Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy

Xinping Lin; Xiaohan Zheng; Juan Zhang; Xiaoli Cui; Daizu Zou; Zheng Zhao; Xiding Pan; Qiong Jie; Yuezhang Wu; Runze Qiu; Junshan Zhou; Nihong Chen; Li Tang; Chun Ge; Jianjun Zou

doi:10.3389/fneur.2022.909403

Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy

Front Neurol. 2022 Aug 19:13:909403. doi: 10.3389/fneur.2022.909403. eCollection 2022.

Authors

Xinping Lin^{1

2}, Xiaohan Zheng^{1

2}, Juan Zhang³, Xiaoli Cui³, Daizu Zou^{1

2}, Zheng Zhao^{2

4}, Xiding Pan^{2

4}, Qiong Jie^{2

4}, Yuezhang Wu^{2

4}, Runze Qiu^{2

4}, Junshan Zhou⁵, Nihong Chen⁵, Li Tang⁶, Chun Ge^{2

4}, Jianjun Zou^{2

4}

Affiliations

¹ School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
² Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
³ Department of Neurology, Nanjing Yuhua Hospital, Yuhua Branch of Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
⁴ Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, China.
⁵ Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
⁶ Department of Pharmacy, Yixing Cancer Hospital, Yixing, China.

Abstract

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. In addition, the 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 had 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 showed that the "Late" XGBoost performed better [AUC, 0.910 (95% CI, 0.837-0.984); Brier, 0.123]. NIHSS after 24 h, 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 h ranks first in relative importance.

Conclusions: The "Early" XGBoost and the "Late" XGBoost allowed us to predict futile recanalization before and after EVT accurately. Our study suggests that including peri-interventional characteristics may lead to superior predictive performance compared to a model based on baseline characteristics only. In addition, NIHSS after 24 h was the most important prognostic factor for futile recanalization.

Keywords: endovascular thrombectomy; futile recanalization; large vessel occlusion; machine learning; predictive model.