Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning : the SCARS model

Hessulf, Fredrik ; Bhatt, Deepak L. ; Engdahl, Johan ; Lundgren, Peter ; Omerovic, Elmir ; Rawshani, Aidin ; Helleryd, Edvin ; Dworeck, Christian ; Friberg, Hans LU and Redfors, Björn , et al. (2023) In EBioMedicine 89. p.1-11
Abstract

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... (More)

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. 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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and , et al. (More)
; ; ; ; ; ; ; ; ; ; ; ; ; and (Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Machine learning, Out-of-hospital cardiac arrest, Prediction model, Web application
in
EBioMedicine
volume
89
article number
104464
pages
1 - 11
publisher
Elsevier
external identifiers
  • pmid:36773348
  • scopus:85147657303
ISSN
2352-3964
DOI
10.1016/j.ebiom.2023.104464
language
English
LU publication?
yes
additional info
Funding Information: 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.Araz Rawshani: Unrestricted grant from the Swedish research council(2019-02019), the Swedish Heart-Lung Foundation (20200261) and the Swedish state (ALFGBG-971482). Publisher Copyright: © 2023 The Author(s)
id
f2016067-4279-4a6b-8f2f-d0702d0d223d
date added to LUP
2023-04-20 19:27:49
date last changed
2024-04-19 21:54:36
@article{f2016067-4279-4a6b-8f2f-d0702d0d223d,
  abstract     = {{<p>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. 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.</p>}},
  author       = {{Hessulf, Fredrik and Bhatt, Deepak L. and Engdahl, Johan and Lundgren, Peter and Omerovic, Elmir and Rawshani, Aidin and Helleryd, Edvin and Dworeck, Christian and Friberg, Hans and Redfors, Björn and Nielsen, Niklas and Myredal, Anna and Frigyesi, Attila and Herlitz, Johan and Rawshani, Araz}},
  issn         = {{2352-3964}},
  keywords     = {{Machine learning; Out-of-hospital cardiac arrest; Prediction model; Web application}},
  language     = {{eng}},
  pages        = {{1--11}},
  publisher    = {{Elsevier}},
  series       = {{EBioMedicine}},
  title        = {{Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning : the SCARS model}},
  url          = {{http://dx.doi.org/10.1016/j.ebiom.2023.104464}},
  doi          = {{10.1016/j.ebiom.2023.104464}},
  volume       = {{89}},
  year         = {{2023}},
}