AIM: We examined the relationship between time from collapse to arrival of emergency medical services (EMS) and survival to hospital discharge for out-of-hospital ventricular fibrillation cardiac arrests in order to determine meaningful interpretations of this association. METHODS: We calculated survival rates in 1-min intervals from collapse to EMS arrival. Additionally, we used logistic regression to determine the absolute probability of survival per minute of delayed EMS arrival. We created a logistic regression model with spline terms for the time variable to examine the decline in survival in intervals that are hypothesized to be physiologically relevant. RESULTS: The observed data showed survival declined, on average, by 3% for each minute that EMS was delayed following collapse. Survival rates did not decline appreciably if the time between collapse and arrival of EMS was 4 min or less but they declined by 5.2% per minute between 5 and 10 min. EMS arrival 11-15 min after collapse showed a less steep decline in survival of 1.9% per minute. The spline model that incorporated changes in slope in the time interval variable modeled this relationship more accurately than a model with a continuous term for time (p=0.01). CONCLUSIONS: The results of our analyses show that survival from out-of-hospital cardiac arrest does not decline at a constant rate following collapse. Models that incorporate changes that reflect the physiological alterations that occur following cardiac arrests are a more accurate way to describe changes in survival rates over time than models that include only a continuous term for time. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
AIM: We examined the relationship between time from collapse to arrival of emergency medical services (EMS) and survival to hospital discharge for out-of-hospital ventricular fibrillation cardiac arrests in order to determine meaningful interpretations of this association. METHODS: We calculated survival rates in 1-min intervals from collapse to EMS arrival. Additionally, we used logistic regression to determine the absolute probability of survival per minute of delayed EMS arrival. We created a logistic regression model with spline terms for the time variable to examine the decline in survival in intervals that are hypothesized to be physiologically relevant. RESULTS: The observed data showed survival declined, on average, by 3% for each minute that EMS was delayed following collapse. Survival rates did not decline appreciably if the time between collapse and arrival of EMS was 4 min or less but they declined by 5.2% per minute between 5 and 10 min. EMS arrival 11-15 min after collapse showed a less steep decline in survival of 1.9% per minute. The spline model that incorporated changes in slope in the time interval variable modeled this relationship more accurately than a model with a continuous term for time (p=0.01). CONCLUSIONS: The results of our analyses show that survival from out-of-hospital cardiac arrest does not decline at a constant rate following collapse. Models that incorporate changes that reflect the physiological alterations that occur following cardiac arrests are a more accurate way to describe changes in survival rates over time than models that include only a continuous term for time. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.
Authors: Catherine O Johnson; Rozenn N Lemaitre; Carol E Fahrenbruch; Stephanie Hesselson; Nona Sotoodehnia; Barbara McKnight; Kenneth M Rice; Pui-Yan Kwok; David S Siscovick; Thomas D Rea Journal: Circ Cardiovasc Genet Date: 2012-06-01
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