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Predicting survival from out-of-hospital cardiac arrest: a graphic model
M P Larsen1, M S Eisenberg, R O Cummins
1Center for Evaluation of Emergency Medical Services, Emergency Medical Services Division, Seattle.
Insights
Sudden cardiac arrest survival decreases with each minute delay in critical interventions like CPR and defibrillation. This model quantizes survival rates based on time to these life-saving emergency medical services (EMS).
Area of Science:
- Emergency Medicine
- Cardiovascular Research
- Public Health
Background:
- Sudden out-of-hospital cardiac arrest (OHCA) presents a critical public health challenge.
- Survival rates are highly dependent on the timeliness of prehospital interventions.
Purpose of the Study:
- To develop a predictive model for OHCA survival.
- To quantify the impact of time intervals to critical interventions on survival rates.
Main Methods:
- Analysis of 1,667 cardiac arrest patients from a long-term surveillance system.
- Utilized multiple linear regression to model survival as a function of time to CPR, defibrillation, and ACLS.
Main Results:
- Developed a model: survival rate = 67% - 2.3%/min to CPR - 1.1%/min to defibrillation - 2.1%/min to ACLS (P < .001).
- Survival declines by 5.5% per minute without immediate intervention.
- Model predictions align with observed survival rates for various EMS response times.
Conclusions:
- The developed model provides a quantitative tool for assessing OHCA survival.
- Useful for planning and comparing emergency medical services (EMS) programs.
- Highlights the critical importance of rapid intervention in improving survival outcomes.
Study Objective:
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.
Participants:
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.
Methods:
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).
Results:
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.
Conclusion:
The model is useful in planning community EMS programs, comparing EMS systems, and showing how different arrival times within a system affect survival rate.