Using Machine Learning to Examine Suicidal Ideation After Traumatic Brain Injury: A Traumatic Brain Injury Model Systems National Database Study

Research on prediction model of adolescent suicide and self-injury behavior based on machine learning algorithm

Objective

To explore the risk factors that affect adolescents' suicidal and self-injurious behaviors and to construct a prediction model for adolescents' suicidal and self-injurious behaviors based on machine learning algorithms.

Methods

Stratified cluster sampling was used to select high school students in Chongqing, yielding 3,000 valid questionnaires. Based on whether students had engaged in suicide or self-injury, they were categorized into a suicide/self-injury group (n=78) and a non-suicide/self-injury group (n=2,922). Gender, age, insomnia, and mental illness data were compared between the two groups, and a logistic regression model was used to analyze independent risk factors for adolescent suicidal and self-injurious behavior. Six methods-multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting-were used to build predictive models. Various model indicators for suicidal and self-injurious behavior were compared across the six algorithms using a confusion matrix to identify the optimal model.

Result

In the self-injury and suicide groups, the proportions of male adolescents, late adolescence, insomnia, and mental illness were significantly higher than in the non-suicide and self-injury groups (p <0.05). Compared with the non-suicidal self-injury group, this group also showed significantly increased scores in cognitive subscales, impulsivity, psychoticism, introversion-extroversion, neuroticism, interpersonal sensitivity, depression, anxiety, hostility, terror, and paranoia (p <0.05). These statistically significant variables were analyzed in a logistic regression model, revealing that gender, impulsivity, psychoticism, neuroticism, interpersonal sensitivity, depression, and paranoia are independent risk factors for adolescent suicide and self-injury. The logistic regression model achieved the highest sensitivity and specificity in predicting adolescent suicide and self-injury behavior (0.9948 and 0.9981, respectively). Performance of the random forest, multi-level perceptron, and extreme gradient models was acceptable, while the K-nearest neighbor algorithm and support vector machine performed poorly.

Conclusion

The detection rate of suicidal and self-injurious behaviors is higher in women than in men. Adolescents displaying impulsiveness, psychoticism, neuroticism, interpersonal sensitivity, depression, and paranoia have a greater likelihood of engaging in such behaviors. The machine learning model for classifying and predicting adolescent suicide and self-injury risk effectively identifies these behaviors, enabling targeted interventions.

The Relationship Between Traumatic Brain Injury and Suicide: A Systematic Review of Risk Factors

Objective

Traumatic brain injury (TBI) significantly increases the risk of suicidal ideation (SI) and behaviors due to neurobiological changes, cognitive impairments, and emotional dysregulation. This review consolidates current evidence on the relationship between TBI and suicide, identifying key risk factors and underlying mechanisms, and highlights the need for further research, especially in civilian populations.

Method

Studies were identified from an online search of PubMed, Web of Science, Cochrane Library, Embase, and Scopus databases with studies published from 2014 to 2024. This review has been registered on Prospero (number CRD42024574643).

Results

Factors indicated such as external causes of injury, comorbidities like depression and substance use disorders, and post-TBI symptoms consistently influence suicide risk. Advanced predictive models emphasize the role of psychological symptoms, particularly depressive features, in forecasting SI post-TBI, underscoring the need for targeted interventions and early symptom management.

Conclusions

The seriousness of TBI significantly impacts the probability of SI and suicide attempts (SA). Research consistently shows that patients with more severe TBIs tend to have higher rates of SI and SA. Psychological disorders, such as depression and substance abuse disorders, greatly increase the likelihood of suicidal actions after a TBI. These conditions not only raise the occurrence of SI but also lead to earlier and more regular SA.

Traumatic Brain Injury and Risk of Incident Comorbidities

Importance

Traumatic brain injury (TBI) is associated with chronic medical conditions. Evidence from diverse clinical administrative datasets may improve care delivery.

Objective

To characterize post-TBI risk of incident neuropsychiatric and medical conditions in a California health care system administrative database and validate findings from a Massachusetts dataset.

Design, Setting, and Participants

In this cohort study, prospective longitudinal cohorts using data from 5 University of California health care settings between 2013 and 2022 were studied. Patients aged 18 years and older with mild (mTBI) or moderate to severe TBI (msTBI) were included. Unexposed individuals were propensity matched by age, race and ethnicity, sex, University of California site, insurance coverage, area deprivation index (ADI) score, and duration from index date to most recent clinical encounter. Patients with study comorbidities of interest before the index date were excluded. Data were analyzed August to October 2024.

