Risk Stratification of New-Onset Psychiatric Disorders Using Clinically Distinct Traumatic Brain Injury Sub-Phenotypes

doi:10.21203/rs.3.rs-4378354/v1

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Research Article

Risk Stratification of New-Onset Psychiatric Disorders Using Clinically Distinct Traumatic Brain Injury Sub-Phenotypes

https://doi.org/10.21203/rs.3.rs-4378354/v1

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Journal Publication

published 02 Aug, 2024

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Background

Traumatic brain injury (TBI) patients constitute a highly heterogeneous population, with varying risks for New-onset Psychiatric Disorders (NPDs). The objectives of this study were to identify TBI phenotypes and determine how NPDs differ among these phenotypes.

Methods

Hospitalized TBI patients from 2003–2019 were obtained from the provincial trauma registry. Propensity score matched samples were used to estimate the risk of TBI on NPDs. To uncover heterogeneity in TBI, latent class analysis-based clustering was applied. The effect of classes on NPDs was assessed using log binomial regression models.

Results

A total of 3,453 TBI patients were included in the analysis. In a conditional regression involving propensity matched TBI and control patients, TBI was significantly associated with the development of NPD-A (OR: 2.78; 95% CI: 2.49–3.09), as well as NPD-P (OR: 2.36; 95% CI: 2.07–2.70). Eight distinct latent classes were identified which differed in the incidence of NPDs. Four classes displayed a 53% (RR:1.53; 95% CI: 1.31–1.78), 48% (RR:1.48; 95% CI: 1.26–1.74), 28% (RR:1.28; 95% CI: 1.08–1.54), and 20% (RR: 1.20, 95%CI: 1.03–1.39), increased NPD risk.

Conclusion

TBI is a significant predictor of NPDs. There are clinically distinguishable phenotypes with different patterns of NPD risk among TBI patients. Identifying individuals with respect to their phenotype may improve risk stratification of TBI patients and promote early intervention for psychiatric care in this vulnerable population.

traumatic brain injury

new-onset psychiatric disorders

latent class analysis

public health

TBI patients have increased odds of New-onset Psychiatric Disorders (NPDs) compared with population-matched controls.
TBI is a heterogenous disorder with clinically distinct sub-phenotypes arising from demographic, injury variables, and pre-injury psychiatric history.
Prolonged hospital stays and discharge to supportive may result in functional and cognitive decline in TBI patients without pre-injury psychiatric conditions, elevating NPD risk.
In patients with pre-injury psychiatric disorders, psychiatric burden is associated with the incidence of NPDs.
A risk stratification approach using latent class analysis resulting in clustering of patients into sub-phenotypes may be effective in identifying TBI patients likely to require early intervention for NPDs.

Traumatic brain injury (TBI) is a significant contributor to global injury burden, with an age-standardized incidence rate of 369 per 100,000 population [1]. Each year in Canada, there are approximately 166,455 TBIs [2], resulting in roughly 20,000 hospitalizations and annual direct medical costs of $120.7 million CAD [3]. TBI is a leading cause of mortality in Canada, accounting for approximately 23% of all injury-related deaths [2].

New-onset psychiatric disorders (NPDs) are common after TBI [4]. Factors such as the location of brain lesion, prior lifetime psychiatric disorders, family function, family psychiatric history, and socioeconomic status have shown significant associations with the development of NPDs following pediatric TBI [5–9]. Furthermore, NPDs are more common in adults with a history of pediatric TBI compared with controls without childhood head injuries, and the presence pre-injury lifetime psychiatric disorders notably predicts the occurrence of NPDs [10].

Despite the growing body of research on the link between TBI and NPDs [4], there remain significant gaps in our knowledge. While a lifetime history of psychiatric disorder before TBI has emerged as a contributing factor to NPDs, it is noteworthy that many patients without such a history also develop post-injury disorders [11]. Nevertheless, few studies have delved into the risk of NPDs in patients without a psychiatric history. Secondly, TBI patients constitute a highly heterogeneous population, with different profiles of risk and long-term outcomes [12–14]. While most studies have examined the relationship between various predictors and NPDs following TBI, none have explored the heterogeneity in the relationship between demographics, injury, psychiatric history and NPDs in TBI patients.

