Screening tools for predicting posttraumatic stress disorder in acutely injured adult trauma patients: A systematic review

Prognostic models for depression and post-traumatic stress disorder symptoms following traumatic brain injury: a CENTER-TBI study

BACKGROUND

Traumatic brain injury (TBI) is associated with an increased risk of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). We aimed to identify predictors and develop models for the prediction of depression and PTSD symptoms at 6 months post-TBI.

METHODS

We analysed data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury study. We used linear regression to model the relationship between predictors and depression (Patient Health Questionnaire-9) and PTSD symptoms (PTSD Checklist for Diagnostic and Statistical Manual for Mental Health Disorders Fifth Edition). Predictors were selected based on Akaike's Information Criterion. Additionally, we fitted logistic models for the endpoints 'probable MDD' and 'probable PTSD'. We also examined the incremental prognostic value of 2-3 weeks of symptoms.

RESULTS

We included 2163 adults (76% Glasgow Coma Scale=13-15). Depending on the scoring criteria, 7-18% screened positive for probable MDD and about 10% for probable PTSD. For both outcomes, the selected models included psychiatric history, employment status, sex, injury cause, alcohol intoxication and total injury severity; and for depression symptoms also preinjury health and education. The performance of the models was modest (proportion of explained variance=R2 8% and 7% for depression and PTSD, respectively). Symptoms assessed at 2-3 weeks had a large incremental prognostic value (delta R2=0.25, 95% CI 0.24 to 0.26 for depression symptoms; delta R2=0.30, 95% CI 0.29 to 0.31 for PTSD).

CONCLUSION

Preinjury characteristics, such as psychiatric history and unemployment, and injury characteristics, such as violent injury cause, can increase the risk of mental health problems after TBI. The identification of patients at risk should be guided by early screening of mental health.

Automatic screening for posttraumatic stress disorder in early adolescents following the Ya'an earthquake using text mining techniques

Background

Self-narratives about traumatic experiences and symptoms are informative for early identification of potential patients; however, their use in clinical screening is limited. This study aimed to develop an automated screening method that analyzes self-narratives of early adolescent earthquake survivors to screen for PTSD in a timely and effective manner.

Methods

An inquiry-based questionnaire consisting of a series of open-ended questions about trauma history and psychological symptoms, was designed to simulate the clinical structured interviews based on the DSM-5 diagnostic criteria, and was used to collect self-narratives from 430 survivors who experienced the Ya'an earthquake in Sichuan Province, China. Meanwhile, participants completed the PTSD Checklist for DSM-5 (PCL-5). Text classification models were constructed using three supervised learning algorithms (BERT, SVM, and KNN) to identify PTSD symptoms and their corresponding behavioral indicators in each sentence of the self-narratives.

Results

The prediction accuracy for symptom-level classification reached 73.2%, and 67.2% for behavioral indicator classification, with the BERT performing the best.

Conclusions

These findings demonstrate that self-narratives combined with text mining techniques provide a promising approach for automated, rapid, and accurate PTSD screening. Moreover, by conducting screenings in community and school settings, this approach equips clinicians and psychiatrists with evidence of PTSD symptoms and associated behavioral indicators, improving the effectiveness of early detection and treatment planning.

Performance replication of the Hospital Mental Health Risk Screen in ethnoracially diverse U.S. patients admitted through emergency care

BACKGROUND

Patients admitted to hospitals after emergency care for injury or acute illness are at risk for later mental health problems. The American College of Surgeons Committee on Trauma Standards for care of injured patients call for mental health risk screening, and the Hospital Mental Health Risk Screen (HMHRS) accurately identified at-risk patients in a developmental study that included patients from five ethnoracial groups. Replication of these findings is essential, because initial positive results for predictive screens can fail to replicate if the items were strongly related to outcomes in the development sample but not in a new sample from the population the screen was intended for.

STUDY DESIGN

Replication of the predictive performance of the 10-item HMHRS was studied prospectively in ethnoracially diverse patients admitted after emergency care for acute illness or injury in three hospitals across the U.S.

