{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:50:15Z","timestamp":1790704215301,"version":"4.1.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"19-20","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neurotrauma"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p>\n                    Progression of intracranial hemorrhage is a common, potentially devastating complication after moderate\/severe traumatic brain injury (TBI). Clinicians have few tools to predict which patients with traumatic intracranial hemorrhage on their initial head computed tomography (hCT) scan will progress. The objective of this investigation was to identify clinical, imaging, and\/or protein biomarkers associated with progression of intracranial hemorrhage (PICH) after moderate\/severe TBI and to create an accurate predictive model of PICH based on clinical features available at presentation. We analyzed a subset of subjects from the phase II double-blind, multi-center, randomized \u201cPrehospital Tranexamic Acid Use for TBI\u201d trial. This subset was limited to the placebo arm of the parent trial with evidence of hemorrhage on the initial hCT and a follow-up hCT 6 h after. PICH was defined as an increase in hemorrhage size by 30% or more, or the development of new hemorrhage in the intra- and extra-axial intracranial vault between the initial and the follow-up hCT. Two independent radiologists evaluated each hCT, and conflicts were adjudicated by a third. Clinical and radiographic characteristics were collected, along with plasma protein biomarkers at admission. Principal component analysis (PCA) was performed, and each principal component (PC) was interrogated for its association with PICH. Finally, expert opinion and recursive feature extraction (RFE) were used to select input features for the construction of several supervised classification models. Their ability to predict PICH was quantified and compared. In this subset of subjects (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 104), 46% (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    = 48) demonstrated PICH. Univariate analyses showed no association between PICH and age, sex, admission Glasgow Coma Scale (GCS), GCS motor subscore, presence of midline shift, admission platelet count or admission INR. Radiographic severity scores (Marshall score [\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.007], Rotterdam score [\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.004]), and initial hematoma volume [\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.005] were associated with PICH. Higher levels of admission glial fibrillary acidic protein (\n                    <jats:italic toggle=\"yes\">p &lt;<\/jats:italic>\n                    0.001) and MAP (\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.011) were also associated with PICH. Of the PCs, PC1 was significantly associated with PICH (\n                    <jats:italic toggle=\"yes\">p<\/jats:italic>\n                    = 0.0125). Using multimodal data input, machine learning classifiers successfully discriminated patients with or without PICH. Models composed of machine-selected features performed better than models composed of expert-selected variables (reaching an average of 77% accuracy, AUC = 0.78 versus AUC = 0.68 for the expert-selected variables). Predictive models utilizing variables measured at admission can accurately predict PICH, confirmed by the 6-hour follow-up hCT. Our best-performing models must now be externally validated in a separate cohort of TBI patients with low GCS and initial hCT positive for hemorrhage.\n                  <\/jats:p>","DOI":"10.1089\/neu.2023.0626","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T04:55:20Z","timestamp":1713156920000},"page":"2349-2361","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Progression of Intracranial Hemorrhage in the Prehospital TXA for TBI Trial"],"prefix":"10.1177","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1598-9782","authenticated-orcid":false,"given":"H.E.","family":"Hinson","sequence":"first","affiliation":[{"name":"Department of Neurology, University of California, San Francisco, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah L.","family":"Radabaugh","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of California, San Francisco, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nincheng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Division of Interventional Radiology University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshinori","family":"Fukuda","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oregon Health and Science University, Portland, Oregon, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey","family":"Pollock","sequence":"additional","affiliation":[{"name":"Department of Radiology, Oregon Health and Science University, Portland, Oregon, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martin","family":"Schreiber","sequence":"additional","affiliation":[{"name":"Donald D. Trunkey Center for Civilian and Combat Casualty Care, Oregon Health and Science University, Portland, Oregon, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Rowell","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Chicago, Chicago, Illinois, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam R.","family":"Ferguson","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, University of California, San Francisco, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_6_2_2","unstructured":"Centers for Disease Control and Prevention U.S. Department of Health and Human Services Peterson AB Thomas KE et al. Surveillance Report of Traumatic Brain Injury-Related Deaths by Age Group Sex and Mechanism of Injury\u2014United States 2018 and 2019. 2022."},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.1227\/00006123-199301000-00004"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.3171\/2015.11.JNS151515"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.3171\/2009.5.JNS081369"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/TA.0000000000002706"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.8958"},{"key":"e_1_3_6_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(18)30231-X"},{"key":"e_1_3_6_9_2","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2017.4994"},{"key":"e_1_3_6_10_2","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa F","year":"2011","unstructured":"Pedregosa F, , Varoquaux G, , Gramfort A, et al. 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