The current study addresses these knowledge gaps by defining NPD as two distinct outcomes experienced by those with and without a psychiatric history. A large population-based dataset of TBI patients and controls was used to demonstrate the difference in the development of NPDs over time. Using demographic, injury, and clinical history data, clustering models were specified and fitted to identify phenotypes of patients at high risk of NPDs. We hypothesized that this approach could identify patient clusters, each with a distinct profile of clinical characteristics associated with significant differences in NPDs.

This investigation addressed three specific research questions: 1) What is the effect of TBI on the development of NPDs in a propensity-matched sample of injured patients and population-based controls? 2) Within the TBI cohort, are there specific phenotypes which differ with respect to demographics, injury variables, and pre-injury psychiatric conditions? 3) Are these phenotypes associated with the development of NPDs?

Data Sources

Details on data sources and linkage are provided in Supplementary methods. Briefly, the TBI cohort was obtained from the Nova Scotia Trauma Registry (NSTR). Pre- and post-injury psychiatric disorders were captured from the Canadian Institute of Health Information- Discharge Abstract Database (CIHI DAD), National Ambulatory Care Reporting System (NACRS), and Provincial Physician Billing database.

Study Setting and Participants

Exposure

The exposure of interest in this study was the occurrence of TBI. We included TBI patients admitted to a provincial hospital between 2003–2019. The severity of TBI was determined using the Abbreviated Injury Scale (AIS). Patients presenting solely with scalp abrasions or superficial lacerations (not representative of intracranial injury), are often categorized as AIS Head score = 1[15], and were excluded from the study cohort. Only subjects with a maximum AIS Head score ≥ 2 were included in the study. This definition for TBI includes a broad range of injury severity and has been used in previous studies [15–18]. Controls were obtained from the provincial health insured registry and did not have a history of TBI or develop TBI in the two years of follow up based on clinical encounters (See Supplementary Data).

Covariates

Guided by the literature, age, sex, medical comorbidities (defined through the Charlson Comorbidity Index [CCI]), pre-injury psychiatric diagnoses, AIS Head score, injury mechanism, discharge destination and length of stay (LOS) in acute care facility were identified as important covariates to consider as potential confounders.

Outcome

The outcome of interest was NPD in the first two years post-TBI. Previous studies have defined NPD (also referred to as novel psychiatric disorder) to occur in one of two conditions [6–8]: 1) the development of a psychiatric disorder after injury in a patient with no lifetime preinjury psychiatric disorder; or 2) the development of a post-injury psychiatric disorder in a patient with a lifetime psychiatric disorder that was never before present (e.g., a subject with a lifetime history of major depressive disorder who develops anxiety disorder after the injury would receive the classification, but would not if only a new episode of major depression occurred). In many cases, the “lifetime psychiatric disorder” variable refers to any psychiatric disorder present prior to the injury [10, 19]. In the current study, we defined NPD as two distinct outcomes, namely, NPD in the absence of psychiatric history (NPD-A) and NPD in the presence of psychiatric history (NPD-P). These outcomes were defined to understand influence of pre-injury psychiatric history on the occurrence of NPDs.

Study Design

The overall study design is presented in Fig. 1. The study is a matched cohort design with a two-year follow-up for outcome collection. The study unfolded in two phases: firstly, we assessed the development of NPDs in a propensity-matched cohort of TBI patients and population-based controls; secondly latent class analysis (LCA) was applied to the TBI cohort, identifying TBI sub-groups.

Data Analysis

Propensity Score Matching

The covariates entered into the propensity score were age, sex, CCI group (no comorbidities [0], mild [1–2], moderate [3–4], and severe [≥ 5]), and mental health diagnoses (ICD9:290–319, ICD10: F04-F99). Cases and controls were matched using greedy nearest neighbor matching without replacement using a calliper width of 0.2 standard deviations of the logit of the propensity score. A matching ratio of 4-to-1 (up to 4 matches of controls per case) was used (See Supplementary Data). The matched cohort was then divided into those with and without pre-injury psychiatric conditions before further analysis.

