Identifying approaches to improve the accuracy of shock outcome prediction for out-of-hospital cardiac arrest

BACKGROUND: Analysis of the electrocardiogram (ECG) can predict if a cardiac arrest patient in ventricular fibrillation is likely to have a return of spontaneous circulation if defibrillated. The accuracy of such methods determines how useful it is clinically and for retrospective analysis. METHODS AND RESULTS: We wanted to identify if there is a potential of improving prediction accuracy by adding peri-arrest factors to an ECG-based prediction system, or constructing a prediction system that adapts to each patient. Therefore, we analysed shock outcome prediction data with a mixed effects logistic regression model to identify if there are random effects (unexplained variation between patients) influencing the prediction accuracy. We also added information about the patients' age, sex and presenting rhythm, ambulance response time and presence of bystander CPR to the model to try to improve it by reducing the random effects. For all the six predictive features analysed random effects where present, with p-values below 10(-3). The random effect size was 73-189% of the feature effect size. Adding the peri-arrest factors to the best ECG-based model gave no significant improvement. CONCLUSIONS: The presence of random effects shows that the shock outcome prediction accuracy can be improved by explaining more of the variation between patients, for example using the approaches outlined above, and that there is within-patient correlation between samples that should be accounted for when evaluating prediction accuracy. The specific peri-arrest factors tested here did not significantly improve prediction accuracy, but other factors should be explored.