Depression is a common consequence of traumatic brain injury (TBI), affecting approximately 30% of individuals with TBI, particularly during the first year post-injury. The etiology of post-TBI depression is complex, involving pre-injury factors, injury characteristics, and post-injury experiences. Despite its prevalence, the long-term trends and determinants of depression trends following TBI remain insufficiently understood.
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Research questions
The current investigation addresses critical knowledge gaps in understanding long-term depression trajectories in TBI patients. The study objectives were to:
(1) characterize the longitudinal course of depression over 10 years post-TBI;
(2) identify sociodemographic and clinical characteristics associated with depression trajectories;
(3) compare the model's predictive performance using population-level information alone (representing average trends) against predictions that incorporate both population-level and subject-specific information; and
(4) evaluate the potential utility of the model for predicting depression trajectories in a clinical setting.
We hypothesized that population-level predictions alone would demonstrate poorer performance compared to models incorporating both population and subject-level effects, and that the magnitude of this difference would be substantial, highlighting the critical importance of accounting for individual heterogeneity in predicting depression trajectories following TBI.
(1) characterize the longitudinal course of depression over 10 years post-TBI;
(2) identify sociodemographic and clinical characteristics associated with depression trajectories;
(3) compare the model's predictive performance using population-level information alone (representing average trends) against predictions that incorporate both population-level and subject-specific information; and
(4) evaluate the potential utility of the model for predicting depression trajectories in a clinical setting.
We hypothesized that population-level predictions alone would demonstrate poorer performance compared to models incorporating both population and subject-level effects, and that the magnitude of this difference would be substantial, highlighting the critical importance of accounting for individual heterogeneity in predicting depression trajectories following TBI.
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Methodology
Data were obtained from the Traumatic Brain Injury Model System (TBIMS) National Data Bank. Depression was measured using the Patient Health Questionnaire-9 (PHQ-9) and collected at 1, 2, 5 and 10 years after injury. Covariates included age, sex, race, employment, education, functional measures, injury severity, pre-injury mental health, and substance use. Linear mixed modelling was used to identify depression trends and factors associated with depression. Predictive performance was evaluated using mean squared error, coverage, and precision.
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Results
The sample comprised 19,397 individuals (mean age 43). Depression scores showed a small decrease over time among those with pre-injury mental health treatment history, but this change was not clinical meaningful. Significant predictors of Year 1 depression included pre-injury mental health treatment (β=1.6), female sex (β=0.86), and prior head injuries (β=0.4). When predicting depression for existing patients using early depression scores, the model achieved precision of 3.7 points, whereas for new patients, the model's precision was 6 points. Conditional predictions outperformed marginal predictions.
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Conclusions
Depression trajectories following TBI exhibit substantial individual heterogeneity. Population-level models alone inadequately capture this complexity, while models incorporating both population and subject-level variations significantly improve predictive performance. This modeling approach demonstrates the potential for predicting depression trajectories in clinical settings, thereby facilitating individualized assessment and intervention.