Latent Class Analysis

LCA was used to derive clinical phenotypes of TBI patients which represent their distinct underlying individual attributes. Details on the LCA methodology are described in the Supplementary methods. LCA was conducted separately for the two TBI cohorts: those with and without pre-injury psychiatric conditions. For those without psychiatric history, indicators included sex, categories of age, categories of CCI, injury mechanism, injury severity (maximum AIS Head), prolonged LOS, and discharge destination. For those with pre-injury psychiatric disorders, we used the same indicators and added past psychiatric conditions as additional indicators. Only psychiatric conditions with a prevalence of > 10% were included: disorders relating to sleep, organic conditions, mood, anxiety, drug substance abuse disorder (SUD), and alcohol SUD. For age, we utilized four categories representing different stages of life: pediatric (0–18 years), young adult (19–35 years), middle-aged adult (36–59), and older adult (≥ 60 years). LOS was dichotomized as brief or prolonged, defined as ≥ 21 days based on previous research [20, 21]. Relative fit was evaluated using the Bayesian Information Criterion (BIC), the sample-size adjusted BIC (SABIC), and Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test (VLMR-LRT) [22]. Empirical robustness was complemented by interpretability during the process of model selection [23].

Characteristics were compared across the identified sub-phenotypes. Categorical variables were reported as number (percentage) and were compared across classes using the chi square test. Risk ratios for NPD-A and NPD-P were estimated using log binomial regression models where latent classes were used as predictors and NPD was modelled as the outcome. A p < 0.05 was considered as statistical significance.

Characteristics of study population

There were 3,453 TBI patients and 13,112 matched controls in the analysis. Table 1 shows the frequencies for the matched variables between cases and controls. TBI and control groups did not significantly differ in age, sex, comorbidities, or history of psychiatric disorders. Psychiatric diagnosis categories are described in Supplementary Table 2. In the two-year follow-up, a significantly greater proportion of TBI patients developed NPD-A (35.6% vs. 16.9%, p < 0.001, Supplementary Table 3) and NPD-P (46.9% vs. 27.7%, p < 0.001) than matched controls.

Table 1

Characteristics of the matched cohort
Characteristic	TBI N = 3,453	Control N = 13,112	p
Age (mean, SD)	51.21 ± 25	51.66 ± 25	0.36
Sex
Male (n, %)	2,430 (70.4)	9,121(69.6)	0.37
Female (n, %)	1,023 (29.6)	3,991 (30.4)	0.37
Charlson comorbidity index
None (n, %)	2,026 (58.7)	7,476 (57.0)	0.41
Mild (n, %)	976 (28.3)	3,892 (29.7)
Moderate (n, %)	277 (8.0)	1,085 )8.3)
Severe (n, %)	174 (5.0)	659 (5.0)
Psychiatric history
None (n, %)	2,206 (63.9)	8,275 (63.1)	0.41
Any (n, %)	1,247 (36.1)	4,837 (36.9)	0.41

Comparison of TBI vs. non-TBI patients for NPDs

In conditional regression analysis performed on the matched groups (Table 2), TBI was significantly associated with the development of NPD-A (OR: 2.78; 95% CI: 2.49–3.09), as well as NPD-P (OR: 2.36; 95% CI: 2.07–2.70).

Table 2

Results of the conditional logistic regression with adjusted odds ratio (OR) and 95%confidence interval (CI) for NPDs in the matched cohort
	NPD-A			NPD-P
Characteristic	Odds ratio¹	95%CI	p	Odds ratio²	95%CI	p
Control	Referent			Referent
TBI	2.78	2.49–3.09	< 0.001	2.36	2.07–2.70	< 0.001
¹Adjusted for matching on age, sex, and comorbidities
² Adjusted for matching on age, sex, comorbidities, and psychiatric history
Odds ratios indicate the probability of TBI vs. control as a characteristic for developing NPD

Latent Class Analysis

Tables 3–4 present fit indices for latent class models ranging from one to five classes. A 4-class solution provided the best fit for both TBI cohorts (those with and without pre-injury psychiatric conditions, Fig. 3 and Fig .4). Supplementary Tables 4–5 provide the mean posterior probabilities of the selected models, representing the average likelihood of individuals being assigned to their respective latent classes. All class assignments were > 80%, demonstrating high class assignment certainty for individuals.

