Chen Ji1, Terry P Brown1, Scott J Booth1, Claire Hawkes1, Jerry P Nolan1,2, James Mapstone3, Rachael T Fothergill1,4, Robert Spaight5, Sarah Black6, Gavin D Perkins1,7. 1. Warwick Clinical Trials Unit, University of Warwick, Coventry CV4 7AL, UK. 2. Royal United Hospitals, Bath BA1 3NG, UK. 3. South of England, Public Health England, UK. 4. London Ambulance Service NHS Trust, London SE1 8SD, UK. 5. East Midlands Ambulance Service NHS Trust, Nottingham NG8 6PY, UK. 6. South Western Ambulance Service NHS Foundation Trust, Exeter EX2 7HY, UK. 7. University Hospitals Birmingham NHS Foundation Trust, Birmingham B91 2JL, UK.
Abstract
AIMS: The out-of-hospital cardiac arrest (OHCA) outcomes project is a national research registry. One of its aims is to explore sources of variation in OHCA survival outcomes. This study reports the development and validation of risk prediction models for return of spontaneous circulation (ROSC) at hospital handover and survival to hospital discharge. METHODS AND RESULTS: The study included OHCA patients who were treated during 2014 and 2015 by emergency medical services (EMS) from seven English National Health Service ambulance services. The 2014 data were used to identify important variables and to develop the risk prediction models, which were validated using the 2015 data. Model prediction was measured by area under the curve (AUC), Hosmer-Lemeshow test, Cox calibration regression, and Brier score. All analyses were conducted using mixed-effects logistic regression models. Important factors included age, gender, witness/bystander cardiopulmonary resuscitation (CPR) combined, aetiology, and initial rhythm. Interaction effects between witness/bystander CPR with gender, aetiology and initial rhythm and between aetiology and initial rhythm were significant in both models. The survival model achieved better discrimination and overall accuracy compared with the ROSC model (AUC = 0.86 vs. 0.67, Brier score = 0.072 vs. 0.194, respectively). Calibration tests showed over- and under-estimation for the ROSC and survival models, respectively. A sensitivity analysis individually assessing Index of Multiple Deprivation scores and location in the final models substantially improved overall accuracy with inconsistent impact on discrimination. CONCLUSION: Our risk prediction models identified and quantified important pre-EMS intervention factors determining survival outcomes in England. The survival model had excellent discrimination. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: The out-of-hospital cardiac arrest (OHCA) outcomes project is a national research registry. One of its aims is to explore sources of variation in OHCA survival outcomes. This study reports the development and validation of risk prediction models for return of spontaneous circulation (ROSC) at hospital handover and survival to hospital discharge. METHODS AND RESULTS: The study included OHCA patients who were treated during 2014 and 2015 by emergency medical services (EMS) from seven English National Health Service ambulance services. The 2014 data were used to identify important variables and to develop the risk prediction models, which were validated using the 2015 data. Model prediction was measured by area under the curve (AUC), Hosmer-Lemeshow test, Cox calibration regression, and Brier score. All analyses were conducted using mixed-effects logistic regression models. Important factors included age, gender, witness/bystander cardiopulmonary resuscitation (CPR) combined, aetiology, and initial rhythm. Interaction effects between witness/bystander CPR with gender, aetiology and initial rhythm and between aetiology and initial rhythm were significant in both models. The survival model achieved better discrimination and overall accuracy compared with the ROSC model (AUC = 0.86 vs. 0.67, Brier score = 0.072 vs. 0.194, respectively). Calibration tests showed over- and under-estimation for the ROSC and survival models, respectively. A sensitivity analysis individually assessing Index of Multiple Deprivation scores and location in the final models substantially improved overall accuracy with inconsistent impact on discrimination. CONCLUSION: Our risk prediction models identified and quantified important pre-EMS intervention factors determining survival outcomes in England. The survival model had excellent discrimination. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: Gavin D Perkins; Andrew S Lockey; Mark A de Belder; Fionna Moore; Peter Weissberg; Huon Gray Journal: Emerg Med J Date: 2015-09-23 Impact factor: 2.740
Authors: Joseph E Tonna; Craig H Selzman; Saket Girotra; Angela P Presson; Ravi R Thiagarajan; Lance B Becker; Chong Zhang; Peter Rycus; Heather T Keenan Journal: JACC Cardiovasc Interv Date: 2022-01-12 Impact factor: 11.195