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<p>Sudden Cardiac Death (SCD) remains a leading cause of mortality worldwide, with outcomes critically dependent on the effective implementation of the “Chain of Survival” — early recognition, early CPR, early defibrillation, and post-resuscitation care. In regional and pre-hospital settings, systemic fragmentation between emergency dispatch, ambulance services, and hospitals undermines this chain. This study presents the development, implementation, and impact evaluation of an integrated, AI-enabled multi-modal emergency care system designed to strengthen the entire Chain of Survival for SCD in a regional context.: We designed and deployed a system integrating a unified information platform, IoT-enabled devices, point-of-care testing (POCT), and AI-driven clinical decision support. The system was implemented phased across three counties in Anyang, China (population ≈ 2.1&#xa0;million) from January 2022 to December 2023. We conducted a quasi-experimental before-and-after study using routinely collected emergency medical services (EMS) data. Primary outcomes were median response time (call receipt to scene arrival), pre-hospital STEMI identification rate, and return of spontaneous circulation (ROSC) for out-of-hospital cardiac arrest (OHCA) of cardiac origin. Data from 1,208 emergency cases (pre-implementation: <i>n</i> = 587; post-implementation: <i>n</i> = 621) were analyzed. Interrupted time series (ITS) analysis was performed to control for secular trends. The median emergency response time decreased from 9.8&#xa0;min (IQR: 7.2–13.1) to 6.7&#xa0;min (IQR: 5.1–9.0) (<i>P</i> &lt; 0.001). The pre-hospital STEMI identification rate improved from 65% to 90% (<i>p</i> &lt; 0.01). For OHCA of cardiac origin, the ROSC rate increased from 18% to 31% (<i>p</i> &lt; 0.05), representing a 72% relative improvement. ITS analysis confirmed a significant level change for response time (β = -2.8&#xa0;min, 95% CI: -3.7 to -1.9, <i>P</i> &lt; 0.001) and for ROSC (β = +12% points, 95% CI: +5 to + 19, <i>P</i> = 0.01) immediately following implementation, with no significant pre-existing trends. The AI models demonstrated robust performance during validation (deterioration prediction AUC 0.89; STEMI detection AUC 0.92). The Anyang Model provides evidence that a systematically integrated, AI-driven platform is feasible and temporally associated with substantial improvements in regional emergency care for SCD. While causal attribution requires further validation, this systems-level approach offers a replicable framework that can be adapted to diverse resource settings.</p>

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Implementation of an ai-enabled multimodal emergency care system is associated with improved sudden cardiac death rescue outcomes in anyang

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Xiaopeng Liu,
  • Chenlong Zhang,
  • Hongjiang Zhang,
  • Junyang Shen,
  • Peng Liu,
  • Yunlong Wang,
  • Rui Li,
  • Sisen Zhang

摘要

Sudden Cardiac Death (SCD) remains a leading cause of mortality worldwide, with outcomes critically dependent on the effective implementation of the “Chain of Survival” — early recognition, early CPR, early defibrillation, and post-resuscitation care. In regional and pre-hospital settings, systemic fragmentation between emergency dispatch, ambulance services, and hospitals undermines this chain. This study presents the development, implementation, and impact evaluation of an integrated, AI-enabled multi-modal emergency care system designed to strengthen the entire Chain of Survival for SCD in a regional context.: We designed and deployed a system integrating a unified information platform, IoT-enabled devices, point-of-care testing (POCT), and AI-driven clinical decision support. The system was implemented phased across three counties in Anyang, China (population ≈ 2.1 million) from January 2022 to December 2023. We conducted a quasi-experimental before-and-after study using routinely collected emergency medical services (EMS) data. Primary outcomes were median response time (call receipt to scene arrival), pre-hospital STEMI identification rate, and return of spontaneous circulation (ROSC) for out-of-hospital cardiac arrest (OHCA) of cardiac origin. Data from 1,208 emergency cases (pre-implementation: n = 587; post-implementation: n = 621) were analyzed. Interrupted time series (ITS) analysis was performed to control for secular trends. The median emergency response time decreased from 9.8 min (IQR: 7.2–13.1) to 6.7 min (IQR: 5.1–9.0) (P < 0.001). The pre-hospital STEMI identification rate improved from 65% to 90% (p < 0.01). For OHCA of cardiac origin, the ROSC rate increased from 18% to 31% (p < 0.05), representing a 72% relative improvement. ITS analysis confirmed a significant level change for response time (β = -2.8 min, 95% CI: -3.7 to -1.9, P < 0.001) and for ROSC (β = +12% points, 95% CI: +5 to + 19, P = 0.01) immediately following implementation, with no significant pre-existing trends. The AI models demonstrated robust performance during validation (deterioration prediction AUC 0.89; STEMI detection AUC 0.92). The Anyang Model provides evidence that a systematically integrated, AI-driven platform is feasible and temporally associated with substantial improvements in regional emergency care for SCD. While causal attribution requires further validation, this systems-level approach offers a replicable framework that can be adapted to diverse resource settings.