PDF(Pubmed)
Abstract：
BACKGROUND: Enhanced detection of large vessel occlusion (LVO) through machine learning (ML) for acute ischemic stroke appears promising. This systematic review explored the capabilities of ML models compared with prehospital stroke scales for LVO prediction.
RESULTS: Six bibliographic databases were searched from inception until October 10, 2023. Meta-analyses pooled the model performance using area under the curve (AUC), sensitivity, specificity, and summary receiver operating characteristic curve. Of 1544 studies screened, 8 retrospective studies were eligible, including 32 prehospital stroke scales and 21 ML models. Of the 9 prehospital scales meta-analyzed, the Rapid Arterial Occlusion Evaluation had the highest pooled AUC (0.82 [95% CI, 0.79-0.84]). Support Vector Machine achieved the highest AUC of 9 ML models included (pooled AUC, 0.89 [95% CI, 0.88-0.89]). Six prehospital stroke scales and 10 ML models were eligible for summary receiver operating characteristic analysis. Pooled sensitivity and specificity for any prehospital stroke scale were 0.72 (95% CI, 0.68-0.75) and 0.77 (95% CI, 0.72-0.81), respectively; summary receiver operating characteristic curve AUC was 0.80 (95% CI, 0.76-0.83). Pooled sensitivity for any ML model for LVO was 0.73 (95% CI, 0.64-0.79), specificity was 0.85 (95% CI, 0.80-0.89), and summary receiver operating characteristic curve AUC was 0.87 (95% CI, 0.83-0.89).
CONCLUSIONS: Both prehospital stroke scales and ML models demonstrated varying accuracies in predicting LVO. Despite ML potential for improved LVO detection in the prehospital setting, application remains limited by the absence of prospective external validation, limited sample sizes, and lack of real-world performance data in a prehospital setting.
RESULTS: Six bibliographic databases were searched from inception until October 10, 2023. Meta-analyses pooled the model performance using area under the curve (AUC), sensitivity, specificity, and summary receiver operating characteristic curve. Of 1544 studies screened, 8 retrospective studies were eligible, including 32 prehospital stroke scales and 21 ML models. Of the 9 prehospital scales meta-analyzed, the Rapid Arterial Occlusion Evaluation had the highest pooled AUC (0.82 [95% CI, 0.79-0.84]). Support Vector Machine achieved the highest AUC of 9 ML models included (pooled AUC, 0.89 [95% CI, 0.88-0.89]). Six prehospital stroke scales and 10 ML models were eligible for summary receiver operating characteristic analysis. Pooled sensitivity and specificity for any prehospital stroke scale were 0.72 (95% CI, 0.68-0.75) and 0.77 (95% CI, 0.72-0.81), respectively; summary receiver operating characteristic curve AUC was 0.80 (95% CI, 0.76-0.83). Pooled sensitivity for any ML model for LVO was 0.73 (95% CI, 0.64-0.79), specificity was 0.85 (95% CI, 0.80-0.89), and summary receiver operating characteristic curve AUC was 0.87 (95% CI, 0.83-0.89).
CONCLUSIONS: Both prehospital stroke scales and ML models demonstrated varying accuracies in predicting LVO. Despite ML potential for improved LVO detection in the prehospital setting, application remains limited by the absence of prospective external validation, limited sample sizes, and lack of real-world performance data in a prehospital setting.
摘要：
背景：通过机器学习（ML）对急性缺血性卒中的大血管闭塞（LVO）的增强检测似乎很有希望。本系统综述探讨了ML模型与院前卒中量表相比对LVO预测的能力。
结果：从开始到2023年10月10日搜索了六个书目数据库。荟萃分析使用曲线下面积(AUC)汇集模型性能，灵敏度,特异性，并总结接收器工作特性曲线。在筛选的1544项研究中，8项回顾性研究符合资格，包括32个院前卒中量表和21个ML模型。在荟萃分析的9个院前量表中，快速动脉闭塞评估的合并AUC最高(0.82[95%CI,0.79-0.84]).支持向量机获得了包括9个ML模型中最高的AUC(合并AUC，0.89[95%CI，0.88-0.89]）。六个院前卒中量表和10个ML模型可用于汇总接收器操作特征分析。任何院前卒中量表的集合敏感性和特异性分别为0.72（95％CI，0.68-0.75）和0.77（95％CI，0.72-0.81），受试者工作特征曲线AUC分别为0.80(95%CI，0.76-0.83)。任何ML模型对LVO的集合灵敏度为0.73(95%CI，0.64-0.79)，特异性为0.85（95%CI，0.80-0.89），受试者工作特征曲线AUC为0.87(95%CI,0.83-0.89)。
结论：院前卒中量表和ML模型在预测LVO方面都表现出不同的准确性。尽管ML在院前环境中具有改善LVO检测的潜力，由于缺乏预期的外部验证，申请仍然受到限制，样本量有限，以及在院前环境中缺乏真实世界的表现数据。
结果：从开始到2023年10月10日搜索了六个书目数据库。荟萃分析使用曲线下面积(AUC)汇集模型性能，灵敏度,特异性，并总结接收器工作特性曲线。在筛选的1544项研究中，8项回顾性研究符合资格，包括32个院前卒中量表和21个ML模型。在荟萃分析的9个院前量表中，快速动脉闭塞评估的合并AUC最高(0.82[95%CI,0.79-0.84]).支持向量机获得了包括9个ML模型中最高的AUC(合并AUC，0.89[95%CI，0.88-0.89]）。六个院前卒中量表和10个ML模型可用于汇总接收器操作特征分析。任何院前卒中量表的集合敏感性和特异性分别为0.72（95％CI，0.68-0.75）和0.77（95％CI，0.72-0.81），受试者工作特征曲线AUC分别为0.80(95%CI，0.76-0.83)。任何ML模型对LVO的集合灵敏度为0.73(95%CI，0.64-0.79)，特异性为0.85（95%CI，0.80-0.89），受试者工作特征曲线AUC为0.87(95%CI,0.83-0.89)。
结论：院前卒中量表和ML模型在预测LVO方面都表现出不同的准确性。尽管ML在院前环境中具有改善LVO检测的潜力，由于缺乏预期的外部验证，申请仍然受到限制，样本量有限，以及在院前环境中缺乏真实世界的表现数据。
