Background Identification of futile recanalisation following endovascular therapy (EVT) in patients with acute ischaemic stroke is both crucial and challenging. Here, we present a novel risk stratification system based on hybrid machine learning method for predicting futile recanalisation. Methods Hybrid machine learning models were developed to address six clinical scenarios within the EVT and perioperative management workflow. These models were trained on a prospective database using hybrid feature selection technique to predict futile recanalisation following EVT. The optimal model was validated and compared with existing models and scoring systems in a multicentre prospective cohort to develop a hybrid machine learning-based risk stratification system for futile recanalisation prediction. Results Using a hybrid feature selection approach, we trained and tested multiple classifiers on two independent patient cohorts (n=1122) to develop a hybrid machine learning-based prediction model. The model demonstrated superior discriminative ability compared with other models and scoring systems (area under the curve=0.80, 95% CI 0.73 to 0.87) and was transformed into a web application (RESCUE-FR Index) that provides a risk stratification system for individual prediction (accessible online at fr-index.biomind.cn/RESCUE-FR/). Conclusions The proposed hybrid machine learning approach could be used as an individualised risk prediction model to facilitate adherence to clinical practice guidelines and shared decision-making for optimal candidate selection and prognosis assessment in patients undergoing EVT. Data are available on reasonable request.

中文翻译：

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急性缺血性卒中血管内治疗后无效再通的预测：混合机器学习模型的开发和验证

背景 急性缺血性卒中患者血管内治疗（EVT）后无效再通的识别既重要又具有挑战性。在这里，我们提出了一种基于混合机器学习方法的新型风险分层系统，用于预测无效的再通。方法 开发混合机器学习模型来解决 EVT 和围手术期管理工作流程中的六种临床场景。这些模型使用混合特征选择技术在前瞻性数据库上进行训练，以预测 EVT 后无效的再通。最佳模型经过验证，并与多中心前瞻性队列中的现有模型和评分系统进行比较，以开发基于混合机器学习的风险分层系统，用于徒劳的再通预测。结果 使用混合特征选择方法，我们在两个独立的患者队列 (n=1122) 上训练和测试了多个分类器，以开发基于混合机器学习的预测模型。与其他模型和评分系统相比，该模型表现出优越的判别能力（曲线下面积=0.80，95% CI 0.73至0.87），并被转化为网络应用程序（RESCUE-FR Index），为个人预测提供风险分层系统（可在线访问 fr-index.biomind.cn/RESCUE-FR/）。结论 所提出的混合机器学习方法可用作个体化风险预测模型，以促进遵守临床实践指南和共享决策，以实现 EVT 患者的最佳候选者选择和预后评估。可根据合理要求提供数据。