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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.
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“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]
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Self-citation
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.
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