{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T16:10:15Z","timestamp":1768320615257,"version":"3.49.0"},"reference-count":28,"publisher":"Journal of Neurosurgery Publishing Group (JNSPG)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:sec>\n<jats:title>OBJECTIVE<\/jats:title>\n<jats:p>The aim of this study was to stratify poly\u2013traumatic brain injury (poly-TBI) patterns into discrete classes and to determine the association of these classes with mortality and withdrawal of life-sustaining treatment (WLST).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>METHODS<\/jats:title>\n<jats:p>The authors performed a single-center retrospective review of their institutional trauma registry from 2018 to 2020 to identify patients with traumatic brain injury (TBI). Patients were included if they had moderate to severe TBI, defined as Glasgow Coma Scale score \u2264 12 and Abbreviated Injury Scale (AIS) head score \u2265 3, and the presence of more than one TBI subtype. TBI subtypes were defined as subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), intracerebral hemorrhage (ICH), and epidural hemorrhage (EDH). Latent class analysis was used to identify patient classes based on TBI subtypes and Rotterdam CT (RCT) scores. The authors then evaluated class membership in relation to categorical outcomes of in-hospital mortality and WLST by using Lanza et al.\u2019s method.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>RESULTS<\/jats:title>\n<jats:p>A total of 125 patients met inclusion criteria for poly-TBI. Latent class analysis yielded 3 poly-TBI classes: class 1\u2013mixed; class 2\u2013SDH\/SAH; and class 3\u2013EDH\/SAH. Class 1\u2013mixed had a higher likelihood of SDH, SAH, and ICH, and a lower likelihood of EDH. Class 2\u2013SDH\/SAH had a higher likelihood of only SDH and SAH. Class 3\u2013EDH\/SAH had a higher likelihood of EDH and SAH, and a lower likelihood of SDH and ICH. Class 1\u2013mixed was relatively more likely to have an RCT score of 2. Class 2\u2013SDH\/SAH was relatively more likely to have an RCT score of 2, 3, and 4. Class 3\u2013EDH\/SAH had a higher likelihood of an RCT score of 3, 4, and 5. Class 1\u2013mixed had significantly lower mortality (\u03c7<jats:sup>2<\/jats:sup> = 7.968; p = 0.005) and less WLST (\u03c7<jats:sup>2<\/jats:sup> = 4.618; p = 0.032) than Class 2\u2013SDH\/SAH. Class 2\u2013SDH\/SAH had the highest probability of death (0.612), followed by class 3\u2013EDH\/SAH (0.385) and class 1\u2013mixed (0.277). Similarly, class 2\u2013SDH\/SAH had the highest WLST probability (0.498), followed by class 3\u2013EDH\/SAH (0.615) and class 1\u2013mixed (0.238).<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title>CONCLUSIONS<\/jats:title>\n<jats:p>Distinct poly-TBI classes were associated with increased in-hospital mortality and WLST. Further research with larger datasets will allow for more comprehensive poly-TBI class definitions and outcomes analysis.<\/jats:p>\n<\/jats:sec>","DOI":"10.3171\/2024.5.jns232842","type":"journal-article","created":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T01:37:11Z","timestamp":1725673031000},"page":"1-8","source":"Crossref","is-referenced-by-count":2,"title":["Novel application of latent class analysis to outcome assessment in traumatic brain injury with multiple injury subtypes or poly-TBI"],"prefix":"10.3171","author":[{"given":"Samuel","family":"Doty","sequence":"first","affiliation":[{"name":"Departments of Neurological Surgery,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jordan C.","family":"Petitt","sequence":"additional","affiliation":[{"name":"Departments of Neurological Surgery,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmed","family":"Kashkoush","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, Cleveland Clinic Foundation, Cleveland, Ohio; and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin B.","family":"Whiting","sequence":"additional","affiliation":[{"name":"Department of Neurological Surgery, Cleveland Clinic Foundation, Cleveland, Ohio; and"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianqi","family":"Xiao","sequence":"additional","affiliation":[{"name":"Departments of Neurological