Development of Risk Prediction Equations for Incident Chronic Kidney Disease

Robert G. Nelson, Morgan E. Grams, Shoshana H. Ballew, Yingying Sang, Fereidoun Azizi, Steven J. Chadban, Layal Chaker, Stephan C. Dunning, Caroline Fox, Yoshihisa Hirakawa, Kunitoshi Iseki, Joachim Ix, Tazeen H. Jafar, Anna Köttgen, David M.J. Naimark, Takayoshi Ohkubo, Gordon J. Prescott, Casey M. Rebholz, Charumathi Sabanayagam, Toshimi SairenchiBen Schöttker, Yugo Shibagaki, Marcello Tonelli, Luxia Zhang, Ron T. Gansevoort, Kunihiro Matsushita, Mark Woodward, Josef Coresh*, Varda Shalev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Importance: Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions. Objective: To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR). Design, Setting, and Participants: Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5222711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2253540). Exposures: Demographic and clinical factors. Main Outcomes and Measures: Incident eGFR of less than 60 mL/min/1.73 m2. Results: Among 4441084 participants without diabetes (mean age, 54 years, 38% women), 660856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781627 participants with diabetes (mean age, 62 years, 13% women), 313646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25. Conclusions and Relevance: Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes..

Original languageEnglish
Pages (from-to)2104-2114
Number of pages11
JournalJAMA - Journal of the American Medical Association
Issue number21
StatePublished - 3 Dec 2019


FundersFunder number
Chief Scientists Office for Scotland
US National Kidney Foundation
National Institutes of Health
National Institute of Diabetes and Digestive and Kidney DiseasesR01DK108803, R01DK100446
Boehringer Ingelheim
National Kidney Foundation Serving Eastern Missouri and Metro East
Omron Healthcare
Kyowa Kirin Pharmaceutical Development
Canadian Institutes of Health Research
Deutsche Forschungsgemeinschaft


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