Serum Biomarkers for Traumatic Brain Injury

Serum Biomarkers for Traumatic Brain Injury

Traumatic brain injury (TBI) is frequently associated with abnormal blood-brain barrier function, resulting in the release of factors that can be used as molecular biomarkers of TBI, among them GFAPUCH-L1S100B, and NSE. Although many experimental studies have been conducted, clinical consolidation of these biomarkers is still needed to increase the predictive power and reduce the poor outcome of TBI. Interestingly, several of these TBI biomarkers are oxidatively modified to carbonyl groups, indicating that markers of oxidative stress could be of predictive value for the selection of therapeutic strategies 1).


Unlike other organ-based diseases where rapid diagnosis employing biomarkers from blood tests are clinically essential to guide diagnosis and treatment, there are no rapid, definitive diagnostic blood tests for TBI. Over the last decade there has been a myriad of studies exploring many promising biomarkers. Despite the large number of published studies there is still a lack of any FDA-approved biomarkers for clinical use in adults and children. There is now an important need to validate and introduce them into the clinical setting 2).


Richter et al. aimed to assess if day of injury serum protein biomarkers could identify critically ill TBI patients in whom the risks of transfer are compensated by the likelihood of detecting management-altering neuroimaging findings.

Data were obtained from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Eligibility criteria included: TBI patients aged ≥ 16 years, Glasgow Coma Score (GCS) < 13 or patient intubated with unrecorded pre-intubation GCS, CT with Marshall score < 3, serum biomarkers (GFAP, NFL, NSE, S100B, Tau, UCH-L1) sampled ≤ 24 h of injury, MRI < 30 days of injury. The degree of axonal injury on MRI was graded using the Adams-Gentry classification. The association between serum concentrations of biomarkers and Adams-Gentry stage was assessed and the optimum threshold concentration identified, assuming different minimum sensitivities for the detection of brainstem injury (Adams-Gentry stage 3). A cost-benefit analysis for the USA and UK health care settings was also performed.

Among 65 included patients (30 moderate-severe, 35 unrecorded) axonal injury was detected in 54 (83%) and brainstem involvement in 33 (51%). In patients with moderate-severe TBI, brainstem injury was associated with higher concentrations of NSETauUCH-L1 and GFAP. If the clinician did not want to miss any brainstem injury, NSE could have avoided MRI transfers in up to 20% of patients. If a 94% sensitivity was accepted considering potential transfer-related complications, GFAP could have avoided 30% of transfers. There was no added net cost, with savings up to £99 (UK) or $612 (US). No associations between proteins and axonal injury were found in intubated patients without a recorded pre-intubation GCS.

Serum protein biomarkers show potential to safely reduce the number of transfers to MRI in critically ill patients with moderate-severe TBI at no added cost 3).

Mozaffari et al. created a comprehensive appraisal of the most prominent serum biomarkers used in the assessment and care of TBI.The PubMed, Scopus, Cochrane, and Web of Science databases were queried with the terms “biomarker” and “traumatic brain injury” as search terms with only full-text, English articles within the past 10 years selected. Non-human studies were excluded, and only adult patients fell within the purview of this analysis. A total of 528 articles were analyzed in the initial search with 289 selected for screening. A further 152 were excluded for primary screening. Of the remaining 137, 54 were included in the final analysis. Serum biomarkers were listed into the following broad categories for ease of discussion: immune markers and markers of inflammationhormones as biomarkers, coagulation and vasculature, genetic polymorphisms, antioxidants and oxidative stressapoptosis and degradation pathways, and protein markers. Glial fibrillary acidic protein(GFAP), S100, and neurons specific enolase (NSE) were the most prominent and frequently cited markers. Amongst these three, no single serum biomarker demonstrated neither superior sensitivity nor specificity compared to the other two, therefore noninvasive panels should incorporate these three serum biomarkers to retain sensitivity and maximize specificity for TBI 4).


