Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.
Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.
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DOI：
10.1038/s41591-023-02396-3
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发表时间：
2023-07
期刊：
影响因子：
82.9
通讯作者：
Callaway, Clifton W.
中科院分区：
文献类型：
--
作者：
Al-Zaiti, Salah S.;Martin-Gill, Christian;Zegre-Hemsey, Jessica K.;Bouzid, Zeineb;Faramand, Ziad;Alrawashdeh, Mohammad O.;Gregg, Richard E.;Helman, Stephanie;Riek, Nathan T.;Kraevsky-Phillips, Karina;Clermont, Gilles;Akcakaya, Murat;Sereika, Susan M.;Van Dam, Peter;Smith, Stephen W.;Birnbaum, Yochai;Saba, Samir;Sejdic, Ervin;Callaway, Clifton W.
患有闭塞性心肌梗死（OMI）且心电图（ECG）显示无ST段抬高的患者数量正在增加。这些患者预后不良，并将受益于立即再灌注治疗，但目前，没有准确的工具，以确定他们在初步分诊。据我们所知，在这里我们报告了第一项观察性队列研究，以开发用于OMI ECG诊断的机器学习模型。使用来自多个临床站点的7，313名连续患者，我们推导并外部验证了一个智能模型，该模型优于执业临床医生和其他广泛使用的商业解释系统，大大提高了精度和灵敏度。我们推导出的OMI风险评分提供了与常规护理相关的增强的纳入和排除准确性，并且，当与训练有素的急救人员的临床判断相结合时，它有助于正确地重新分类三分之一的胸痛患者。驱动我们模型的ECG特征得到了临床专家的验证，提供了与心肌损伤的合理机制联系。开发用于检测心电图无ST段抬高的闭塞性心肌梗死的机器学习算法，在诊断评估方面优于临床医生。
Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury. A machine learning algorithm, developed to detect occlusion myocardial infarction with no-ST elevation from electrocardiogram, outperforms clinicians in diagnostic assessments.
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DOI：
10.1016/j.ajem.2018.06.020
发表时间：
2019-03
期刊：
The American journal of emergency medicine
影响因子：
--
作者：
Al-Zaiti SS;Faramand Z;Alrawashdeh MO;Sereika SM;Martin-Gill C;Callaway C
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