Enhancing the golden hour: classification of traumatic brain injury, severity, and concomitant clinical phenotypes using prehospital continuous physiological data during air transport.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Enhancing the golden hour: classification of traumatic brain injury, severity, and concomitant clinical phenotypes using prehospital continuous physiological data during air transport.
Συγγραφείς:	Woodward MR; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Departments of Neurology, University of Maryland School of Medicine, Baltimore, United States of America.; Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, United States of America., Yang S; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Anesthesiology, University of Maryland School of Medicine, Baltimore, United States of America.; Institute for Healthcare Computing, University of Maryland, Baltimore, United States of America., Somalinga M; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America., Dalton K; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Departments of Neurology, University of Maryland School of Medicine, Baltimore, United States of America., Felix RB; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Fischell Department of Biomedical Engineering, University of Maryland, College Park, United States of America., Podell J; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Departments of Neurology, University of Maryland School of Medicine, Baltimore, United States of America., Chen L; Department of Information Systems, University of Maryland, Baltimore County, Baltimore, United States of America., Schwartzbauer G; Neurosurgery, University of Maryland School of Medicine, Baltimore, United States of America., Teeter W; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Emergency Medicine, University of Maryland School of Medicine, Baltimore, United States of America., Floccare D; Emergency Medicine, University of Maryland School of Medicine, Baltimore, United States of America., Hu P; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Anesthesiology, University of Maryland School of Medicine, Baltimore, United States of America.; Institute for Healthcare Computing, University of Maryland, Baltimore, United States of America., Badjatia N; Program in Trauma, University of Maryland School of Medicine, Baltimore, United States of America.; Departments of Neurology, University of Maryland School of Medicine, Baltimore, United States of America.
Πηγή:	Physiological measurement [Physiol Meas] 2026 Aug 03; Vol. 47 (8). Date of Electronic Publication: 2026 Aug 03.
Τύπος έκδοσης:	Journal Article; Validation Study
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: IOP Pub. Ltd Country of Publication: England NLM ID: 9306921 Publication Model: Electronic Cited Medium: Internet ISSN: 1361-6579 (Electronic) Linking ISSN: 09673334 NLM ISO Abbreviation: Physiol Meas Subsets: MEDLINE
Imprint Name(s):	Original Publication: Bristol, UK : IOP Pub. Ltd., c1993-
Ιατρικοί όροι (MeSH):	Brain Injuries, Traumatic/classification , Brain Injuries, Traumatic/diagnosis , Brain Injuries, Traumatic/physiopathology , Emergency Medical Services/statistics & numerical data , Monitoring, Physiologic/methods , Predictive Learning Models , Classification Algorithms*, Phenotype ; Air Ambulances ; Vital Signs ; Humans ; Male ; Female ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Aged ; Glasgow Coma Scale ; Retrospective Studies ; Reproducibility of Results ; Predictive Value of Tests
Περίληψη:	Background.During emergency transport, clinical assessment and vital signs (VS) may lack the sensitivity to identify traumatic brain injury (TBI) and identify specific TBI subtypes which may have implications for triaging and timely delivery of life-saving interventions.Objective.To evaluate the ability of machine learning (ML) algorithms to identify the presence of TBI with or without specific important concomitant clinical phenotypes including shock, coagulopathy and polytrauma during air transport to a trauma center.Methods.We identified a cohort of consecutive trauma patients aged 18-65 transported from the scene of injury via helicopter to an urban academic trauma center and collected prehospital clinical data and continuous VSs. We used ElasticNet (regularized regression) and XGBoost (gradient boosting), comparing three variable sets: clinical variables only, continuous physiologic monitoring data only, and combined clinical and physiological data, to develop four predictive models: (1) presence/absence of TBI, (2) mild vs moderate-severe TBI, (3) presence/absence of polytrauma in moderate-severe TBI, (4) presence/absence of coagulopathy in TBI, and (5) presence/absence of shock in TBI.Results.1025 patients (median age 38, interquartile range (IQR): 27-53; 70% male; median Glasgow coma scale 15 (IQR: 13-15) were identified. Across all predictive models, ML algorithms exhibited good predictive discrimination, with area under the receiver operator curve of 0.79 (0.75-0.84), 0.79 (0.74-0.83), 0.89 (0.85-0.92), 0.77 (0.67-0.86), and 0.78 (0.72-0.84) for TBI, TBI severity, polytrauma, coagulopathy, and shock, respectively. Clinical data best predicted TBI severity and polytrauma, while physiologic data improved prediction of shock and coagulopathy.Conclusions.ML algorithms integrating clinical and continuous physiological monitoring can improve identification of TBI and concomitant clinical phenotypes during prehospital transport. (© 2026 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)
Contributed Indexing:	Keywords: coagulopathy; polytrauma; prehospital care; shock; traumatic brain injury
Entry Date(s):	Date Created: 20260717 Date Completed: 20261006 Latest Revision: 20261006
Update Code:	20261007
DOI:	10.1088/1361-6579/ae8ca7
PMID:	42468566
Βάση Δεδομένων:	MEDLINE

Περιγραφή
ISSN:	1361-6579
DOI:	10.1088/1361-6579/ae8ca7