1)

Mendes Arent A, de Souza LF, Walz R, Dafre AL. Perspectives on Molecular Biomarkers of Oxidative Stress and Antioxidant Strategies in Traumatic Brain Injury. Biomed Res Int. 2014;2014:723060. Epub 2014 Feb 13. Review. PubMed PMID: 24689052.
2)

Papa L, Edwards D, Ramia M. Exploring Serum Biomarkers for Mild Traumatic Brain Injury. In: Kobeissy FH, editor. Brain Neurotrauma: Molecular, Neuropsychological, and Rehabilitation Aspects. Boca Raton (FL): CRC Press/Taylor & Francis; 2015. Chapter 22. PubMed PMID: 26269900.
3)

Richter S, Winzeck S, Czeiter E, Amrein K, Kornaropoulos EN, Verheyden J, Sugar G, Yang Z, Wang K, Maas AIR, Steyerberg E, Büki A, Newcombe VFJ, Menon DK; Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury Magnetic Resonance Imaging (CENTER-TBI MRI) Sub-study Participants and Investigators. Serum biomarkers identify critically ill traumatic brain injury patients for MRI. Crit Care. 2022 Nov 29;26(1):369. doi: 10.1186/s13054-022-04250-3. PMID: 36447266.
4)

Mozaffari K, Dejam D, Duong C, Ding K, French A, Ng E, Preet K, Franks A, Kwan I, Phillips HW, Kim DY, Yang I. Systematic Review of Serum Biomarkers in Traumatic Brain Injury. Cureus. 2021 Aug 10;13(8):e17056. doi: 10.7759/cureus.17056. PMID: 34522534; PMCID: PMC8428323.

Traumatic brain injury epidemiology in Europe

Traumatic brain injury epidemiology in Europe

In 2018 a systematic review provided a comprehensive, up-to-date summary of traumatic brain injury (TBI) epidemiology in Europe, describing incidence, mortality, age, and sex distribution, plus severity, mechanism of injury, and time trends. PubMed, CINAHL, EMBASE, and Web of Science were searched in January 2015 for observational, descriptive, English language studies reporting incidence, mortality, or case fatality of TBI in Europe. There were no limitations according to date, age, or TBI severity. Methodological quality was assessed using the Methodological Evaluation of Observational Research checklist. Data were presented narratively. Sixty-six studies were included in the review. Country-level data were provided in 22 studies, regional population or treatment center catchment area data were reported by 44 studies. Crude incidence rates varied widely. For all ages and TBI severities, crude incidence rates ranged from 47.3 per 100,000, to 694 per 100,000 population per year (country-level studies) and 83.3 per 100,000, to 849 per 100,000 population per year (regional-level studies). Crude mortality rates ranged from 9 to 28.10 per 100,000 population per year (country-level studies), and 3.3 to 24.4 per 100,000 population per year (regional-level studies.) The most common mechanisms of injury were traffic accidents and falls. Over time, the contribution of traffic accidents to total TBI events may be reducing. Case ascertainment and definitions of TBI are variable. Improved standardization would enable more accurate comparisons 1).


In 2016 aimed to estimate the hospital-based incidence, population-wide mortality, and the contribution of TBI to injury-related mortalities in European countries, and to provide European summary estimates for these indicators.

For this cross-sectional analysis, we obtained population data from Eurostat for hospital discharges and causes of death in European countries in 2012. Outcomes of interest were TBIs that required hospital admission or were fatal. We calculated age-adjusted hospital discharge rates and mortality rates and extrapolated data to 28 European Union countries and all 48 states in Europe. We present between-country comparisons, pooled age-adjusted rates, and comparisons with all-injury rates.

