Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation.
Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation.
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人工智能模型预测心脏骤停表现为无脉电活动与心室颤动。
DOI：
10.1161/circep.123.012338
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Chugh,SumeetS
中科院分区：
文献类型：
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作者：
Holmstrom,Lauri;Bednarski,Bryan;Chugh,Harpriya;Aziz,Habiba;Pham,HoangNhat;Sargsyan,Arayik;Uy-Evanado,Audrey;Dey,Damini;Salvucci,Angelo;Jui,Jonathan;Reinier,Kyndaron;Slomka,PiotrJ;Chugh,SumeetS
研究背景对于表现为无脉性电活动（PEA）的心脏骤停（SCA）没有特异性治疗方法，存活率很低;不像心室颤动（VF），可以通过除颤治疗。开发新的治疗方法需要基本的临床研究，但获得真正的初始节奏一直是一个限制因素。METHODSUsing人口统计学和详细的临床变量，我们训练和测试的AI模型（极端梯度提升），以区分PEA-SCA与VF-SCA在一个新的设置，提供了真正的初始节奏。SCA的一个亚组由紧急医疗服务人员见证，并且由于响应时间为零，因此记录了真实的SCA初始节律。内部队列包括421例急诊医疗服务见证的院外SCA，PEA或VF作为波特兰，俄勒冈州大都市区的初始节奏。在来自加利福尼亚州文图拉的220个紧急医疗服务见证的SCA中进行了外部验证。在内部队列中，人工智能模型实现了0.68（95%CI，0.61-0.76）的受试者工作特征曲线下面积。外部队列的模型性能相似，接受者工作特征曲线下面积为0.72（95% CI，0.59-0.84）。贫血，年龄较大，体重增加，呼吸困难作为警告症状的PEA-SCA的最重要的功能;年轻，胸痛作为警告症状和建立冠状动脉疾病的重要功能与VF。CONCLUSIONSSThe人工智能模型识别PEA-SCA的新功能，从VF-SCA区分，并成功地复制在外部队列。这些发现增强了对PEA-SCA的机械理解，对开发新的管理策略具有潜在的影响。
BACKGROUNDThere is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor.METHODSUsing demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA.RESULTSIn the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61–0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59–0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF.CONCLUSIONSThe artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies.