Why the study?
Prediction of CKD progression to ESKD typically relies on baseline eGFR alone, and whether more complex modeling approaches using longitudinal kidney function trajectories improve prediction remained uncertain.
Do complex longitudinal modeling approaches improve the prediction of ESKD compared to traditional baseline models in patients with CKD?
Do complex longitudinal modeling approaches improve the prediction of ESKD compared to traditional baseline models in patients with CKD?
Complex joint models and time-varying eGFR models do not offer significant improvement in predicting ESKD over traditional baseline eGFR models or the Kidney Failure Risk Equation in cohorts with rare outcomes and complete follow-up.
Answers are grounded in this paper and cite their sources.
Supports simpler baseline eGFR models or KFRE for ESKD prediction in CKD; leaves open value of complex longitudinal approaches in other settings.
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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Brand et al. (2019) studied this question.
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