Table 3

Information criterion for all models for the TBI cohort without pre-injury psychiatric disorders
Model	AIC¹	BIC²	SABIC³	LR⁴	Entropy	LRT⁵
2 class	27026.67	27248.66	26952.52	2511.56	0.82
3 class	26290.58	26626.42	26176.43	1841.49	0.82	< 0.001
4 class*	26131.58	26581.26	25977.43	1693.07	0.78	< 0.001
5 class	26077.50	26641.01	25883.34	1594.64	0.72	< 0.001
¹ Akaike information criterion
² Bayesian information criterion
³ Sample-size adjusted BIC
⁴ Likelihood ratio G² statistic
⁵ Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test
*For the TBI cohort without pre-injury psychiatric conditions, the SABIC and LRT indicated the 5-class model as the most appropriate. Lower values for BIC and SABIC indicate relatively better balance between parsimony and model fit. Emphasis was placed on BIC, SABIC, and VLMR-LRT given evidence showing their unique strength in identifying the ideal number of classes. The 4-class model provided a more clearly defined representation of the data over the 5-class models. For these reasons, the 4-class model was selected.

Table 4

Information criterion for all models for the TBI cohort with pre-injury psychiatric disorders
Model	AIC¹	BIC²	SABIC³	LR⁴	Entropy	LRT⁵
2 class	24702.05	24973.73	24599.62	6570.99	0.80
3 class	24317.83	24727.92	24161.40	6192.17	0.81	< 0.001
4 class*	24160.17	24708.67	23949.74	6077.18	0.83	< 0.001
5 class	24016.84	24703.73	23752.40	5870.54	0.88	< 0.001
¹ Akaike information criterion
² Bayesian information criterion
³ Sample-size adjusted BIC
⁴ Likelihood ratio G² statistic
⁵ Vuong-Lo-Mendell-Rubin adjusted likelihood ratio test
*For the TBI cohort with pre-injury psychiatric conditions, the SABIC and LRT indicated the 5-class model as the most appropriate. Lower values for BIC and SABIC indicate relatively better balance between parsimony and model fit. Emphasis was placed on BIC, SABIC, and VLMR-LRT given evidence showing their unique strength in identifying the ideal number of classes. The 4-class model provided a more clearly defined representation of the data. For these reasons, the 4-class model was selected.

Class descriptions for TBI cohort without pre-injury psychiatric conditions

Supplementary Table 6 summarizes the descriptive statistics of the four classes and a visual representation is shown in Fig. 2. Significant differences were observed across all characteristics between classes. Additionally, the incidence of NPD-A showed a significant variation between classes (p < 0.001).

Older males/timely discharge/home recovery

This class was composed largely of older adult (85%) males (70%) who experienced falls (79%). They were discharged home (87%) following a brief LOS (95%).

Older adults/extended recovery/supportive care class: This class was composed largely of older adults (98%) who experienced falls (89%). They were discharged to other acute care facilities (43%) or home with support services (29%) after a prolonged LOS (46%).

Young males/timely discharge /home recovery class: This class was composed of young adult (41%) males (83%) whose injuries were caused by MVCs (39%). These individuals were discharged home (92%) after a brief LOS (98%).

Young-middle-aged males/extended recovery/rehabilitative care class: Members of this class were young adult (42%) and middle age (38%) males (82%). Their head injury was mostly caused by MVCs (69%) and resulted in a severe injury (AIS = 5; 42%). The majority of this class had a high probability of being discharged to a rehabilitation facility (70%) after a prolonged LOS (84%).

