Browsing by Author "Perrin, Paul B."

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    Depression, Anxiety, and Suicidality in Individuals With Chronic Traumatic Brain Injury Before and During the COVID-19 Pandemic: A National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems Study
    (Elsevier, 2023) Katta-Charles, Sheryl; Adams, Leah M.; Chiaravalloti, Nancy D.; Hammond, Flora M.; Perrin, Paul B.; Rabinowitz, Amanda R.; Venkatesan, Umesh M.; Weintraub, Alan H.; Bombardier, Charles H.; Physical Medicine and Rehabilitation, School of Medicine
    Objective: To examine the prevalence, severity, and correlates of depression, anxiety, and suicidal ideation in people with traumatic brain injury (TBI) assessed before and during the COVID-19 pandemic. Design: Retrospective cohort study using data collected through the Traumatic Brain Injury Model Systems (TBIMS) network at 1, 2, 5, 10, 15, 20, 25, or 30 years post TBI. Setting: United States-based TBIMS rehabilitation centers with telephone assessment of community residing participants. Participants: Adults (72.4% male; mean age, 47.2 years) who enrolled in the TBIMS National Database and completed mental health questionnaires prepandemic (January 1, 2017 to February 29, 2020; n=5000) or during pandemic (April 1, 2022 to June 30, 2021; n=2009) (N=7009). Interventions: Not applicable. Main outcome measures: Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 questionnaire. Results: Separate linear and logistic regressions were constructed with demographic, psychosocial, injury-related, and functional characteristics, along with a binary indicator of COVID-19 pandemic period (prepandemic vs during pandemic), as predictors of mental health outcomes. No meaningful differences in depression, anxiety, or suicidal ideation were observed before vs during the COVID-19 pandemic. Correlations between predictors and mental health outcomes were similar before and during the pandemic. Conclusions: Contrary to our predictions, the prevalence, severity, and correlates of mental health conditions were similar before and during the COVID-19 pandemic. Results may reflect generalized resilience and are consistent with the most recent findings from the general population that indicate only small, transient increases in psychological distress associated with the pandemic. While unworsened, depression, anxiety, and suicidal ideation remain prevalent and merit focused treatment and research efforts.
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    Societal Participation of People With Traumatic Brain Injury Before and During the COVID-19 Pandemic: A NIDILRR Traumatic Brain Injury Model Systems Study
    (Elsevier, 2023) Venkatesan, Umesh M.; Adams, Leah M.; Rabinowitz, Amanda R.; Agtarap, Stephanie; Bombardier, Charles H.; Bushnik, Tamara; Chiaravalloti, Nancy D.; Juengst, Shannon B.; Katta-Charles, Sheryl; Perrin, Paul B.; Pinto, Shanti M.; Weintraub, Alan H.; Whiteneck, Gale G.; Hammond, Flora M.; Physical Medicine and Rehabilitation, School of Medicine
    Objective: To examine the effect of the COVID-19 pandemic on societal participation in people with moderate-to-severe traumatic brain injury (TBI). Design: Cross-sectional retrospective cohort. Setting: National TBI Model Systems centers, United States. Participants: TBI 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%). Interventions: Not applicable. Main outcome measure: The 3 subscales of the Participation Assessment with Recombined Tools-Objective: Out and About (community involvement), Productivity, and Social Relations. Results: Out 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. Conclusions: Consistent 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.
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    Using Machine Learning to Examine Suicidal Ideation After TBI: A TBI Model Systems National Database Study
    (Wolters Kluwer, 2023) Fisher, Lauren B.; Curtiss, Joshua E.; Klyce, Daniel W.; Perrin, Paul B.; Juengst, Shannon B.; Gary, Kelli W.; Niemeier, Janet P.; McConnell Hammond, Flora; Bergquist, Thomas F.; Wagner, Amy K.; Rabinowitz, Amanda R.; Giacino, Joseph T.; Zafonte, Ross D.; Physical Medicine and Rehabilitation, School of Medicine
    Objective: The aim of the study was to predict suicidal ideation 1 yr after moderate to severe traumatic brain injury. Design: This study used a cross-sectional design with data collected through the prospective, longitudinal Traumatic Brain Injury Model Systems network at hospitalization and 1 yr after injury. Participants who completed the Patient Health Questionnaire-9 suicide item at year 1 follow-up ( N = 4328) were included. Results: A gradient boosting machine algorithm demonstrated the best performance in predicting suicidal ideation 1 yr after traumatic brain injury. Predictors were Patient Health Questionnaire-9 items (except suicidality), Generalized Anxiety Disorder-7 items, and a measure of heavy drinking. Results of the 10-fold cross-validation gradient boosting machine analysis indicated excellent classification performance with an area under the curve of 0.882. Sensitivity was 0.85 and specificity was 0.77. Accuracy was 0.78 (95% confidence interval, 0.77-0.79). Feature importance analyses revealed that depressed mood and guilt were the most important predictors of suicidal ideation, followed by anhedonia, concentration difficulties, and psychomotor disturbance. Conclusions: Overall, depression symptoms were most predictive of suicidal ideation. Despite the limited clinical impact of the present findings, machine learning has potential to improve prediction of suicidal behavior, leveraging electronic health record data, to identify individuals at greatest risk, thereby facilitating intervention and optimization of long-term outcomes after traumatic brain injury.