Predicting survival from out-of-hospital cardiac arrest: A graphic model

To develop a graphic model that describes survival from sudden out-of-hospital cardiac arrest as a function of time intervals to critical prehospital interventions. From a cardiac arrest surveillance system in place since 1976 in King County, Washington, we selected 1,667 cardiac arrest patients with a high likelihood of survival: they had underlying heart disease, were in ventricular fibrillation, and had arrested before arrival of emergency medical services (EMS) personnel. For each patient, we obtained the time intervals from collapse to CPR, to first defibrillatory shock, and to initiation of advanced cardiac life support (ACLS). A multiple linear regression model fitting the data gave the following equation: survival rate = 67% − 2.3% per minute to CPR − 1.1% per minute to defibrillation − 2.1% per minute to ACLS, which was significant at P < .001. The first term, 67%, represents the survival rate if all three interventions were to occur immediately on collapse. Without treatment (CPR, defibrillatory shock, or definitive care), the decline in survival rate is the sum of the three coefficients, or 5.5% per minute. Survival rates predicted by the model for given EMS response times approximated published observed rates for EMS systems in which paramedics respond with or without emergency medical technicians. The model is useful in planning community EMS programs, comparing EMS systems, and showing how different arrival times within a system affect survival rate.