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Abstract

Acute traumatic brain injury (TBI) in older veterans is an under-recognized public health emergency for the Veterans Health Administration (VHA). The fastest rising incidence of TBI in the United States is in older adults, who have higher mortality, lower rates of functional recovery, and higher risk for post-TBI dementia. Pre-existing TBI, medical/psychiatric conditions, and substance use—common in older veterans—are emerging risk factors for TBI and worse outcomes thereafter. There is an urgent need to characterize acute TBI in older veterans to inform effective interventions to optimize outcomes. We aim to characterize military, clinical, and biological features using a combination of TBI common data elements (CDEs) and validated dementia and geriatrics research assessments. This single-site, longitudinal, observational research study aims to enroll 70 older veterans with acute TBI who receive computed tomography in the emergency department and 30 matched non-neurotrauma controls who present to the San Francisco VA Medical Center within 14 days of their event. Participants and study partners complete pre-injury health/military relevant exposure assessments and multi-domain geriatric and TBI CDE follow-up assessments at 2 weeks, 3, 6, and 12 months. Blood for proteomic biomarkers is collected at baseline, 6 months, and 12 months. Willing participants also undergo magnetic resonance imaging at 2 weeks, 6 months, and 12 months. This study aims for comprehensive characterization of baseline and longitudinal endophenotypes of an intentionally heterogeneous, “real-world,” population of older veterans presenting with acute TBI. Findings will inform the development of future studies focused on optimizing outcomes and evaluating interventions in TBI. This study will provide critical insights into the unique characteristics and the natural history of incident TBI in older veterans, paving the way for larger, Veteran Affairs-based, multicenter prospective studies of acute TBI to inform prevention, support correct diagnosis, and optimize short- and long-term recovery.

Keywords

elderly MRI TBI TBI biomarkers TBI imaging veterans

Introduction

Acute traumatic brain injury (TBI) in older veterans is an under-recognized public health emergency for the Veterans Health Administration (VHA). Nearly 2.9 million Americans seek medical attention for TBI annually, leading to an estimated annual cost of over $75 billion.¹ Older adults have the highest and fastest rising incidence of TBI-related emergency department (ED) visits, hospitalizations, and deaths of any other age group, with more than 1 in 10 people over age 65 seeking medical attention for acute TBI in an 18-year period.² Compared with younger patients, older adults with TBI have higher mortality, lower rates of functional recovery, and may be at especially high risk for post-TBI dementia. Details of clinical and biological characteristics of older veterans with acute TBI are incomplete and insufficient to implement evidence-based practice guidelines or plan clinical trials. Pre-existing TBI, neurological and psychiatric conditions,^3–5 and substance use,^6,7 all common in older veterans, are emerging risk factors for sustaining TBI and for worse outcomes after TBI. Thus, it is likely that the incidence of TBI in older veterans is as high, if not higher, than what has been reported in older civilians. Until now, VHA has understandably focused research efforts on TBI sustained during military service. Due to population aging and the shifting demographics of TBI in the United States, however, research is urgently needed on the silent epidemic of older veterans who sustain TBI after return to civilian life.

As of 2023, there were 8.1 million veterans—roughly half of all U.S. Veterans—aged 65 years or older, with the largest age group representing male veterans in their late 1970s. This group is composed of draftees and enlistees who were 18–20 years old in 1964, when the United States entered the Vietnam War.⁸ A recent study of Medicare claims found that 12.9% of older adults experienced TBI over an 18-year period,² which suggests that, conservatively, more than 58,000 older veterans sustain a TBI annually. Given that veterans have higher rates of risk factors than the general population for sustaining a TBI, in actuality, this number is likely higher than calculated. Importantly, the TBI prevalence in older veterans outstrips that of active-duty service members. From 2000 to 2024 Q3, the number of TBIs in active-duty members is reported as 514,583, far less than the 1.4 million TBIs estimated in the elderly veteran population during that same period.⁹

Given the scale of this problem, it is crucial to expand our understanding of acute TBI in the geriatric veteran population. Because veterans often share unique characteristics related to their military service, studies of TBI in the elderly general population can fail to generalize to the veteran community. Veterans often face a combination of military-relevant exposures (MREs) such as blast injuries, environmental toxins, and combat stress, which can have long-lasting effects on brain health.^10–14 These MREs, alongside the typical comorbidities of aging (hypertension, diabetes, cardiovascular disease, etc.), make the recovery process from TBI in older veterans distinct from that of their civilian counterparts.^15–18 The effects of these exposures may not be fully understood until later in life, complicating TBI diagnosis and treatment for aging veterans.¹⁹ Moreover, veterans are at a higher risk of neurodegenerative disease, post-traumatic stress disorder (PTSD), and dementia, conditions that can exacerbate TBI outcomes.^20–22 Given that 99% of older adults with new TBI have at least one pre-existing condition and 10% have dementia, there is a pressing need to tailor diagnostic, therapeutic, and rehabilitative strategies that account for the synergism of MREs and age-related health challenges of this population.²³

To begin to address age-related barriers within TBI research, the Transforming Research and Clinical Knowledge in Geriatric TBI (TRACK-GERI; R01 NS110944 2019–2024) study was funded, a 5-year 2-site TRACK-TBI Network prospective cohort of acute geriatric TBI in patients presenting to Level 1 trauma centers. However, as that study recruited exclusively at Level 1 trauma centers, it did not include any VA facilities. As a result, TRACK-GERI has not elucidated the unique features of geriatric TBI in veterans presenting with mostly low-acuity TBI to VA EDs.

Thus, to further characterize the unique veteran population, the current TRACK-VA study was developed. This longitudinal, observational trial will assemble a cohort of older veterans with incident TBI and deeply characterize clinical characteristics, prior exposures, current phenotype (including imaging and blood-based biomarkers), and natural history over its 12-month course. It is integrated into the expansive TRACK-TBI network and will be the first network study to enroll patients from a “regular ED.” To facilitate data pooling and direct comparison between this study and the wider TRACK-TBI Network, we have leveraged its infrastructure design for imaging protocols, biospecimens, clinical assessments, and data collection and curation. This standardization allows TRACK-VA to align with the most current TBI CDE consensus recommendations and expand the implications of our findings. Additionally, the data gathered in this study are curated and reported into the Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR), which will allow for future data sharing and contribution to the national advancement of TBI research.

