Estimating Effectiveness of Cardiac Arrest Interventions: A Logistic Regression Survival Model

Abstract

Background The study objective was to develop a simple, generalizable predictive model for survival after out-of-hospital cardiac arrest due to ventricular fibrillation. Methods and Results Logistic regression analysis of two retrospective series (n=205 and n=1667, respectively) of out-of-hospital cardiac arrests was performed on data sets from a Southwestern city (population, 415 000; area, 406 km 2 ) and a Northwestern county (population, 1 038 000; area, 1399 km 2 ). Both are served by similar two-tiered emergency response systems. All arrests were witnessed and occurred before the arrival of emergency responders, and the initial cardiac rhythm observed was ventricular fibrillation. The main outcome measure was survival to hospital discharge. Patient age, initiation of CPR by bystanders, interval from collapse to CPR, interval from collapse to defibrillation, bystander CPR/collapse-to-CPR interval interaction, and collapse-to-CPR/collapse-to-defibrillation interval interaction were significantly associated with survival. There was not a significant difference between observed survival rates at the two sites after control for significant predictors. A simplified predictive model retaining only collapse to CPR and collapse to defibrillation intervals performed comparably to the more complicated explanatory model. Conclusions The effectiveness of prehospital interventions for out-of-hospital cardiac arrest may be estimated from their influence on collapse to CPR and collapse to defibrillation intervals. A model derived from combined data from two geographically distinct populations did not identify site as a predictor of survival if clinically relevant predictor variables were controlled for. This model can be generalized to other US populations and used to project the local effectiveness of interventions to improve cardiac arrest survival.