In 2012, 1 375 974 hospital discharges (data from 24 countries) and 33 415 deaths (25 countries) related to TBI were identified. The pooled age-adjusted hospital discharge rate was 287·2 per 100 000 (95% CI 232·9-341·5) and the pooled age-adjusted mortality rate was 11·7 per 100 000 (9·9-13·6). TBI caused 37% (95% CI 36-38) of all injury-related deaths in the analysed countries. Extrapolating our results, we estimate 56 946 (95% CI 47 286-66 099) TBI-related deaths and 1 445 526 (1 172 996-1 717 039) hospital discharges occurred in 2012 in the European Union (population 508·5 million) and about 82 000 deaths and about 2·1 million hospital discharges in the whole of Europe (population 737 million). We noted substantial between-country differences.

TBI is an important cause of death and hospital admissions in Europe. The substantial between-country differences observed warrant further study and suggest that the true burden of TBI in Europe has not yet been captured. Rigorous epidemiological studies are needed to fully quantify the effect of TBI on society. Despite a great degree of consistency in data reporting across countries already being achieved, further efforts in this respect could improve the validity of between-country comparisons 2).


In 2015 a total, 28 epidemiological studies on TBI from 16 European countries were identified in the literature. A great variation was found in case definitions and case ascertainment between studies. Falls and road traffic accidents (RTA) were the two most frequent causes of TBI, with falls being reported more frequently than RTA 3).

A search was conducted in the PubMed electronic database using the terms: epidemiology, incidence, brain injur*, head injur* and Europe. Only articles published in English and reporting on data collected in Europe between 1990 and 2014 were included. In total, 28 epidemiological studies on TBI from 16 European countries were identified in the literature. A great variation was found in case definitions and case ascertainment between studies. Falls and road traffic accidents (RTA) were the two most frequent causes of TBI, with falls being reported more frequently than RTA. In most of the studies a peak TBI incidence was seen in the oldest age groups. In the meta-analysis, an overall incidence rate of 262 per 100,000 for admitted TBI was derived.

Interpretation of published epidemiologic studies is confounded by differences in inclusion criteria and case ascertainment. Nevertheless, changes in epidemiological patterns are found: falls are now the most common cause of TBI, most notably in elderly patients. Improvement of the quality of standardised data collection for TBI is mandatory for reliable monitoring of epidemiological trends and to inform appropriate targeting of prevention campaigns 4).

In 2006 it was difficult to reach a consensus on all epidemiological findings across the 23 published European studies because of critical differences in methods employed across the reports 5).

In a retrospectivelongitudinal study of all TBI patients treated in ICU between 2013-2018, 77% (n=171) were male and the median age was 46 (Q1-Q3: 28-62). The most common mechanism of injury was fall from less than two meters (<2m) followed by road traffic accidents (RTA). The proportion of injuries due to RTA increased over the six-year period (p=0.006). 41.4% (n=92) of injuries had reported alcohol involvement. Patients with falls <2m had double the median age and double the rate of alcohol involvement compared to those suffering RTA (p<0.001, p<0.001). The neurosurgical intervention rate was 74% (n=165). The median duration of ICU admission and of intracranial pressure monitoring, advanced ventilation, and inotropic therapy increased over the six-year period (p=0.031, p=0.038, p=0.033, p<0.001). This study’s findings could inform precise and impactful public prevention measures. The increasing duration of ICU admission and of other interventions should be examined further for their effect on patient outcomes and resource consumption 6).

Traumatic brain injury epidemiology in Finland

A coordinated strategy to evaluate this public health problem in Romania would first of all rely on a related advanced monitoring system, to provide precise information about the epidemiology, clinical and paraclinical data, but concerning the social and economic connected consequences, too 7).

Traumatic brain injury epidemiology in Spain


1)

Brazinova A, Rehorcikova V, Taylor MS, Buckova V, Majdan M, Psota M, Peeters W, Feigin V, Theadom A, Holkovic L, Synnot A. Epidemiology of Traumatic Brain Injury in Europe: A Living Systematic Review. J Neurotrauma. 2018 Dec 19. doi: 10.1089/neu.2015.4126. Epub ahead of print. PMID: 26537996.
2)

