Abstract: FR-PO0571
"PD-PREDICT": A Machine-Learning Tool to Predict Patient Survival in UK Patients on Peritoneal Dialysis, with External Validation in Norway
Session Information
- Home Dialysis: Clinical Epidemiology
November 07, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Dialysis
- 802 Dialysis: Home Dialysis and Peritoneal Dialysis
Authors
- Ali, Hatem, University Hospital of Wales, Cardiff, Wales, United Kingdom
- Pajek, Jernej, Ljubljanska metropolija, Ljubljana, Slovenia
- Daoud, Ahmed, Medical University of South Carolina, Charleston, South Carolina, United States
- Fulop, Tibor, Medical University of South Carolina, Charleston, South Carolina, United States
Background
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.
Methods
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.
Results
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% CI 0.79–0.83). Temporal validation produced a C-index of 0.80, and external validation in Norway yielded 0.77.
Conclusion
PD-PREDICT provides robust, dynamic mortality risk predictions for PD patients, outperforming traditional methods and maintaining accuracy across temporal and geographic validations.