In the present study, a matched cohort was used to determine the increased risk of NPDs in those with and without TBI. Additionally, the potential effects of demographics, injury variables, medical comorbidities, and pre-injury psychiatric conditions on the NPDs experienced by TBI patients were described. We established that TBI is a heterogenous population demonstrating distinct clinical profiles (phenotypes), that show significant differences in the development of NPDs, suggesting their potential utility in risk stratification. This study is a proof-of-concept and further research is needed to assess the utility of these phenotypes to guide clinical management of TBI patients.

Previous research indicates that patients with TBI are at risk for developing NPDs with rates ranging from 18.3 to 60.8% in the first year post-injury [11, 28]. Our results are consistent with these findings as the rates of NPD-A and NPD-P at two years in the current study were between 36 and 47%. Results from a conditional regression model in the matched cohort revealed that in the cohort without pre-injury psychiatric conditions, the odds of developing NPD were 2.8 times greater in those with TBI. In the cohort with pre-injury psychiatric conditions, those with TBI had 2.4 greater odds for developing NPD compared to controls. Thus, head injury alone and in combination with pre-injury psychiatric conditions appears to contribute to increased risk of post-injury psychiatric disorders compared to controls. A previous population-based study demonstrated an increase in risk for all psychiatric outcomes after head injury in patients without pre-injury psychiatric history [29]. Prior studies also support the finding that among TBI patients, pre-injury disorders are significant predictors of post-injury disorders [12, 30].

We speculate that there are several justifications for how TBI may act independently and in conjunction with pre-injury psychiatric disorders to increase the risk of NPDs. These mechanisms involve an interplay of disrupted connectivity, neuroinflammation, neurochemical imbalances, psychosocial factors, and environmental influences. Firstly, the neuroinflammatory response triggered by TBI can persist long after the initial injury. Chronic neuroinflammation has been linked to alterations in brain function and structure, affecting regions critical for emotional regulation, such as the prefrontal cortex and the limbic system, thereby contributing to the development and exacerbation of psychiatric disorders [31]. Additionally, TBI affects the brain’s structural and functional connectivity, leading to impairments in the communication between different brain regions involved in mood regulation, cognition, and behavior [32]. Disrupted connectivity in the default mode network, salience network, and fronto-limbic circuits has been associated with various psychiatric disorders, including depression, bipolar disorder, schizophrenia, autism, and PTSD [33]. TBI can lead to imbalances in neurotransmitters such as serotonin, dopamine, and glutamate, which play crucial roles in mood regulation, and their disruption can contribute to the development of various psychiatric disorders. Furthermore, the psychological distress of sustaining a TBI, such as loss of independence, changes in social roles, and difficulties with daily activities, may contribute to the development of psychiatric disorders like depression, anxiety, and PTSD [34]. Finally, pre-existing genetic vulnerabilities and environmental factors, including stress, trauma, and lack of social support, may interact with the neurobiological changes caused by TBI, increasing the risk of manifestation of psychiatric conditions [4].

Among TBI patients without any pre-injury psychiatric history, the emerging phenotype was predominately determined by LOS and discharge destination while accounting for the effects of age-related vulnerability to psychiatric disorders. The young males/timely discharge/home recovery phenotype was characterized by a notable proportion of young adults and pediatric patients and showed the lowest NPD-A incidence among all phenotypes. Age-related resilience against physical trauma and NPDs likely underpins the lower incidence of NPDs in this particular phenotype. The highest risk of NPD-A was evident in the older adults/extended recovery/supportive care class. Previous research has shown that prolonged LOS, especially in the intensive care unit, is a risk factor for long-term psychiatric disorders. Recovering at home with support services and relocation to a nursing home is a challenging transition for older adults, often resulting in high rates of persistent depression and anxiety [35]. These environmental changes and age-related vulnerability coupled with the recovery from a head injury may exacerbate mental health symptoms with subsequent development of NPD-A. The young-middle-aged males/extended recovery/rehabilitative care phenotype was also associated with a high incidence of NPD-A. Interestingly, the severity of head injuries observed within this class was especially higher when compared to the other subgroups, suggesting that the extent of functional loss resulting from these injuries necessitated specialized rehabilitative care. Importantly, non-routine discharge to a supportive facility often implies that an individual has experienced functional and/or cognitive decline, which may serve as mediators in the emergence of NPDs. Individuals experiencing limitations in their ability to perform activities of daily living may develop feelings of helplessness and frustration, potentially leading to the development of NPDs. Similarly, cognitive decline after TBI may impede one’s ability to process and cope with stressors, increasing vulnerability to psychiatric disorders. Therefore, non-routine discharge to supportive facilities serves as an indicator of functional and/or cognitive loss, which in turn can play a mediating role in the development of NPDs. The current study and data sources did not allow for an analysis of how functional, cognitive, and social outcomes of head injury may be confounders, mediators, and/or modifiers of the association between TBI and NPDs. We acknowledge that a mediation analysis to quantify the extent to which the relationship between TBI and NPDs can be explained by one or more intermediate variables is critical in expanding our understanding of the complex causal pathway between head injury and NPDs.

