Optimizing automated external defibrillator deployment within the walking golden window for out-of-hospital cardiac arrest cases: a case study from a Chinese city - Qin, Zhaohui; Li, Jia; Zheng, Shuyao; Xu, Da; Zhang, Wei; Lu, Lu; Yan, Xianliang; Xu, Tie; Zhao, Ningjun; Xu, Yan (Frontiers in Public Health, 2025)
BackgroundIrreversible brain injury may begin 4–6 min after the onset of out-of-hospital cardiac arrest (OHCA) if no cardiopulmonary resuscitation (CPR) is provided. This period is commonly referred to as the “golden window” in China. Based on the walking distance within this window, we proposed an improved public access defibrillation (PAD) deployment strategy to enhance automated external defibrillator (AED) efficiency in typical Chinese cities.MethodsThis observational study used two datasets (an AED inventory and an OHCA registry) to assess the current effectiveness of AED deployment in the urban area of the Xuzhou city, Jiangsu Province. Using Geographic Information System (GIS) to determine the optimal AED placement distance based on the golden window walking-route distance. We also used python to simulate the improved model.ResultsIn the model, a total of 1,350 OHCAs and 1,238 AEDs were included and 78.4% of OHCAs occurred in the community. The AED coverage rate within 100 m was 7.93 and 7.33% based on the straight-line model and walking-route model. The proportion of OHCAs where an AED was accessible within the walking distance of the golden window accounted for 53.04% on average, with an average of 1.19 AEDs per case. The optimal deployment distance for AEDs to achieve maximum efficiency and approximate the standards of developed cities (Average = 1, Proportion = 40%) is computed to be 270–280 m in straight line. The simulation demonstration of the improved model shows that the benefit is significantly improved.ConclusionOur model verified the current mismatch between AED deployment and OHCA cases in Xuzhou city. Based on this, we proposed an improved allocation model, which demonstrated the potential to optimize AED deployment more effectively. Furthermore, by integrating updated PAD strategies, our model can be further adapted to support drone-based AED delivery systems, offering a flexible and data-driven approach for future implementation.