Study goals and objectives

The study goals are to (1) advance scientific knowledge of acute geriatric TBI in veterans by characterizing clinical, blood-based biomarker, and neuroimaging features of older veterans presenting acutely with TBI to a typical VA ED and (2) contribute to optimization, diagnosis, and precision medicine treatments to improve short- and long-term outcomes of geriatric TBI in veterans. The specific aims are to (1) assemble a new prospective cohort of veterans age ≥ 65 years presenting to SFVA ED ≤14 days after TBI who received computed tomography (CT; N = 70) and demographically similar non-injured ED controls (N = 30) and deeply phenotype clinical and biological features over 12 months using TBI common data elements (CDEs), (2) characterize neuroimaging features of acute geriatric TBI in veterans using TBI CDEs and quantitative structural and functional magnetic resonance imaging (MRI), and (3) determine accuracy of blood-based glial fibrillary acidic protein (GFAP), hyperphosphorylated-tau, total tau, brain-derived tau, amyloid beta 42, amyloid beta 40, and neurofilament light chain for TBI diagnosis, prognosis of outcome, and disease monitoring in older veterans (Fig. 1).

FIG. 1.

Specific aims: Study goals and timeline of the TRACK-VA longitudinal observational study. TRACK-VA, Transforming Research and Clinical Knowledge in Older Veterans with Acute Traumatic Brain Injury.

Study Design

Overall design

The TRACK-VA study is a single-center, prospective, longitudinal cohort study.

Data management

All study data will be initially collected on paper case report forms (CRFs) and stored locally in locked cabinets behind locked doors. Data will then be entered and electronically stored in QuesGen Systems. All participants will be assigned to a random Study ID corresponding to their data. Qualitative CT and MRI data will be stored in QuesGen Systems and REDCap data management platforms. MRI images will be read on RadiAnt. These platforms have been selected to ascertain compatibility with FITBIR, to harmonize with the existing TRACK-TBI and TRACK-GERI databases, and to allow for data validation and quality assurance with data entry. Data will be curated at regular intervals by research coordinators to ensure quality and consistency.

Following publication of our main results, data will be uploaded to the FITBIR database. Research data will be kept indefinitely. If subjects decide later that they would like to remove research data from FITBIR or other national health research databases, they can notify our study team, and any remaining specimens and information will be destroyed if they are no longer needed for their care. However, if any research has already been done, this data will be kept and analyzed. We plan for data to remain secure in these databases during enrollment, follow-up, data analysis, and publication. We anticipate that analysis will continue for several years after the completion of subject enrollment.

Ethical considerations

Study participants will be enrolled in accordance with established standards surrounding informed consent. Informed consent procedures will ensure that potential participants are fit for enrollment. If a patient does not pass any one pre-consent orientation assessment (the Galveston Orientation and Amnesia Test, the Assessment of Post Traumatic Amnesia, and the Capacity Assessment Record), efforts will be made to enroll the patient through consent by a legal authorized representative (LAR) (e.g., consultee or proxy) who may infer the patient’s volition to participate. Thus, we aim to decrease under-enrollment in TBI studies of patients with comorbidities such as cognitive impairment. To aid in this goal, a study partner should be consented for each patient, serving as their study proxy.

Study subjects are notified of all study procedures and potential risks within the consent process. Subjects and study partners may withdraw from the study at any time. Subjects may also be withdrawn by their LAR if necessary. Taking part in or leaving the study will not affect medical care. The study team will inform about new information or changes in the study that may affect the subject’s health or willingness to continue in the study. All study data will be de-identified when sharing with collaborators to safeguard privacy. Structural MRI data will be made available to subjects who have reportable findings. Finally, appropriate compensation will be made to study subjects, aligned with the number of follow-up visits completed.

Methodology

Enrollment

This study will enroll 70 veterans aged 65 or older presenting to the SFVAMC ED within 14 days of a head injury of sufficient severity to warrant a head CT, along with 70 co-enrolled study partners. We will also enroll 30 demographically similar ED controls who deny a history of TBI in the past year, with medical or orthopedic conditions discharged from the ED, and 30 co-enrolled control study partners. The study will comprehensively characterize baseline and longitudinal neurobehavioral, blood-based biomarker, and neuroimaging features within this cohort.

Inclusion/exclusion criteria for patients

Inclusion criteria include English fluency, capacity, with the ability to provide informed consent via self or study partner, and the ability to identify and enroll a study partner. Study partners must be 18 or older, designated by the patient, present with the patient to the ED, listed in the patient’s medical chart, or be the patient’s case manager/social worker. A study partner must know the patient well enough to comment on the patient’s health and functional status, have fluency in English, and have the ability to provide informed consent for themselves.

Exclusion criteria include penetrating TBI, spinal cord injury with ASIA Impairment Scale score of C or worse, polytrauma, acute disabling neurological injury such as acute stroke or global anoxic injury, current incarceration, active psychiatric hold (e.g., 5150, 5270), current participation in an interventional trial, and low likelihood of follow-up.

Importantly, we will not exclude for pre-existing medical or neurological conditions apart from those listed above.

Inclusion/exclusion criteria for control subjects

Control subject inclusion/exclusion criteria mirror those of TBI patients (see above), with the exception of exclusion for having suffered a medically diagnosed TBI in the last year, and/or hospital admission post-ED presentation.

