Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • harvard-cite-them-right
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model
Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Anesthesiology and Intensive Care Medicine, Sahlgrenska University Hospital, Mölndal, Sweden.ORCID-id: 0000-0003-2730-8710
Mount Sinai Heart, Icahn School of Medicine at Mount Sinai Health System, New York, NY, United States.
Karolinska Institutet, Department of Medicine, Karolinska University Hospital Danderyd, Stockholm, Sweden.
Högskolan i Borås, Akademin för vård, arbetsliv och välfärd. a Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Visa övriga samt affilieringar
2023 (Engelska)Ingår i: eBioMedicine, ISSN 2352-3964, Vol. 89, artikel-id 104464Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Background: A prediction model that estimates survival and neurological outcome in out-of-hospital cardiac arrest patients has the potential to improve clinical management in emergency rooms.

Methods: We used the Swedish Registry for Cardiopulmonary Resuscitation to study all out-of-hospital cardiac arrest (OHCA) cases in Sweden from 2010 to 2020. We had 393 candidate predictors describing the circumstances at cardiac arrest, critical time intervals, patient demographics, initial presentation, spatiotemporal data, socioeconomic status, medications, and comorbidities before arrest. To develop, evaluate and test an array of prediction models, we created stratified (on the outcome measure) random samples of our study population. We created a training set (60% of data), evaluation set (20% of data), and test set (20% of data). We assessed the 30-day survival and cerebral performance category (CPC) score at discharge using several machine learning frameworks with hyperparameter tuning. Parsimonious models with the top 1 to 20 strongest predictors were tested. We calibrated the decision threshold to assess the cut-off yielding 95% sensitivity for survival. The final model was deployed as a web application.

Findings: We included 55,615 cases of OHCA. Initial presentation, prehospital interventions, and critical time intervals variables were the most important. At a sensitivity of 95%, specificity was 89%, positive predictive value 52%, and negative predictive value 99% in test data to predict 30-day survival. The area under the receiver characteristic curve was 0.97 in test data using all 393 predictors or only the ten most important predictors. The final model showed excellent calibration. The web application allowed for near-instantaneous survival calculations.

Interpretation: Thirty-day survival and neurological outcome in OHCA can rapidly and reliably be estimated during ongoing cardiopulmonary resuscitation in the emergency room using a machine learning model incorporating widely available variables.

Ort, förlag, år, upplaga, sidor
2023. Vol. 89, artikel-id 104464
Nyckelord [en]
Machine learning, Out-of-hospital cardiac arrest, Prediction model, Web application
Nationell ämneskategori
Kardiologi
Forskningsämne
Människan i vården
Identifikatorer
URN: urn:nbn:se:hb:diva-29456DOI: 10.1016/j.ebiom.2023.104464Scopus ID: 2-s2.0-85147657303OAI: oai:DiVA.org:hb-29456DiVA, id: diva2:1738054
Forskningsfinansiär
Vetenskapsrådet, 2019–02019
Anmärkning

Funding: Swedish Research Council (2019–02019); Swedish state under the agreement between the Swedish government, and the county councils (ALFGBG-971482); The Wallenberg Centre for Molecular and Translational Medicine

Tillgänglig från: 2023-02-20 Skapad: 2023-02-20 Senast uppdaterad: 2024-02-01Bibliografiskt granskad

Open Access i DiVA

fulltext(442 kB)61 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 442 kBChecksumma SHA-512
0e6a1da47311d8eb4a8638e881f915578362330a615b8998e9296c2fb22dddf8b23ff3d656439caf8489e32f3684ec8ba3086421dd9a6e1f1826d08a3b400cf3
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextScopus

Person

Lundgren, PeterHerlitz, Johan

Sök vidare i DiVA

Av författaren/redaktören
Hessulf, FredrikLundgren, PeterHerlitz, Johan
Av organisationen
Akademin för vård, arbetsliv och välfärd
Kardiologi

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 61 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
urn-nbn

Altmetricpoäng

doi
urn-nbn
Totalt: 56 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • harvard-cite-them-right
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf