Overcoming Spatial and Temporal Barriers to Public Access Defibrillators Via Optimization

Abstract

Background: Immediate access to an automated external defibrillator (AED) increases the chance of survival for out-of-hospital cardiac arrest (OHCA). Current deployment usually considers spatial AED access, assuming AEDs are available 24 h a day.

Objectives: The goal of this study was to develop an optimization model for AED deployment, accounting for spatial and temporal accessibility, to evaluate if OHCA coverage would improve compared with deployment based on spatial accessibility alone.

Methods: This study was a retrospective population-based cohort trial using data from the Toronto Regional RescuNET Epistry cardiac arrest database. We identified all nontraumatic public location OHCAs in Toronto, Ontario, Canada (January 2006 through August 2014) and obtained a list of registered AEDs (March 2015) from Toronto Paramedic Services. Coverage loss due to limited temporal access was quantified by comparing the number of OHCAs that occurred within 100 meters of a registered AED (assumed coverage 24 h per day, 7 days per week) with the number that occurred both within 100 meters of a registered AED and when the AED was available (actual coverage). A spatiotemporal optimization model was then developed that determined AED locations to maximize OHCA actual coverage and overcome the reported coverage loss. The coverage gain between the spatiotemporal model and a spatial-only model was computed by using 10-fold cross-validation.

Results: A total of 2,440 nontraumatic public OHCAs and 737 registered AED locations were identified. A total of 451 OHCAs were covered by registered AEDs under assumed coverage 24 h per day, 7 days per week, and 354 OHCAs under actual coverage, representing a coverage loss of 21.5% (p < 0.001). Using the spatiotemporal model to optimize AED deployment, a 25.3% relative increase in actual coverage was achieved compared with the spatial-only approach (p < 0.001).

Conclusions: One in 5 OHCAs occurred near an inaccessible AED at the time of the OHCA. Potential AED use was significantly improved with a spatiotemporal optimization model guiding deployment.

Keywords: automated external defibrillator; cardiac arrest; emergency cardiac care; resuscitation.

Copyright © 2016 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

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CENTRAL ILLUSTRATION. Optimizing Spatiotemporal AED Access: Time Point Comparisons of Registered AEDs

Of a total of 737 registered automated external defibrillators (AED) in Toronto, 95.9% (707 of 737 AEDs) were available at 12 PM and 30.9% (228 of 737 AEDs) were available at 12 AM. The AEDs included in these maps were available at least 5 days of the week at their respective examined times. Potential AED use was significantly improved with a spatiotemporal optimization model guiding deployment, overcoming decreased accessibility in the evening and night.

FIGURE 1. Registered AED Availability

Automated external defibrillator (AED) availability varied by time of day.

FIGURE 2. OHCA Coverage Gain with Spatiotemporal Model

Overall coverage gain in actual coverage of testing set out-of-hospital cardiac arrests (OHCA) by the spatiotemporal model was statistically significant for all categories (p

FIGURE 3. Comparing Spatiotemporal and Spatial-only Model OHCA Coverage

The difference in actual coverage of testing set OHCAs by prospective AED locations determined by the spatiotemporal and spatial-only models was statistically significant for all N (p