Majdan M, Plancikova D, Brazinova A, Rusnak M, Nieboer D, Feigin V, Maas A. Epidemiology of traumatic brain injuries in Europe: a cross-sectional analysis. Lancet Public Health. 2016 Dec;1(2):e76-e83. doi: 10.1016/S2468-2667(16)30017-2. Epub 2016 Nov 29. PMID: 29253420.
3) , 4)

Peeters W, van den Brande R, Polinder S, Brazinova A, Steyerberg EW, Lingsma HF, Maas AI. Epidemiology of traumatic brain injury in Europe. Acta Neurochir (Wien). 2015 Oct;157(10):1683-96. doi: 10.1007/s00701-015-2512-7. Epub 2015 Aug 14. PubMed PMID: 26269030.
5)

Tagliaferri F, Compagnone C, Korsic M, Servadei F, Kraus J. A systematic review of brain injury epidemiology in Europe. Acta Neurochir (Wien). 2006 Mar;148(3):255-68; discussion 268. Review. PubMed PMID: 16311842.
6)

Forrest C, Healy V, Plant R. Temporal Trends in Traumatic Brain Injury. Ir Med J. 2022 May 25;115(5):597. PMID: 35696279.
7)

Popescu C, Anghelescu A, Daia C, Onose G. Actual data on epidemiological evolution and prevention endeavours regarding traumatic brain injury. J Med Life. 2015 Jul-Sep;8(3):272-7. Review. PubMed PMID: 26351526; PubMed Central PMCID: PMC4556905.

Severe traumatic brain injury outcome

Severe traumatic brain injury outcome

Females exhibited more favorable cerebral physiology post-Traumatic Brain Injury, particularly better mitochondrial function, and reduced excitotoxicity, but this did not translate into better clinical outcomes compared to males. Future studies need to further explore potential sex differences in secondary injury mechanisms in TBI 1).


deep learning model of head computed tomography and clinical information can be used to predict 6-month severe traumatic brain injury outcome 2).


Younger age, modified Fisher scale (mFS) score, and Intracerebral hemorrhage volume are associated with Intracranial pressure elevation in patients with a severe traumatic brain injury. Imaging features may stratify patients by their risk of subsequent ICP elevation 3).


There has been a secular trend towards reduced incidence of severe traumatic brain injury in the first world, driven by public health interventions such as seatbelt legislation, helmet use, and workplace health and safety regulations. This has paralleled improved outcomes following TBI delivered in a large part by the widespread establishment of specialised neurointensive care 4).

The impact of a moderate to severe brain injury depends on the following:

Severity of initial injury

Rate/completeness of physiological recovery

Functions affected

Meaning of dysfunction to the individual

Resources available to aid recovery

Areas of function not affected by TBI

see Effect of trauma center designation in severe traumatic brain injury outcome


Mortality or severe disability affects the majority of patients after severe traumatic brain injury (TBI). Adherence to the brain trauma foundation severe traumatic brain injury guidelines has overall improved outcomes; however, traditional as well as novel interventions towards intracranial hypertension and secondary brain injury have come under scrutiny after series of negative randomized controlled trials. In fact, it would not be unfair to say there has been no single major breakthrough in the management of severe TBI in the last two decades. One plausible hypothesis for the aforementioned failures is that by the time treatment is initiated for neuroprotection, or physiologic optimization, irreversible brain injury has already set in. Lazaridis et al., and others, have developed predictive models based on machine learning from continuous time series of intracranial pressure and partial pressure of brain tissue oxygen. These models provide accurate predictions of physiologic crises events in a timely fashion, offering the opportunity for an earlier application of targeted interventions. In a article, Lazaridis et al., review the rationale for prediction, discuss available predictive models with examples, and offer suggestions for their future prospective testing in conjunction with preventive clinical algorithm5).


