Abstract 16111: Modeling and Spatial Assessment of a Bystander Notification System for Out-of-hospital Cardiac Arrests in Pittsburgh, PA
Introduction: Rapid cardiopulmonary resuscitation (CPR) or defibrillation with an automated external defibrillator (AED) can increase rate of survival after an OHCA. Bystander notification systems (BNS) alert and direct trained responders within proximity to OHCAs to initiate CPR and provide immediate treatment. This study aimed to assess the characteristics and impact of BNS, if it were deployed in Pittsburgh, PA.
Methods: Locations of OHCAs from 2009-2014 were obtained from the Pittsburgh Site for Resuscitation Outcomes Consortium (n=2912). AED locations were gathered from the Pittsburgh PULSE Program and the HeartMap Challenge (n=531). Analysis for this assessment was conducted using Quantum GIS (QGIS) and MATLAB (Mathworks, INC).
Simulations incorporating actual AEDs, OHCAs, and population characteristics were run to assess bystander deployment strategies. Factors to calculate number of bystanders in each census tract were the following: fixed random points, population, population density, shape area, cardiac arrest incidence, cardiovascular disease mortality (CVD) and age over 65. Maps were created for each factor to calculate total distance and time for a random layperson to travel to an AED and then to an OHCA. Repeated simulations (k=500) produced statistical summaries for total distance and time traveled. Using the factor that produced the lowest distance, spatial analysis was conducted to determine the degree of difference in response time between deployment of bystanders and emergency medical services (EMS). A one-month survival outcome model was estimated using BNS and EMS assessed times.
Results: CVD and Fixed Random Points produced the lowest total distance traveled from the repeated simulations. In 92% of all of the census tracts, bystanders arrived prior to EMS. The survival outcome model predicts that 17.2% (Median = 17.1%, Standard Error = 3.7%) of victims will survive when assessed by layperson and 7.9% (Median = 7.9%, Standard Error = 0.179) will survive with EMS.
Conclusion: Total time of arrival to an OHCA by a layperson may decrease with the use of BNS. With further research and use of real data, BNS may have potential to improve overall survival rates.
Author Disclosures: S. Srinivasan: None. D.D. Salcido: Research Grant; Significant; Laerdal Foundation Small Grant, Henry L. Hillman Foundation Project Grant, NIH/NHLBI Career Development in Emergency Medicine Program (K12HL109068).
- © 2016 by American Heart Association, Inc.