PDF(Pubmed)
Abstract：
UNASSIGNED: This study hypothesized that monitoring electrocardiogram (ECG) waveforms in patients with out-of-hospital cardiac arrest (OHCA) could have predictive value for survival or neurological outcomes. We aimed to establish a new prognostication model based on the single variable of monitoring ECG waveforms in patients with OHCA using machine learning (ML) techniques.
UNASSIGNED: This observational retrospective study included successfully resuscitated patients with OHCA aged ≥ 18 years admitted to an intensive care unit in Japan between April 2010 and April 2020. Waveforms from ECG monitoring for 1 h after admission were obtained from medical records and examined. Based on the open-access PTB-XL dataset, a large publicly available 12-lead ECG waveform dataset, we built an ML-supported premodel that transformed the II-lead waveforms of the monitoring ECG into diagnostic labels. The ECG diagnostic labels of the patients in this study were analyzed for prognosis using another model supported by ML. The endpoints were favorable neurological outcomes (cerebral performance category 1 or 2) and survival to hospital discharge.
UNASSIGNED: In total, 590 patients with OHCA were included in this study and randomly divided into 3 groups (training set, n = 283; validation set, n = 70; and test set, n = 237). In the test set, our ML model predicted neurological and survival outcomes, with the highest areas under the receiver operating characteristic curves of 0.688 (95% CI: 0.682-0.694) and 0.684 (95% CI: 0.680-0.689), respectively.
UNASSIGNED: Our ML predictive model showed that monitoring ECG waveforms soon after resuscitation could predict neurological and survival outcomes in patients with OHCA.
UNASSIGNED: This observational retrospective study included successfully resuscitated patients with OHCA aged ≥ 18 years admitted to an intensive care unit in Japan between April 2010 and April 2020. Waveforms from ECG monitoring for 1 h after admission were obtained from medical records and examined. Based on the open-access PTB-XL dataset, a large publicly available 12-lead ECG waveform dataset, we built an ML-supported premodel that transformed the II-lead waveforms of the monitoring ECG into diagnostic labels. The ECG diagnostic labels of the patients in this study were analyzed for prognosis using another model supported by ML. The endpoints were favorable neurological outcomes (cerebral performance category 1 or 2) and survival to hospital discharge.
UNASSIGNED: In total, 590 patients with OHCA were included in this study and randomly divided into 3 groups (training set, n = 283; validation set, n = 70; and test set, n = 237). In the test set, our ML model predicted neurological and survival outcomes, with the highest areas under the receiver operating characteristic curves of 0.688 (95% CI: 0.682-0.694) and 0.684 (95% CI: 0.680-0.689), respectively.
UNASSIGNED: Our ML predictive model showed that monitoring ECG waveforms soon after resuscitation could predict neurological and survival outcomes in patients with OHCA.
摘要：
■本研究假设监测院外心脏骤停（OHCA）患者的心电图（ECG）波形可能对生存或神经系统结局具有预测价值。我们旨在基于使用机器学习（ML）技术监测OHCA患者ECG波形的单个变量，建立新的预测模型。
这项观察性回顾性研究纳入了2010年4月至2020年4月期间在日本接受重症监护病房治疗的18岁以上OHCA患者成功复苏。从病历中获得入院后1小时的ECG监测波形并进行检查。基于开放存取PTB-XL数据集，一个大型公开可用的12导联心电图波形数据集，我们构建了支持ML的预模型,将监测心电图的II导联波形转换为诊断标签.使用ML支持的另一个模型分析了本研究中患者的ECG诊断标签的预后。终点是良好的神经系统结局（脑功能类别1或2）和出院后的生存率。
■总共，590名OHCA患者被纳入本研究，并随机分为3组(训练集，n=283；验证集，n=70；和测试集，n=237）。在测试集中，我们的ML模型预测了神经和生存结果，接收器工作特性曲线下的最高面积为0.688（95％CI：0.682-0.694）和0.684（95％CI：0.680-0.689），分别。
■我们的ML预测模型表明，在复苏后不久监测ECG波形可以预测OHCA患者的神经和生存结果。
这项观察性回顾性研究纳入了2010年4月至2020年4月期间在日本接受重症监护病房治疗的18岁以上OHCA患者成功复苏。从病历中获得入院后1小时的ECG监测波形并进行检查。基于开放存取PTB-XL数据集，一个大型公开可用的12导联心电图波形数据集，我们构建了支持ML的预模型,将监测心电图的II导联波形转换为诊断标签.使用ML支持的另一个模型分析了本研究中患者的ECG诊断标签的预后。终点是良好的神经系统结局（脑功能类别1或2）和出院后的生存率。
■总共，590名OHCA患者被纳入本研究，并随机分为3组(训练集，n=283；验证集，n=70；和测试集，n=237）。在测试集中，我们的ML模型预测了神经和生存结果，接收器工作特性曲线下的最高面积为0.688（95％CI：0.682-0.694）和0.684（95％CI：0.680-0.689），分别。
■我们的ML预测模型表明，在复苏后不久监测ECG波形可以预测OHCA患者的神经和生存结果。
