Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy - Lin, Xinping; Zheng, Xiaohan; Zhang, Juan; Cui, Xiaoli; Zou, Daizu; Zhao, Zheng; Pan, Xiding; Jie, Qiong; Wu, Yuezhang; Qiu, Runze; Zhou, Junshan; Chen, Nihong; et al. (Frontiers in Neurology, 2022)
Background and Purpose: Futile recanalization occurs when the endovascular thrombectomy (EVT) is a technical success but fails to achieve a favorable outcome. This study aimed to use machine learning (ML) algorithms to develop a pre-EVT model and a post-EVT model to predict the risk of futile recanalization and to provide meaningful insights to assess the prognostic factors associated with futile recanalization. Methods: Consecutive acute ischemic stroke patients with large vessel occlusion (LVO) undergoing EVT at the National Advanced Stroke Center of Nanjing First Hospital (China) between April 2017 and May 2021 were analyzed. The baseline characteristics and peri-interventional characteristics were assessed using four ML algorithms. The predictive performance was evaluated by the area under curve (AUC) of receiver operating characteristic and calibration curve. SHapley Additive exPlanations (SHAP) approach and partial dependence plot were introduced to understand the relative importance and the influence of a single feature. Results: A total of 312 patients were included in this study. Of the four ML models that include baseline characteristics, the “Early” XGBoost has a better performance (AUC, 0.790 [95% confidence intervals (CI), 0.677-0.903]; Brier, 0.191). Subsequent inclusion of peri-interventional characteristics into the “Early” XGBoost shows that the “Late” XGBoost performed better (AUC, 0.910 [95% CI, 0.837-0.984]; Brier, 0.123). NIHSS after 24 hours, age, groin to recanalization, and the number of passages were the critical prognostic factors associated with futile recanalization, and the SHAP approach shows that NIHSS after 24 hours ranks first in relative importance. Conclusions: The “Early” XGBoost and the “Late” XGBoost allowed to accurately predict futile recanalization before and after EVT, respectively. Our study suggests that the inclusion of peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 hours was the most important prognostic factor for futile recanalization.