Development and validation of an explainable machine learning prediction model for futile recanalization after mechanical thrombectomy in acute large vessel occlusion stroke

Abstract

BACKGROUND: Mechanical thrombectomy (MT) is the primary treatment for acute ischemic stroke (AIS) caused by large vessel occlusion (LVO). However, the likelihood of futile recanalization (FR) at 90 days post-MT remains high. METHODS: This study included 534 AIS patients with anterior circulation LVO who underwent MT, with the primary outcome being FR. The derivation cohort consisted of 445 patients (June 2018-June 2023), while the temporal validation cohort had 89 patients (July 2023-June 2024). The derivation cohort was split into 70% training and 30% internal validation sets. Eleven machine learning (ML) models were trained, tested, and compared, and the best-performing model was selected for optimization and temporal validation. SHapley Additive exPlanations (SHAP) were used for model interpretation. RESULTS: The CatBoost model showed the best discriminative ability among the 11 ML models. After feature selection and dimensionality reduction, a final explainable CatBoost model with 12 features was established, accurately predicting FR in both internal (area under the curve (AUC)=0.915) and temporal (AUC=0.930) validations. The model has been deployed as a web application for clinical use. CONCLUSION: We developed a ML prediction model with 12 key features that demonstrates excellent performance in predicting FR. The deployment of this model as a web application offers a promising tool for clinicians to assess FR risk, potentially enhancing patient selection and improving personalized stroke care.

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