Predicting Kidney Failure from Longitudinal Kidney Function Trajectory: A Comparison of Models

Item does not contain fulltextMinimal datasets used to train and validate models described in Predicting Kidney Failure from Longitudinal Kidney Function Trajectory: A Comparison of Models. Goal: Early prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD). Study design: Prospective cohort Setting & participants: We re-used data from two CKD cohorts including patients with baseline estimated glomerular filtration rate (eGFR) >30ml/min per 1.73m2. MASTERPLAN (N=505; 55 ESKD events) was used as development dataset, and NephroTest (N=1385; 72 events) for validation. Predictors: All models included age, sex, eGFR, and albuminuria. Analytical Approach: We trained the models on the MASTERPLAN data and determined discrimination and calibration for each model at 2 years follow-up for a prediction horizon of 2 years in the NephroTest cohort. We benchmarked the predictive performance against the Kidney Failure Risk Equation (KFRE). Results: The C-statistics for the KFRE was 0.94 (95%CI 0.86 to 1.01). Performance was similar for the Cox model with time-varying eGFR (0.92 [0.84 to 0.97]), eGFR (0.95 [0.90 to 1.00]), and the joint model 0.91 [0.87 to 0.96]). The Cox model with eGFR slope showed the best calibration. For the analysis scripts please refer to the supplementary file in the publiciation