Interpretable machine learning-based prediction of 90-day mortality after endovascular treatment in acute large vessel occlusion stroke.

Background: Despite high recanalization rates after endovascular treatment (EVT) for acute ischemic stroke due to large vessel occlusion (AIS-LVO), 15%-20% of patients still die within 90 days. Existing prognostic models predominantly target functional disability rather than mortality, and rarely incorporate calibration, clinical utility, or interpretability. We aimed to develop and internally validate interpretable machine learning (ML) models for dynamic post-EVT in-hospital prediction of 90-day all-cause mortality in AIS-LVO patients with successful recanalization. Methods: We retrospectively enrolled 449 AIS-LVO patients with successful EVT (modified Thrombolysis in Cerebral Infarction grade 2b-3) at a nationally certified Advanced Stroke Center in Guangxi, southwestern China, between January 2022 and May 2025. Candidate predictors included baseline characteristics, admission laboratory indicators, procedural variables, and post-procedural complications available during hospitalization. Patients were randomly split 8:2 into training ( Results: The 90-day mortality rate was 19.6% (88/449). Random Forest achieved favorable overall test-set performance (AUC = 0.806; Brier score = 0.128; Hosmer-Lemeshow Conclusions: A Random Forest-based ML model with an online calculator and SHAP-based interpretability supports dynamic in-hospital post-EVT mortality risk stratification, with complication predictors incorporated only after they clinically occur. Prospective external validation in geographically diverse multicenter cohorts is warranted.