TY - JOUR
T1 - The development of a risk-adjustment strategy to benchmark emergency medical service (EMS) performance in relation to out-of-hospital cardiac arrest in Australia and New Zealand
AU - Howell, Stuart
AU - Smith, Karen
AU - Finn, Judith
AU - Cameron, Peter
AU - Ball, Stephen
AU - Bosley, Emma
AU - Doan, Tan
AU - Dicker, Bridget
AU - Faddy, Steven
AU - Nehme, Ziad
AU - Swain, Andy
AU - Thorrowgood, Melanie
AU - Thomas, Andrew
AU - Perillo, Samuel
AU - McDermott, Mike
AU - Smith, Tony
AU - Bray, Janet
AU - on behalf of the Aus-ROC OHCA Epistry Management Committee
N1 - Funding Information:
This study was funded by an Australian Resuscitation Council Victorian Branch grant. SH was funded by the National Health and Medical Research Council (NHMRC) Prehospital Emergency Care Centre of Research Excellence (#116453) and a Heart Foundation of Australia Vanguard Grant (#103010). JB (#104751) and ZN (#105690) were funded by Heart Foundation of Australia Fellowships. JF was supported by NHMRC Investigator Grant (#1174838) and PC by a Medical Research Future Fund Fellowship (#1139686).
Funding Information:
Non-author members of the Epistry Management Committee: Gavin Perkins, Melissa Dyson, Natalie Heriot, Nicole Packham, Patrick Meere, Matt Green, Rudi Brits. We thank Ben Beck (Monash University) for his early work on this project. This study was funded by an Australian Resuscitation Council Victorian Branch grant. SH was funded by the National Health and Medical Research Council (NHMRC) Prehospital Emergency Care Centre of Research Excellence (#116453) and a Heart Foundation of Australia Vanguard Grant (#103010). JB (#104751) and ZN (#105690) were funded by Heart Foundation of Australia Fellowships. JF was supported by NHMRC Investigator Grant (#1174838) and PC by a Medical Research Future Fund Fellowship (#1139686).
Publisher Copyright:
© 2023 The Authors
PY - 2023/7
Y1 - 2023/7
N2 - Introduction: The aim of this study was to develop a risk adjustment strategy, including effect modifiers, for benchmarking emergency medical service (EMS) performance for out-of-hospital cardiac arrest (OHCA) in Australia and New Zealand. Method: Using 2017–2019 data from the Australasian Resuscitation Outcomes Consortium (Aus-ROC) OHCA Epistry, we included adults who received an EMS attempted resuscitation for a presumed medical OHCA. Logistic regression was applied to develop risk adjustment models for event survival (return of spontaneous circulation at hospital handover) and survival to hospital discharge/30 days. We examined potential effect modifiers, and assessed model discrimination and validity. Results: Both OHCA survival outcome models included EMS agency and the Utstein variables (age, sex, location of arrest, witnessed arrest, initial rhythm, bystander cardiopulmonary resuscitation, defibrillation prior to EMS arrival, and EMS response time). The model for event survival had good discrimination according to the concordance statistic (0.77) and explained 28% of the variation in survival. The corresponding figures for survival to hospital discharge/30 days were 0.87 and 49%. The addition of effect modifiers did little to improve the performance of either model. Conclusion: The development of risk adjustment models with good discrimination is an important step in benchmarking EMS performance for OHCA. The Utstein variables are important in risk-adjustment, but only explain a small proportion of the variation in survival. Further research is required to understand what factors contribute to the variation in survival between EMS.
AB - Introduction: The aim of this study was to develop a risk adjustment strategy, including effect modifiers, for benchmarking emergency medical service (EMS) performance for out-of-hospital cardiac arrest (OHCA) in Australia and New Zealand. Method: Using 2017–2019 data from the Australasian Resuscitation Outcomes Consortium (Aus-ROC) OHCA Epistry, we included adults who received an EMS attempted resuscitation for a presumed medical OHCA. Logistic regression was applied to develop risk adjustment models for event survival (return of spontaneous circulation at hospital handover) and survival to hospital discharge/30 days. We examined potential effect modifiers, and assessed model discrimination and validity. Results: Both OHCA survival outcome models included EMS agency and the Utstein variables (age, sex, location of arrest, witnessed arrest, initial rhythm, bystander cardiopulmonary resuscitation, defibrillation prior to EMS arrival, and EMS response time). The model for event survival had good discrimination according to the concordance statistic (0.77) and explained 28% of the variation in survival. The corresponding figures for survival to hospital discharge/30 days were 0.87 and 49%. The addition of effect modifiers did little to improve the performance of either model. Conclusion: The development of risk adjustment models with good discrimination is an important step in benchmarking EMS performance for OHCA. The Utstein variables are important in risk-adjustment, but only explain a small proportion of the variation in survival. Further research is required to understand what factors contribute to the variation in survival between EMS.
KW - Emergency medical services
KW - Heart arrest
KW - Out of hospital cardiac arrest
KW - Registries
KW - Resuscitation
UR - http://www.scopus.com/inward/record.url?scp=85160632798&partnerID=8YFLogxK
U2 - 10.1016/j.resuscitation.2023.109847
DO - 10.1016/j.resuscitation.2023.109847
M3 - Article
C2 - 37211232
AN - SCOPUS:85160632798
SN - 0300-9572
VL - 188
JO - Resuscitation
JF - Resuscitation
M1 - 109847
ER -