Surgery,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John J.","family":"Francis","sequence":"additional","affiliation":[{"name":"Departments of Neurological Surgery,"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Douglas","family":"Gunzler","sequence":"additional","affiliation":[{"name":"Population and Quantitative Health Sciences, and"},{"name":"Population Health Research Institute, Center for Health Policy & Research, MetroHealth Medical Center, Cleveland, Ohio"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary Joan","family":"Roach","sequence":"additional","affiliation":[{"name":"Population Health Research Institute, Center for Health Policy & Research, MetroHealth Medical Center, Cleveland, Ohio"},{"name":"Physical Medicine and Rehabilitation, Case Western Reserve University School of Medicine, MetroHealth Medical Center, Cleveland, Ohio;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael L.","family":"Kelly","sequence":"additional","affiliation":[{"name":"Departments of Neurological Surgery,"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1754","reference":[{"issue":"2","key":"b1","series-title":"Med Clin North Am","first-page":"213","article-title":"Traumatic brain injury: an overview of epidemiology, pathophysiology, and medical management","volume":"104","author":"Capizzi A","year":"2020","unstructured":"Capizzi A, Woo J, Verduzco-Gutierrez M. Traumatic brain injury: an overview of epidemiology, pathophysiology, and medical management. Med Clin North Am. 2020;104(2):213\u2013238."},{"key":"b2","series-title":"Front Neurol","first-page":"112","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":"Garza N","year":"2020","unstructured":"Garza N, Toussi A, Wilson M, Shahlaie K, Martin R. 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. Front Neurol. 2020;11:112."},{"issue":"6","key":"b3","series-title":"J Neurosurg","first-page":"1640","article-title":"Sequential changes in Rotterdam CT scores related to outcomes for patients with traumatic brain injury who undergo decompressive craniectomy","volume":"124","author":"Fujimoto K","year":"2016","unstructured":"Fujimoto K, Miura M, Otsuka T, Kuratsu J. Sequential changes in Rotterdam CT scores related to outcomes for patients with traumatic brain injury who undergo decompressive craniectomy. J Neurosurg. 2016;124(6):1640\u20131645."},{"issue":"11","key":"b4","series-title":"Acta Neurochir (Wien)","first-page":"2033","article-title":"Comparison of predictability of Marshall and Rotterdam CT scan scoring system in determining early mortality after traumatic brain injury","volume":"157","author":"Deepika A","year":"2015","unstructured":"Deepika A, Prabhuraj AR, Saikia A, Shukla D. Comparison of predictability of Marshall and Rotterdam CT scan scoring system in determining early mortality after traumatic brain injury. Acta Neurochir (Wien). 2015;157(11):2033\u20132038."},{"issue":"12","key":"b5","series-title":"Nat Rev Neurol","first-page":"711","article-title":"Traumatic brain injury: sex, gender and intersecting vulnerabilities","volume":"14","author":"Mollayeva T","year":"2018","unstructured":"Mollayeva T, Mollayeva S, Colantonio A. Traumatic brain injury: sex, gender and intersecting vulnerabilities. Nat Rev Neurol. 2018;14(12):711\u2013722."},{"issue":"8","key":"b6","series-title":"PLoS Med","first-page":"e165","article-title":"Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics","volume":"5","author":"Steyerberg EW","year":"2008","unstructured":"Steyerberg EW, Mushkudiani N, Perel P,  Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med. 2008;5(8):e165."},{"key":"b7","article-title":"Structural Equation Modeling for Health and Medicine","author":"Gunzler DD","year":"2021","unstructured":"Gunzler DD, Perzynski AT, Carle AC. Structural Equation Modeling for Health and Medicine. 1st ed. Routledge; 2021."},{"issue":"11","key":"b8","series-title":"Am J Respir Crit Care Med","first-page":"1274","article-title":"Latent class analysis reveals COVID-19-related acute respiratory distress syndrome subgroups with differential responses to corticosteroids","volume":"204","author":"Sinha P","year":"2021","unstructured":"Sinha P, Furfaro D, Cummings MJ,  Latent class analysis reveals COVID-19-related acute respiratory distress syndrome subgroups with differential responses to corticosteroids. Am J Respir Crit Care Med. 2021;204(11):1274\u20131285."