Background. Out-of-hospital cardiac arrest (OHCA) is a major health problem worldwide, and neurologic injury remains the leading cause of morbidity and mortality among survivors of OHCA. The purpose of this study was to investigate whether a machine learning algorithm could detect complex dependencies between clinical variables in emergency departments in OHCA survivors and perform reliable predictions of favorable neurologic outcomes. Methods. This study included adults (≥18 years of age) with a sustained return of spontaneous circulation after successful resuscitation from OHCA between 1 January 2004 and 31 December 2014. We applied three machine learning algorithms, including logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The primary outcome was a favorable neurological outcome at hospital discharge, defined as a Glasgow-Pittsburgh cerebral performance category of 1 to 2. The secondary outcome was a 30-day survival rate and survival-to-discharge rate. Results. The final analysis included 1071 participants from the study period. For neurologic outcome prediction, the area under the receiver operating curve (AUC) was 0.819, 0.771, and 0.956 in LR, SVM, and XGB, respectively. The sensitivity and specificity were 0.875 and 0.751 in LR, 0.687 and 0.793 in SVM, and 0.875 and 0.904 in XGB. The AUC was 0.766 and 0.732 in LR, 0.749 and 0.725 in SVM, and 0.866 and 0.831 in XGB, for survival-to-discharge and 30-day survival, respectively. Conclusions. Prognostic models trained with ML technique showed appropriate calibration and high discrimination for survival and neurologic outcome of OHCA without using prehospital data, with XGB exhibiting the best performance.

中文翻译：

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用于院外心脏骤停患者生存和神经结果预测的机器学习模型

背景。院外心脏骤停 (OHCA) 是世界范围内的主要健康问题，神经损伤仍然是 OHCA 幸存者发病和死亡的主要原因。本研究的目的是调查机器学习算法是否可以检测 OHCA 幸存者急诊科临床变量之间的复杂依赖关系，并对有利的神经系统结果进行可靠预测。方法. 本研究包括在 2004 年 1 月 1 日至 2014 年 12 月 31 日期间从 OHCA 成功复苏后自主循环持续恢复的成年人（≥18 岁）。我们应用了三种机器学习算法，包括逻辑回归 (LR)、支持向量机 ( SVM）和极端梯度提升（XGB）。主要结果是出院时良好的神经系统结果，定义为 1 至 2 级的 Glasgow-Pittsburgh 脑性能类别。次要结果是 30 天存活率和出院存活率。结果. 最终分析包括研究期间的 1071 名参与者。对于神经系统结果预测，LR、SVM 和 XGB 的受试者工作曲线下面积 (AUC) 分别为 0.819、0.771 和 0.956。LR 的敏感性和特异性分别为 0.875 和 0.751，SVM 的敏感性和特异性分别为 0.687 和 0.793，XGB 的敏感性和特异性分别为 0.875 和 0.904。LR 中的 AUC 分别为 0.766 和 0.732，SVM 中的 AUC 为 0.749 和 0.725，XGB 中的 AUC 分别为 0.866 和 0.831，用于出院生存期和 30 天生存期。结论。使用 ML 技术训练的预后模型在不使用院前数据的情况下显示出对 OHCA 的生存和神经系统结果的适当校准和高度区分，其中 XGB 表现出最佳性能。