Workflow

Participants are enrolled in the ED or by telephone after discharge. Participants and study partners complete pre-injury health, MRE, multi-domain geriatric, and TBI CDE follow-up assessments at 2 weeks, 3, 6 and 12 months. Upon enrollment, we collect demographic and socioeconomic data. We also systematically measure pre-existing medical, psychiatric, and cognitive conditions through interview and geriatric-specific assessment including systematic ascertainment of comorbidities, frailty, and cognitive status. Pre-injury cognitive status is determined through a 30-min study partner Clinical Dementia Rating interview to characterize cognitive trajectory and function over the past several years prior to the index ED visit, thereby facilitating categorization of participants as pre-existing normal cognition, mild cognitive impairment, or dementia. We also collect data on the lifetime history of prior TBI, history of PTSD, military service information, and MREs (history of combat, deployment, and blast exposure). We collect data on Glasgow Coma Score (GCS), loss of consciousness (LOC), post-traumatic amnesia, alteration of consciousness, peripheral injury, cause of injury, hospital unit, blood pressure, lab values, TBI-related seizures, and details of hospital course including length of stay, medications, neurosurgical interventions, and discharge location.

Time frame for enrollment

The timeline for this project is expected to be 4 years. Study recruitment was initiated in October 2022 but was halted in December 2022 due to the need for additional staff support. Once proper talent acquisition had been accomplished, enrollment resumed in July 2023 and was completed in December 2025. The index follow-up for outcome assessment will be 12 months. Final analysis of the complete database is expected to begin in mid-2026, following data refinement.

Blood sampling

Blood samples are drawn in all patients after informed consent, within 14 days of head injury, at 6 months, and at 12 months. Details of specimen collection are input into CRFs. When possible, the baseline blood sample is drawn from an arterial or venous catheter placed as a part of standard care. In most cases, and during follow-up visits, patients receive a separate venipuncture.

Samples are processed, aliquoted when appropriate, and stored in a −80°C freezer within 2 h of collection. Sample acquisition, processing, and storage are performed following the TBI-CDEs Biospecimens and Biomarkers Working Group Guidelines.²⁴ Upon study completion, samples are shipped overnight on dry ice to the analyzing laboratory for analysis. The final set of biomarker assays is determined based on the latest relevant evidence, as the field of blood-based TBI biomarkers is rapidly advancing.

Neuroimaging

All TBI study subjects must have had a non-contrast cranial CT performed as part of clinical care. Each patient’s head CT is characterized using the TBI CDEs, a consensus-based recommendation for data collection, data definitions, and best practices in TBI research established jointly by the National Institute of Neurological Disorders and Stroke, Defense Centers of Excellence, National Institute on Disability and Rehabilitation Research, and Veterans Administration.^25–27 The free text of the dictated reports for the clinical CT and any clinical MRI scans is copied into a database for manual coding by the neuroradiologist. Head CT data are de-identified and uploaded to a VA HIPAA-compliant data server as Digital Imaging and Communications in Medicine files. A board-certified neuroradiologist is reviewing each head CT blinded to all clinical data except age and sex, without accessing other patients imaging.

A follow-up MRI is done for 50 TBI participants and 25 controls at 2 weeks, 6 months, and 12 months post-injury/enrollment. The 1-h MRI scan protocol is implemented on a 3 Tesla (3T) Siemens Skyra platform. A ∼15-min fast scan is also available for participants who are unable or unwilling to undergo the 1-h protocol due to physical or psychological comorbidities. Acute intracranial trauma is characterized on 2-week MRI, and longitudinal structural and functional changes are characterized on 6- and 12-month MRI using quantitative metrics. Each MRI is subsequently reviewed by a board-certified neuroradiologist, blinded to all clinical data except age and sex, and scans are characterized using the TBI CDEs.^48–50 The 1-h protocol sequences include a Localizer, SAG MPRAGE, AX_SWI, 3D T2 TSE, DTI_dir64_dir b=1300, 8 b=0, DTI_PA, DTI_dir64_dir b=3000,8 b=0, DTI_PA_TE111, Field Mapping, Sagittal 3D Flair, Resting State fMRI extended ADNI, Field Mapping_fMRI, and HighResHippo. The FAST Protocol Sequences include BRN_LOC_AAHScout, SAG_T1 mprage fast, AX_T2 fast, AX T2 Flair-fs, Ax_3D_SWI, AX_DTI 2mm ISO (FAST) 230 FOV, and COR T2 FAST BRAIN.

If there are significant findings during acute intracranial trauma characterization on a 2-week MRI, the patient is promptly notified by the principal investigator. If the findings require follow-up or intervention, the principal investigator also notifies the patient’s clinical team.

Initial and ongoing assessments

Patients who pass screening will consent for themselves and may complete all self-assessments. Patients who do not pass screening, either due to the severity of TBI or pre-existing conditions, will be consented by proxy and will complete as many self-assessments as possible. Test completion codes will be used to indicate the reason for missing or invalid data. Study partners may assist patients with interviews. Controls will complete a modified set of measures that have been edited to eliminate reference to TBI.

To optimize enrollment and retention, follow-up measures will be completed both by phone and in-person visits. If a participant is unable to travel, phlebotomy and physical exams may be completed in the patient’s home. By using proxy-reported assessments, this study will capture clinical outcomes even in the most disabled patients. An overview of outcome assessments done per timepoint is detailed in Table 1.

Table 1.

