Prehospital
Literature Index
Cardiac arrest & resuscitation

Identifying optimal locations for automated external defibrillators (AED) in Freiburg: development and validation of a machine learning model based on demographic and infrastructural data.

Julian Ganter, Hannah Bakker, Stefan Nickel, Elisa-Sophie Reichling, Alicia Wittmer, Niklas Werner, Thomas Brucklacher, Robert Wunderlich, Georg Trummer, Hans-Jörg Busch, Michael Patrick Müller

BMC Emergency Medicine · 2025-12-13 · Method & validation

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Abstract

INTRODUCTION: Out-of-hospital cardiac arrest (OHCA) is a critical medical emergency where rapid access to automated external defibrillators (AED) can significantly improve survival rates. However, there is currently a lack of well-established frameworks and guidelines concerning the optimal placement of AED. Additionally, historical data on the locations of OHCA incidents is often unavailable or incomplete. This study seeks to address these gaps by analyzing the most effective AED placement strategies and evaluating the impact of additional AED locations on suspected OHCA cases. To achieve this, a machine learning (ML) model is developed that relies exclusively on demographic and infrastructural factors, without the need for historical OHCA location data. METHODS: In this data-driven predictive modelling study, 5,076 alerts of suspected OHCA and 95 AED locations in Freiburg were analysed (October 7, 2018, to May 28, 2024). Demographic and infrastructural data were integrated into a three-step approach to identify and prioritize optimal AED placements. A Decision Tree was trained to predict OHCA risk at possible locations, followed by the application of a greedy algorithm to determine AED locations. The models were validated using several performance metrics and historical OHCA data to ensure accuracy. Additionally, different scenarios were evaluated to maximize AED coverage of OHCA incidents. RESULTS: Optimizing AED placement using predicted data increased coverage from 21.6% to 42.4%, without adding more devices. The ML model's coverage was only 6.7% lower than that achieved using historical alert data. Adding 19 AEDs (a 20% increase) to the existing network raised coverage to 30.5%. CONCLUSION: The findings demonstrate the feasibility of using ML models for AED placement in regions lacking comprehensive historical data. Integrating advanced ML techniques can further refine strategies for AED deployment in urban areas, ultimately improving emergency response effectiveness. TRIAL REGISTRATION: The trial is registered with the German Clinical Trials Register (DRKS, ID: DRKS00016625 15/04/2019 and DRKS00032957 30/10/2023), which is a WHO primary register.

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Tags

Automated external defibrillator · Decision support tools · Urban

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