A stacking ensemble machine learning model predicted AAA-related mortality in patients undergoing endovascular aneurysm repair with a time-dependent C-index of 0.759 at 30 days and 0.716 at 365 days.
Observational (n=12,312)
Does a stacking ensemble machine learning model accurately predict long-term AAA-related mortality in patients undergoing EVAR?
A stacking ensemble machine learning model provides effective time-to-event prediction for long-term AAA-related mortality after EVAR, identifying dynamic changes in predictor importance.
Effect estimate: C-index 0.759 at 30 days, 0.716 at 365 days
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.
Choi et al. (Fri,) conducted a observational in Abdominal aortic aneurysm (AAA) (n=12,312). Stacking ensemble machine learning model vs. Other machine learning algorithms was evaluated on AAA-related mortality (C-index 0.759 at 30 days, 0.716 at 365 days). A stacking ensemble machine learning model predicted AAA-related mortality in patients undergoing endovascular aneurysm repair with a time-dependent C-index of 0.759 at 30 days and 0.716 at 365 days.