Patient and Study Partner Assessments

Category	Measure	Timepoint
Screening	1. Assessment of Speech Intelligibility 2. Galveston Orientation and Amnesia Test 3. Post-Traumatic Amnesia Assessment 4. Capacity Assessment Record	Baseline, 2 weeks, and then as needed
Consent	1. Patient (or proxy) consent	Baseline
Blood draw	1. Plasma, serum, buffy coat (baseline only), and whole blood for biomarkers, APOE, and banking	Baseline, 6 months, 12 months
Initial interview	1. ED/hospital/acute injury variables 2. Demographic variables 3. Vocational history 4. Pre-injury medical, psychiatric, drug use, and medication history 5. Prior TBI screen (Ohio State TBI Identification Method)⁵⁵ 6. Alcohol Use Disorders Identification Test⁵⁶ 7. Military Service Information 8. Combat Exposure Scale⁵⁷ 9. Deployment Risk and Resilience Inventory-2⁵⁸	Baseline
Geriatric assessments	1. Comorbidities 2. Clinical Dementia Rating⁵⁹ 3. Groningen Frailty Indicator	Baseline, 6 months, 12 months
	4. Activities of daily living (ADL)/mobility5. Functional Activities Questionnaire (instrumental ADLs)6. Everyday Cognition⁶⁰7. Neuropsychiatric Inventory⁶¹8. Gardner Parkinsonism Screening Questions	Baseline, 2 weeks, 3 months, 6 months, 12 months
TBI CDE outcomes	1. Glasgow Outcome Scale—Extended⁶²2. Follow-up interview (post-traumatic epilepsy, rehabilitation, repeat injuries)3. Rivermead Post-concussion Symptoms Questionnaire⁶³4. Patient Health Questionnaire—Depression Module (PHQ-9)⁶⁴5. Columbia Suicide Severity Rating Scale Screening (triggered by PHQ-9)6. Post-Traumatic Stress Disorder Checklist for DSM-5⁶⁵7. Quality of life after brain injury⁶⁶	2 weeks, 3 months, 6 months, 12 months
MRI brain	1. MRI1-h protocol sequences: Localizer, SAG MPRAGE, AX_SWI, 3D T2 TSE, DTI_dir64_dir b=1300, 8 b=0, DTI_PA, DTI_dir64_dir b=3000,8 b=0, DTI_PA_TE111, Field Mapping, Sagittal 3D Flair, Resting State fMRI extended ADNI, Field Mapping_fMRI, HighResHippoFAST Protocol Sequences: BRN_LOC_AAHScout, SAG_T1 mprage fast, AX_T2 fast, AX T2 Flair-fs, Ax_3D_SWI, AX_DTI 2mm ISO (FAST) 230 FOV, COR_T2 FAST BRAIN	2 weeks, 6 months, 12 months
Cognitive testing	1. Blind MoCA2. Brief Test of Adult Cognition by Telephone⁶⁷3. Digit Span (WAIS III)4. Controlled Oral Word Association Test⁶⁸5. Full RAVLT6. WAIS IV7. Neuropsych Hearing Test	2 weeks, 6 months, 12 months
Physical exam	1. Height, weight, seated blood pressure, resting heart rate2. Short Physical Performance Battery⁶⁹3. Unified Parkinson Disease Rating Scale⁷⁰	2 weeks, 6 months, 12 months

CDE, common data element; DSM‐5, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; ED, emergency department; MRI, magnetic resonance imaging; TBI, traumatic brain injury.

Tests will only be administered by trained study personnel. If clinically relevant problems are detected during outcome assessment administration, research personnel will escalate the issue to medical staff or the principal investigator according to local clinical protocols.

Preliminary baseline characteristics

At present, the study has enrolled 45 out of 70 TBI participants and 9 out of 30 controls. Our TBI participant enrollments resulted from approximately 1,100 screened veterans since August 2022. Methods of recruitment included an in-person approach or post-discharge telephone contact after distributing a study flyer or mailing a study letter. Of enrolled participants, 24 were enrolled via an in-person approach, 12 via letter, and 9 via flyer. Baseline data and imaging are available for 42 enrolled veterans. Participants range in age from 65 to 95, with an average age of 77.8 years. There was a strong predominance of the male gender (95.2%), as expected in a VA population. The average number of comorbidities is 6.6, and 100% presented with a GCS score of 13–15. Participants were admitted to the hospital after ED presentation 5.1% of the time and had evidence of acute intracranial trauma on head CT in 5.4% of cases. Notably, 58% of participants had a history of pre-existing TBI, 61.3% had at least one psychiatric condition, and 15.8% reported alcohol/substance use disorder.

Power analysis

The sample size for this study is based on realistic logistical considerations as well as calculations to detect poor global outcome (primary end-point) and poor cognitive outcome (secondary end-point) with a power of 80%. For our descriptive analysis of clinical and biological phenotypes, estimates will have a 95% confidence interval (CI) for the mean with half-width of approximately 0.25 standard deviations. For binary variables, estimates will have a 95% CI for a binary variable with 20–50% prevalence with half-width of 10–12% for veterans. Poor global outcome is estimated at >50% for TBI patients in this cohort. This prediction is based on a prior report of poor global outcome in 49% of subjects in TRACK-TBI with high-GCS, CT-negative presentation (previously referred to as mild TBI).²⁸ Poor cognitive outcome is estimated at ∼30% for TBI patients in this cohort based on our own preliminary study. For comparison of continuous variables among TBI versus controls, we will have 80% power (α = 0.05) to detect medium effect sizes (Cohen’s d = 0.6–0.7). For comparison of categorical variables, we will have 80% power (α = 0.05) to detect differences in proportion of 18–30% (all calculated using G*Power).

The sample size for the MRI cohort is 50 patients and 25 controls, each undergoing scans at 2 weeks, 6 months, and 12 months. This sample size is comparable to our prior MRI studies that have established the value of MRI for predicting outcome after mild TBI in cohorts of mostly younger adults (average age 30–40), and thus, we anticipate sufficient power to validate our prior findings in this elderly veteran cohort.^29–32

Our analysis of blood-based biomarkers seeks to distinguish TBI patients from controls and to predict neuroimaging positivity and outcome. Thus, we aim for a minimally clinically relevant diagnostic/prognostic area under the receiver operating characteristic curve of 0.7 from the null, with power of 80%, for discriminating TBI versus controls. Based on our preliminary data, we anticipate requiring 40 patients and 17 controls. Among those with TBI, to discriminate imaging features or predict future outcomes, we would require a maximum of 70 patients for outcomes with low (20%) prevalence and 48 patients for outcomes with high (50%) prevalence.

Discussion

TRACK-VA is the first TRACK-TBI Network study of acute TBI in older veterans presenting to a VA ED. It is also the first TRACK-TBI Network study of acute TBI in individuals presenting to a non-trauma center ED. This study leverages extensive methodological expertise in prospective studies of acute TBI, as it has sufficient points of overlap with existing TRACK-TBI Network studies to permit comparison and pooling of data.