},{"issue":"4","key":"b9","series-title":"Emerg Med Clin North Am","first-page":"803","article-title":"Subarachnoid hemorrhage: updates in diagnosis and management","volume":"35","author":"Long B","year":"2017","unstructured":"Long B, Koyfman A, Runyon MS. Subarachnoid hemorrhage: updates in diagnosis and management. Emerg Med Clin North Am. 2017;35(4):803\u2013824."},{"issue":"6","key":"b10","series-title":"Neurosurgery","first-page":"1173","article-title":"Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors","volume":"57","author":"Maas AI","year":"2005","unstructured":"Maas AI, Hukkelhoven CW, Marshall LF, Steyerberg EW. Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery. 2005;57(6):1173\u20131182."},{"issue":"10","key":"b11","series-title":"Intensive Care Med","first-page":"1115","article-title":"Occurrence and timing of withdrawal of life-sustaining measures in traumatic brain injury patients: a CENTER-TBI study","volume":"47","author":"van Veen E","year":"2021","unstructured":"van Veen E, van der Jagt M, Citerio G,  Occurrence and timing of withdrawal of life-sustaining measures in traumatic brain injury patients: a CENTER-TBI study. Intensive Care Med. 2021;47(10):1115\u20131129."},{"key":"b12","article-title":"Latent Structure Analysis","author":"Lazarsfeld PF","year":"1968","unstructured":"Lazarsfeld PF, Henry NW. Latent Structure Analysis. Houghton, Mifflin; 1968."},{"key":"b13","series-title":"Berkeley Symp Math Stat Prob","first-page":"221","article-title":"The behavior of maximum likelihood estimates under nonstandard conditions","author":"Huber PJ","year":"1967","unstructured":"Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. Berkeley Symp Math Stat Prob. 1967:221-233."},{"key":"b14","series-title":"Muth\u00e9n & Muth\u00e9n","first-page":"5","article-title":"Mplus User\u2019s Guide","author":"Muth\u00e9n LK","year":"2012","unstructured":"Muth\u00e9n LK, Muth\u00e9n BO. Mplus User\u2019s Guide. 7th ed. Muth\u00e9n & Muth\u00e9n; 2012:5 Accessed June 30, 2024.https:\/\/www.statmodel.com\/download\/usersguide\/Mplus%20user%20guide%20Ver_7_r6_web.pdf"},{"issue":"2","key":"b15","series-title":"Prev Sci","first-page":"157","article-title":"Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment","volume":"14","author":"Lanza ST","year":"2013","unstructured":"Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14(2):157\u2013168."},{"key":"b16","series-title":"Mult Scler Relat Disord","first-page":"102912","article-title":"Integrating patient-reported outcomes and quantitative timed tasks to identify relapsing remitting multiple sclerosis patient subgroups: a latent profile analysis","volume":"51","author":"Briggs FBS","year":"2021","unstructured":"Briggs FBS, Conway DS, De Nadai AS, Ontaneda D, Gunzler DD. Integrating patient-reported outcomes and quantitative timed tasks to identify relapsing remitting multiple sclerosis patient subgroups: a latent profile analysis. Mult Scler Relat Disord. 2021;51:102912."},{"issue":"1","key":"b17","series-title":"Struct Equ Modeling","first-page":"1","article-title":"Latent class analysis with distal outcomes: a flexible model-based approach","volume":"20","author":"Lanza ST","year":"2013","unstructured":"Lanza ST, Tan X, Bray BC. Latent class analysis with distal outcomes: a flexible model-based approach. Struct Equ Modeling. 2013;20(1):1\u201326."},{"key":"b18","series-title":"Muth\u00e9n & Muth\u00e9n","article-title":"Mplus User\u2019s Guide","author":"Muth\u00e9n LK","year":"2017","unstructured":"Muth\u00e9n LK, Muth\u00e9n B. Mplus User\u2019s Guide. 8th ed. Muth\u00e9n & Muth\u00e9n; 2017. Accessed June 30, 2024.https:\/\/www.statmodel.com\/download\/usersguide\/MplusUserGuideVer_8.pdf"},{"issue":"4","key":"b19","series-title":"Struct Equ Modeling","first-page":"621","article-title":"MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus","volume":"25","author":"Hallquist MN","year":"2018","unstructured":"Hallquist MN, Wiley JF. MplusAutomation: An R package for facilitating large-scale latent variable analyses in Mplus. Struct Equ Modeling. 