Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

Humans Machine Learning Male Female Disease Progression Brain Injuries, Traumatic / diagnostic imaging Middle Aged Retrospective Studies Adult Cerebral Hemorrhage / diagnostic imaging Tomography, X-Ray Computed Aged Risk Assessment / methods
Computed tomography Extreme gradient boosting Intracerebral hemorrhage Machine learning Random forest algorithm Traumatic brain injury

Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
22 May 2024
Historique:
received: 24 01 2024
accepted: 09 05 2024
medline: 23 5 2024
pubmed: 23 5 2024
entrez: 22 5 2024
Statut: epublish

Résumé

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model's precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.

Identifiants

DOI: 10.1038/s41598-024-61739-3 PMID: 38778144
pubmed: 38778144
doi: 10.1038/s41598-024-61739-3
pii: 10.1038/s41598-024-61739-3
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

11690

Informations de copyright

© 2024. The Author(s).

Références

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Auteurs

Heui Seung Lee (HS)

Department of Neurosurgery, College of Medicine, Hallym Sacred Heart Hospital, Hallym University, Anyang-si, Korea.
Interdisciplinary Program for Bioinformatics, Graduate School, Seoul National University, Seoul, Korea.

Ji Hee Kim (JH)

Department of Neurosurgery, College of Medicine, Hallym Sacred Heart Hospital, Hallym University, Anyang-si, Korea.

Jiye Son (J)

Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, Korea.
Integrated Major in Innovative Medical Science, Graduate School, Seoul National University, Seoul, Korea.

Hyeryun Park (H)

Interdisciplinary Program for Bioengineering, Graduate School, Seoul National University, Seoul, Korea.
Integrated Major in Innovative Medical Science, Graduate School, Seoul National University, Seoul, Korea.

Jinwook Choi (J)

Department of Biomedical Engineering, College of Medicine, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. jinchoi@snu.ac.kr.
Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea. jinchoi@snu.ac.kr.

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