It is well documented that the veteran population has a unique comorbidity and exposure profile; moreover, there is evidence that older veterans may have different clinical and biological features of acute TBI as compared with the civilian population.^3–5 Interestingly, the baseline CT-positive rate in our cohort is 5%, which is consistent with the rate of CT-positivity in larger studies of civilians in community EDs, so the distribution of TBI severity in our cohort likely mirrors that of the general population presenting to non-trauma centers.³³ Nevertheless, for several years, we have been studying chronic neurobehavioral effects of remote TBI in prospective cohorts of older veterans and civilians. In older veterans, we have identified significant TBI-associated executive cognitive impairment, motor impairment, and elevations in several blood-based biomarkers of neurodegeneration.^34–36 However, in a nationally representative cohort of civilians with detailed cognitive testing, we did not identify any TBI-associated objective cognitive impairment.³⁷ While most prior epidemiological studies evaluating the risk of dementia after TBI have identified an increase, several have not.^22,38 Some of this discrepancy may be due to rudimentary phenotyping of TBI, which all relied on administrative data or self-report. However, we hypothesize that there is also a difference in pre-existing brain health between veterans and civilians,³⁹ and possibly also a synergistic interaction between MREs and TBI. In support of the latter is a case–control study showing more than additive interaction between TBI and exposure to paraquat, a toxic herbicide used by the military, on risk of Parkinson’s disease.⁴⁰ Additionally, there is mounting evidence from studies of mostly younger adults that pre-existing TBI and mental health conditions, common in veterans, are associated with worse outcomes after TBI.^3–7,41,42

A key component of our endophenotyping effort is to investigate the role of blood-based biomarkers in TBI management in this population. Older adults with cognitive impairment frequently present with an unwitnessed fall. In these patients, a clinical diagnosis of TBI can be difficult, which leads to unnecessary ED visits, imaging, and workup, with high associated costs. Reliable blood-based TBI biomarkers, akin to a troponin for myocardial infarction, are currently being investigated as a potential guide to diagnosis/prognosis.⁴³ It is of particular interest to validate these biomarkers in the elderly veteran population, where the majority of patients present with low-velocity, CT-negative TBI in a delayed fashion.⁴⁴ Thus, biomarkers specific to the acute phase of TBI (<48 h) such as GFAP and ubiquitin carboxyl-terminal hydrolase L1 may need to be supplemented with long-term biomarkers for TBI and neurodegeneration, such as NFL and tau moieties.^45–54 Another important aspect of this investigation is the role of imaging, specifically with MRI. A 2-week MRI has shown great promise as an early prognostic indicator in mild TBI.^29,30,45 However, these blood-based and MRI biomarkers have never been studied specifically in older veterans, who have high burdens of comorbid medical and psychiatric conditions. Moreover, the impact of pre-existing MREs on these biomarkers has never been evaluated in this population. It is therefore imperative to validate both established and emerging biomarkers specifically in this population.

Our focus on geriatric TBI is an opportunity to study an accelerated phenotype of post-TBI dementia. TRACK-VA is based on and utilizes the existing TRACK-TBI infrastructure and protocols, which were created in collaboration with the National Institute of Health (NIH) and Department of Defense (DOD), among many others. Since the majority of TBI research within the DOD has focused on injuries acquired during active service, TRACK-VA was an opportunity for the VHA to understand what is now the larger source of TBIs, namely, those suffered by retired, aging service members. Thus, the project was awarded sponsorship by the VHA, making it effectively a collaboration between the VHA, the NIH, and the DOD to accelerate and advance research within the field of geriatric TBI. As such, in addition to internal data analysis, the study will be sharing CDEs with FITBIR, so that other future studies may leverage this unique dataset. Thus, our findings could be used to design well-powered clinical trials to prevent, delay, or manage post-TBI dementia in older veterans. Our findings will also inform the application of emerging blood-based and imaging TBI biomarkers to older veterans in clinical practice and trials. Through our extensive baseline patient and study partner interviews, we will comprehensively characterize pre-injury cognitive status, functional status, mobility, and MREs. We are optimistic that this will both improve care and outcomes of TBI in this population and pave a path forward for future larger multicenter trials of acute TBI in veterans presenting to VA EDs using a comprehensive TRACK-TBI type protocol.

Conclusion

The TRACK-VA study will provide critical insights into the unique characteristics and the natural history of incident TBI in older veterans. It will deeply characterize clinical characteristics, prior exposures, imaging and blood-based biomarkers, and natural history over the 12 months following injury. As the first TRACK-TBI network study to recruit veterans presenting to a VA ED, this study will also pave the way for larger, state-of-the-art, VA-based, multicenter prospective studies and trials of acute TBI in this burdened and understudied population that are urgently needed to inform prevention, support correct diagnosis, and optimize short- and long-term recovery.

Transparency Statement

This trial was not registered because it is a non-interventional, purely observational trial. The analysis plan was not formally pre-registered, but the team member with primary responsibility for the analysis (K.Y., P.E.T) certifies that the analysis plan was pre-specified. A sample size of 70 subjects and 30 controls is planned based on preliminary results for the primary outcome measure. For continuous variables, sample size is calculated to yield 80% power to detect medium effect sizes (Cohen’s d = 0.6–0.7) with a 95% CI for the mean; for categorical variables, sample size is calculated to yield 80% power (α = 0.05) to detect differences in proportion of 18–30% (G*Power). For the secondary outcome measure of MRI-based biomarkers, sample size is calculated at 50 patients and 25 controls, based on preliminary data, to yield 80% power to validate prior findings. For the secondary outcome measure of blood-based biomarker levels in TBI versus control subjects, we aim for an AUC of 0.7 from the null, with a power of 80%; based on preliminary data, we anticipate requiring 70 patients for outcomes with low (20%) prevalence and 48 patients for outcomes with high (50%) prevalence. Data collection and analysis are performed by investigators who are aware of relevant participant characteristics. All surveys and questionnaires used to develop prognostic models are available from the authors. The key inclusion criteria and outcome evaluations are established standards. Screening, recruitment, data collection, and data analysis are ongoing. This article will be published under a Creative Commons Open Access license and, upon publication, will be freely available at https://www.liebertpub.com/loi/neu.

