Time-to-event ensemble machine learning approach for predicting long-term survival of abdominal aortic aneurysm patients undergoing endovascular aneurysm repair - Hong-Jae Choi; Changhee Lee; Joon Seo Lim; You Jung Ok; Jae-Sung Choi; Jae Hwa Jeong; Yong Won Seong; Hyeon Jong Moon; Se Jin Oh (PLoS ONE, 2026)
Background Endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) is associated with risks such as endoleaks and late aneurysm rupture, highlighting the importance of long-term survival prediction. Despite recent advancements in machine learning (ML), predictive models utilizing time-to-event analysis remain limited for AAA patients undergoing EVAR. We aimed to develop a stacking ensemble ML model to predict long-term outcomes in EVAR-treated AAA patients. Methods From 2002 to 2019, a total of 12,312 patients underwent EVAR. The primary outcome was AAA-related mortality, with follow-up until December 31, 2019. Using 5 ML algorithms, we developed a model comprising 34 variables. Model performance was assessed using the time-dependent C-index and Brier score. Variable importance was evaluated through permutation-based and partial dependent plots. Results The stacking ensemble model showed the best predictive performance among the tested models (time-dependent C-index: 0.759 at 30 days, 0.716 at 365 days). The time-dependent Brier scores generally increased slightly over time but remained stable across all ML algorithms. Important predictors included age, smoking status, duration between diagnosis and surgery, household income, renal function, and blood pressure. Variable importance differed over time, and each predictor presented a nonlinear relationship with AAA-related mortality risk. Conclusion The stacking ensemble ML model for time-to-event prediction identified dynamic, time-varying changes in predictor importance, providing improved risk stratification and phase-specific management after EVAR.