Enhancing out-of-hospital cardiac arrest survival in China through the 5-minute social rescue circle implementation.

IF 6.5 1区 医学 Q1 CRITICAL CARE MEDICINE
Wenwu Zhang, Lijuan Wu, Xinrong He, Jizhe Xia, Jun Xu, Can Ma, Liyi Mai, Jinxin Huang, Xueyun Zhan, Guohua Yin, Hanhan Hu, Conghua Wang, Yujie Li, Qingli Dou, Abdelouahab Bellou, Jinle Lin, Xin Li
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引用次数: 0

Abstract

Background: Out-of-hospital cardiac arrest (OHCA) survival in China remains critically low due to limited bystander cardiopulmonary resuscitation (CPR) training, insufficient automated external defibrillator (AED) deployment, and delayed community rescue responses. This study quantitatively assessed the impact of the intervention and the 5-minute social rescue circle (5MSRC) on OHCA outcomes in Baoan District, Shenzhen, China.

Methods: We employed a patient-centered approach to integrate three datasets from Baoan, Shenzhen from March 2019 to September 2024: (a) 3586 OHCA cases; (b) 332,507 CPR-trained residents; (c) 4,707 AED deployments. A WeChat-based 5MSRC system was developed to integrate video-guided CPR with emergency dispatch. Using the integrated dataset, we applied geospatial tracking, machine learning, and SHapley Additive exPlanations (SHAP) analysis to identify key factors associated with improvedreturn of spontaneous circulation (ROSC) and 30-day survival.

Results: Among 3586 OHCA cases, 3.88 % achieved prehospital ROSC and 3.82 % survived after 30 days. Arrests in public locations showed significantly better outcomes than residential settings (ROSC: 66.2 % vs 33.8 %; 30-day survival: 68.6 % vs 31.4 %, both p < 0.001). During post-pandemic recovery (2020-2024), OHCA outcomes demonstrated significant improvement: bystander CPR rates increased from 12.32 % to 27.27 %, AED application rose from 1.96 % to 4.64 %, and 30-day survival improved from 2.27 % to 6.51 % (all p < 0.05). The machine learning models achieved excellent predictive performance for prehospital ROSC (best ROC-AUC: 0.92, 95 % CI 0.91-0.93) and good performance for 30-day survival (best ROC-AUC: 0.97, 95 % CI 0.96-0.98), demonstrating robust predictive capability for both acute and longer-term outcomes. Feature importance analysis revealed that community-level factors-particularly recent CPR training rates (e.g., 6-month), AED proximity, and rapid response systems-drove prehospital ROSC, while hospital-based interventions (e.g., percutaneous coronary intervention) became increasingly important for 30-day survival.

Conclusions: These findings underscore the critical synergy between community preparedness (frequent CPR training, AED accessibility) and advanced hospital care in optimizing OHCA outcomes. The high-performing prediction models demonstrate the potential of machine learning to identify strategic intervention points across the continuum of cardiac arrest care.

通过5分钟社会救助圈的实施提高中国院外心脏骤停生存率
背景:由于旁观者心肺复苏(CPR)培训有限、自动体外除颤器(AED)部署不足以及社区救援响应延迟,中国院外心脏骤停(OHCA)生存率仍然非常低。本研究定量评估了干预和5分钟社会救援圈(5MSRC)对中国深圳宝安区OHCA结果的影响。方法:采用以患者为中心的方法,整合2019年3月至2024年9月深圳宝安的三个数据集:(a) 3,586例OHCA病例;(b)接受心肺复苏术训练的住院医师332,507名;(c) 4 707个AED部署。开发了基于微信的5MSRC系统,将视频引导CPR与应急调度相结合。利用集成的数据集,我们应用了地理空间跟踪、机器学习和SHapley加性解释(SHAP)分析,以确定与改善自然循环(ROSC)和30天存活率相关的关键因素。结果:3586例OHCA患者院前ROSC达到3.88%,30 d生存率为3.82%。公共场所的逮捕效果明显好于居住场所(ROSC: 66.2% vs 33.8%;结论:这些发现强调了社区准备(频繁的心肺复苏培训、AED的可及性)和先进的医院护理在优化OHCA结果方面的关键协同作用。高性能预测模型展示了机器学习在心脏骤停护理连续体中识别战略干预点的潜力。
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来源期刊
Resuscitation
Resuscitation 医学-急救医学
CiteScore
12.00
自引率
18.50%
发文量
556
审稿时长
21 days
期刊介绍: Resuscitation is a monthly international and interdisciplinary medical journal. The papers published deal with the aetiology, pathophysiology and prevention of cardiac arrest, resuscitation training, clinical resuscitation, and experimental resuscitation research, although papers relating to animal studies will be published only if they are of exceptional interest and related directly to clinical cardiopulmonary resuscitation. Papers relating to trauma are published occasionally but the majority of these concern traumatic cardiac arrest.
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