Authors’ Contributions

P.E.T.: Formal analysis, writing—original draft, writing—review and editing, supervision, and project administration. M.J.C.-D.: Investigation, data curation, writing—original draft, and writing—review and editing. D.H.L. and K.H.K.: Investigation and data curation. Y.R.L.: Data Curation. K.Y.: Methodology and formal analysis. G.T.M.: Conceptualization, methodology, funding acquisition, and project administration. P.M.: Formal analysis, writing—review and editing, supervision, funding acquisition, and project administration. R.C.G.: Conceptualization, methodology, formal analysis, supervision, funding acquisition, and project administration. All authors meet criteria for authorship as defined by the International Committee of Medical Journal Editors. The principal investigators (R.C.G. and P.E.T.) had overall responsibility for study design, regulatory oversight, data integrity, and final approval of the article.

Footnotes

Acknowledgments

The authors thank Justin Wong for data entry and data curation, and Sky Raptentsetsang for clinical research coordination and regulatory support. They acknowledge the San Francisco VA Medical Center, particularly the Emergency Department, for facilitating study conduct and participant identification. They also thank Rebecca Yu, VA research grants manager, for administrative and grants management support. Finally, they are grateful to the study partners of the participating subjects, whose time and involvement made this work possible. The contents of this article do not represent the views of the U.S. Department of Veterans Affairs or the United States Government.

Author Disclosure Statement

The authors have no competing interests to disclose.

Funding Information

This work was supported by the U.S. Department of Veterans Affairs, VHA, Office of Research and Development, through a VA Merit Award (CX002346-01).

Abbreviations Used

References

1. Peterson

, Xu

, Daughterty

, et al. Surveillance Report of Traumatic Brain Injury-related Emergency Department Visits, Hospitalizations, and Deaths United States, 2014 Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, U.S. Department of Health and Human Services: Atlanta, Georgia; 2019.

2. Kornblith

, Diaz-Ramirez

, Yaffe

, et al. Incidence of traumatic brain injury in a longitudinal cohort of older adults. JAMA Netw Open 2024;7(5):e2414223; doi: 10.1001/jamanetworkopen.2024.14223

3. Kornblith

, Bahorik

, Li

, et al. Traumatic brain injury, cardiovascular disease, and risk of dementia among older US Veterans. Brain Inj 2022;36(5):628–632; doi: 10.1080/02699052.2022.2033842

4. Schneider

ALC

, Peltz

, Li

, et al. Traumatic brain injury and long-term risk of stroke among US military veterans. Stroke 2023;54(8):2059–2068; doi: 10.1161/STROKEAHA.123.042360

5. Albrecht

, Gardner

, Bahorik

, et al. Psychiatric disorders are common among older US veterans prior to traumatic brain injury. J Head Trauma Rehabil 2024;39(6):E525–E531; doi: 10.1097/HTR.0000000000000959

6. Teeters

, Lancaster

, Brown

, et al. Substance use disorders in military veterans: Prevalence and treatment challenges. Subst Abuse Rehabil 2017;8:69–77; doi: 10.2147/SAR.S116720

7.Substance Use and Military Life Drug Facts. 2019.

8. Vespa

. Aging Veterans: America’s Veteran Population in Later Life. 2023.

9.DOD TBI Worldwide Numbers. 2025. Available from: https://health.mil/Military-Health-Topics/Centers-of-Excellence/Traumatic-Brain-Injury-Center-of-Excellence/DOD-TBI-Worldwide-Numbers.

10.

10. Phipps

, Mondello

, Wilson

, et al. Characteristics and impact of U.S. military blast-related mild traumatic brain injury: A systematic review. Front Neurol 2020;11:559318; doi: 10.3389/fneur.2020.559318

11.

11. Brooks

, Sandri

, Nixon

, et al. Neuroinflammation and brain health risks in veterans exposed to burn pit toxins. Int J Mol Sci 2024;25(18):9759; doi: 10.3390/ijms25189759

12.

12. McKee

, Robinson

. Military-related traumatic brain injury and neurodegeneration. Alzheimers Dement 2014;10(3 Suppl):S242–S53; doi: 10.1016/j.jalz.2014.04.003

13.

13. Massaad

, Kiapour

. Long-Term health outcomes of traumatic brain injury in veterans. JAMA Netw Open 2024;7(2):e2354546; doi: 10.1001/jamanetworkopen.2023.54546

14.

14. Clausen

, Clarke

, Phillips

, et al.; VA Mid-Atlantic MIRECC Workgroup. Combat exposure, posttraumatic stress disorder, and head injuries differentially relate to alterations in cortical thickness in military Veterans. Neuropsychopharmacology 2020;45(3):491–498; doi: 10.1038/s41386-019-0539-9

15.

15. McKinnon

, Garvin

. Weight reduction goal achievement among veterans with mental health diagnoses. J Am Psychiatr Nurses Assoc 2019;25(4):257–265; doi: 10.1177/1078390318800594

16.

16. Liu

, Collins

, Wang

, et al. The prevalence and trend of depression among veterans in the United States. J Affect Disord 2019;245:724–727; doi: 10.1016/j.jad.2018.11.031

17.

17. Rivera

, Amuan

, Morris

, et al. Arthritis, comorbidities, and care utilization in veterans of operations enduring and Iraqi Freedom. J Orthop Res 2017;35(3):682–687; doi: 10.1002/jor.23323

18.

18. Betancourt

, Dolezel

, Shanmugam

, et al. The health status of the US veterans: A longitudinal analysis of surveillance data prior to and during the COVID-19 pandemic. Healthcare (Basel) 2023;11(14):2049; doi: 10.3390/healthcare11142049

19.

