{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T16:39:33Z","timestamp":1787503173728,"version":"build-2736575974"},"reference-count":27,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,2]]},"abstract":"\n OBJECTIVE<\/jats:title>\n Traumatic intracranial hemorrhage (tICH) is a significant source of morbidity and mortality in trauma patients. While prognostic models for tICH outcomes may assist in alerting clinicians to high-risk patients, previously developed models face limitations, including low accuracy, poor generalizability, and the use of more prognostic variables than is practical. This study aimed to construct a simpler and more accurate method of risk stratification for all tICH patients.<\/jats:p>\n <\/jats:sec>\n \n METHODS<\/jats:title>\n \n The authors retrospectively identified a consecutive series of 4110 patients admitted to their institution\u2019s level 1 trauma center between 2003 and 2013. For each admission, they collected the patient\u2019s sex, age, systolic blood pressure, blood alcohol concentration, antiplatelet\/anticoagulant use, Glasgow Coma Scale (GCS) score, Injury Severity Score, presence of epidural hemorrhage, presence of subdural hemorrhage, presence of subarachnoid hemorrhage, and presence of intraparenchymal hemorrhage. The final study population comprised 3564 patients following exclusion of records with missing data. The dependent variable under study was patient death. A\n k<\/jats:italic>\n -fold cross-validation was carried out with the best models selected via the Akaike Information Criterion. These models risk stratified the study partitions into grade I (< 1% predicted mortality), grade II (1%\u201310% predicted mortality), grade III (10%\u201340% predicted mortality), or grade IV (> 40% predicted mortality) tICH. Predicted mortalities were compared with actual mortalities within grades to assess calibration. Concordance was also evaluated. A final model was constructed using the entire data set. Subgroup analysis was conducted for each hemorrhage type.\n <\/jats:p>\n <\/jats:sec>\n \n RESULTS<\/jats:title>\n Cross-validation demonstrated good calibration (p < 0.001 for all grades) with a mean concordance of 0.881 (95% CI 0.865\u22120.898). In the authors\u2019 final model, older age, lower blood alcohol concentration, antiplatelet\/anticoagulant use, lower GCS score, and higher Injury Severity Score were all associated with greater mortality. Subgroup analysis showed successful stratification for subarachnoid, intraparenchymal, grade II\u2013IV subdural, and grade I epidural hemorrhages.<\/jats:p>\n <\/jats:sec>\n \n CONCLUSIONS<\/jats:title>\n The authors developed a risk stratification model for tICH of any GCS score with concordance comparable to prior models and excellent calibration. These findings are applicable to multiple hemorrhage subtypes and can assist in identifying low-risk patients for more efficient resource allocation, facilitate family conversations regarding goals of care, and stratify patients for research purposes. Future work will include testing of more variables, validation of this model across institutions, as well as creation of a simplified model whose outputs can be calculated mentally.<\/jats:p>\n <\/jats:sec>","DOI":"10.3171\/2018.11.jns182199","type":"journal-article","created":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T19:16:02Z","timestamp":1551208562000},"page":"552-559","source":"Crossref","is-referenced-by-count":34,"title":["Predicting mortality in traumatic intracranial hemorrhage"],"prefix":"10.3171","volume":"132","author":[{"given":"Andrew Y.","family":"Powers","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Warren Alpert Medical School of Brown University;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauricio B.","family":"Pinto","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Warren Alpert Medical School of Brown University;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver Y.","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Warren Alpert Medical School of Brown University;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Shu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Warren Alpert Medical School of Brown University;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cody","family":"Doberstein","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Warren Alpert Medical School of Brown University;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wael F.","family":"Asaad","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Warren Alpert Medical School of Brown University;"},{"name":"Carney Institute for Brain Science, Brown University;"},{"name":"Department of Neuroscience, Brown University; and"},{"name":"Norman Prince Neurosciences Institute and"},{"name":"Department of Neurosurgery, Rhode Island Hospital, Providence, Rhode Island"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1754","reference":[{"key":"b1","series-title":"World Neurosurg","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1016\/j.wneu.2017.08.147","article-title":"Models of mortality and morbidity in severe traumatic brain injury: an analysis of a Singapore neurotrauma database","volume":"108","author":"Han JX","year":"2017","unstructured":"Han JX, See AAQ, Gandhi M, King NKK: Models of mortality and morbidity in severe traumatic brain injury: an analysis of a Singapore neurotrauma database. 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