RESULTS

Risk screen scores and follow-up mental health outcomes were obtained for 452 of 631 patients enrolled (72%). A cut score of 10 on the HMHRS correctly identified 79% of the patients who reported elevated levels of depression, anxiety, and PTSD symptoms two months post-admission (sensitivity) and 72% of the patients whose symptoms were not elevated (specificity). HMHRS scores also predicted well for patients with acute illness, for patients with injuries, and for patients who reported an Asian American/Pacific Islander, Black, Latinx, Multirace, or White identity.

CONCLUSIONS

Predictive performance of the HMHRS was strong overall and within all five ethnoracial subgroups. Routine screening could reduce suffering and health care costs, increase health and mental health equity, and foster preventive care research and implementation. The performance of the HMHRS should be studied in other countries and in other populations of recent trauma survivors, such as survivors of disaster or mass violence.

Development of a brief bedside tool to screen women sexual assault survivors for risk of persistent posttraumatic stress six months after sexual assault

This study aims to develop and validate a brief bedside tool to screen women survivors presenting for emergency care following sexual assault for risk of persistent elevated posttraumatic stress symptoms (PTSS) six months after assault. Participants were 547 cisgender women sexual assault survivors who presented to one of 13 sexual assault nurse examiner (SANE) programs for medical care within 72 h of a sexual assault and completed surveys one week and six months after the assault. Data on 222 potential predictors from the SANE visit and the week one survey spanning seven broadly-defined risk factor domains were candidates for inclusion in the screening tool. Elevated PTSS six months after assault were defined as PCL-5 > 38. LASSO logistic regression was applied to 20 randomly selected bootstrapped samples to evaluate variable importance. Logistic regression models comprised of the top 10, 20, and 30 candidate predictors were tested in 10 cross-validation samples drawn from 80% of the sample. The resulting instrument was validated in the remaining 20% of the sample. AUC of the finalized eight-item prediction tool was 0.77 and the Brier Score was 0.19. A raw score of 41 on the screener corresponds to a 70% risk of elevated PTSS at 6 months. Similar performance was observed for elevated PTSS at one year. This brief, eight-item risk stratification tool consists of easy-to-collect information and, if validated, may be useful for clinical trial enrichment and/or patient screening.

A comparison of the psychometric properties of a person-administered vs. automated screening tool for posttraumatic stress disorder (PTSD) in traumatically injured patients

BACKGROUND

The American College of Surgeons Committee on Trauma (ACS-CoT) mandated that trauma centers have mental health screening and referral protocols in place by 2023. This study compares the Injured Trauma Survivor Screen (ITSS) and the Automated Electronic Medical Record (EMR) Screen to assess their performance in predicting risk for posttraumatic stress disorder (PTSD) within the same sample of trauma patients to inform trauma centers' decision when selecting a tool to best fit their current clinical practice.

METHODS

This was a secondary analysis of three prospective cohort studies of traumatically injured patients (N = 255). The ITSS and Automated EMR Screen were compared using receiver operating characteristic curves to predict risk of subsequent PTSD development. PTSD diagnosis at 6-month follow-up was assessed using the Clinician Administered PTSD Scale for DSM-5.

RESULTS

Just over half the sample screened positive on the ITSS (57.7%), while 67.8% screened positive on the Automated EMR Screen. The area under the curve (AUC) for the two screens was not significantly different (ITSS AUC = 0.745 versus Automated EMR Screen AUC = 0.694, p = 0.21), similar performance in PTSD risk predication within the same general trauma population. The ITSS and Automated EMR Screen had similar sensitivities (86.5%, 89.2%), and specificities (52.5%, 40.9%) respectively at their recommended cut-off points.

CONCLUSION

Both screens are psychometrically comparable. Therefore, trauma centers considering screening tools for PTSD risk to comply with the ACS-CoT 2023 mandate should consider their local resources and patient population. Regardless of screen selection, screening must be accompanied by a referral process to address the identified risk.