We identified four distinct latent classes among TBI patients with pre-injury psychiatric history, each exhibiting a unique profile and highlighting the interaction between psychiatric burden and NPD-P incidence. The young adult/low psychiatric burden phenotype was characterized by the lowest burden of pre-existing psychiatric conditions and the lowest NPD-P incidence among all phenotypes. The reduced burden of psychiatric conditions is a likely explanation of the lower incidence of NPD-P in this particular phenotype. In contrast, the psychiatric complexity/high comorbidity class had the highest pre-injury psychiatric comorbidity levels and were discharged home post-injury. The high burden of psychiatric conditions coupled with the potential lack of screening and monitoring for NPDs in a home environment, may explain why this phenotype had the greatest NPD-P incidence among four classes.

Results of conditional logistic regression for each phenotype allowed for the comparison of TBI patients with their matched controls within homogeneous subgroups. We demonstrated that within each phenotype, patients with TBI are more likely to have NPD than matched controls, corresponding to meaningful differences in NPD risk when matched controls are considered. It is important to note that latent classes were derived using a broader set of indicators, including demographics and injury-related variables for patients with TBI. The matched controls, however, were matched to patients with TBI patients based on age, sex, comorbidities, and psychiatric conditions. This difference should be taken into account when interpreting the results of the conditional logistic regression with each phenotype.

Guidelines from the American College of Surgeons emphasize the importance of postinjury mental health disorder screening and intervention for trauma patients [36]. Triaging the risk of NPDs at the time of discharge from hospital may lead to better post-injury mental health outcomes and quality of life for patients with TBI. Our approach of identifying interpretable phenotypes with different risk of NPDs, and the accurate classification of individuals into these phenotypes, allows for a practical risk stratification approach of patients with TBI. The parameter estimates from the LCA model on the original data can be applied to calculate the posterior class membership probabilities for new patients. This process facilitates risk assessment by classifying new patients into one of the defined phenotypes.

This study has several strengths. The linked data sources provided detailed information for a population-based cohort, limiting selection and recall biases, while also ensuring robust statistical power. The variables used in our modelling approach are available in electronic health records, and therefore the insights from our analyses are applicable within similar settings. To assess the health burden of NPDs among individuals with and without TBI, a direct comparison of data between TBI cases and uninjured controls is essential. Hence, we employed a demographically similar uninjured cohort as the matched group for patients with TBI. This matching approach enables the exploration of differences in NPD prevalence between injured individuals and healthy controls.

There are limitations to be considered when interpreting the current findings. Our results are dependent on patient encounters documented in administrative databases, with the potential for under-reporting of diagnoses, lack of specificity in coding, and inaccuracies in designating diagnostic categories. Given that the current study used administrative health databases, we did not capture lifetime psychiatric history using structured clinical interviews. Our study may have potentially underestimated the true prevalence of pre-injury psychiatric history and its impact on the development of novel psychiatric disorders post-TBI using a lookback period of two years to ascertain pre-injury psychiatric burden. For patients with pre-existing psychiatric disorders, we were unable to determine whether these disorders worsened in severity based on ICD coding. Additionally, our study did not capture changes in life satisfaction, functional independence, and social participation, all of which are known to influence mental health functioning after injury [37, 38]. It is also important to acknowledge that while the control group was selected to match the TBI cohort demographically and did not have a recorded history of TBI or develop TBI during the two years of follow-up, some controls may have experienced head injuries which did not result in clinical encounters. Finally, the current study did not perform external validation of the identified phenotypes. To confirm the generalizability of these phenotypes, future research should apply the posterior membership probabilities derived from our main analysis to a new, independent sample of TBI patients. Nevertheless, the study findings provide a method to identify homogeneous subgroups of TBI patients in relation to NPDs, thereby facilitating both research and clinical applications. We demonstrate that LCA can distinguish clinically meaningful phenotypes using routinely collected variables. Using these standard variables may be a useful method to stratify patients into more homogeneous groups for enrollment into clinical trials, selection of pharmacological and non-pharmacological interventions, and prediction of clinical outcomes. From a clinical standpoint, the risk phenotypes would be useful for counselling patients and predicting their psychiatric rehabilitation needs.