Clinical prediction models for progression of chronic kidney disease to end-stage kidney failure under pre-dialysis nephrology care: results from the Chronic Kidney Disease Japan Cohort Study

Abstract

Background: Reliable prediction tools are needed to identify patients with chronic kidney disease (CKD) at greater risk of developing end-stage kidney failure (ESKF). We developed and validated clinical prediction models (CPMs) for CKD progression to ESKF under pre-dialysis nephrology care using CKD-Japan Cohort (CKD-JAC) data. Methods: We prospectively followed up 2034 participants with CKD, defined as an estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m 2 , aged 20–75 years for a mean of 3.15 years. We randomly divided the overall analysis set into development and validation cohorts. In the development cohort, CPMs were developed using Cox proportional hazard regression, and the goodness of fit was evaluated. In the validation cohort, discrimination and calibration of the developed CPMs were evaluated. We also validated developed CPMs in the dataset with the bootstrap method. Results: ESKF onset was observed in 206 and 216 patients in the development (20.3%) and validation (21.2%) cohorts, respectively. Goodness of fit, discrimination, and calibration were worse for a simple model including age, sex, and eGFR than for a complicated model (plus albuminuria, systolic blood pressure, diabetes, serum albumin, and hemoglobin). The mean absolute difference between the observed and predictive probabilities of ESKF onset at 3 years was lower for the complicated model than for the simple model (1.57 vs. 1.87%). Conclusions: CPMs employing readily available data could precisely predict progression to ESKF in patients with CKD stage G3a to G5. These developed CPMs may facilitate more appropriate clinical care and shared decision-making between clinicians and patients.