The risk ratio (RR) of NPD-A in the older females/extended recovery/supportive care class was 53% higher than the young-middle-aged males/timely discharge/home recovery class (RR:1.53; 95% CI: 1.31–1.78, Supplementary Table 7). Additionally, the young-middle-aged males/extended recovery/rehabilitative care class exhibited a 48% higher RR (RR:1.48; 95% CI: 1.26–1.74) and the older males/timely discharge/home recovery class demonstrated a 20% higher RR (RR: 1.20, 95%CI: 1.03–1.39) than the young-middle-aged males/timely discharge/home recovery class.

Class descriptions for TBI cohort with pre-injury psychiatric conditions

Supplementary Table 8 summarizes the estimated profiles of the 4-class model for the TBI cohort with pre-injury psychiatric conditions and a visual representation is shown in Figs. 3–4. Significant differences were observed across all indicators between classes. Moreover, the incidence of NPD-P showed a significant variation between classes (p = 0.02). The distinct classes identified in this cohort are as follows:

Young adult/low psychiatric burden class: Individuals in this class were mostly young adult (49%) males (82%). MVCs were the major cause of TBI (57%). These individuals were discharged home (67%) after a brief LOS (72%). This class had low probabilities of pre-injury psychiatric conditions (mood disorders [19%], anxiety disorders [33%], drug SUD [10%], alcohol SUD [6%]), and low probabilities of comorbid psychiatric disorders (17%).

Older females/intermediate psychiatric burden class: Members of this class were older (98%) females (60%) with a high probability of falls as the cause of TBI (94%). This class was discharged home with support services (23%) or to a nursing home (30%). These individuals mainly had pre-injury organic disorders (59%), mood disorders (29%), anxiety (27%), and a 49% probability of comorbid conditions.

Anxiety predominant/intermediate psychiatric burden class: This class was composed of middle-age (46%) and older adult (53%) males (69%) who experienced falls (71%). They were discharged home (64%). Pre-injury anxiety (46%) was the most common disorder, but there were but no comorbid disorders in this class.

Psychiatric complexity/high comorbidity class: This class was composed of middle-aged (52%) males (67%), who experienced falls (42%) and were discharged home (59%). This class had the highest probabilities of pre-injury psychiatric disorders: sleep (16%), mood (57%), anxiety (76%), drug-related substance abuse (35%), alcohol-related substance abuse, (26%), and the highest probability of comorbid conditions (100%).

The psychiatric complexity/high comorbidity class exhibited a 28% higher risk (RR:1.28; 95% CI: 1.08–1.54) of NPD-P compared to the young adult/low psychiatric burden class (Supplementary Table 9).

In the present study, we describe potential effects of the relationships between demographics, injury variables, medical comorbidities, and pre-injury psychiatric conditions on NPDs experienced by TBI patients. Using a matched cohort we first determined the increased risk of NPDs in those with and without TBI. Then, 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 TBI patients are at greater risk for developing NPDs with rates ranging from 18.3–60.8% in the first year post-injury [24, 25]. Our results are consistent with these findings as the rates of NPD-A and NPD-P at two years in the current study were 36–47%. Results from a 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 [26]. Prior studies also support the finding that among TBI patients, pre-injury disorders are significant predictors of post-injury disorders [11, 27]. 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. TBI induces neurochemical imbalances, neuroinflammation, and disruptions in neural circuits, which are all known to contribute to the emergence of psychiatric symptoms [28]. Furthermore, even in the absence of pre-injury psychiatric conditions, the psychological and physiological stress triggered by injury may lead to the development of NPDs. Among individuals with pre-injury psychiatric conditions, the alteration in the brain's resilience and adaptive mechanisms, make it more vulnerable to the neurometabolic cascade following head injury [29]. TBI often results in structural and functional changes in the brain which may interact and overlap with the shared macroscopic and microscopic changes in brain structure seen in many psychiatric conditions, thereby increasing the risk of NPD [29], especially in those with pre-injury conditions.

