Browsing by Author "Klyce, Daniel W."

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    Perceived care partner burden at 1-year post-injury and associations with emotional awareness, functioning, and empathy after TBI: A TBI model systems study
    (IOS Press, 2023) Klyce, Daniel W.; Merced, Kritzianel; Erickson, Alexander; Neumann, Dawn M.; Hammond, Flora M.; Sander, Angelle M.; Bogner, Jennifer A.; Bushnik, Tamara; Chung, Joyce S.; Finn, Jacob A.; Physical Medicine and Rehabilitation, School of Medicine
    Background: People with traumatic brain injury (TBI) can lack awareness of their own emotions and often have problems with emotion dysregulation, affective disorders, and empathy deficits. These impairments are known to impact psychosocial behaviors and may contribute to the burden experienced by care partners of individuals with TBI. Objective: To examine the associations of emotional awareness, emotional functioning, and empathy among participants with TBI with care partner burden. Method: This multisite, cross-sectional, observational study used data from 90 dyads (participants with TBI and their care partner) 1-year post-injury. Participants with TBI completed the Difficulty with Emotional Regulation Scale (DERS; Awareness, Clarity, Goals, Impulse, Nonacceptance, and Strategies subscales); PTSD Checklist-Civilian Version; NIH Toolbox Anger-Affect, Hostility and Aggression Subdomains; PHQ-9; GAD-7; and the Interpersonal Reactivity Index (empathic concern and perspective taking subscales). Care partners completed the Zarit Burden Inventory (ZBI) and provided demographic information. Results: Care partners were predominately female (77%), and most were either a spouse/partner (55.2%) or parent (34.4%). In an unadjusted model that included assessments of emotional awareness, emotional functioning, and empathy of the participant with TBI, the DERS-Awareness and NIH-Hostility subscales accounted for a significant amount of variance associated with care partner burden. These findings persisted after adjusting for care partner age, relationship, education, and the functional status of the participant with TBI (β= 0.493 and β= 0.328, respectively). Conclusion: These findings suggest that high levels of hostility and low emotional self-awareness can significantly affect the burden felt by TBI care partners.
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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.