Identifying locations for public access defibrillators using mathematical optimization.

BACKGROUND: Geospatial methods using mathematical optimization to identify clusters of cardiac arrests and prioritize public locations for defibrillator deployment have not been studied. Our objective was to develop such a method and test its performance against a population-guided approach. METHODS AND RESULTS: All public location cardiac arrests in Toronto, Ontario, Canada, from December 16, 2005, to July 15, 2010, and all automated external defibrillator (AED) locations registered with Toronto Emergency Medical Services as of September 2009 were plotted geographically. Current AED coverage was quantified by determining the number of cardiac arrests occurring within 100 m of a registered AED. Clusters of cardiac arrests without a registered AED within 100 m were identified. With the use of mathematical optimization techniques, cardiac arrest coverage improvements were computed and shown to be superior to results from a population-guided deployment method. There were 1310 eligible public location cardiac arrests and 1669 registered AEDs. Of the eligible cardiac arrests, 304 were within 100 m of at least 1 registered AED (23% coverage). The average distance from a cardiac arrest to the closest AED was 281 m. With AEDs deployed in the top 30 locations, an additional 112 historical cardiac arrests would be covered (32% total coverage), and the average distance to the closest AED would be 262 m. CONCLUSIONS: Geographic clusters of cardiac arrests can be easily identified and prioritized with the use of mathematical modeling. Optimized AED deployment can increase cardiac arrest coverage and decrease the distance to the closest AED. Mathematical modeling can augment public AED deployment programs.

Determining risk for out-of-hospital cardiac arrest by location type in a Canadian urban setting to guide future public access defibrillator placement.

STUDY OBJECTIVE: Automated external defibrillator use by lay bystanders during out-of-hospital cardiac arrest rarely occurs but can improve survival. We seek to estimate risk for out-of-hospital cardiac arrest by location type and evaluate current automated external defibrillator deployment in a Canadian urban setting to guide future automated external defibrillator deployment. METHODS: This was a retrospective analysis of a population-based out-of-hospital cardiac arrest database. We included consecutive public location, nontraumatic, out-of-hospital cardiac arrests occurring in Toronto from January 1, 2006, to June 30, 2010, captured in the Resuscitation Outcomes Consortium Epistry database. Two investigators independently categorized each out-of-hospital cardiac arrest and automated external defibrillator location into one of 38 categories. Total site counts in each location category were used to estimate average annual per-site cardiac arrest incidence and determine the relative automated external defibrillator coverage for each location type. RESULTS: There were 608 eligible out-of-hospital cardiac arrest cases. The top 5 location categories by average annual out-of-hospital cardiac arrests per site were race track/casino (0.67; 95% confidence interval [CI] 0 to 1.63), jail (0.62; 95% CI 0.3 to 1.06), hotel/motel (0.15; 95% CI 0.12 to 0.18), hostel/shelter (0.14; 95% CI 0.067 to 0.19), and convention center (0.11; 95% CI 0 to 0.43). Although schools were relatively lower risk for cardiac arrest, they represented 72.5% of automated external defibrillator-covered locations in the study region. Some higher-risk location types such as hotel/motel, hostel/shelter, and rail station were severely underrepresented with respect to automated external defibrillator coverage. CONCLUSION: We have identified types of locations with higher per-site risk for cardiac arrest relative to others. We have also identified potential mismatches between cardiac arrest risk by location type and registered automated external defibrillator distribution in a Canadian urban setting.