19. Betancourt

, Granados

, Pacheco

, et al. Exploring health outcomes for U.S. veterans compared to non-veterans from 2003 to 2019. Healthcare (Basel) 2021;9(5):604; doi: 10.3390/healthcare9050604

20.

20. Howlett

, Nelson

, Stein

. Mental health consequences of traumatic brain injury. Biol Psychiatry 2022;91(5):413–420; doi: 10.1016/j.biopsych.2021.09.024

21.

21. Loignon

, Ouellet

, Belleville

. A systematic review and meta-analysis on PTSD following TBI among military/veteran and civilian populations. J Head Trauma Rehabil 2020;35(1):E21–E35; doi: 10.1097/HTR.0000000000000514

22.

22. Gardner

, Bahorik

, Kornblith

, et al. Systematic review, meta-analysis, and population attributable risk of dementia associated with traumatic brain injury in civilians and veterans. J Neurotrauma 2023;40(7–8):620–634; doi: 10.1089/neu.2022.0041

23.

23. Thompson

, McCormick

, Kagan

. Traumatic brain injury in older adults: Epidemiology, outcomes, and future implications. J Am Geriatr Soc 2006;54(10):1590–1595.

24.

24. Manley

, Diaz-Arrastia

, Brophy

, et al. Common data elements for traumatic brain injury: Recommendations from the biospecimens and biomarkers working group. Arch Phys Med Rehabil 2010;91(11):1667–1672; doi: 10.1016/j.apmr.2010.05.018

25.

25. Haacke

, Duhaime

, Gean

, et al. Common data elements in radiologic imaging of traumatic brain injury. J Magn Reson Imaging 2010;32(3):516–543; doi: 10.1002/jmri.22259

26.

26. Duhaime

, Gean

, Haacke

, et al.; Common Data Elements Neuroimaging Working Group Members, Pediatric Working Group Members. Common data elements in radiologic imaging of traumatic brain injury. Arch Phys Med Rehabil 2010;91(11):1661–1666; doi: 10.1016/j.apmr.2010.07.238

27.

27. Whyte

, Vasterling

, Manley

. Common data elements for research on traumatic brain injury and psychological health: Current status and future development. Arch Phys Med Rehabil 2010;91(11):1692–1696; doi: 10.1016/j.apmr.2010.06.031

28.

28. Nelson

, Temkin

, Dikmen

, et al.; and the TRACK-TBI Investigators. Recovery after mild traumatic brain injury in patients presenting to US level I trauma centers: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study. JAMA Neurol 2019;76(9):1049–1059.

29.

29. Yuh

, Cooper

, Mukherjee

, et al.; TRACK-TBI INVESTIGATORS. Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: A TRACK-TBI study. J Neurotrauma 2014;31(17):1457–1477.

30.

30. Yuh

, Mukherjee

, Lingsma

, et al.; TRACK-TBI Investigators. Magnetic resonance imaging improves 3-month outcome prediction in mild traumatic brain injury. Ann Neurol 2013;73(2):224–235.

31.

31. Palacios

, Owen

, Yuh

, et al.; TRACK-TBI Investigators. The evolution of white matter microstructural changes after mild traumatic brain injury: A longitudinal DTI and NODDI study. Sci Adv 2020;6(32):eaaz6892.

32.

32. Yuh

, Cooper

, Ferguson

, et al. Quantitative CT improves outcome prediction in acute traumatic brain injury. J Neurotrauma 2012;29(5):735–746.

33.

33. Yang

, Lassaren

, Londi

, et al. Risk factors for traumatic intracranial hemorrhage in mild traumatic brain injury patients at the emergency department: A systematic review and meta-analysis. Scand J Trauma Resusc Emerg Med 2024;32(1):91; doi: 10.1186/s13049-024-01262-6

34.

34. Peltz

, Kenney

, Gill

, et al. Blood biomarkers of traumatic brain injury and cognitive impairment in older veterans. Neurology 2020;95(9):e1126–e1133.

35.

35. Peltz

, Gardner

, Kenney

, et al. Neurobehavioral characteristics of older veterans with remote traumatic brain injury. J Head Trauma Rehabil 2017;32(1):E8–E15.

36.

36. Kaup

, Peltz

, Kenney

, et al. Neuropsychological profile of lifetime traumatic brain injury in older veterans. J Int Neuropsychol Soc 2017;23(1):56–64.

37.

37. Gardner

, Langa

, Yaffe

. Subjective and objective cognitive function among older adults with a history of traumatic brain injury: A population-based cohort study. PLoS Med 2017;14(3):e1002246.

38.

38. Dams-O’Connor

, Guetta

, Hahn-Ketter

, et al. Traumatic brain injury as a risk factor for Alzheimer’s disease: Current knowledge and future directions. Neurodegener Dis Manag 2016;6(5):417–429.

39.

39. Mathias

, Wheaton

. Contribution of brain or biological reserve and cognitive or neural reserve to outcome after TBI: A meta-analysis (prior to 2015). Neurosci Biobehav Rev 2015;55:573–593.

40.

40. Lee

, Bordelon

, Bronstein

, et al. Traumatic brain injury, paraquat exposure, and their relationship to Parkinson disease. Neurology 2012;79(20):2061–2066.

41.

41. Dams-O’Connor

, Spielman

, Singh

, et al.; TRACK-TBI Investigators. The impact of previous traumatic brain injury on health and functioning: A TRACK-TBI study. J Neurotrauma 2013;30(24):2014–2020.

42.

42. Yue

, Cnossen

, Winkler

, et al.; TRACK-TBI Investigators. Pre-injury comorbidities are associated with functional impairment and post-concussive symptoms at 3- and 6-months after mild traumatic brain injury: A TRACK-TBI study. Front Neurol 2019;10:343.

43.

43. Wang

, Yang

, Zhu

, et al. An update on diagnostic and prognostic biomarkers for traumatic brain injury. Expert Rev Mol Diagn 2018;18(2):165–180.

44.

