Dataset Information

Longitudinal Effects of Medical Comorbidities on Functional Outcome and Life Satisfaction After Traumatic Brain Injury: An Individual Growth Curve Analysis of NIDILRR Traumatic Brain Injury Model System Data.

ABSTRACT: OBJECTIVE:To explore associations of specific physical and neuropsychiatric medical conditions to motor and cognitive functioning and life satisfaction over the first 10 years following traumatic brain injury (TBI). SETTING:Telephone follow-up through 6 TBI Model System centers. PARTICIPANTS:In total, 404 individuals or proxies with TBI enrolled in the TBI Model System longitudinal study participating in 10-year follow-up. DESIGN:Individual growth curve analysis. MAIN MEASURES:FIM Motor and Cognitive subscales, Satisfaction With Life Scales, and Medical and Mental Health Comorbidities Interview. RESULTS:Hypertension, diabetes, cancers, rheumatoid arthritis, and anxiety negatively affected the trajectory of motor functioning over time. Diabetes, cancers, chronic bronchitis, anxiety, and depression negatively impacted cognitive functioning. Numerous neuropsychiatric conditions (sleep disorder, alcoholism, drug addiction, anxiety, panic attacks, posttraumatic stress disorder, depression, and bipolar disorder), as well as hypertension, liver disease, and cancers, diminished life satisfaction. Other medical conditions had a negative effect on functioning and satisfaction at specific follow-up periods. CONCLUSION:Natural recovery after TBI may include delayed onset of functional decline or early recovery, followed by progressive deterioration, and is negatively affected by medical comorbidities. Results contribute to the growing evidence that TBI is most appropriately treated as a chronic medical condition complicated by a variety of comorbid conditions.

SUBMITTER: Malec JF

PROVIDER: S-EPMC6711835 | biostudies-literature | 2019 Sep/Oct

REPOSITORIES: biostudies-literature

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Longitudinal Effects of Medical Comorbidities on Functional Outcome and Life Satisfaction After Traumatic Brain Injury: An Individual Growth Curve Analysis of NIDILRR Traumatic Brain Injury Model System Data.

Malec James F JF Ketchum Jessica M JM Hammond Flora M FM Corrigan John D JD Dams-OʼConnor Kristen K Hart Tessa T Novack Thomas T Dahdah Marie M Whiteneck Gale G GG Bogner Jennifer J

The Journal of head trauma rehabilitation 20190901 5

<h4>Objective</h4>To explore associations of specific physical and neuropsychiatric medical conditions to motor and cognitive functioning and life satisfaction over the first 10 years following traumatic brain injury (TBI).<h4>Setting</h4>Telephone follow-up through 6 TBI Model System centers.<h4>Participants</h4>In total, 404 individuals or proxies with TBI enrolled in the TBI Model System longitudinal study participating in 10-year follow-up.<h4>Design</h4>Individual growth curve analysis.<h4> ...[more]

PMID: 30829813

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Project description:ObjectiveTo examine the effect of the COVID-19 pandemic on societal participation in people with moderate-to-severe traumatic brain injury (TBI).DesignCross-sectional retrospective cohort.SettingNational TBI Model Systems centers, United States.ParticipantsTBI Model Systems enrollees (N=7003), ages 16 and older and 1-30 years postinjury, interviewed either prepandemic (PP) or during the pandemic (DP). The sample was primarily male (72.4%) and White (69.5%), with motor vehicle collisions as the most common cause of injury (55.1%).InterventionsNot applicable.Main outcome measureThe 3 subscales of the Participation Assessment with Recombined Tools-Objective: Out and About (community involvement), Productivity, and Social Relations.ResultsOut and About, but not Productivity or Social Relations, scores were appreciably lower among DP participants compared to PP participants (medium effect). Demographic and clinical characteristics showed similar patterns of association with participation domains across PP and DP. When their unique contributions were examined in regression models, age, self-identified race, education level, employment status, marital status, income level, disability severity, and life satisfaction were variably predictive of participation domains, though most effects were small or medium in size. Depression and anxiety symptom severities each showed small zero-order correlations with participation domains across PP and DP but had negligible effects in regression analyses.ConclusionsConsistent with the effect of COVID-19 on participation levels in the general population, people with TBI reported less community involvement during the pandemic, potentially compounding existing postinjury challenges to societal integration. The pandemic does not appear to have altered patterns of association between demographic/clinical characteristics and participation. Assessing and addressing barriers to community involvement should be a priority for TBI treatment providers. Longitudinal studies of TBI that consider pandemic-related effects on participation and other societally linked outcomes will help to elucidate the potential longer-term effect the pandemic has on behavioral health in this population.

Project description:Poor outcomes after traumatic brain injury (TBI) are common yet remain difficult to predict. Diffuse axonal injury is important for outcomes, but its assessment remains limited in the clinical setting. Currently, axonal injury is diagnosed based on clinical presentation, visible damage to the white matter or via surrogate markers of axonal injury such as microbleeds. These do not accurately quantify axonal injury leading to misdiagnosis in a proportion of patients. Diffusion tensor imaging provides a quantitative measure of axonal injury in vivo, with fractional anisotropy often used as a proxy for white matter damage. Diffusion imaging has been widely used in TBI but is not routinely applied clinically. This is in part because robust analysis methods to diagnose axonal injury at the individual level have not yet been developed. Here, we present a pipeline for diffusion imaging analysis designed to accurately assess the presence of axonal injury in large white matter tracts in individuals. Average fractional anisotropy is calculated from tracts selected on the basis of high test-retest reliability, good anatomical coverage and their association to cognitive and clinical impairments after TBI. We test our pipeline for common methodological issues such as the impact of varying control sample sizes, focal lesions and age-related changes to demonstrate high specificity, sensitivity and test-retest reliability. We assess 92 patients with moderate-severe TBI in the chronic phase (≥6 months post-injury), 25 patients in the subacute phase (10 days to 6 weeks post-injury) with 6-month follow-up and a large control cohort (n = 103). Evidence of axonal injury is identified in 52% of chronic and 28% of subacute patients. Those classified with axonal injury had significantly poorer cognitive and functional outcomes than those without, a difference not seen for focal lesions or microbleeds. Almost a third of patients with unremarkable standard MRIs had evidence of axonal injury, whilst 40% of patients with visible microbleeds had no diffusion evidence of axonal injury. More diffusion abnormality was seen with greater time since injury, across individuals at various chronic injury times and within individuals between subacute and 6-month scans. We provide evidence that this pipeline can be used to diagnose axonal injury in individual patients at subacute and chronic time points, and that diffusion MRI provides a sensitive and complementary measure when compared to susceptibility weighted imaging, which measures diffuse vascular injury. Guidelines for the implementation of this pipeline in a clinical setting are discussed.

Dataset Information

Longitudinal Effects of Medical Comorbidities on Functional Outcome and Life Satisfaction After Traumatic Brain Injury: An Individual Growth Curve Analysis of NIDILRR Traumatic Brain Injury Model System Data.

Publications

Longitudinal Effects of Medical Comorbidities on Functional Outcome and Life Satisfaction After Traumatic Brain Injury: An Individual Growth Curve Analysis of NIDILRR Traumatic Brain Injury Model System Data.

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