Laboratory Trajectories Improve Kidney Failure Risk Estimation

Abstract

Accurate kidney failure risk assessment is critical to timely intervention in chronic kidney disease (CKD). Existing equations (e.g. Kidney Failure Risk Equation; KFRE) rely on single laboratory measurements to estimate short- and long-term kidney failure risk, leaving longitudinal laboratory patterns unused. Here we introduce Clalit Longitudinal Assessment of Risk of Kidney Failure (CLARK), an interpretable longitudinal extension of latest-value methods which incorporates routinely collected repeat laboratory measures. We develop CLARK using data from 5.4 million individuals, identifying 270,009 patients with CKD to create one of the largest longitudinal CKD cohorts to date, with 12,087 kidney replacement therapy initiation events and a median follow-up of 10.4 years. Across laboratory configurations and prediction horizons, CLARK demonstrated improved discrimination over static models (e.g., 2-year average precision 0.541 vs 0.516 in the eGFR-only setting). At intervention thresholds, trajectory-based models improved identification of high-risk patients, especially for longer-term prediction, suggesting that interpretable longitudinal laboratory features may enhance kidney failure risk assessment through improved identification of patients most likely to benefit from timely intervention.