Lin Xinping, Zheng Xiaohan, Zhang Juan, Cui Xiaoli, Zou Daizu, Zhao Zheng, Pan Xiding, Jie Qiong, Wu Yuezhang, Qiu Runze, Zhou Junshan, Chen Nihong, Tang Li, Ge Chun, Zou Jianjun
School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Department of Pharmacy Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Front Neurol. 2022 Aug 19;13:909403. doi: 10.3389/fneur.2022.909403. eCollection 2022.
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.
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.
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.
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.
当血管内血栓切除术（EVT）在技术上成功但未能取得良好预后时，就会出现无效再通。本研究旨在使用机器学习（ML）算法开发一个EVT前模型和一个EVT后模型，以预测无效再通的风险，并为评估与无效再通相关的预后因素提供有意义的见解。
对2017年4月至2021年5月期间在中国南京医科大学第一附属医院国家高级卒中中心接受EVT治疗的连续性急性缺血性卒中伴大血管闭塞（LVO）患者进行分析。使用四种ML算法评估基线特征和围手术期特征。通过受试者操作特征曲线下面积（AUC）和校准曲线评估预测性能。此外，引入了SHapley加性解释（SHAP）方法和偏依赖图，以了解单个特征的相对重要性和影响。
本研究共纳入312例患者。在包含基线特征的四个ML模型中，“早期”XGBoost表现更佳{AUC，0.790[95%置信区间（CI），0.677 - 0.903]；布里尔评分，0.191}。随后将围手术期特征纳入“早期”XGBoost模型，结果显示“晚期”XGBoost表现更好[AUC，0.910（95%CI，0.837 - 0.984）；布里尔评分，0.123]。24小时后的美国国立卫生研究院卒中量表（NIHSS）评分、年龄、腹股沟至再通时间以及穿刺次数是与无效再通相关的关键预后因素，且SHAP方法显示24小时后的NIHSS评分在相对重要性方面排名第一。
“早期”XGBoost和“晚期”XGBoost使我们能够准确预测EVT前后的无效再通情况。我们的研究表明，与仅基于基线特征的模型相比，纳入围手术期特征可能会带来更好的预测性能。此外，24小时后的NIHSS评分是无效再通最重要的预后因素。