Determining the prognostic significance of clinical factors for patients with severe head injury can lead to an improved understanding of the pathophysiology of head injury and to improvement in therapy. A technique known as the sequential Bayes method has been used previously for the purpose of prognosis. The application of this method assumes that prognostic factors are statistically independent. It is now known that they are not. Violation of the assumption of independence may produce errors in determining prognosis. As an alternative technique for predicting the outcome of patients with severe head injury, a logistic regression model is proposed. A preliminary evaluation of the method using data from 115 patients with head injury shows the feasibility of using early data to predict outcome accurately and of being able to rank input variables in order of their prognostc significance. 6).


A prospective and consecutive series of 225 patients with severe head injuries who were managed in a uniform way was analyzed to relate outcome to several clinical variables. Good recovery or moderate disability were achieved by 56% of the patients, 10% remained severely disabled or vegetative, and 34% died. Factors important in predicting a poor outcome included the presence of intracranial hematoma, increasing agemotor impairment, impaired or absent eye movements or pupillary light reflexes, early hypotensionhypoxemia or hypercarbia, and raised intracranial pressure over 20 mm Hg despite artificial ventilation. Most of these predictive factors were assessed on admission, but a subset of 158 patients was identified in whom coma was present on admission and was known to have persisted at least until the following day. Although the mortality in this subset (40%) was higher than in the total series, it was lower than in several comparable reported series of patients with severe head injury. Predictive correlations were equally strong in the entire series and in the subset of 158 patients with coma. A plea is made for inclusion in the definition of “severe head injury” of all patients who do not obey commands or utter recognizable words on admission to the hospital after early resuscitation 7).


Survival rate of isolated severe TBI patients who required an emergent neurosurgical intervention could be time dependent. These patients might benefit from expedited process (computed tomographic scan, neurosurgical consultation, etc.) to shorten the time to surgical intervention 8).

The impact of a moderate to severe brain injury can include:

Cognitive deficits including difficulties with:

Attention Concentration Distractibility Memory Speed of Processing Confusion Perseveration Impulsiveness Language Processing “Executive functions” Speech and Language

not understanding the spoken word (receptive aphasia) difficulty speaking and being understood (expressive aphasia) slurred speech speaking very fast or very slow problems reading problems writing Sensory

difficulties with interpretation of touch, temperature, movement, limb position and fine discrimination Perceptual

the integration or patterning of sensory impressions into psychologically meaningful data Vision

partial or total loss of vision weakness of eye muscles and double vision (diplopia) blurred vision problems judging distance involuntary eye movements (nystagmus) intolerance of light (photophobia) Hearing

decrease or loss of hearing ringing in the ears (tinnitus) increased sensitivity to sounds Smell

loss or diminished sense of smell (anosmia) Taste

loss or diminished sense of taste Seizures

the convulsions associated with epilepsy that can be several types and can involve disruption in consciousness, sensory perception, or motor movements Physical Changes

Physical paralysis/spasticity Chronic pain Control of bowel and bladder Sleep disorders Loss of stamina Appetite changes Regulation of body temperature Menstrual difficulties Social-Emotional

Dependent behaviors Emotional ability Lack of motivation Irritability Aggression Depression Disinhibition Denial/lack of awareness


Both single predictors from early clinical examination and multiple hospitalization variables/parameters can be used to determine the long-term prognosis of TBI. Predictive models like the IMPACT or CRASH prognosis calculator (based on large sample sizes) can predict mortality and unfavorable outcomes. Moreover, imaging techniques like MRI (Magnetic Resonance Imaging) can also predict consciousness recovery and mental recovery in severe TBI, while biomarkers associated with stress correlate with, and hence can be used to predict, severity and mortality. All predictors have limitations in clinical application. Further studies comparing different predictors and models are required to resolve limitations of current predictors 9).

Clinical outcome prediction following traumatic brain injury (TBI) is a widely investigated field of research. Several outcome prediction models have been developed for prognosis after TBI. There are two main prognostic models: International Mission for Prognosis and Clinical Trials in Traumatic Brain Injury (IMPACT) prognosis calculator and the Corticosteroid Randomization after Significant Head Injury (CRASH) prognosis calculator. The prognosis model has three or four levels:

(1) model A included age, motor GCS, and pupil reactivity

(2) model B included predictors from model A with CT characteristics

(3) model C included predictors from model B with laboratory parameters.

