Cost-effectiveness analysis of a 'Termination of Resuscitation' protocol for the management of out-of-hospital cardiac arrest.

BACKGROUND: Historically in Singapore, all out-of-hospital cardiac arrests (OHCA) were transported to hospital for pronouncement of death. A 'Termination of Resuscitation' (TOR) protocol, implemented from 2019 onwards, enables emergency responders to pronounce death at-scene in Singapore. This study aims to evaluate the cost-effectiveness of the TOR protocol for OHCA management. METHODS: Adopting a healthcare provider's perspective, a Markov model was developed to evaluate three competing options: No TOR, Observed TOR reflecting existing practice, and Full TOR if TOR is exercised fully. The model had a cycle duration of 30 days after the initial state of having a cardiac arrest, and was evaluated over a 10-year time horizon. Probabilistic sensitivity analysis was performed to account for uncertainties. The costs per quality adjusted life years (QALY) was calculated. RESULTS: A total of 3,695 OHCA cases eligible for the TOR protocol were analysed; mean age of 73.0 ± 15.5 years. For every 10,000 hypothetical patients, Observed TOR and Full TOR had more deaths by approximately 19 and 31 patients, respectively, compared to No TOR. Full TOR had the least costs and QALYs at $19,633,369 (95% Uncertainty Interval (UI) 19,469,973 to 19,796,764) and 0 QALYs. If TOR is exercised for every eligible case, it could expect to save approximately $400,440 per QALY loss compared to No TOR, and $821,151 per QALY loss compared to Observed TOR. CONCLUSION: The application of the TOR protocol for the management of OHCA was found to be cost-effective within acceptable willingness-to-pay thresholds, providing some justification for sustainable adoption.

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS.

Background: Shock-refractory ventricular fibrillation (VF) or ventricular tachycardia (VT) is a treatment challenge in out-of-hospital cardiac arrest (OHCA). This study aimed to develop and validate machine learning models that could be implemented by emergency medical services (EMS) to predict refractory VF/VT in OHCA patients. Methods: This was a retrospective study examining adult non-traumatic OHCA patients brought into the emergency department by Singapore EMS from the Pan-Asian Resuscitation Outcomes Study (PAROS) registry. Data from April 2010 to March 2020 were extracted for this study. Refractory VF/VT was defined as VF/VT persisting or recurring after at least one shock. Features were selected based on expert clinical opinion and availability to dispatch prior to arrival at scene. Multivariable logistic regression (MVR), LASSO and random forest (RF) models were investigated. Model performance was evaluated using receiver operator characteristic (ROC) area under curve (AUC) analysis and calibration plots. Results: 20,713 patients were included in this study, of which 860 (4.1%) fulfilled the criteria for refractory VF/VT. All models performed comparably and were moderately well-calibrated. ROC-AUC were 0.732 (95% CI, 0.695 - 0.769) for MVR, 0.738 (95% CI, 0.701 - 0.774) for LASSO, and 0.731 (95% CI, 0.690 - 0.773) for RF. The shared important predictors across all models included male gender and public location. Conclusion: The machine learning models developed have potential clinical utility to improve outcomes in cases of refractory VF/VT OHCA. Prediction of refractory VF/VT prior to arrival at patient's side may allow for increased options for intervention both by EMS and tertiary care centres.