“PD-PREDICT”: A Machine Learning Model for Patient Survival in Peritoneal Dialysis

Résumé fourni par la source

Accurate survival prediction in peritoneal dialysis (PD) patients is essential for personalized treatment planning and shared decision-making. We developed and validated PD-PREDICT, an XGBoost-based model to generate dynamic mortality risk estimates in incident PD patients. We conducted a retrospective cohort study using data from the UK Renal Registry (UKRR), comprising 22,711 incident PD patients treated between January 1, 2007, and September 1, 2022. The development cohort (n = 14,650; January 2007-December 2016) was split into training and internal test sets. Temporal validation employed an independent UKRR cohort (n = 8,061; January 2017-December 2021). External validation used 2,180 patients from the Norwegian Renal Registry. Model performance was assessed by Harrell's concordance index ( C index), Integrated Brier Score (IBS), decision curve analysis, and 50 iteration bootstrap for C index stability. In the development cohort, PD-PREDICT achieved a training C index of 0.83 and test C index of 0.81 (IBS: 0.09). The decision tree baseline model yielded a test C index of 0.78 (IBS: 0.13). Bootstrap analysis confirmed C index stability (0.81; 95% confidence interval [CI], 0.79-0.83). Temporal validation produced a C index of 0.80, and external validation in Norway yielded 0.77. PD-PREDICT provides robust, dynamic mortality risk predictions for PD patients, outperforming traditional methods and maintaining accuracy across temporal and geographic validations.

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