In consideration of the fact that interventions after admission, such as ICP management also have prognostic value for outcome predictions and may improve the models’ performance, Yuan F et al developed another prediction model (model D) which includes ICP. With the development of molecular biology, a handful of brain injury biomarkers were reported that may improve the predictive power of prognostic models, including neuron-specific enolase (NSE), glial fibrillary acid protein (GFAP), S-100β protein, tumour necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), myelin basic protein (MBP), cleaved tau protein (C-tau), spectrin breakdown products (SBDPs), and ubiquitin C-terminal hydrolase-L1 (UCH-L1), and sex hormones. A total of 40 manuscripts reporting 11 biomarkers were identified in the literature. Many substances have been implicated as potential biomarkers for TBI; however, no single biomarker has shown the necessary sensitivity and specificity for predicting outcome. The limited number of publications in this field underscores the need for further investigation. Through fluid biomarker analysis, the advent of multi-analyte profiling technology has enabled substantial advances in the diagnosis and treatment of a variety of conditions. Application of this technology to create a bio-signature for TBI using multiple biomarkers in combination will hopefully facilitate much-needed advances. We believe that further investigations about brain injury biomarkers may improve the predictive power of the contemporary outcome calculators and prognostic models, and eventually improve the care of patients with TBI 10).


Injury site, injury type, and injury degree are the main risk factors for post-traumatic epilepsyTraumatic brain injury outcome can be affected by early post-traumatic epilepsy11).

Insurance and racial disparities continue to exist for TBI patients. Insurance status appears to have an impact on short- and long-term outcomes to a greater degree than patient race 12).

CRASH

IMPACT

Traumatic brain injury mortality.

see Quality of Life after Brain Injury.

Traumatic brain injury complications.

Statins have been shown to improve traumatic brain injury outcome in animal models. The aim of a study was to determine the effect of preinjury statins on outcomes in TBI patients.

Lokhandwala et al. performed a 4-y (2014-2017) review of a TBI database and included all patients aged ≥18 y with severe isolated TBI. Patients were stratified into those who were on statins and those who were not and were matched (1:2 ratio) using propensity score matching. The primary outcome was in-hospital mortality. The secondary outcomes were skilled nursing facility disposition, Glasgow Outcome Scale-extended score, and hospital and intensive care unit length of stay (LOS).

They identified 1359 patients, of which 270 were matched (statin: 90, no-statin: 180). Mean age was 55 ± 8y, median Glasgow Coma Scale was 10 (8-12), and median head-abbreviated injury scale was 3 (3-5). Matched groups were similar in age, mechanism of injury, Glasgow Coma Scale, Injury Severity Score, neurosurgical intervention, type and size of intracranial hemorrhage, and preinjury anticoagulant or antiplatelet use. The overall in-hospital mortality rate was 18%. Patients who received statins had lower rates of in-hospital mortality (11% versus 21%, P = 0.01), skilled nursing facility disposition (19% versus 28%; P = 0.04), and a higher median Glasgow Outcome Scale-extended (11 [9-13] versus 9 [8-10]; P = 0.04). No differences were found between the two groups in terms of hospital LOS (6 [4-9] versus 5 [3-8]; P = 0.34) and intensive care unit LOS (3 [3-6] versus 4 [3-5]; P = 0.09).

Preinjury statin use in isolated traumatic brain injury patients is associated with improved outcomes. This finding warrants further investigations to evaluate the potential beneficial role of statins as a therapeutic drug in a TBI 13).


