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Predicting recurrent cardiac arrest in individuals surviving Out-of-Hospital cardiac arrest
Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.ORCID iD: 0000-0002-7438-230X
Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.
Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.
Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.
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2023 (English)In: Resuscitation, ISSN 0300-9572, E-ISSN 1873-1570, Vol. 184, article id 109678Article in journal (Refereed) Published
Abstract [en]

Background: Despite improvements in short-term survival for Out-of-Hospital Cardiac Arrest (OHCA) in the past two decades, long-term survival is still not well studied. Furthermore, the contribution of different variables on long-term survival have not been fully investigated.

Aim: Examine the 1-year prognosis of patients discharged from hospital after an OHCA. Furthermore, identify factors predicting re-arrest and/or death during 1-year follow-up.

Methods: All patients 18 years or older surviving an OHCA and discharged from the hospital were identified from the Swedish Register for Cardiopulmonary Resuscitation (SRCR). Data on diagnoses, medications and socioeconomic factors was gathered from other Swedish registers. A machine learning model was constructed with 886 variables and evaluated for its predictive capabilities. Variable importance was gathered from the model and new models with the most important variables were created.

Results: Out of the 5098 patients included, 902 (∼18%) suffered a recurrent cardiac arrest or death within a year. For the outcome death or re-arrest within 1 year from discharge the model achieved an ROC (receiver operating characteristics) AUC (area under the curve) of 0.73. A model with the 15 most important variables achieved an AUC of 0.69.

Conclusions: Survivors of an OHCA have a high risk of suffering a re-arrest or death within 1 year from hospital discharge. A machine learning model with 15 different variables, among which age, socioeconomic factors and neurofunctional status at hospital discharge, achieved almost the same predictive capabilities with reasonable precision as the full model with 886 variables.

 

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 184, article id 109678
Keywords [en]
Machine learning, Out-of-hospital cardiac arrest, Prognosis
National Category
Cardiac and Cardiovascular Systems
Identifiers
URN: urn:nbn:se:hb:diva-29226DOI: 10.1016/j.resuscitation.2022.109678ISI: 000949949100001Scopus ID: 2-s2.0-85146093009OAI: oai:DiVA.org:hb-29226DiVA, id: diva2:1725476
Available from: 2023-01-11 Created: 2023-01-11 Last updated: 2024-02-01Bibliographically approved

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Herlitz, Johan

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