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Search: WFRF:(Nordberg Per) > (2020-2021) > Axelsson Christer > Identifying the rel...

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

Al-Dury, Nooraldeen, 1986 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin,Institute of Medicine
Ravn-Fischer, Annica, 1974 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
Hollenberg, Jacob (author)
Karolinska Institutet
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Israelsson, Johan (author)
Linnéuniversitetet,Sjöfartshögskolan (SJÖ),Kalmar county hospital, Sweden,Linnéuniversitetet, Sjöfartshögskolan (SJÖ)
Nordberg, Per (author)
Karolinska Institutet
Strömsöe, Anneli, 1969- (author)
Mälardalen University, Sweden
Axelsson, Christer (author)
University of Borås, Sweden
Herlitz, Johan, 1949- (author)
Gothenburg University,Göteborgs universitet,Högskolan i Borås,Akademin för vård, arbetsliv och välfärd,University of Gothenburg, Sweden;University of Borås, Sweden,PreHospen,Högskolan i Borås, Akademin för vård, arbetsliv och välfärd,Institutionen för medicin,Institute of Medicine
Rawshani, Araz, 1986 (author)
Gothenburg University,Göteborgs universitet,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
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 (creator_code:org_t)
2020-06-25
2020
English.
In: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. - : BioMed Central. - 1757-7241. ; 28:1, s. 1-8
  • Journal article (peer-reviewed)
Abstract Subject headings
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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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kardiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)

Keyword

Människan i vården
The Human Perspective in Care
Människan i vården
The Human Perspective in Care
Människan i vården
The Human Perspective in Care
Människan i vården
The Human Perspective in Care
Människan i vården
The Human Perspective in Care
Människan i vården
The Human Perspective in Care
Människan i vården
The Human Perspective in Care
Medicin

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