Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: AUC 0.888 (internal validation), AUC 0.850 (temporal validation)

• Primary Outcome: 6-month all-cause mortality prediction

• Nnt Or Sensitivity: Accuracy 87.9% (internal), 87.4% (temporal validation)

• Confidence Interval: Not reported

Clinical Application

High feasibility using routinely collected clinical data and laboratory parameters Relevant to Australian nephrology practice; could integrate with existing ANZDATA registry data collection and support advance care planning discussions required under Australian healthcare frameworks CAPD patients in nephrology centers with access to routine laboratory monitoring

Abstract

Objective: This study aimed to develop and validate a time-updated, explainable machine learning (ML) early-warning system for short-term mortality risk in patients with continuous ambulatory peritoneal dialysis (CAPD). Methods: Multiple supervised ML techniques were validated to stratify individuals at high risk of 6-month death, classifiers were trained, internally validated and independently temporally validated in this retrospective study of 1,484 CAPD patients in our PD center. We collected time-updated markers such as patient demographics, clinical characteristics, and laboratory data to inform the ML models and the performance of eight ML models was assessed via the area under the curve (AUC) and accuracy. The SHapley Additive exPlanation (SHAP) method was selected to further interpret the predictive models and link findings to clinical actionability. Results: In internal validation cohort, the light gradient boosting machine (lightGBM) model demonstrated best performance with AUC of 0.888 and highest accuracy of 0.879. In temporal validation cohort, the lightGBM model also exhibited good classification performance, achieving AUC of 0.850 and good accuracy of 0.874. Moreover, SHAP analysis identified several key features that contributed to the accurate prediction of the lightGBM model. Conclusions: The time-updated lightGBM early-warning system helps clinicians to early identify CAPD patients with high risk of death within six months based on easily accessible and time-updated data and provides a basis for personalized treatment. We developed and validated a time-updated, explainable ML early-warning system that accurately predicts 6-month all-cause mortality in CAPD patients using routinely, available laboratory data.The LightGBM model achieved excellent discriminative performance, with an AUC of 0.888 in internal validation and 0.850 in independent temporal validation.This model provides a scalable, low-cost early-warning tool for routine CAPD care, with a standardized risk-stratified intervention framework to guide timely, targeted care and advance care planning.