1)

Svedung Wettervik TM, Hånell A, Howells T, Enblad P, Lewén A. Females Exhibit Better Cerebral Pressure Autoregulation, Less Mitochondrial Dysfunction, and Reduced Excitotoxicity following Severe Traumatic Brain Injury. J Neurotrauma. 2022 May 19. doi: 10.1089/neu.2022.0097. Epub ahead of print. PMID: 35587145.
2)

Pease M, Arefan D, Barber J, Yuh E, Puccio A, Hochberger K, Nwachuku E, Roy S, Casillo S, Temkin N, Okonkwo DO, Wu S; TRACK-TBI Investigators. Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans. Radiology. 2022 Apr 26:212181. doi: 10.1148/radiol.212181. Epub ahead of print. PMID: 35471108.
3)

Murray NM, Wolman DN, Mlynash M, Threlkeld ZD, Christensen S, Heit JJ, Harris OA, Hirsch KG. Early Head Computed Tomography Abnormalities Associated with Elevated Intracranial Pressure in Severe Traumatic Brain Injury. J Neuroimaging. 2020 Nov 4. doi: 10.1111/jon.12799. Epub ahead of print. PMID: 33146933.
4)

Khellaf A, Khan DZ, Helmy A. Recent advances in traumatic brain injury. J Neurol. 2019 Sep 28. doi: 10.1007/s00415-019-09541-4. [Epub ahead of print] PubMed PMID: 31563989.
5)

Lazaridis C, Rusin CG, Robertson CS. Secondary Brain Injury: Predicting and Preventing Insults. Neuropharmacology. 2018 Jun 6. pii: S0028-3908(18)30279-X. doi: 10.1016/j.neuropharm.2018.06.005. [Epub ahead of print] Review. PubMed PMID: 29885419.
6)

Stablein DM, Miller JD, Choi SC, Becker DP. Statistical methods for determining prognosis in severe head injury. Neurosurgery. 1980 Mar;6(3):243-8. PubMed PMID: 6770283.
7)

Miller JD, Butterworth JF, Gudeman SK, Faulkner JE, Choi SC, Selhorst JB, Harbison JW, Lutz HA, Young HF, Becker DP. Further experience in the management of severe head injury. J Neurosurg. 1981 Mar;54(3):289-99. PubMed PMID: 7463128.
8)

Matsushima K, Inaba K, Siboni S, Skiada D, Strumwasser AM, Magee GA, Sung GY, Benjaminm ER, Lam L, Demetriades D. Emergent operation for isolated severe traumatic brain injury: Does time matter? J Trauma Acute Care Surg. 2015 Aug 28. [Epub ahead of print] PubMed PMID: 26317818.
9)

Gao L, Wu X. Prediction of clinical outcome in severe traumatic brain injury. Front Biosci (Landmark Ed). 2015 Jan 1;20:763-771. PubMed PMID: 25553477.
10)

Gao J, Zheng Z. Development of prognostic models for patients with traumatic brain injury: a systematic review. Int J Clin Exp Med. 2015 Nov 15;8(11):19881-5. eCollection 2015. Review. PubMed PMID: 26884899; PubMed Central PMCID: PMC4723744.
11)

Liu Z, Chen Q, Chen Z, Wang J, Tian D, Wang L, Liu B, Zhang S. Clinical analysis on risk factors and prognosis of early post-traumatic epilepsy. Arq Neuropsiquiatr. 2019 Jul 15;77(6):375-380. doi: 10.1590/0004-282×20190071. PubMed PMID: 31314838.
12)

Schiraldi M, Patil CG, Mukherjee D, Ugiliweneza B, Nuño M, Lad SP, Boakye M. Effect of Insurance and Racial Disparities on Outcomes in Traumatic Brain Injury. J Neurol Surg A Cent Eur Neurosurg. 2015 Mar 23. [Epub ahead of print] PubMed PMID: 25798799.
13)

Lokhandwala A, Hanna K, Gries L, Zeeshan M, Ditillo M, Tang A, Hamidi M, Joseph B. Preinjury Statins Are Associated With Improved Survival in Patients With Traumatic Brain Injury. J Surg Res. 2019 Aug 16;245:367-372. doi: 10.1016/j.jss.2019.07.081. [Epub ahead of print] PubMed PMID: 31425877.
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