Exposure

TBI.

Main Outcomes and Measures

International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes were used to identify patients with TBI and patients with up to 22 comorbidities within neurological, psychiatric, cardiovascular, and endocrine umbrella groupings. Cox proportional hazard models were used to generate yearly hazard ratios (HRs) from 6 months up to 10 years after a TBI. Models were further stratified by age and ADI score.

Results

The study consisted of 20 400 patients (9264 female [45.4%]; 1576 Black [7.7%], 3944 Latinx [19.3%], and 10 480 White [51.4%]), including 5100 patients with mTBI (median [IQR] age, 36.0 [25.0-51.0] years), 5100 patients with msTBI (median [IQR age, 35.0 [25.0-52.0] years), and 10 200 matched patients in the control group (median [IQR] age, 36.0 [25.0-51.0] years). By ADI score quintile, there were 2757 unexposed patients (27.0%), 1561 patients with mTBI (30.6%), and 1550 patients with msTBI (30.4%) in the lowest (1-2) quintiles and 1523 unexposed patients (14.9%), 769 patients with mTBI (15.1%), and 804 patients with msTBI (15.8%) in the highest quintiles (9-10). TBI of any severity was associated with increased risk of nearly all conditions (mTBI HRs ranged from 1.30; 95% CI, 1.07-1.57 for hypothyroidism to 4.06; 95% CI, 3.06-5.39 for dementia, and msTBI HRs ranged from 1.35; 95% CI, 1.12-1.62 for hypothyroidism to 3.45; 95% CI, 2.73-4.35 for seizure disorder). Separate age and ADI stratifications revealed patient populations at increased risk, including middle-age adults (ages 41-60 years), with increased risk of suicidality (mTBI: HR, 4.84; 95% CI, 3.01-7.78; msTBI: HR, 4.08; 95% CI, 2.51-6.62). Suicidality risk persisted for patients with mTBI in the high ADI subgroup (HR, 2.23; 95% CI, 1.36-3.66).

Conclusions and Relevance

In this cohort study, TBI was a risk factor associated with treatable incident neuropsychiatric and other medical conditions, validating similar findings from a Massachusetts dataset. Additional exploratory findings suggested varying demographic and regional risk patterns, which may generate causal hypotheses for further research and inform clinical surveillance strategies.

Association Between Impulsivity, Self-Harm, Suicidal Ideation, and Suicide Attempts in Patients with Traumatic Brain Injury

Traumatic brain injury (TBI) affects over 48 million people worldwide each year. Suicide is common in TBI, and there are several known contributing factors, including severe TBI, depression, alcohol use, and male sex. Impulsivity, or the tendency to act quickly with little thought, may be an early predictor of suicidality following TBI. The purpose of this study was to evaluate the risk of suicidality in patients with a prior history of impulsivity following a TBI. Using de-identified electronic health records from the TriNetX U.S. Collaborative Network with Natural Language Processing, three cohorts were generated: the impulsivity TBI cohort (I+TBI+) included subjects with a diagnosis of impulsivity before a diagnosis of TBI; the no impulsivity TBI cohort (I-TBI+) included patients with TBI but no impulsivity; the impulsivity no TBI cohort (I+TBI-) included patients with impulsivity but TBI. Two analyses were conducted, including analysis 1 (impulsivity TBI vs. no impulsivity TBI) and analysis 2 (impulsivity TBI vs. impulsivity no TBI). Patients were 1:1 matched by age, sex, race, ethnicity, psychiatric diagnoses, and antidepressant use. Outcomes included a diagnosis of self-harm, suicidal ideation, or a suicide attempt within 1 year after the index event. The all-time incidence of each outcome was assessed across different age categories. The chi-square test (categorical variables) and t-test (numerical variables) were used to assess for differences between groups. A total of 1,292,776 patients with TBI were identified in the study. After 1:1 matching, there were 20,694 patients (mean [standard deviation, SD] age, 48.1 [21.8]; 8,424 females [40.7%]) with impulsivity and TBI (I+TBI+), 1,272,082 patients (mean [SD] age, 46.0 [23.1]; 562,705 females [44.2%]) with TBI alone (I-TBI+), and 90,669 patients (mean [SD] age, 43.7 [22.6]; 45,188 females [49.8%]) with impulsivity alone (I+TBI-). Within the first year after a TBI, patients with impulsivity were more likely to exhibit self-harm (p < 0.001), suicidal ideation (p < 0.001), or a suicide attempt (p < 0.001). Compared with patients with TBI without impulsivity, those with impulsivity had a 4-fold increase in the incidence of self-harm (2.81% vs. 0.63%), an 8-fold increase in suicidal ideation (52.42% vs. 6.41%), and a 21-fold increase in suicide attempts (32.02% vs. 1.50%). This study suggests that impulsivity diagnosed before a TBI may increase the risk of post-traumatic suicidality, with a 4-fold increased risk of self-harm, an 8-fold increased risk of suicidal ideation and a 21-fold increased risk of suicide attempts. This characterizes a group of at-risk individuals who may benefit from early psychiatric support and targeted interventions following a TBI.

