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Cited by 7Cardiac Arrest and ResuscitationPalliative Care and End-of-Life IssuesMechanical Circulatory Support Devices

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System for Predicting Neurological Outcomes Following Cardiac Arrest Based on Clinical Predictors Using a Machine Learning Method: The Neurological Outcomes After Cardiac Arrest Method

Kim, Tae Jung, Suh, Jungyo, Park, Soo-Hyun, Kim, Youngjoon, Ko, Sang-Bae·2025·Neurocritical Care
DOI 10.1007/s12028-025-02222-3
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Abstract

Background A multimodal approach may prove effective for predicting clinical outcomes following cardiac arrest (CA). We aimed to develop a practical predictive model that incorporates clinical factors related to CA and multiple prognostic tests using machine learning methods. Methods The neurological outcomes after CA (NOCA) method for predicting poor outcomes were developed using data from 390 patients with CA between May 2018 and June 2023. The outcome was poor neurological outcome, defined as a Cerebral Performance Category score of 3-5 at discharge. We analyzed 31 variables describing the circumstances at CA, demographics, comorbidities, and prognostic studies. The prognostic method was developed based on an extreme gradient-boosting algorithm with threefold cross-validation and hyperparameter optimization. The performance of the predictive model was evaluated using the receiver operating characteristic curve analysis and calculating the area under the curve (AUC). Results Of the 390 total patients (mean age 64.2 years; 71.3% male), 235 (60.3%) experienced poor outcomes at discharge. We selected variables to predict poor neurological outcomes using least absolute shrinkage and selection operator regression. The Glasgow Coma Scale-M (best motor response), electroencephalographic features, the neurological pupil index, time from CA to return of spontaneous circulation, and brain imaging were found to be important key parameters in the NOCA score. The AUC of the NOCA method…

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  • Multimodal Artificial Intelligence for Precision Critical Care: A Scoping Review

    Yang, M., Shi, N., Chen, H., Po, E. Y. P., Ng, P. Y., Lai, P. C. K., Wu, Y.-C., & Fong, D. Y. T. (2026) · Health Data Science · doi:10.34133/hds.0356 · Cited by 3

    “…AI due to high storage requirements and their primary relevance to specific clinical tasks, such as neurological outcome prediction with EEG [ 19 , 52 , 53 , 56 , 61 ] or MRI [ 19 , 52 ], pneumonia detection via chest x-ray [ 62 , 63 ], and forecasting mechanical circulatory support needs from…”

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Metadata

Authors
Kim, T. J., Suh, J., Park, S.-H., Kim, Y., & Ko, S.-B.
Affiliations
  • Seoul National University (KR)
  • Asan Medical Center (KR)
  • Soonchunhyang University (KR)
Corresponding author
Ko, Sang-Bae
ORCID
Venue
Neurocritical Care
Publisher
Springer Science and Business Media LLC
Published
2025-02-20
Volume
42
Issue
3
Pages
829-838
ISSN
  • 1541-6933 (ISSN-L)
  • 1556-0961
DOI
10.1007/s12028-025-02222-3
OpenAlex
W4407791108
Cited by
7
References
3
Citations by year
2026: 6 · 2025: 1
FWCI
7.37
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Neurocritical Care (publishedVersion)
  • PubMed (publishedVersion)
Funders
Seoul National University Hospital (0320210350)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Cardiac Arrest and Resuscitation
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Palliative Care and End-of-Life Issues
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Mechanical Circulatory Support Devices
Keywords
cardiac arrest, neurological outcome prediction, poor neurological outcome, extreme gradient boosting, prognostic model, cerebral performance category, electroencephalographic features, neurological pupil index, neuroimaging, clinical predictors
MeSH terms
Machine Learning, Aged, Electroencephalography, Female, Heart Arrest, Humans, Male, Middle Aged, Prognosis, Glasgow Coma Scale, Outcome Assessment, Health Care

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