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Do patient characteristics or factors at resuscitation influence long-term outcome in patients surviving to be discharged following inhospital cardiac arrest?
Critical Care volume 11, Article number: P326 (2007)
Mortality among patients surviving to be discharged following inhospital cardiac arrest (IHCA) is high. The present study assesses whether this might be explained by differences in patient factor or in factors at resuscitation.
An analysis of IHCA data collected from one Swedish tertiary hospital and from five Finnish secondary hospitals over a 10-year period. The study was limited to patients surviving to be discharged from the hospital. Multiple logistic regression analysis was used to identify factors associated with survival at 1 year from the arrest.
Of a total of 1,578 resuscitated patients, 441 (28%) survived to hospital discharge and 359 (80%) were alive at 12 months. Factors associated with survival at 12 months were age (odds ratio (OR) 0.96, 95% confidence interval (CI) 0.935–0.979), no renal disease (OR 0.4, CI 0.2–0.9), good functional status at discharge (OR 2.9, CI 1.4–6.0), and arrest occurring at (compared with arrests at general wards) the emergency ward (OR 5.8, CI 1.8–18), cardiac care unit (OR 2.9, CI 1.3–6.3), ICU (OR 2.6, CI 1.1–6.2), ward for thoracic surgery (OR 12.9, CI 3.4–49.1) and unit for interventional radiology (OR 16.4, CI 4.4–61.2). There was no difference in initial rhythm, delay to defibrillation or delay to return of spontaneous circulation between survivors and nonsurvivors at 12 months.
Several patient factors, mainly age, functional status and co-morbid disease, influence long-term survival following IHCA. Location of the arrest also influences survival, but the initial rhythm, the delays to defibrillation and return of spontaneous circulation do not.
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Skrifvars, M., Castrén, M., Nurmi, J. et al. Do patient characteristics or factors at resuscitation influence long-term outcome in patients surviving to be discharged following inhospital cardiac arrest?. Crit Care 11, P326 (2007). https://0-doi-org.brum.beds.ac.uk/10.1186/cc5486
- Public Health
- Confidence Interval
- Regression Analysis
- Logistic Regression
- Logistic Regression Analysis