Published in:
Open Access 01-12-2020 | Cardiopulmonary Resuscitation | Original research
Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study
Published in: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine | Issue 1/2020
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Introduction
Studies examining the factors linked to survival after out of hospital cardiac arrest (OHCA) have either aimed to describe the characteristics and outcomes of OHCA in different parts of the world, or focused on certain factors and whether they were associated with survival. Unfortunately, this approach does not measure how strong each factor is in predicting survival after OHCA.
Aim
To investigate the relative importance of 16 well-recognized factors in OHCA at the time point of ambulance arrival, and before any interventions or medications were given, by using a machine learning approach that implies building models directly from the data, and arranging those factors in order of importance in predicting survival.
Methods
Using a data-driven approach with a machine learning algorithm, we studied the relative importance of 16 factors assessed during the pre-hospital phase of OHCA. We examined 45,000 cases of OHCA between 2008 and 2016.
Results
Overall, the top five factors to predict survival in order of importance were: initial rhythm, age, early Cardiopulmonary Resuscitation (CPR, time to CPR and CPR before arrival of EMS), time from EMS dispatch until EMS arrival, and place of cardiac arrest. The largest difference in importance was noted between initial rhythm and the remaining predictors. A number of factors, including time of arrest and sex were of little importance.
Conclusion
Using machine learning, we confirm that the most important predictor of survival in OHCA is initial rhythm, followed by age, time to start of CPR, EMS response time and place of OHCA. Several factors traditionally viewed as important, e.g. sex, were of little importance.