Citation

Bombardier CH, Hoekstra T, Dikmen S, Fann JR. J. Neurotrauma 2016; 33(23): 2115-2124.

Affiliation

University of Washington, Psychiatry & Behavioral Sciences , Box 356560 , 1959 NE Pacific Street , Seattle, Washington, United States , 98195-6560 ; fann@u.washington.edu.

Copyright

(Copyright © 2016, Mary Ann Liebert Publishers)

DOI

10.1089/neu.2015.4349

PMID

26979826

Abstract

Major depression is prevalent following traumatic brain injury (TBI) and associated with poor outcomes. Little is known about the course of depression after TBI. Participants were 559 consecutively admitted patients with mild to severe TBI recruited from inpatient units at Harborview Medical Center, a Level I trauma center in Seattle WA. Participants were assessed with the Patient Health Questionnaire-9 (PHQ-9) depression measure at months 1-6, 8, 10 and 12 post-injury. We used linear latent class growth mixture modeling (LCGMM) of PHQ-9 total scores to identify homogeneous subgroups with distinct longitudinal trajectories. A four-class LCGMM had good fit indices and clinical interpretability. Trajectory groups were: low depression (70.1%), delayed depression (13.2%), depression recovery (10.4%) and persistent depression (6.3%). Multinomial logistic regression analyses were used to distinguish trajectory classes based on baseline demographic, psychiatric history, and clinical variables. Relative to the low depression group, the other 3 groups were consistently less likely to be on Medicare versus Commercial insurance and more likely to have a preinjury history of MDD, other mental health disorders or, major depressive disorder, and alcohol dependence. They were also more likely to have a positive toxicology screen for cocaine or amphetamines at the time of injury, and a history of alcohol dependence. They were less likely to be on Medicare versus commercial insurance. Trajectories based on LCGMM are an empirical and clinically meaningful way to characterize distinct courses of depression following TBI. When combined with baseline predictors, this line of research may improve our ability to predict prognosis and target groups who may benefit from in greatest need of treatment or secondary prevention efforts (e.g., proactive telephone counseling).

Language: en