Subject(s)
Out-of-Hospital Cardiac Arrest/mortality , Out-of-Hospital Cardiac Arrest/therapy , Time-to-Treatment , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , Cardiopulmonary Resuscitation , Emergency Medical Services , Female , Humans , Logistic Models , Male , Middle Aged , Registries , Retrospective Studies , Sex Distribution , Sweden/epidemiology , Time Factors , Treatment Outcome , Young AdultABSTRACT
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.
Subject(s)
Algorithms , Machine Learning , Out-of-Hospital Cardiac Arrest/mortality , Survival Analysis , Age Factors , Aged , Cardiopulmonary Resuscitation , Emergency Medical Services , Female , Heart Rate , Humans , Male , Out-of-Hospital Cardiac Arrest/therapy , Sweden , Time-to-TreatmentABSTRACT
AIM: To investigate characteristics and outcome among patients suffering in-hospital cardiac arrest (IHCA) with the emphasis on gender and age. METHODS: Using the Swedish Register of Cardiopulmonary Resuscitation, we analyzed associations between gender, age and co-morbidities, etiology, management, 30-day survival and cerebral function among survivors in 14,933 cases of IHCA. Age was divided into three ordered categories: young (18-49years), middle-aged (50-64years) and older (65years and above). Comparisons between men and women were age adjusted. RESULTS: The mean age was 72.7years and women were significantly older than men. Renal dysfunction was the most prevalent co-morbidity. Myocardial infarction/ischemia was the most common condition preceding IHCA, with men having 27% higher odds of having MI as the underlying etiology. A shockable rhythm was found in 31.8% of patients, with men having 52% higher odds of being found in VT/VF. After adjusting for various confounders, it was found that men had a 10% lower chance than women of surviving to 30days. Older individuals were managed less aggressively than younger patients. Increasing age was associated with lower 30-day survival but not with poorer cerebral function among survivors. CONCLUSION: When adjusting for various confounders, it was found that men had a 10% lower chance than women of surviving to 30days after in-hospital cardiac arrest. Older individuals were managed less aggressively than younger patients, despite a lower chance of survival. Higher age was, however, not associated with poorer cerebral function among survivors.
