Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes

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 m(2)) 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 A(1c), 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.

Automated summary

This study aims to predict adverse kidney outcomes and improve medical management and clinical trial design. By developing and validating models using various factors, the risk of a significant decline in glomerular filtration rate or kidney failure can be successfully predicted. These models are applicable to individuals with or without diabetes, as well as those with high or low eGFR.