<body><div><img src="https://img.particlenews.com/image.php?type=thumbnail_580x000&amp;url=08AzV8_1AIORNmG00" alt="https://img.particlenews.com/image.php?url=08AzV8_1AIORNmG00" width="600" height="350" >Out-of-hospital sudden cardiac arrest is common and usually fatal, and survival depends on rapid defibrillation. More...Many communities deploy automated external defibrillators without precise guidance, which can hinder timely access. To help address this challenge, investigators at Cedars-Sinai Health Sciences University (Los Angeles, CA, USA) and colleagues developed a data-driven algorithm to optimize public AED placement. A new study describes how the approach targets locations where cardiac arrests cluster.The algorithm analyzes historical sudden cardiac arrest events using geospatial clustering to pinpoint hotspots. It identifies sites where three or more incidents occurred within a 100-meter radius. It then proposes automated external defibrillator placement within 200 meters of each cluster to reduce retrieval distance for bystanders.Investigators reviewed incident records from Ventura County, California, and Multnomah County, Oregon, spanning 2012 to 2023. Using these data, the model generated proposed device sites that encompassed each event within the stated distance threshold. Findings were published in IJC Heart &amp; Vasculature.The work reflects collaboration with emergency medical services and fire department partners involved in community-based research. The investigators noted that future studies will be needed to determine whether the algorithm outperforms current AED location strategies used by municipalities and health systems. They also indicated an interest in seeing the method tested to evaluate reductions in deaths from sudden cardiac arrest.“Research shows that the sooner a bystander can locate and use an AED, the higher the likelihood that the person experiencing sudden cardiac arrest survives,” said Sumeet Chugh, MD, vice dean and chief AI Health Research Officer at Cedars-Sinai, and senior author of the paper."These community-based studies are a huge team effort that involves ongoing collaborations with colleagues in emergency medical services and the fire department. We hope researchers use this algorithm to study how the placement of AEDs reduces deaths from sudden cardiac arrest," said Chugh, who is also director of the Center for Cardiac Arrest Prevention in the Smidt Heart Institute.</div></body>

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Out-of-hospital sudden cardiac arrest is common and usually fatal, and survival depends on rapid defibrillation. More... Many communities deploy automated external defibrillators without precise guidance, which can hinder timely access. To help address this challenge, investigators at Cedars-Sinai Health Sciences University (Los Angeles, CA, USA) and colleagues developed a data-driven algorithm to optimize public AED placement. A new study describes how the approach targets locations where cardiac arrests cluster.

Algorithm Identifies Cardiac Arrest Hotspots to Guide AED Placement

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Out-of-hospital sudden cardiac arrest is common and usually fatal, and survival depends on rapid defibrillation. More... Many communities deploy automated 

Algorithm Identifies Cardiac Arrest Hotspots to Guide AED Placement

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