Lee Sijin, Lee Kwang-Sig, Park Sang-Hyun, Lee Sung Woo, Kim Su Jin
Department of Emergency Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea.
AI Center, Korea University College of Medicine, Seoul 02841, Republic of Korea.
J Clin Med. 2024 Dec 13;13(24):7600. doi: 10.3390/jcm13247600.
This study uses machine learning and multicenter registry data for analyzing the determinants of a favorable neurological outcome in patients with out-of-hospital cardiac arrest (OHCA) and developing decision support systems for various subgroups. The data came from the Korean Cardiac Arrest Research Consortium registry, with 2679 patients who underwent OHCA aged 18 or above with the return of spontaneous circulation (ROSC). The dependent variable was a favorable neurological outcome (Cerebral Performance Category score 1-2), and 68 independent variables were included, e.g., first monitored rhythm, in-hospital cardiopulmonary resuscitation (CPR) duration and post-ROSC pH. A random forest was used for identifying the major determinants of the favorable neurological outcome and developing decision support systems for the various subgroups stratified by the major variables. Based on the random forest variable importance, the major determinants of the OHCA patient outcomes were the in-hospital CPR duration (0.0824), in-hospital electrocardiogram on emergency room arrival (0.0692), post-ROSC pH (0.0579), prehospital ROSC before emergency room arrival (0.0565), coronary angiography (0.0527), age (0.0415), first monitored rhythm (EMS) (0.0402), first monitored rhythm (community) (0.0401), early coronary angiography within 24 h (0.0304) and time from scene arrival to CPR stop (0.0301). It was also found that the patients could be divided into six subgroups in terms of their prehospital ROSC and first monitored rhythm (EMS), and that a decision tree could be developed as a decision support system for each subgroup to find the effective cut-off points regarding the in-hospital CPR duration, post-ROSC pH, age and hemoglobin. We identified the major determinants of favorable neurological outcomes in successfully resuscitated patients who underwent OHCA using machine learning. This study demonstrates the strengths of a random forest as an effective decision support system for each stratified subgroup (prehospital ROSC and first monitored rhythm by EMS) to find its own optimal cut-off points for the major in-hospital variables (in-hospital CPR duration, post-ROSC pH, age and hemoglobin).
本研究使用机器学习和多中心注册数据，分析院外心脏骤停（OHCA）患者获得良好神经功能预后的决定因素，并为不同亚组开发决策支持系统。数据来自韩国心脏骤停研究联盟注册库，共有2679例18岁及以上经历OHCA且恢复自主循环（ROSC）的患者。因变量是良好的神经功能预后（脑功能分类评分1 - 2），纳入了68个自变量，例如首次监测到的心律、院内心肺复苏（CPR）持续时间和ROSC后pH值。使用随机森林来识别良好神经功能预后的主要决定因素，并为按主要变量分层的不同亚组开发决策支持系统。基于随机森林变量重要性，OHCA患者预后的主要决定因素是院内CPR持续时间（0.0824）、急诊室到达时的院内心电图（0.0692）、ROSC后pH值（0.0579）、急诊室到达前的院外ROSC（0.0565）、冠状动脉造影（0.0527）、年龄（0.0415）、首次监测到的心律（急救医疗服务[EMS]）（0.0402）、首次监测到的心律（社区）（0.0401）、24小时内早期冠状动脉造影（0.0304）以及从现场到达至CPR停止的时间（0.0301）。还发现，根据患者的院外ROSC和首次监测到的心律（EMS），可将患者分为六个亚组，并且可以为每个亚组开发决策树作为决策支持系统，以找到关于院内CPR持续时间、ROSC后pH值、年龄和血红蛋白的有效截断点。我们使用机器学习确定了成功复苏的OHCA患者获得良好神经功能预后的主要决定因素。本研究证明了随机森林作为一种有效的决策支持系统，可为每个分层亚组（院外ROSC和EMS首次监测到的心律）找到其自身关于主要院内变量（院内CPR持续时间、ROSC后pH值、年龄和血红蛋白）的最佳截断点的优势。
