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 = 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., 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, antihypertensive medication use, history of heart failure, coronary heart disease, atrial fibrillation, smoking status, 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] = 0.753, 0.782) in the diabetes and higher-eGFR validation cohorts; 0.769 (IQR = 0.758, 0.808) in the diabetes and lower-eGFR validation cohorts; 0.740 (IQR = 0.717, 0.763) in the no diabetes and higher-eGFR validation cohorts; and 0.750 (IQR = 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 successfully for use in the general population in persons with and without diabetes with higher or lower eGFR.
|Number of pages||8|
|Early online date||20 Jul 2022|
|Publication status||Published - 1 Sep 2022|
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Data from: Development and validation of prediction models of adverse kidney outcomes in the population with and without diabetes mellitus
Grams, M. E. (Creator), Brunskill, N. J. (Creator), Ballew, S. H. (Creator), Sang, Y. (Creator), Coresh, J. (Creator), Matsushita, K. (Creator) & Sawhney, S. (Creator), University of Aberdeen, 2022