Objective

To determine the feasibility of using a machine learning algorithm to screen for large vessel occlusions (LVO) in the Emergency Department (ED).

Materials and methods

A retrospective cohort of consecutive ED stroke alerts at a large comprehensive stroke center was analyzed. The primary outcome was diagnosis of LVO at discharge. Components of the National Institutes of Health Stroke Scale (NIHSS) were used in various clinical methods and machine learning algorithms to predict LVO, and the results were compared with the baseline method (aggregate NIHSS score with threshold of 6). The Area-Under-Curve (AUC) was used to measure the overall performance of the models. Bootstrapping (n = 1000) was applied for the statistical analysis.

Results

Of 1133 total patients, 67 were diagnosed with LVO. A Gaussian Process (GP) algorithm significantly outperformed other methods including the baseline methods. AUC score for the GP algorithm was 0.874 ± 0.025, compared with the simple aggregate NIHSS score, which had an AUC score of 0.819 ± 0.024. A dual-stage GP algorithm is proposed, which offers flexible threshold settings for different patient populations, and achieved an overall sensitivity of 0.903 and specificity of 0.626, in which sensitivity of 0.99 was achieved for high-risk patients (defined as initial NIHSS score > 6).

Conclusion

Machine learning using a Gaussian Process algorithm outperformed a clinical cutoff using the aggregate NIHSS score for LVO diagnosis. Future studies would be beneficial in exploring prospective interventions developed using machine learning in screening for LVOs in the emergent setting.

中文翻译：

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使用美国国立卫生研究院卒中量表组件在急诊科预测大血管闭塞：一种机器学习方法

客观的

确定在急诊科 (ED) 中使用机器学习算法筛查大血管闭塞 (LVO) 的可行性。

材料和方法

对大型综合卒中中心的连续 ED 卒中警报的回顾性队列进行了分析。主要结果是出院时诊断为 LVO。在各种临床方法和机器学习算法中使用美国国立卫生研究院卒中量表 (NIHSS) 的组成部分来预测 LVO，并将结果与​​基线方法进行比较（NIHSS 总分，阈值为 6）。曲线下面积 (AUC) 用于衡量模型的整体性能。Bootstrapping ( n  = 1000) 用于统计分析。

结果

在总共 1133 名患者中，67 名被诊断为 LVO。高斯过程 (GP) 算法明显优于其他方法，包括基线方法。GP 算法的 AUC 分数为 0.874 ± 0.025，而简单汇总 NIHSS 分数的 AUC 分数为 0.819 ± 0.024。提出了一种双阶段 GP 算法，该算法为不同的患者群体提供灵活的阈值设置，总体敏感性为 0.903，特异性为 0.626，其中对高危患者（定义为初始 NIHSS 评分 > 6).

结论

使用高斯过程算法的机器学习优于使用总 NIHSS 评分进行 LVO 诊断的临床截止值。未来的研究将有利于探索使用机器学习开发的前瞻性干预措施来筛查紧急情况下的 LVO。