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 sub-phenotype was characterized by a notable proportion of young adults and pediatric patients and showed the lowest NPD-A incidence among all sub-phenotypes. The age-related resilience against physical trauma and NPDs likely underpins the lower incidence of NPDs in this particular sub-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 mental health conditions. 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 (Davison et al., 2022). These environmental changes and age-related vulnerability coupled with the recovery from a head injury may worsen the progression of mental health with subsequent development of NPD-A. The young-middle-aged males/extended recovery/rehabilitative care sub-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 a crucial 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 sub-phenotype was characterized by the lowest burden of pre-existing psychiatric conditions and the lowest NPD-P incidence among all sub-phenotypes. The reduced burden of psychiatric conditions is a likely explanation of the lower incidence of NPD-P in this particular sub-phenotype. In contrast, the psychiatric complexity/high comorbidity class had the highest pre-injury psychiatric comorbidity levels (52%) 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 sub-phenotype had the greatest NPD-P incidence among four classes. Finally, the two sub-phenotypes with intermediate psychiatric burden demonstrated a corresponding intermediate risk of NPDs compared with the other two classes.

Guidelines from the American College of Surgeons emphasize the importance of postinjury mental health screening and intervention for trauma patients [30]. Our approach of identifying interpretable sub-phenotypes with different risk of NPDs, and the accurate classification of individuals into these sub-phenotypes, allows for a practical risk stratification approach of TBI patients. 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 TBI patients.

Strengths and limitations

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 TBI patients. 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. It is also important to acknowledge that while the control group was selected to match TBI patients 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.

This study highlights the significant post-injury mental health needs experienced by over half of the survivors of TBI. Regardless of psychiatric conditions in the two years prior to injury, these patients have a significantly greater burden of NPDs compared with population-matched controls. TBI is a heterogenous disorder comprised of clinically distinguishable phenotypes with different patterns of NPD risk. Using this approach for risk stratification at the time of discharge from acute care may help guide early and targeted interventions for those individuals most at risk of developing NPDs following TBI.

Funding: This project was supported by the Canadian Institutes of Health Research (CIHR) through the Canada Graduate Scholarships-Doctoral (CGS-D) Award, the Department of Surgery, Dalhousie University, and the Nova Scotia Health Research Fund. The funders had no role in the preparation of this manuscript.

Conflict of interest: The authors have no conflict of interest to declare.

Ethics approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the Nova Scotia Health Research Ethics Board, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent to participate: A waiver of consent was approved by the Nova Scotia Health Research Ethics Board.

Consent for publication: Not applicable.

Availability of data and material: Not applicable.

Code availability: Not applicable.

Author contributions: NK contributed to the conception and design of the study, acquisition and analysis of the data, and drafting of the manuscript. AN, SSRA and CF contributed to the conception and design of the study and data analysis. DBC contributed to the conception and design of the study and the acquisition of the data. This work was part of the PhD thesis of NK in the Interdisciplinary PhD program at Dalhousie University under the supervision of SSRA and CF. All authors interpreted the data, revised the manuscript critically for important intellectual content, and provided final approval of the version to be published.

Acknowledgements: Data used in this research were made available by the Nova Scotia Department of Health and Wellness. Any opinions expressed by the authors do not necessarily reflect the opinion of the Nova Scotia Department of Health and Wellness, Nova Scotia Health Trauma Program, or Health Data Nova Scotia.

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No competing interests reported.

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published 02 Aug, 2024

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Risk Stratification of New-Onset Psychiatric Disorders Using Clinically Distinct Traumatic Brain Injury Sub-Phenotypes

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Abstract

Background

Methods

Results

Conclusion

Figures

Contributions to the Literature

Introduction

Methods

Data Sources

Study Setting and Participants

Exposure

Covariates

Outcome

Study Design

Data Analysis

Propensity Score Matching

Latent Class Analysis

Results

Characteristics of study population

Comparison of TBI vs. non-TBI patients for NPDs

Latent Class Analysis

Class descriptions for TBI cohort without pre-injury psychiatric conditions

Class descriptions for TBI cohort with pre-injury psychiatric conditions

Discussion

Strengths and limitations

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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Journal Publication

Version 1