/mnh.0000000000000553 · Cited by 23
An independent validation of the kidney failure risk equation in an Asian population
Kang, M. W., Tangri, N., Kim, Y. C., An, J. N., Lee, J., Li, L., Oh, Y. K., Kim, D. K., Joo, K. W., Kim, Y. S., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69715-3 · Cited by 23
Self-citationIs a COVID-19 Vaccine Likely to Make Things Worse?
Abo, S. M. C., & Smith, R. J. (2020) · Vaccines · doi:10.3390/vaccines8040761 · Cited by 23
Ali, I., Chinnadurai, R., Ibrahim, S. T., & Kalra, P. A. (2021) · BMC Nephrology · doi:10.1186/s12882-021-02282-5 · Cited by 23
Lee, K., Chu, Y.-C., Tsai, M., Tseng, W., Lin, Y., Ou, S., & Tarng, D. (2022) · Biomedicines · doi:10.3390/biomedicines10030546 · Cited by 23
A Holistic Framework for the Evaluation of Kidney Function in a Gender-Diverse Landscape
Miranda, K. T., Greene, D. N., Collister, D., Krasowski, M. D., Ahmed, S. B., Cirrincione, L. R., Rosas, S. E., Saad, N., & Pierre, C. C. (2024) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2024.01.522 · Cited by 23
Oskoui, T., Pandya, R., Weiner, D. E., Wong, J. B., Koch‐Weser, S., & Ladin, K. (2020) · Kidney Medicine · doi:10.1016/j.xkme.2019.11.002 · Cited by 23
Hayes, J., Osborn, D., Francis, E., Ambler, G., Tomlinson, L. A., Boman, M., Wong, I. C. K., Geddes, J. R., Dalman, C., & Lewis, G. (2021) · BMC Medicine · doi:10.1186/s12916-021-01964-z · Cited by 23
Smekal, M. D., Tam‐Tham, H., Finlay, J. L., Donald, M., Benterud, E., Thomas, C. M., Quinn, R. R., Tam, K., Manns, B., Tonelli, M. A., et al. (2018) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358118763809 · Cited by 23
Self-citationÖzbek, L., Abdel‐Rahman, S. M., Unlu, S., Güldan, M., Çöpür, S., Burlacu, A., Covic, A. C., & Kanbay, M. (2024) · Medicina · doi:10.3390/medicina60101668 · Cited by 23
Akbari, A., Tangri, N., Brown, P. A., Biyani, M., Rhodes, E., Kumar, T., Shabana, W. M., & Sood, M. M. (2020) · Canadian Journal of Kidney Health and Disease · doi:10.1177/2054358120911274 · Cited by 22
Self-citationPrescribing and deprescribing in chronic kidney disease
Manski-Nankervis, J.-A. E., McMorrow, R., Nelson, C., Jesudason, S., & Sluggett, J. K. (2021) · Australian Journal of General Practice · doi:10.31128/ajgp-11-20-5752 · Cited by 22
Lin, C., Lai, Y.-C., Chang, T., Jiang, Y., Chang, Y., & Chuang, L. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109834 · Cited by 22
Machine Learning Improves Upon Clinicians' Prediction of End Stage Kidney Disease
Chuah, A., Walters, G. D., Christiadi, D., Karpe, K. M., Kennard, A. L., Singer, R. F., Talaulikar, G. S., Ge, W., Suominen, H., Andrews, T. D., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.837232 · Cited by 22
Biopsy-proven CKD etiology and outcomes: the Chronic Kidney Disease Japan Cohort (CKD-JAC) study
Hamano, T., Imaizumi, T., Hasegawa, T., Fujii, N., Komaba, H., Ando, M., Nangaku, M., Nitta, K., Hirakata, H., Isaka, Y., et al. (2022) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfac134 · Cited by 22
Wang, M., Peter, S. S., Chu, C. D., Tuot, D. S., & Chen, J. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.25797 · Cited by 22
Chen, F., Kantagowit, P., Nopsopon, T., Chuklin, A., & Pongpirul, K. (2023) · PLoS ONE · doi:10.1371/journal.pone.0278729 · Cited by 26
Metadata
- Authors
- Tangri, N., Grams, M. E., Levey, A. S., Coresh, J., Appel, L. J., Astor, B. C., Chodick, G., Collins, A. J., Djurdjev, O., Elley, C. R., Evans, M., Garg, A. X., Hallan, S. I., Inker, L. A., Ito, S., Jee, S. H., Kovesdy, C. P., Kronenberg, F., Heerspink, H. J. L., Marks, A., Nadkarni, G. N., Navaneethan, S. D., Nelson, R. G., Titze, S., Sarnak, M. J., Stengel, B., Woodward, M., Iseki, K., & for the CKD Prognosis Consortium
