Clara E Stoesser1, Justin J Boutilier2, Christopher L F Sun3, Steven C Brooks4, Sheldon Cheskes5, Katie N Dainty6, Michael Feldman7, Dennis T Ko8, Steve Lin9, Laurie J Morrison10, Damon C Scales11, Timothy C Y Chan12. 1. Departmentof Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada. 2. Departmentof Industrial and Systems Engineering, University of Wisconsin - Madison, Madison, WI, USA. Electronic address: jboutilier@wisc.edu. 3. SloanSchool of Management, Massachusetts Institute of Technology, Cambridge, MA, USA; HealthcareSystems Engineering, Massachusetts General Hospital, Boston, MA, USA. 4. LiKa Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Departmentsof Emergency Medicine and Public Health Sciences, Queen's University, Kingston, ON, Canada. 5. LiKa Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Departmentof Family and Community Medicine, Division of Emergency Medicine, University of Toronto, Toronto, ON, Canada; SunnybrookCenter for Prehospital Medicine, Toronto, ON, Canada. 6. Instituteof Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; NorthYork General Hospital, Toronto, ON, Canada. 7. SunnybrookCenter for Prehospital Medicine, Toronto, ON, Canada. 8. Instituteof Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; Institutefor Clinical Evaluation Sciences, Toronto, ON, Canada; SchulichHeart Program, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Departmentof Medicine, University of Toronto, Toronto, ON, Canada. 9. LiKa Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Instituteof Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; Departmentof Medicine, University of Toronto, Toronto, ON, Canada. 10. LiKa Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Departmentof Medicine, University of Toronto, Toronto, ON, Canada. 11. LiKa Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada; Instituteof Health Policy, Management and Evaluation (IHPME), University of Toronto, Toronto, ON, Canada; Institutefor Clinical Evaluation Sciences, Toronto, ON, Canada; Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada. 12. Departmentof Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada; LiKa Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada.
Abstract
BACKGROUND: Although several Utstein variables are known to independently improve survival, how they moderate the effect of emergency medical service (EMS) response times on survival is unknown. OBJECTIVES: To quantify how public location, witnessed status, bystander CPR, and bystander AED shock individually and jointly moderate the effect of EMS response time delays on OHCA survival. METHODS: This retrospective cohort study was a secondary analysis of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database (December 2005 to June 2015). We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCAs from eleven sites across the US and Canada. We trained a logistic regression model with standard Utstein control variables and interaction terms between EMS response time and the four aforementioned OHCA characteristics. RESULTS: 102,216 patients were included. Three of the four characteristics - witnessed OHCAs (OR = 0.962), bystander CPR (OR = 0.968) and public location (OR = 0.980) - increased the negative effect of a one-minute delay on the odds of survival. In contrast, a bystander AED shock decreased the negative effect of a one-minute response time delay on the odds of survival (OR = 1.064). The magnitude of the effect of a one-minute delay in EMS response time on the odds of survival ranged from 1.3% to 9.8% (average: 5.3%), depending on the underlying OHCA characteristics. CONCLUSIONS: Delays in EMS response time had the largest reduction in survival odds for OHCAs that did not receive a bystander AED shock but were witnessed, occurred in public, and/or received bystander CPR. A bystander AED shock appears to be protective against a delay in EMS response time.
BACKGROUND: Although several Utstein variables are known to independently improve survival, how they moderate the effect of emergency medical service (EMS) response times on survival is unknown. OBJECTIVES: To quantify how public location, witnessed status, bystander CPR, and bystander AED shock individually and jointly moderate the effect of EMS response time delays on OHCA survival. METHODS: This retrospective cohort study was a secondary analysis of the Resuscitation Outcomes Consortium Epistry-Cardiac Arrest database (December 2005 to June 2015). We included all adult, non-traumatic, non-EMS witnessed, and EMS-treated OHCAs from eleven sites across the US and Canada. We trained a logistic regression model with standard Utstein control variables and interaction terms between EMS response time and the four aforementioned OHCA characteristics. RESULTS: 102,216 patients were included. Three of the four characteristics - witnessed OHCAs (OR = 0.962), bystander CPR (OR = 0.968) and public location (OR = 0.980) - increased the negative effect of a one-minute delay on the odds of survival. In contrast, a bystander AED shock decreased the negative effect of a one-minute response time delay on the odds of survival (OR = 1.064). The magnitude of the effect of a one-minute delay in EMS response time on the odds of survival ranged from 1.3% to 9.8% (average: 5.3%), depending on the underlying OHCA characteristics. CONCLUSIONS: Delays in EMS response time had the largest reduction in survival odds for OHCAs that did not receive a bystander AED shock but were witnessed, occurred in public, and/or received bystander CPR. A bystander AED shock appears to be protective against a delay in EMS response time.