Elsevier

Resuscitation

Volume 34, Issue 1, February 1997, Pages 27-34
Resuscitation

Predicting survival from out-of-hospital cardiac arrest: a multivariate analysis

https://doi.org/10.1016/S0300-9572(96)01031-3Get rights and content

Abstract

From 954 attempts to resuscitate patients from out-of-hospital cardiac arrest two datasets were derived, namely 861 cases of cardiac arrest and 906 cases of either cardiac or primary respiratory arrest. For each dataset, multivariate analysis was performed by fitting a number of explanatory variables with respect to the outcomes of admission to hospital and discharge home in logistic regression models. There were numerous interactions between these variables.

Being conscious at the time of the arrival of the ambulance crew and subsequently having cardiac arrest strongly predicted survival, as did both the presence of a witness to the arrest and the initiation of cardiopulmonary resuscitation (CPR) by a bystander; this latter effect was a marker for early CPR. The strongest predictor of a poor outcome was delay to CPR or delay to advanced cardiac life support.

Introduction

The likelihood that an individual will be admitted to hospital, and later discharged, following an episode of pre-hospital cardiorespiratory arrest is related to a variety of factors. These factors include characteristics of the patient (age, gender, social class, racial group and previous medical history), details of the arrest (place of collapse, presence of a witness, aetiology and initial rhythm) and characteristics of the resuscitation attempt (bystander-initiated cardiopulmonary resuscitation (CPR), delay from collapse to basic life support (BLS), early defibrillation and delay from collapse to advanced cardiac life support (ACLS)) 1, 2, 3, 4, 5.

Many of the reported series of resuscitations from pre-hospital cardiac arrest have analysed these factors but have failed to take into account the complex interactions that exist between them. In this study we use logistic regression analysis to describe the association of a number of factors, most of which can be assessed at the time of attempted resuscitation, with successful admission to hospital and discharge home.

Section snippets

South Glamorgan emergency medical service (EMS)

The data used in this paper are drawn from 954 attempted resuscitations by members of South Glamorgan EMS during a 138-week period between 1989 and 1992. The demography of the county and the structure of the EMS are described in detail in a following article [6]. Briefly, South Glamorgan is an urban and semi-rural county with a resident population of approximately 400 000 contained within 484 square km. During the period of data collection the EMS consisted of ambulances crewed by emergency

Results

The characteristics of the 954 attempted resuscitations are outlined in Table 1. Of these, 136 (14.3%) were admitted to the hospital wards and 68 (7.1%) were discharged.

Limitations of analysis

Univariate analysis reveals many significant associations between characteristics of cardiac arrest victims, the location of the arrest, resuscitation attempts and the EMS provision with both admission and discharge. However these characteristics are themselves associated and there may be many interactions between variables. For example, patients who collapse in a private house are less likely to have a witnessed arrest or to receive CPR from a bystander than those collapsing in the street 8, 9

Conclusion

Our data has demonstrated a number of independent factors associated with improved survival following out-of-hospital cardiac arrest. Whereas some of these are only of value in developing predictive models of success of resuscitation and are beyond the direct control of EMS planners, the importance of rapid response and treatment lies in the ability of these two variables to be influenced by emergency systems.

Acknowledgements

This work was funded by a Welsh Office Operational Research Grant and by the Prophit-Rosser Scholarship of the Royal College of Physicians of London. We thank Dr Michael Stephens, Dr Peter Donnelly and Ms Carolyn Lester.

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