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

Stuart Howell; Karen Smith; Judith Finn; Peter Cameron; Stephen Ball; Emma Bosley; Tan Doan; Bridget Dicker; Steven Faddy; Ziad Nehme; Andy Swain; Melanie Thorrowgood; Andrew Thomas; Samuel Perillo; Mike McDermott; Tony Smith; Janet Bray; Aus-ROC OHCA Epistry Management Committee

doi:10.1016/j.resuscitation.2023.109847

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

Resuscitation. 2023 Jul:188:109847. doi: 10.1016/j.resuscitation.2023.109847. Epub 2023 May 19.

Authors

Stuart Howell¹, Karen Smith², Judith Finn³, Peter Cameron⁴, Stephen Ball⁵, Emma Bosley⁶, Tan Doan⁷, Bridget Dicker⁸, Steven Faddy⁹, Ziad Nehme¹⁰, Andy Swain¹¹, Melanie Thorrowgood¹², Andrew Thomas¹³, Samuel Perillo¹⁴, Mike McDermott¹⁵, Tony Smith¹⁶, Janet Bray¹⁷; Aus-ROC OHCA Epistry Management Committee

Affiliations

¹ School of Public Health and Preventive Medicine, Monash University, Victoria, Australia.
² School of Public Health and Preventive Medicine, Monash University, Victoria, Australia; Department of Paramedicine, Monash University, Victoria, Australia.
³ School of Public Health and Preventive Medicine, Monash University, Victoria, Australia; Prehospital, Resuscitation and Emergency Care Research Unit (PRECRU), Curtin University, Western Australia, Australia; St John Western Australia, Western Australia, Australia.
⁴ School of Public Health and Preventive Medicine, Monash University, Victoria, Australia; Emergency and Trauma Centre, The Alfred, Melbourne, Victoria, Australia.
⁵ Prehospital, Resuscitation and Emergency Care Research Unit (PRECRU), Curtin University, Western Australia, Australia; St John Western Australia, Western Australia, Australia.
⁶ Queensland Ambulance Service, Queensland, Australia; School of Clinical Sciences, Queensland University of Technology, Queensland, Australia.
⁷ Queensland Ambulance Service, Queensland, Australia.
⁸ St John New Zealand, Auckland, New Zealand; Auckland University of Technology, Auckland, New Zealand.
⁹ NSW Ambulance, New South Wales, Australia.
¹⁰ School of Public Health and Preventive Medicine, Monash University, Victoria, Australia; Ambulance Victoria, Victoria, Australia.
¹¹ Wellington Free Ambulance, New Zealand.
¹² SA Ambulance Service, South Australia, Australia.
¹³ St John Ambulance NT, Northern Territory, Australia.
¹⁴ ACT Ambulance, Australian Capital Territory, Australia.
¹⁵ Ambulance Tasmania, Tasmania, Australia.
¹⁶ St John New Zealand, Auckland, New Zealand.
¹⁷ School of Public Health and Preventive Medicine, Monash University, Victoria, Australia; Prehospital, Resuscitation and Emergency Care Research Unit (PRECRU), Curtin University, Western Australia, Australia; Emergency and Trauma Centre, The Alfred, Melbourne, Victoria, Australia. Electronic address: janet.bray@monash.edu.

PMID: 37211232
DOI: 10.1016/j.resuscitation.2023.109847

Abstract

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.

Keywords: Emergency medical services; Heart arrest; Out of hospital cardiac arrest; Registries; Resuscitation.

MeSH terms

Adult
Australia / epidemiology
Benchmarking
Cardiopulmonary Resuscitation*
Cohort Studies
Emergency Medical Services*
Humans
New Zealand / epidemiology
Out-of-Hospital Cardiac Arrest* / therapy
Registries
Risk Adjustment