Elola Andoni, Aramendi Elisabete, Rueda Enrique, Irusta Unai, Wang Henry, Idris Ahamed
Department of Communications Engineering, University of the Basque Country, 48013 Bilbao, Spain.
Department of Emergency Medicine, University of Texas Health Science Center, Houston, TX 77030, USA.
Entropy (Basel). 2020 Jul 9;22(7):758. doi: 10.3390/e22070758.
A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision-recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.
院外心脏骤停（OHCA）后恢复自主循环的患者中二次心脏骤停很常见。再次心脏骤停事件与更差的患者预后相关，但对于导致再次心脏骤停的心脏动力学了解甚少。再次心脏骤停的预测有助于改善OHCA患者的预后。本研究的目的是开发一种机器学习模型来预测再次心脏骤停。设计了一种基于21种心率变异性（HRV）和心电图（ECG）特征的随机森林分类器。使用自主循环恢复后2分钟的分析间隔来计算这些特征。该模型在162例OHCA患者（55例发生再次心脏骤停）的队列中，采用重复交叉验证程序进行训练和测试。该模型的中位数（四分位间距）敏感性（再次心脏骤停）和特异性（未发生再次心脏骤停）分别为67.3%（9.1%）和67.3%（10.3%），受试者工作特征曲线下面积和精确召回率曲线下面积的中位数分别为0.69和0.53。这是首个预测再次心脏骤停的机器学习模型，将以自动化方式为临床医生提供具有临床价值的信息。
