TY - JOUR
T1 - Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes
AU - CKD Prognosis Consortium
AU - Grams, Morgan E.
AU - Brunskill, Nigel J.
AU - Ballew, Shoshana H.
AU - Sang, Yingying
AU - Coresh, Josef
AU - Matsushita, Kunihiro
AU - Surapaneni, Aditya
AU - Bell, Samira
AU - Carrero, Juan J.
AU - Chodick, Gabriel
AU - Evans, Marie
AU - Heerspink, Hiddo J.L.
AU - Inker, Lesley A.
AU - Iseki, Kunitoshi
AU - Kalra, Philip A.
AU - Kirchner, H. Lester
AU - Lee, Brian J.
AU - Levin, Adeera
AU - Major, Rupert W.
AU - Medcalf, James
AU - Nadkarni, Girish N.
AU - Naimark, David M.J.
AU - Ricardo, Ana C.
AU - Sawhney, Simon
AU - Sood, Manish M.
AU - Staplin, Natalie
AU - Stempniewicz, Nikita
AU - Stengel, Benedicte
AU - Sumida, Keiichi
AU - Traynor, Jamie P.
AU - van den Brand, Jan
AU - Wen, Chi Pang
AU - Woodward, Mark
AU - Yang, Jae Won
AU - Wang, Angela Yee Moon
AU - Tangri, Navdeep
N1 - Publisher Copyright:
© 2022 by the American Diabetes Association.
PY - 2022/9
Y1 - 2022/9
N2 - OBJECTIVE To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design. RESEARCH DESIGN AND METHODS In this meta-analysis of individual participant data, 43 cohorts (N 5 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 estimated glomerular filtration rate (eGFR; ‡60 or <60 mL/min/ 1.73 m2) to predict a composite of ‡40% decline in eGFR or kidney failure (i.e., re-ceipt of kidney replacement therapy) over 2–3 years. RESULTS There were 17,399 and 24,591 events in development and validation cohorts, re-spectively. Models predicting ‡40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking sta-tus, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] 5 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR 5 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR 5 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR 5 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts. CONCLUSIONS Novel prediction equations for a decline of ‡40% in eGFR can be applied success-fully for use in the general population in persons with and without diabetes with higher or lower eGFR.
AB - OBJECTIVE To predict adverse kidney outcomes for use in optimizing medical management and clinical trial design. RESEARCH DESIGN AND METHODS In this meta-analysis of individual participant data, 43 cohorts (N 5 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 estimated glomerular filtration rate (eGFR; ‡60 or <60 mL/min/ 1.73 m2) to predict a composite of ‡40% decline in eGFR or kidney failure (i.e., re-ceipt of kidney replacement therapy) over 2–3 years. RESULTS There were 17,399 and 24,591 events in development and validation cohorts, re-spectively. Models predicting ‡40% eGFR decline or kidney failure incorporated age, sex, eGFR, albuminuria, systolic blood pressure, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking sta-tus, and BMI, and, in those with diabetes, hemoglobin A1c, insulin use, and oral diabetes medication use. The median C-statistic was 0.774 (interquartile range [IQR] 5 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR 5 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR 5 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR 5 0.731, 0.785) in the no diabetes and lower-eGFR validation cohorts. Incorporating the previous 2-year eGFR slope minimally improved model performance, and then only in the higher-eGFR cohorts. CONCLUSIONS Novel prediction equations for a decline of ‡40% in eGFR can be applied success-fully for use in the general population in persons with and without diabetes with higher or lower eGFR.
UR - http://www.scopus.com/inward/record.url?scp=85136755249&partnerID=8YFLogxK
U2 - 10.2337/dc22-0698
DO - 10.2337/dc22-0698
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C2 - 35856507
AN - SCOPUS:85136755249
SN - 0149-5992
VL - 45
SP - 2055
EP - 2063
JO - Diabetes Care
JF - Diabetes Care
IS - 9
ER -