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- Al-Dury, Nooraldeen, 1986, et al.
(author)
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Characteristics and outcome among 14,933 adult cases of in-hospital cardiac arrest : A nationwide study with the emphasis on gender and age.
- 2017
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In: American Journal of Emergency Medicine. - : Elsevier. - 0735-6757 .- 1532-8171. ; 35:12, s. 1839-1844
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Journal article (peer-reviewed)abstract
- 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.
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| 2. |
- Al-Dury, Nooraldeen, 1986, et al.
(author)
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Identifying the relative importance of predictors of survival in out of hospital cardiac arrest : a machine learning study
- 2020
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In: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - : BioMed Central. - 1757-7241. ; 28:1, s. 1-8
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Journal article (peer-reviewed)abstract
- 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.
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- Al-Dury, Nooraldeen, 1986
(author)
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Predictors of survival in cardiac arrest
- 2021
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Doctoral thesis (other academic/artistic)abstract
- Cardiac arrest (CA) refers to the cessation of cardiac function. Survival is around 30% for in-hospital cardiac arrest (IHCA), and 10% for out-of-hospital cardiac arrest (OHCA). Many factors influence survival, ranging from the patient’s age, gender and comorbidities, to the conditions surrounding the arrest, to the emergency medical service (EMS) response time, to post-arrest treatment strategies. In study I, the characteristics and outcome of ca 15,000 cases of IHCA were studied from a national perspective. We found men to have a 10% lower chance than women of surviving to 30 days. Older individuals were managed less aggressively, and had a lower 30-day survival, but a similar cerebral function among survivors compared with younger patients. In study II, machine learning (ML) was used to rank the most important predictors of survival in ca 5,000 cases of IHCA. A shockable presenting rhythm was by far the strongest predictor of survival, followed by the location and the cause of CA, the presence of hypoxia within one hour before the arrest, and then age. The delays to start of CPR and to defibrillation were short in the majority of patients, which may explain why delay was not the most important factor for outcome. Gender did not seem important when using ML. Study III examines ca 22,000 bystander-witnessed cases of OHCA to determine the influence of age and gender on the delays to treatment, and on the association between delay and survival. Patients aged >70 years had a longer delay from collapse to start of CPR after OHCA. The decrease in survival with increasing delay to CPR was more marked in men than in women, whereas the decrease in survival with increasing delay to treatment was similar between older and younger patients. Study IV utilizes machine learning (ML) to rank the most important predictors of survival in ca 45,000 cases of OHCA. The top five predictors of survival after OHCA appear to be: initial rhythm, age, early CPR, EMS response time, and place of CA. Gender did not seem important when using ML.
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