{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:35:41Z","timestamp":1790746541134,"version":"4.1.0"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T00:00:00Z","timestamp":1738540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R01HD107280"],"award-info":[{"award-number":["R01HD107280"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec030\">\n<jats:title>Background<\/jats:title>\n<jats:p>Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a significant ongoing challenge in the management of geriatric TBI. As the population ages and co-existing medical conditions complexify<jats:italic>, so does the need to improve the quality of care for this<\/jats:italic> population. Utilizing early hospital admission variables, this study will create and validate a multinomial decision tree that predicts the discharge disposition of older patients with fall-related TBI.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec031\">\n<jats:title>Methods<\/jats:title>\n<jats:p>From the National Trauma Data Bank, we retrospectively analyzed 11,977 older patients with a fall-related TBI (2017\u20132021). Clinical variables included Glasgow Coma Scale (GCS) score, intracranial pressure monitor use, venous thromboembolism (VTE) prophylaxis, and initial vital signs. Outcomes included hospital discharge disposition re-categorized into home, care facility, or deceased. Data were split into two sets, where 80% developed a decision tree, and 20% tested predictive performance. We employed a conditional inference tree algorithm with bootstrap (B\u2009=\u2009100) and grid search options to grow the decision tree and measure discrimination ability using the area under the curve (AUC) and calibration plots.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec032\">\n<jats:title>Results<\/jats:title>\n<jats:p>Our decision tree used seven admission variables to predict the discharge disposition of older TBI patients. Significant non-modifiable variables included total GCS and injury severity scores, while VTE prophylaxis type was the most important interventional variable. Patients who did not receive VTE prophylaxis treatment had a higher probability of death. The predictive performance of the tree in terms of AUC value (95% confidence intervals) in the training cohort for death, care, and home were 0.66 (0.65\u20130.67), 0.75 (0.73\u20130.76), and 0.77 (0.76\u20130.79), respectively. In the test cohort, the values were 0.64 (0.62\u20130.67), 0.75 (0.72\u20130.77), and 0.77 (0.73\u20130.79).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec033\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>We have developed and internally validated a multinomial decision tree to predict the discharge destination of older patients with TBI. This tree could serve as a decision support tool for caregivers to manage older patients better and inform decision-making. However, the tree must be externally validated using prospective data to ascertain its predictive and clinical importance.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0316462","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T18:52:50Z","timestamp":1738608770000},"page":"e0316462","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1499-0896","authenticated-orcid":true,"given":"Biche","family":"Osong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1691-8559","authenticated-orcid":true,"given":"Eric","family":"Sribnick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Groner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Stanley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren","family":"Schulz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence","family":"Cook","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8112-319X","authenticated-orcid":true,"given":"Henry","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,2,3]]},"reference":[{"key":"pone.0316462.ref001","author":"Centers for Disease Control and Prevention","year":"2023"},{"key":"pone.0316462.ref002","doi-asserted-by":"crossref","first-page":"112","DOI":"10.3389\/fneur.2020.00112","article-title":"The increasing age of TBI patients at a single level 1 trauma center and the discordance between GCS and CT rotterdam scores in the elderly","volume":"11","author":"N Garza","year":"2020","journal-title":"Front Neurol"},{"key":"pone.0316462.ref003","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1097\/00005373-200208000-00004","article-title":"Traumatic brain injury in the elderly: increased mortality and worse functional outcome at discharge despite lower injury severity","volume":"53","author":"M Susman","year":"2002","journal-title":"J Trauma Acute Care Surg"},{"issue":"7","key":"pone.0316462.ref004","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1089\/neu.2017.5371","article-title":"Geriatric traumatic brain injury: epidemiology, outcomes, knowledge gaps, and future directions","volume":"35","author":"RC Gardner","year":"2018","journal-title":"J Neurotrauma"},{"issue":"1","key":"pone.0316462.ref005","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.radonc.2014.04.012","article-title":"A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making","volume":"112","author":"C Oberije","year":"2014","journal-title":"Radiother Oncol"},{"issue":"27","key":"pone.0316462.ref006","doi-asserted-by":"crossref","first-page":"4268","DOI":"10.1200\/JCO.2010.28.5478","article-title":"Rapid-learning system for cancer care","volume":"28","author":"AP Abernethy","year":"2010","journal-title":"J Clin Oncol"},{"issue":"7641","key":"pone.0316462.ref007","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1136\/bmj.39461.643438.25","article-title":"Predicting outcome after traumatic brain injury: practical prognostic models based on large cohort of international patients","volume":"336","author":"MRC CRASH Trial Collaborators","year":"2008","journal-title":"BMJ"},{"issue":"8","key":"pone.0316462.ref008","doi-asserted-by":"crossref","first-page":"e165; 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R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2014."