- Affiliations
- Seven Oaks General Hospital (CA)
- Johns Hopkins University (US)
- Tufts Medical Center (US)
- University of Wisconsin–Madison (US)
- Tel Aviv University (IL)
- University of Minnesota (US)
- Provincial Health Services Authority (CA)
- University of Auckland (NZ)
- Karolinska Institutet (SE)
- Western University (CA)
- Norwegian University of Science and Technology (NO)
- Tohoku University (JP)
- Yonsei University (KR)
- University of Tennessee Health Science Center (US)
- Innsbruck Medical University (AT)
- University Medical Center Groningen (NL)
- University of Aberdeen (GB)
- Icahn School of Medicine at Mount Sinai (US)
- Cleveland Clinic (US)
- National Institute of Diabetes and Digestive and Kidney Diseases (US)
- Friedrich-Alexander-Universität Erlangen-Nürnberg (DE)
- CESP, INSERM, Villejuif, France29Université Paris-Saclay, Université Paris-Sud, UVSQ, Villejuif, France
- The University of Sydney (AU)
- University of the Ryukyus University Hospital (JP)
- Corresponding author
- Coresh, Josef
- ORCID
- Tangri, Navdeep
- Grams, Morgan E.
- Levey, Andrew S.
- Coresh, Josef
- Appel, Lawrence J.
- Astor, Brad C.
- Chodick, Gabriel
- Elley, C. Raina
- Evans, Marie
- Garg, Amit X.
- Hallan, Stein I.
- Inker, Lesley A.
- Ito, Sadayoshi
- Jee, Sun Ha
- Kovesdy, Csaba P.
- Kronenberg, Florian
- Heerspink, Hiddo J. L.
- Marks, Angharad
- Nadkarni, Girish N.
- Navaneethan, Sankar D.
- Nelson, Robert G.
- Titze, Stephanie
- Sarnak, Mark J.
- Woodward, Mark
- Iseki, Kunitoshi
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2016-01-12
- Volume
- 315
- Issue
- 2
- Pages
- 164
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC4752167
- OpenAlex
- W2234357320
- Cited by
- 737
- References
- 50
- Citations by year
- 2026: 90 · 2025: 87 · 2024: 90 · 2023: 80 · 2022: 91 · 2021: 83 · 2020: 62 · 2019: 49 · 2018: 42 · 2017: 32 · 2016: 29
- FWCI
- 6.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Amgen
- Kaiser Permanente
- Cleveland Clinic
- Institute for Clinical Evaluative Sciences
- University of Oxford
- University of Aberdeen
- Institut National de la Santé et de la Recherche Médicale
- Rijksuniversiteit Groningen
- Universitair Medisch Centrum Groningen
- Université Paris-Saclay
- Université Paris-Sud
- Medizinische Universität Innsbruck
- Universität Innsbruck
- Université de Versailles Saint-Quentin-en-Yvelines
- George Institute for Global Health
- National Institutes of Health (UL1TR001079, UL1TR000424, UL1TR000003)
- Health Science Center, University of Tennessee
- National Heart, Lung, and Blood Institute (HHSN268201 100012C, HHSN268201100007C, HHSN2682 01100008C, HH-SN-268201100006C, HHSN268201100011C, HHSN268201100005C, HHSN268201100010C, HHSN26820 1100009C)
- National Cancer Institute (R01CA165001)
- National Institute of Diabetes and Digestive and Kidney Diseases (K23DK067303, U01DK060990, K23DK002904, K08DK092287, U01DK035073, R01DK139225, U01DK060984, U01DK060902, R01DK100446, U01DK060980, U01DK061022, U01DK060963, U01DK061028)
- National Institute of Neurological Disorders and Stroke (U01NS041588)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Keywords
- chronic kidney disease progression, kidney failure risk prediction, risk prediction equations, end-stage renal disease, dialysis, kidney transplantation, regional calibration, individual patient data meta-analysis, estimated glomerular filtration rate, calcium and phosphate, discrimination and calibration, diabetes mellitus, race, age, nephrology care
- MeSH terms
- Humans, Prognosis, Cohort Studies, Proportional Hazards Models, Disease Progression, Risk Assessment, Renal Insufficiency, Chronic, Renal Insufficiency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review