American Diabetes Association
5.4a_Supplement_DiabtesCare_20220422.pdf (1.15 MB)

Development and validation of prediction models of adverse kidney outcomes in the population with and without diabetes mellitus

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posted on 2022-07-20, 11:48 authored by Morgan E Grams, Nigel J Brunskill, Shoshana H Ballew, Yingying Sang, Josef Coresh, Kunihiro Matsushita, Aditya Surapaneni, Samira Bell, Juan J Carrero, Gabriel Chodick, Marie Evans, Hiddo JL Heerspink, Lesley A Inker, Kunitoshi Iseki, Philip A Kalra, H Lester Kirchner, Brian J Lee, Adeera Levin, Rupert W Major, James Medcalf, Girish N Nadkarni, David MJ Naimark, Ana C Ricardo, Simon Sawhney, Manish M Sood, Natalie Staplin, Nikita Stempniewicz, Benedicte Stengel, Keiichi Sumida, Jamie P Traynor, Jan van den Brand, Chi-Pang Wen, Mark Woodward, Jae Won Yang, Angela Yee-Moon Wang, Navdeep Tangri, the CKD Prognosis Consortium


Objective: To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design.

Research Design and Methods: In this individual participant data meta-analysis, 43 cohorts (N=1,621,817) from research studies, electronic medical records, and clinical trials with global representation were separated into development and validation cohorts. Models were developed and validated within strata of diabetes mellitus (presence or absence) and eGFR (≥60 or <60 ml/min/1.73 m2) to predict a composite of ≥40% decline in eGFR or kidney failure (receipt of kidney replacement therapy) over 2-3 years.

Results: There were 17,399 and 24,591 events in development and validation cohorts, respectively. Models predicting ≥40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, anti-hypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, and body-mass index (and hemoglobin A1c, insulin use, and oral diabetes medication use in those with diabetes). The median C-statistic was 0.774 (interquartile range [IQR]: 0.753, 0.782) in the diabetes/higher eGFR validation cohorts, 0.769 (IQR: 0.758, 0.808) in the diabetes/lower eGFR validation cohorts, 0.740 (interquartile range [IQR]: 0.717, 0.763) in the no diabetes/higher eGFR validation cohorts, and 0.750 (IQR: 0.731, 0.785) in the no diabetes/lower eGFR validation cohorts. Incorporating previous 2-year eGFR slope minimally improved model performance, and only in the higher eGFR cohorts. 

Conclusions: Novel prediction equations for an eGFR decline of ≥40% eGFR can be applied successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.


The CKD Prognosis Consortium (CKD-PC) Data Coordinating Center is funded in part by a program grant from the US National Kidney Foundation and the National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100446). 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 Appendix 3. Some of the data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government.


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