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Browsing by Author "Fisher, Lauren B."
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Item Prevalence of suicidal behaviour following traumatic brain injury: Longitudinal follow-up data from the NIDRR Traumatic Brain Injury Model Systems(Taylor & Francis, 2016) Fisher, Lauren B.; Pedrelli, Paola; Iverson, Grant L.; Bergquist, Thomas F.; Bombardier, Charles H.; Hammond, Flora M.; Hart, Tessa; Ketchum, Jessica M.; Giacino, Joseph; Zafonte, Ross; Department of Physical Medicine and Rehabilitation, IU School of MedicineObjective: This study utilized the Traumatic Brain Injury Model Systems (TBIMS) National Database to examine the prevalence of depression and suicidal behaviour in a large cohort of patients who sustained moderate-to-severe TBI. Method: Participants presented to a TBIMS acute care hospital within 72 hours of injury and received acute care and comprehensive rehabilitation in a TBIMS designated brain injury inpatient rehabilitation programme. Depression and suicidal ideation were measured with the Patient Health Questionnaire (PHQ-9). Self-reported suicide attempts during the past year were recorded at each follow-up examination, at 1, 2, 3, 10, 15 and 20 years post-injury. Results: Throughout the 20 years of follow-up, rates of depression ranged from 24.8–28.1%, suicidal ideation ranged from 7.0–10.1% and suicide attempts (past year) ranged from 0.8–1.7%. Participants who endorsed depression and/or suicidal behaviour at year 1 demonstrated consistently elevated rates of depression and suicidal behaviour 5 years after TBI. Conclusion: Compared to the general population, individuals with TBI are at greater risk for depression and suicidal behaviour many years after TBI. The significant psychiatric symptoms evidenced by individuals with TBI highlight the need for routine screening and mental health treatment in this population.Item 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 MedicineObjective: 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.