Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models

Abstract : RATIONALE & OBJECTIVE: Early prediction of chronic kidney disease (CKD) progression to end-stage kidney disease (ESKD) currently use Cox models including baseline estimated glomerular filtration rate (eGFR) only. Alternative approaches include a Cox model that includes eGFR slope determined over a baseline period of time, a Cox model with time varying GFR, or a joint modeling approach. We studied if these more complex approaches may further improve ESKD prediction. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: We re-used data from two CKD cohorts including patients with baseline 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, known prognostic markers for ESKD. 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. CONCLUSION: In the present studies, where the outcome was rare and follow-up data was highly complete, the joint models did not offer improvement in predictive performance over more traditional approaches such as a survival model with time-varying eGFR, or a model with eGFR slope.
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Submitted on : Monday, August 5, 2019 - 9:22:13 AM
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Jan van den Brand, Tjeerd Dijkstra, Jack Wetzels, Bénédicte Stengel, Marie Metzger, et al.. Predicting kidney failure from longitudinal kidney function trajectory: A comparison of models. PLoS ONE, Public Library of Science, 2019, 14 (5), pp.e0216559. ⟨10.1371/journal.pone.0216559⟩. ⟨inserm-02263510⟩

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