2018;25(4):621\u2013638."},{"key":"b20","article-title":"An Introduction to R: Revised and Updated","author":"Venables WN","year":"2002","unstructured":"Venables WN, Smith DM. An Introduction to R: Revised and Updated. Network Theory Ltd; 2002."},{"key":"b21","series-title":"World Neurosurg","first-page":"e166","article-title":"Clinical outcome of epidural hematoma treated surgically in the era of modern resuscitation and trauma care","volume":"118","author":"Gutowski P","year":"2018","unstructured":"Gutowski P, Meier U, Rohde V, Lemcke J, von der Brelie C. Clinical outcome of epidural hematoma treated surgically in the era of modern resuscitation and trauma care. World Neurosurg. 2018;118:e166\u2013e174."},{"issue":"6","key":"b22","series-title":"J Neurosurg","first-page":"1510","article-title":"Neurological outcome in patients with traumatic brain injury and its relationship with computed tomography patterns of traumatic subarachnoid hemorrhage","volume":"114","author":"Wong GK","year":"2011","unstructured":"Wong GK, Yeung JH, Graham CA, Zhu XL, Rainer TH, Poon WS. Neurological outcome in patients with traumatic brain injury and its relationship with computed tomography patterns of traumatic subarachnoid hemorrhage. J Neurosurg. 2011;114(6):1510\u20131515."},{"issue":"23","key":"b23","series-title":"Int J Environ Res Public Health","first-page":"4787","article-title":"Concurrent types of intracranial hemorrhage are associated with a higher mortality rate in adult patients with traumatic subarachnoid hemorrhage: a cross-sectional retrospective study","volume":"16","author":"Rau CS","year":"2019","unstructured":"Rau CS, Wu SC, Hsu SY,  Concurrent types of intracranial hemorrhage are associated with a higher mortality rate in adult patients with traumatic subarachnoid hemorrhage: a cross-sectional retrospective study. Int J Environ Res Public Health. 2019;16(23):4787."},{"issue":"1","key":"b24","series-title":"J Neurosurg","first-page":"236","article-title":"Subdural hematoma as a major determinant of short-term outcomes in traumatic brain injury","volume":"128","author":"Lee JJ","year":"2018","unstructured":"Lee JJ, Segar DJ, Morrison JF, Mangham WM, Lee S, Asaad WF. Subdural hematoma as a major determinant of short-term outcomes in traumatic brain injury. J Neurosurg. 2018;128(1):236\u2013249."},{"issue":"4","key":"b25","series-title":"Neurosurg Focus","first-page":"E22","article-title":"Morbidity and mortality in elderly patients undergoing evacuation of acute traumatic subdural hematoma","volume":"49","author":"Kerezoudis P","year":"2020","unstructured":"Kerezoudis P, Goyal A, Puffer RC, Parney IF, Meyer FB, Bydon M. Morbidity and mortality in elderly patients undergoing evacuation of acute traumatic subdural hematoma. Neurosurg Focus. 2020;49(4):E22."},{"issue":"2","key":"b26","series-title":"J Neurosurg","first-page":"552","article-title":"Predicting mortality in traumatic intracranial hemorrhage","volume":"132","author":"Powers AY","year":"2019","unstructured":"Powers AY, Pinto MB, Tang OY, Chen JS, Doberstein C, Asaad WF. Predicting mortality in traumatic intracranial hemorrhage. J Neurosurg. 2019;132(2):552\u2013559."},{"issue":"1","key":"b27","series-title":"J Negat Results Biomed","first-page":"10","article-title":"Hemispheric differences in the surgical outcomes of patients with traumatic acute subdural hematoma","volume":"13","author":"Inamasu J","year":"2014","unstructured":"Inamasu J, Hasegawa M, Hayashi T, Kato Y, Hirose Y. Hemispheric differences in the surgical outcomes of patients with traumatic acute subdural hematoma. J Negat Results Biomed. 2014;13(1):10."},{"issue":"6","key":"b28","series-title":"J Neurosurg","first-page":"1621","article-title":"Simplifying the use of prognostic information in traumatic brain injury. Part 2: graphical presentation of probabilities","volume":"128","author":"Murray GD","year":"2018","unstructured":"Murray GD, Brennan PM, Teasdale GM. Simplifying the use of prognostic information in traumatic brain injury. Part 2: graphical presentation of probabilities. 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