Explainable machine learning model for predicting functional outcomes in posterior circulation stroke after thrombectomy

作者：Zhelv Yao, Qiuhong Ji, Xuefeng Zang, Wenwei Yun, Yun Luo, Jie Cao, Jingxian Xu, Zhihong Ke, Ziyi Xie, Chenglu Mao, Qiaochu Guan, Weiping Lv, Zhengyang Zhu, Yanan Huang, Ya Peng, Yun Xu · 发表于：Journal of NeuroInterventional Surgery · 年份：2025 · DOI：10.1136/jnis-2025-023624 · 被引用次数：2 · 研究领域：Acute Ischemic Stroke Management、Artificial Intelligence in Healthcare and Education、Explainable Artificial Intelligence (XAI)

BACKGROUND: The early prediction of functional outcomes in patients with posterior circulation stroke (PCS) is crucial for timely interventions and optimizing treatment plans. We have developed and validated a machine learning (ML) model for predicting 3-month functional outcomes in patients with PCS undergoing endovascular thrombectomy (EVT). METHODS: The derivation cohort, consisting of 202 patients with PCS who underwent EVT at four medical centers from January 2020 to December 2023, was separated for training and internal validation, and an external dataset of 54 patients admitted from January 2020 to July 2023 was used for external validation. The target outcome was a good functional outcome, defined as a modified Rankin Scale score of 0-3 at 3 months. Seven ML models were trained using preoperative features, with the primary evaluation metric being the area under the receiver operating characteristic curve (AUC). The top performing model was further trained using intraoperative and postoperative features. Model interpretations were generated using the Shapley additive explanations (SHAP) method. RESULTS: The Random Forest model demonstrated the best discriminative ability among the models considered. After feature selection, the final preoperative model used seven features, achieving an AUC of 0.83 in the test set and 0.81 in the external validation cohort. The inclusion of intraoperative and postoperative features further enhanced the model's performance, resulting in ...