Suicide Attempts and Ideation Among Veterans/Service Members and Non-Veterans Over 5 Years Following Traumatic Brain Injury: A Combined NIDILRR and VA TBI Model Systems Study

OBJECTIVE

This study compared rates of suicide attempt (SA) and suicidal ideation (SI) during the first 5 years after traumatic brain injury (TBI) among veterans and service members (V/SMs) in the Veterans Affairs (VA) and the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) Model Systems National Databases to each other and to non-veterans (non-Vs) in the NIDILRR database.

SETTING

Twenty-one NIDILRR and 5 VA TBI Model Systems (TBIMS) inpatient rehabilitation facilities in the United States.

PARTICIPANTS

Participants with TBI were discharged from rehabilitation alive, had a known military status recorded (either non-V or history of military service), and successful 1-, 2-, and/or 5-year follow-up interviews completed between 2009 and 2021. The year 1 cohort included 8737 unique participants (8347 with SA data and 3987 with SI data); the year 2 (7628 participants) and year 5 (4837 participants) cohorts both had similar demographic characteristics to the year 1 cohort.

DESIGN

Longitudinal design with data collected across TBIMS centers at 1, 2, and 5 years post-injury.

MAIN OUTCOMES AND MEASURES

History of SA in past year and SI in past 2 weeks assessed by the Patient Health Questionnaire-9 (PHQ-9). Patient demographics, injury characteristics, and rehabilitation outcomes were also assessed.

RESULTS

Full sample rates of SA were 1.9%, 1.5%, and 1.6%, and rates of SI were 9.6%, 10.1%, and 8.7% (respectively at years 1, 2, and 5). There were significant differences among groups based on demographic, injury-related, mental/behavioral health, and functional outcome variables. Characteristics predicting SA/SI related to mental health history, substance use, younger age, lower functional independence, and greater levels of disability.

CONCLUSIONS

Compared with participants with TBI in the NIDILRR system, higher rates of SI among V/SMs with TBI in the VA system appear associated with risk factors observed within this group, including mental/behavioral health characteristics and overall levels of disability.

Traumatic Brain Injury Rehabilitation Outcome Prediction Using Machine Learning Methods

Objective

To investigate the performance of machine learning (ML) methods for predicting outcomes from inpatient rehabilitation for subjects with TBI using a dataset with a large number of predictor variables. Our second objective was to identify top predictive features selected by the ML models for each outcome and to validate the interpretability of the models.

Design

Secondary analysis using computational modeling of relationships between patients, injury and treatment activities and 6 outcomes, applied to the large multi-site, prospective, longitudinal observational dataset collected during the traumatic brain injury inpatient rehabilitation study.

Setting

Acute inpatient rehabilitation.

Participants

1946 patients aged 14 years or older, who sustained a severe, moderate, or complicated mild TBI, and were admitted to 1 of 9 US inpatient rehabilitation sites between 2008 and 2011 (N=1946).

Main Outcome Measures

Rehabilitation length of stay, discharge to home, FIM cognitive and FIM motor at discharge and at 9-months post discharge.

Results

Advanced ML models, specifically gradient boosting tree model, performed consistently better than all other models, including classical linear regression models. Top ranked predictive features were identified for each of the 6 outcome variables. Level of effort, days to rehabilitation admission, age at rehabilitation admission, and advanced mobility activities were the most frequently top ranked predictive features. The highest-ranking predictive feature differed across the specific outcome variable.

Conclusions

Identifying patient, injury, and rehabilitation treatment variables that are predictive of better outcomes will contribute to cost-effective care delivery and guide evidence-based clinical practice. ML methods can contribute to these efforts.