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June 25, 2020SHILAP Revista de lepidopterologíaOpen Access

Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: a machine learning study

Nooraldeen Al-Dury Annica Ravn‐Fischer Jacob Hollenberg Johan Israelsson Per Nordberg Anneli Strömsöe Christer Axelsson Johan Herlitz Araz Rawshani

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Abstract

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 et al. (2020) studied this question.

synapsesocial.com/papers/69df47fa9b582f29b9591925 https://doi.org/10.1186/s13049-020-00742-9

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Recent Trends in Survival From Out-of-Hospital Cardiac Arrest in the United Statescohort · 2014 · 573 citations
2Trends in Outcomes for Out-of-Hospital Cardiac Arrest by Age in JapanOriginal Research · 2015 · 57 citations
3A short delay from out of hospital cardiac arrest to call for ambulance increases survivalcohort · 2003 · 52 citations
4Trends in Survival After In-Hospital Cardiac Arrest During Nights and Weekendscohort · 2018 · 116 citations
5Improved outcome in Sweden after out-of-hospital cardiac arrest and possible association with improvements in every link in the chain of survivalcohort · 2014 · 160 citations

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