44. Lee

, Begley

. Delays in seeking health care: Comparison of veterans and the general population. J Public Health Manag Pract 2017;23(2):160–168; doi: 10.1097/PHH.0000000000000420

45.

45. Yue

, Yuh

, Korley

, et al.; TRACK-TBI Investigators. Association between plasma GFAP concentrations and MRI abnormalities in patients with CT-negative traumatic brain injury in the TRACK-TBI cohort: A prospective multicentre study. Lancet Neurol 2019;18(10):953–961.

46.

46. Hossain

, Mohammadian

, Takala

RSK

, et al. Early levels of glial fibrillary acidic protein and neurofilament light protein in predicting the outcome of mild traumatic brain injury. J Neurotrauma 2019;36(10):1551–1560.

47.

47. Shahim

, Politis

, van der Merwe

, et al. Time course and diagnostic utility of NfL, tau, GFAP, and UCH-L1 in subacute and chronic TBI. Neurology 2020;95(6):e623–e636.

48.

48. Asken

, Elahi

, La Joie

, et al. Plasma glial fibrillary acidic protein levels differ along the spectra of amyloid burden and clinical disease stage. J Alzheimers Dis 2020;78(1):265–276.

49.

49. Khalil

, Pirpamer

, Hofer

, et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun 2020;11(1):812.

50.

50. Mattsson

, Cullen

, Andreasson

, et al. Association between longitudinal plasma neurofilament light and neurodegeneration in patients with Alzheimer disease. JAMA Neurol 2019;76(7):791–799.

51.

51. Ashton

, Pascoal

, Karikari

, et al. Plasma p-tau231: A new biomarker for incipient Alzheimer’s disease pathology. Acta Neuropathol 2021;141(5):709–724.

52.

52. Janelidze

, Mattsson

, Palmqvist

, et al. Plasma P-tau181 in Alzheimer’s disease: Relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer’s dementia. Nat Med 2020;26(3):379–386.

53.

53. Thijssen

, La Joie

, Wolf

, et al.; Advancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) investigators. Diagnostic value of plasma phosphorylated tau181 in Alzheimer’s disease and frontotemporal lobar degeneration. Nat Med 2020;26(3):387–397.

54.

54. Gardner

, Rubenstein

, Wang

KKW

, et al. Age-Related differences in diagnostic accuracy of plasma glial fibrillary acidic protein and tau for identifying acute intracranial trauma on computed tomography: A TRACK-TBI study. J Neurotrauma 2018;35(20):2341–2350.

55.

55. Corrigan

, Bogner

. Initial reliability and validity of the Ohio State University TBI Identification Method. J Head Trauma Rehabil 2007;22(6):318–329.

56.

56. Grossbard

, Malte

, Lapham

, et al. Prevalence of alcohol misuse and follow-up care in a national sample of OEF/OIF VA patients with and without TBI. Psychiatr Serv 2017;68(1):48–55.

57.

57. Keane

, Fairbank

, Caddell

, et al. Clinical evaluation of a measure to assess combat exposure (PDF). Psychological Assessment 1989;1:53–55.

58.

58. Vogt

, Smith

, King

, et al., Deployment Risk and Resilience Inventory-2 (DRRI-2): An updated tool for assessing psychosocial risk and resilience factors among Service members and Veterans (PDF). J Traumatic Stress 2013;26(6):710–717.

59.

Morris

. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 1993;43(11).

60.

60. Farias

, Mungas

, Reed

, et al. The measurement of everyday cognition (ECog): scale development and psychometric properties. Neuropsychology 2008;22(4):531–544.

61.

61. Cummings

, Mega

, Gray

, et al. The Neuropsychiatric Inventory: comprehensive assessment of psychopathology in dementia. Neurology 1994;44(12).

62.

62. Wilson

, Pettigrew

, Teasdale

. Structured interviews for the Glasgow Outcome Scale and the extended Glasgow Outcome Scale: guidelines for their use. J Neurotrauma 1998;15(8):573–585.

63.

63. King

, Crawford

, Wenden

, et al. The Rivermead Post Concussion Symptoms Questionnaire: a measure of symptoms commonly experienced after head injury and its reliability. J Neurol 1995;242(9):587–592.

64.

64. Kroenke

, Spitzer

, Williams

. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16(9):606–613.

65.

65. Blevins

, Weathers

, Davis

, et al. The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. J Traumatic Stress 2015;28(6):489–498.

66.

66. Von Steinbuechel

, Wilson

, Gibbons

, et al. QOLIBRI overall scale: a brief index of health-related quality of life after traumatic brain injury. J Neurol Neurosurg Psychiatry 2012;83(11):1041–1047.

67.

67. Tun

, Lachman

. Telephone assessment of cognitive function in adulthood: the Brief Test of Adult Cognition by Telephone. Age Ageing 2006;35(6):629–632.

68.

68. Ruff

, Light

, Parker

, et al. Benton Controlled Oral Word Association test: Reliability and updated norms. Arch Clin Neuropsychol 1996;11(4):329–338.

69.

69. Guralnik

, Ferrucci

, Simonsick

, et al. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med 1995;332(9):556–561.

70.

70.Fahn SERL. Unified Parkinson’s Disease Rating Scale - Recent developments in Parkinson’s disease. McMillan Health Care: New Jersey; 1987; pp. 153–163.

Transforming Research and Clinical Knowledge in Older Veterans with Acute Traumatic Brain Injury: A Longitudinal Observational Study

Abstract

Keywords

Introduction

Study goals and objectives

Study Design

Overall design

Data management

Ethical considerations

Methodology

Enrollment

Inclusion/exclusion criteria for patients

Inclusion/exclusion criteria for control subjects

Workflow

Time frame for enrollment

Blood sampling

Neuroimaging

Initial and ongoing assessments

Preliminary baseline characteristics

Power analysis

Discussion

Conclusion

Transparency Statement

Authors’ Contributions

Footnotes

Acknowledgments

Author Disclosure Statement

Funding Information

Abbreviations Used

References