A prediction tool for initial out-of-hospital cardiac arrest survivors

AbstractAimImprovement in predicting survival after out-of-hospital cardiac arrest is of major medical, scientific and socioeconomic interest. The current study aimed at developing an accurate outcome-prediction tool for patients following out-of-hospital cardiac arrests.MethodsThis retrospective cohort study was based on a cardiac arrest registry. From out-of-hospital cardiac arrest patients (n=1932), a set of variables established before restoration of spontaneous circulation was explored using multivariable logistic regression. To obtain reliable estimates of the classification performance the patients were allocated to training (oldest 80%) and validation (most recent 20%) sets. The main performance parameter was the area under the ROC curve (AUC), classifying patients into survivors/non-survivors after 30 days. Based on rankings of importance, a subset of variables was selected that would have the same predictive power as the entire set. This reduced-variable set was used to derive a comprehensive score to predict mortality.ResultsThe average AUC was 0.827 (CI 0.793–0.861) for a logistic regression model using all 21 variables. This was significantly better than the AUC for any single considered variable. The total amount of adrenaline, number of minutes to sustained restoration of spontaneous circulation, patient age and first rhythm had the same predictive power as all 21 variables. Based on this finding, our score was built and had excellent predictive accuracy (the AUC was 0.810), discriminating patients into 10%, 30%, 50%, 70%, and 90% survival probabilities.ConclusionThe current results are promising to increase prognostication accuracy, and we are confident that our score will be helpful in the daily clinical routine