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Meas."],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Background.<\/jats:italic>\n                    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.\n                    <jats:italic>Objective.<\/jats:italic>\n                    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.\n                    <jats:italic>Methods.<\/jats:italic>\n                    We identified a cohort of consecutive trauma patients aged 18\u201365 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.\n                    <jats:italic>Results.<\/jats:italic>\n                    1025 patients (median age 38, interquartile range (IQR): 27\u201353; 70% male; median Glasgow coma scale 15 (IQR: 13\u201315) were identified. Across all predictive models, ML algorithms exhibited good predictive discrimination, with area under the receiver operator curve of 0.79 (0.75\u20130.84), 0.79 (0.74\u20130.83), 0.89 (0.85\u20130.92), 0.77 (0.67\u20130.86), and 0.78 (0.72\u20130.84) 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.\n                    <jats:italic>Conclusions.<\/jats:italic>\n                    ML algorithms integrating clinical and continuous physiological monitoring can improve identification of TBI and concomitant clinical phenotypes during prehospital transport.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae8ca7","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T22:55:46Z","timestamp":1784328946000},"page":"085001","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing the golden hour: classification of traumatic brain injury, severity, and concomitant clinical phenotypes using prehospital continuous physiological data during air 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