Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis

Navdeep Tangri, Morgan E Grams, Andrew S Levey, Josef Coresh, Lawrence J Appel, Brad C Astor, Gabriel Chodick, Allan J Collins, Ognjenka Djurdjev, C Raina Elley, Marie Evans, Amit X Garg, Stein I Hallan, Lesley A Inker, Sadayoshi Ito, Sun Ha Jee, Csaba P Kovesdy, Florian Kronenberg, Hiddo J Lambers Heerspink, Angharad MarksGirish N Nadkarni, Sankar D Navaneethan, Robert G Nelson, Stephanie Titze, Mark J Sarnak, Benedicte Stengel, Mark Woodward, Kunitoshi Iseki, CKD Prognosis Consortium

Research output: Contribution to journalArticlepeer-review

438 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)164-174
Number of pages11
Issue number2
Publication statusPublished - 12 Jan 2016

Bibliographical note

The CKD-PC Data Coordinating Center is funded in part by a program grant from the US National Kidney Foundation (NKF funding sources include AbbVie, Amgen, and Merck) and grant R01DK100446-01 from the National Institute of Diabetes and Digestive and Kidney Diseases. Dr Grams is supported by grant K08DK092287 from the National Institute of Diabetes and Digestive and Kidney Diseases. A variety of sources have supported enrollment and data collection including laboratory measurements, and follow-up in the collaborating cohorts of the CKD-PC. These funding sources include government agencies such as national institutes of health and medical research councils as well as foundations and industry sponsors listed in eAppendix 4 in the Supplement.


Dive into the research topics of 'Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis'. Together they form a unique fingerprint.

Cite this