Ramos Lucas A, Kappelhof Manon, van Os Hendrikus J A, Chalos Vicky, Van Kranendonk Katinka, Kruyt Nyika D, Roos Yvo B W E M, van der Lugt Aad, van Zwam Wim H, van der Schaaf Irene C, Zwinderman Aeilko H, Strijkers Gustav J, van Walderveen Marianne A A, Wermer Mariekke J H, Olabarriaga Silvia D, Majoie Charles B L M, Marquering Henk A
Department of Biomedical Engineering and Physics, University of Amsterdam, Amsterdam, Netherlands.
Department of Clinical Epidemiology and Biostatistics, University of Amsterdam, Amsterdam, Netherlands.
Front Neurol. 2020 Oct 15;11:580957. doi: 10.3389/fneur.2020.580957. eCollection 2020.
Although endovascular treatment (EVT) has greatly improved outcomes in acute ischemic stroke, still one third of patients die or remain severely disabled after stroke. If we could select patients with poor clinical outcome despite EVT, we could prevent futile treatment, avoid treatment complications, and further improve stroke care. We aimed to determine the accuracy of poor functional outcome prediction, defined as 90-day modified Rankin Scale (mRS) score ≥5, despite EVT treatment. We included 1,526 patients from the MR CLEAN Registry, a prospective, observational, multicenter registry of ischemic stroke patients treated with EVT. We developed machine learning prediction models using all variables available at baseline before treatment. We optimized the models for both maximizing the area under the curve (AUC), reducing the number of false positives. From 1,526 patients included, 480 (31%) of patients showed poor outcome. The highest AUC was 0.81 for random forest. The highest area under the precision recall curve was 0.69 for the support vector machine. The highest achieved specificity was 95% with a sensitivity of 34% for neural networks, indicating that all models contained false positives in their predictions. From 921 mRS 0-4 patients, 27-61 (3-6%) were incorrectly classified as poor outcome. From 480 poor outcome patients in the registry, 99-163 (21-34%) were correctly identified by the models. All prediction models showed a high AUC. The best-performing models correctly identified 34% of the poor outcome patients at a cost of misclassifying 4% of non-poor outcome patients. Further studies are necessary to determine whether these accuracies are reproducible before implementation in clinical practice.
尽管血管内治疗（EVT）极大地改善了急性缺血性中风的治疗效果，但仍有三分之一的患者在中风后死亡或严重致残。如果我们能够筛选出尽管接受了EVT治疗但临床结局仍不佳的患者，就可以避免无效治疗，避免治疗并发症，并进一步改善中风护理。我们旨在确定尽管接受了EVT治疗，但功能结局不佳预测（定义为90天改良Rankin量表[mRS]评分≥5）的准确性。我们纳入了1526例来自MR CLEAN注册研究的患者，这是一个前瞻性、观察性、多中心的缺血性中风患者接受EVT治疗的注册研究。我们使用治疗前基线时可用的所有变量开发了机器学习预测模型。我们对模型进行了优化，以最大化曲线下面积（AUC）并减少假阳性数量。在纳入的1526例患者中，480例（31%）患者结局不佳。随机森林的最高AUC为0.81。支持向量机的精确召回率曲线下最高面积为0.69。神经网络实现的最高特异性为95%，敏感性为34%，这表明所有模型在预测中都包含假阳性。在921例mRS 0-4的患者中，27-61例（3-6%）被错误分类为结局不佳。在注册研究中的480例结局不佳的患者中，模型正确识别出99-163例（21-34%）。所有预测模型均显示出较高的AUC。表现最佳的模型正确识别出34%的结局不佳患者，但代价是将4%的非结局不佳患者错误分类。在临床实践中实施之前，有必要进行进一步研究以确定这些准确性是否可重复。
