Spatiotemporal AED optimization is generalizable

Abstract

Aims

Mathematical optimization of automated external defibrillator (AED) placements has the potential to improve out-of-hospital cardiac arrest (OHCA) coverage and reverse the negative effects of limited AED accessibility. However, the generalizability of optimization approaches has not yet been investigated. Our goal is to examine the performance and generalizability of a spatiotemporal AED placement optimization methodology, initially developed for Toronto, Canada, to the new study setting of Copenhagen, Denmark.

Methods

We identified all public OHCAs (1994–2016) and all registered AEDs (2016) in Copenhagen, Denmark. We calculated the coverage loss associated with limited temporal accessibility of registered AEDs, and used a spatiotemporal optimization model to quantify the potential coverage gain of optimized AED deployment. Coverage gain of spatiotemporal deployment over a spatial-only solution was quantified through 10-fold cross-validation. Statistical testing was performed using χ² and McNemar’s tests.

Results

We found 2149 public OHCAs and 1573 registered AED locations. Coverage loss was found to be 24.4% (1104 OHCAs covered under assumed 24/7 coverage, and 835 OHCAs under actual coverage). The coverage gain from using the spatiotemporal model over a spatial-only approach was 15.3%. Temporal and geographical trends in coverage gain were similar to Toronto.

Conclusions

Without modification, a previously developed spatiotemporal AED optimization approach was applied to Copenhagen, resulting in similar OHCA coverage findings as Toronto, despite large geographic and cultural differences between the two cities. In addition to reinforcing the importance of temporal accessibility of AEDs, these similarities demonstrate the generalizability of optimization approaches to improve AED placement and accessibility.

Introduction

Out-of-hospital cardiac arrest (OHCA) affects over 700,000 people a year in North America and Europe [1,2]. Survival from OHCA decreases rapidly for every minute delay in treatment [3]. Treatment options include cardiopulmonary resuscitation (CPR) and defibrillation. In particular, publicly located automated external defibrillators (AEDs) can be used by bystanders to reduce the delay to defibrillation for OHCA victims [[3], [4], [5], [6]]. Consequently, much effort has focused on implementing public access defibrillator (PAD) programs and developing guidelines for strategic AED placement, which recommend AEDs be placed in high-risk areas and be easily reachable within a few minutes [7,8]. Prior research has focused on quantifying OHCA risk in different location types in cities worldwide, demonstrating generalizability of many of the findings [3,6,[9], [10], [11], [12], [13], [14]]. For example, transportation and recreation facilities have been established in multiple studies as high-risk areas that can benefit from AED placement [3,6,9,15]. In practice, AEDs may be positioned based on local or political decisions, resulting in paradoxical placement in low risk areas [9,16].

For an AED to be used it needs to be accessible. Previous research has shown that in North America and Europe, inaccessible AEDs can significantly decrease OHCA coverage [17], in particular by over 50% during the weekends, evening, and night times [18]. To better guide AED placement and temporal AED accessibility, current research has focused on mathematical optimization of AED placements [17,[19], [20], [21], [22], [23]]. Studies from Toronto, Canada, suggest that optimizing AED locations can outperform population-guided strategies [19], reverse the negative effects of limited temporal availability [17], and be cost-effective [22]. However, unlike the findings on OHCA risk in different location types, it is currently unclear whether the optimization methodologies and results are generalizable. Establishing generalizability is particularly important since the potential financial benefits of optimization strategies can be realized through more efficient PAD programs, many of which have low utilization despite widespread and costly AED deployment [24].

The current paper presents the first study to determine generalizability of previous optimization research for AED placement. In particular, we use the methodology from the spatiotemporal optimization study from Toronto, Canada [17], and apply it to a new study setting of Copenhagen, Denmark. We perform two analyses using Copenhagen data: 1) quantify the temporal availabilities and OHCA coverage of existing registered AEDs, and 2) measure the improvement in AED accessibility and OHCA coverage from spatiotemporal optimization of AED locations. Copenhagen and Toronto are contrasting in size, population, city structure, existing AED networks, and working hours [9,17,[25], [26], [27], [28], [29], [30], [31]]. Given these differences, establishing generalizability to Copenhagen suggests that optimization will be effective in other settings as well.