Four Questions Nurses Can Ask to Predict PTSD 1 Year After a Motor Vehicle Crash.

BACKGROUND: The role of nurses in screening for posttraumatic stress disorder is crucial in trauma units. OBJECTIVES: To create and evaluate an easy and brief tool for nurses to predict chronic posttraumatic stress disorder 1 year after a motor vehicle crash. METHODS: We performed a 1-year follow-up multicenter study from 2007 to 2015, including 274 patients injured in a motor vehicle crash who were hospitalized in an orthopedic trauma unit. Nurses administered the DEPITAC questionnaire. Posttraumatic stress disorder was measured by the Post-Traumatic Stress Disorder Checklist of symptoms during the first year following the crash. A multivariable logistic regression model was implemented to select items significantly associated with posttraumatic stress disorder to improve the DEPITAC questionnaire. Predictive performance to predict posttraumatic stress disorder 1 year after the motor vehicle crash was examined for these different models. RESULTS: Of 274 patients studied, a total of 75.9% completed the questionnaire at 1 year of follow-up. We found that only two questions and two simple elements of the patient's medical record (other injury or a person dying during the crash, perception of vital threat, number of children, and length of stay in trauma) predicted posttraumatic stress disorder 1 year after a motor vehicle crash. CONCLUSIONS: The brevity of this evaluation, simple scoring rules, and screening test performance suggest that this new screening tool can be easily administered in the acute care setting by nurses.

Factors Associated With Mental Health Disorders Among University Students in France Confined During the COVID-19 Pandemic.

Importance: The coronavirus disease 2019 (COVID-19) pandemic and quarantine measures have raised concerns regarding their psychological effects on populations. Among the general population, university students appear to be particularly susceptible to experiencing mental health problems. Objectives: To measure the prevalence of self-reported mental health symptoms, to identify associated factors, and to assess care seeking among university students who experienced the COVID-19 quarantine in France. Design, Setting, and Participants: This survey study collected data from April 17 to May 4, 2020, from 69 054 students living in France during the COVID-19 quarantine. All French universities were asked to send an email to their students asking them to complete an online questionnaire. The targeted population was approximately 1 600 000 students. Exposure: Living in France during the COVID-19 quarantine. Main Outcomes and Measures: The rates of self-reported suicidal thoughts, severe distress, stress, anxiety, and depression were assessed using the 22-item Impact of Events Scale-Revised, the 10-item Perceived Stress Scale, the 20-item State-Trait Anxiety Inventory (State subscale), and the 13-item Beck Depression Inventory, respectively. Covariates were sociodemographic characteristics, precariousness indicators (ie, loss of income or poor quality housing), health-related data, information on the social environment, and media consumption. Data pertaining to care seeking were also collected. Multivariable logistic regression analyses were performed to identify risk factors. Results: A total of 69 054 students completed the survey (response rate, 4.3%). The median (interquartile range) age was 20 (18-22) years. The sample was mainly composed of women (50 251 [72.8%]) and first-year students (32 424 [47.0%]). The prevalence of suicidal thoughts, severe distress, high level of perceived stress, severe depression, and high level of anxiety were 11.4% (7891 students), 22.4% (15 463 students), 24.7% (17 093 students), 16.1% (11 133 students), and 27.5% (18 970 students), respectively, with 29 564 students (42.8%) reporting at least 1 outcome, among whom 3675 (12.4%) reported seeing a health professional. Among risk factors identified, reporting at least 1 mental health outcome was associated with female gender (odds ratio [OR], 2.10; 95% CI, 2.02-2.19; P < .001) or nonbinary gender (OR, 3.57; 95% CI, 2.99-4.27; P < .001), precariousness (loss of income: OR, 1.28; 95% CI, 1.22-1.33; P < .001; low-quality housing: OR, 2.30; 95% CI, 2.06-2.57; P < .001), history of psychiatric follow-up (OR, 3.28; 95% CI, 3.09-3.48; P < .001), symptoms compatible with COVID-19 (OR, 1.55; 95% CI, 1.49-1.61; P < .001), social isolation (weak sense of integration: OR, 3.63; 95% CI, 3.35-3.92; P < .001; low quality of social relations: OR, 2.62; 95% CI, 2.49-2.75; P < .001), and low quality of the information received (OR, 1.56; 95% CI, 1.49-1.64; P < .001). Conclusions and Relevance: The results of this survey study suggest a high prevalence of mental health issues among students who experienced quarantine, underlining the need to reinforce prevention, surveillance, and access to care.

Temporal analysis of heart rate variability as a predictor of post traumatic stress disorder in road traffic accidents survivors.

BACKGROUND: Road Traffic Accidents (RTA) are most probably the leading cause of post traumatic stress disorder (PTSD) in developed countries. The autonomic nervous system (ANS) disturbances, due to psychological trauma, are part of the pathophysiology of PTSD. The aim of the present study was to determine whether early heart rate variability (HRV) measurement, a biomarker of the ANS function, could act as a predictor of PTSD development after a RTA. METHODS: We prospectively investigated 35 survivors of RTA with both physical injury and psychological trauma. HRV data were obtained from 24-h Holter ECG monitoring, which was performed on the second day after the accident. Time domain analysis was applied to the inter-beat (RR) interval time series to calculate the various parameters of HRV. PTSD status was assessed 2 and 6 months after RTA. RESULTS: There was a global diminution of HRV measurements in the PTSD group at both 2 and 6 months. The variability index was the best predictor of PTSD with the area under the receiveroperating curve for discriminating PTSD at 6 months at 0.92 (95% CI: 0.785; 1.046). A cut-off at 2.19% yielded a sensitivity of 85.7% and a specificity of 81.8% for PTSD. Positive and negative predictive values were respectively 75% and 90%. However, initial heart rate (HR) data were relevant at 2 months but not at 6 months. CONCLUSION: RTA survivors exhibiting lower parasympathetic modulation of HR, indexed by temporal analysis of HRV, are more susceptible to developing PTSD as a short and long-term outcome.