Table_1_Machine learning to predict futile recanalization of large vessel occlusion before and after endovascular thrombectomy.XLSX

Background and purposeFutile 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.
MethodsConsecutive 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.
ResultsA 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.
ConclusionsThe “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.

背景与目的
无效再通指血管内取栓术（endovascular thrombectomy, EVT）操作成功但未获得良好预后。本研究旨在通过机器学习（machine learning, ML）算法分别构建EVT术前模型与术后模型，以预测无效再通风险，并为明确与无效再通相关的预后因素提供有价值的参考。

方法
分析2017年4月至2021年5月期间，于中国南京第一医院国家高级卒中中心接受EVT治疗的连续性大血管闭塞（large vessel occlusion, LVO）急性缺血性卒中患者。采用四种机器学习算法评估患者的基线特征与围手术期特征，通过受试者工作特征曲线下面积（area under curve, AUC）与校准曲线评估模型的预测性能。此外，引入沙普利可加解释（SHapley Additive exPlanations, SHAP）方法与偏依赖图，以分析特征的相对重要性及单个特征的影响效应。

结果
本研究共纳入312例患者。在纳入基线特征的四种机器学习模型中，“早期”XGBoost模型表现更优[AUC为0.790，95%置信区间（confidence intervals, CI）：0.677~0.903；布里尔评分为0.191]。后续将围手术期特征纳入“早期”XGBoost模型后，“晚期”XGBoost模型性能更佳[AUC为0.910，95%CI：0.837~0.984；布里尔评分为0.123]。24小时美国国立卫生研究院卒中量表（National Institutes of Health Stroke Scale, NIHSS）评分、年龄、股动脉穿刺至再通时间及取栓次数是与无效再通相关的关键预后因素；SHAP方法显示，24小时NIHSS评分的相对重要性位居首位。

结论
“早期”XGBoost与“晚期”XGBoost模型可分别精准预测EVT术前、术后的无效再通风险。本研究表明，相较于仅基于基线特征构建的模型，纳入围手术期特征可获得更优的预测性能。此外，24小时NIHSS评分是预测无效再通的最重要预后因素。