},{"issue":"9","key":"pone.0316462.ref018","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1089\/neu.2004.21.1131","article-title":"Inaccurate early assessment of neurological severity in head injury","volume":"21","author":"N Stocchetti","year":"2004","journal-title":"J Neurotrauma"},{"issue":"7","key":"pone.0316462.ref019","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1001\/jamasurg.2014.13","article-title":"The effect of age on Glasgow Coma Scale score in patients with traumatic brain injury","volume":"149","author":"K Salottolo","year":"2014","journal-title":"JAMA Surg"},{"key":"pone.0316462.ref020","article-title":"Glasgow coma scale is unreliable for the prediction of severe head injury in elderly trauma patients","author":"A Kehoe","year":"2014","journal-title":"Emerg Med J"},{"issue":"11","key":"pone.0316462.ref021","doi-asserted-by":"crossref","first-page":"1378","DOI":"10.3390\/ijerph14111378","article-title":"Effect of age on glasgow coma scale in patients with moderate and severe traumatic brain injury: an approach with propensity score-matched population","volume":"14","author":"C-S Rau","year":"2017","journal-title":"Int J Environ Res Public Health"},{"issue":"4","key":"pone.0316462.ref022","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1054\/jocn.1999.0198","article-title":"A study of the functional outcome and mortality in elderly patients with head injuries","volume":"7","author":"PD Ritchie","year":"2000","journal-title":"J Clin Neurosci"},{"key":"pone.0316462.ref023","doi-asserted-by":"crossref","first-page":"e664","DOI":"10.1016\/j.wneu.2019.12.095","article-title":"The impact of drug and alcohol intoxication on glasgow coma scale assessment in patients with traumatic brain injury","volume":"135","author":"AM DiGiorgio","year":"2020","journal-title":"World 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unit","volume":"7","author":"SH Hosseini","year":"2016","journal-title":"Anesth Pain Med"},{"issue":"1","key":"pone.0316462.ref027","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.injury.2022.09.035","article-title":"Glasgow coma scale compared to other trauma scores in discriminating in-hospital mortality of traumatic brain injury patients admitted to urban Indian hospitals: a multicentre prospective cohort study","volume":"54","author":"D Basak","year":"2023","journal-title":"Injury"},{"issue":"9","key":"pone.0316462.ref028","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.injury.2010.09.019","article-title":"Correlation between Glasgow coma score components and survival in patients with traumatic brain injury","volume":"42","author":"W-M Kung","year":"2011","journal-title":"Injury"},{"issue":"10","key":"pone.0316462.ref029","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1111\/j.1553-2712.2011.01164.x","article-title":"Modification of 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Brandi","year":"2020","journal-title":"Anaesthesiol Intensive Ther"},{"issue":"5","key":"pone.0316462.ref034","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1097\/TA.0000000000002830","article-title":"Updated guidelines to reduce venous thromboembolism in trauma patients: a western trauma association critical decisions algorithm","volume":"89","author":"EJ Ley","year":"2020","journal-title":"J Trauma Acute Care Surg"},{"issue":"1","key":"pone.0316462.ref035","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1186\/s13054-022-04243-2","article-title":"Perspective: the top 11 priorities to improve trauma outcomes, from system to patient level","volume":"26","author":"MC Reade","year":"2022","journal-title":"Crit Care"},{"key":"pone.0316462.ref036","doi-asserted-by":"crossref","first-page":"100168","DOI":"10.1016\/j.sipas.2023.100168","article-title":"Retrospective evaluation of chemical venous thromboembolism prophylaxis in traumatic brain injury","volume":"13","author":"EK Hollfelder","year":"2023","journal-title":"Surg Prac Sci"},{"issue":"8","key":"pone.0316462.ref037","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1111\/j.1538-7836.2004.00842.x","article-title":"Challenges in the prevention of venous thromboembolism in the elderly","volume":"2","author":"G Di Minno","year":"2004","journal-title":"J Thromb Haemost"},{"issue":"1","key":"pone.0316462.ref038","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.cger.2005.09.003","article-title":"Prevention of venous thromboembolism in the geriatric patient","volume":"22","author":"AK Jaffer","year":"2006","journal-title":"Clin Geriatr Med"},{"issue":"1","key":"pone.0316462.ref039","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1093\/bja\/ael110","article-title":"Monitoring the injured brain: ICP and CBF","volume":"97","author":"LA Steiner","year":"2006","journal-title":"Br J 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