Image_2_Associations Between PTSD Symptom Custers and Longitudinal Changes in Suicidal Ideation: Comparison Between 4-Factor and 7-Factor Models of DSM-5 PTSD Symptoms.TIF

Objective: The association between posttraumatic stress disorder (PTSD) and suicidal ideation (SI) is well-known. However, a few studies have investigated the associations between PTSD symptom clusters based on the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) and changes in suicide risk longitudinally.Methods: We adopted a longitudinal study design using data from the National Survey for Stress and Health of 3,090 of the Japanese population. The first and second surveys were conducted on November 2016 and March 2017, respectively. The suicidal ideation attributes scale was applied to assess the severity of suicidal ideation at baseline and the follow-up period. A multivariate linear regression model was conducted to examine the associations between the 4- or 7-factor model of PTSD symptom clusters at baseline and longitudinal changes in SI.Results: Overall, 3,090 subjects were analyzed (mean age, 44.9 ± 10.9 years; 48.8% female) at Baseline, and 2,163 completed the second survey. In the 4-factor model, we found that the severity of negative alternations in cognition and mood were significantly associated with increased SI after 4 months. In the 7-factor model, we found that the severity of anhedonia and externalizing behavior at baseline was significantly associated with increased SI during the follow-up period.Conclusions: We found that the seven-factor model of DSM-5 PTSD symptoms may provide greater specificity in predicting longitudinal SI change in the general population. Closely monitoring specific PTSD core symptoms may be more effective in mitigating key clinical and functional outcomes.</p

Data_Sheet_1_A machine-learning model to predict suicide risk in Japan based on national survey data.DOCX

ObjectiveSeveral prognostic models of suicide risk have been published; however, few have been implemented in Japan using longitudinal cohort data. The aim of this study was to identify suicide risk factors for suicidal ideation in the Japanese population and to develop a machine-learning model to predict suicide risk in Japan.Materials and MethodsData was obtained from Wave1 Time 1 (November 2016) and Time 2 (March 2017) of the National Survey for Stress and Health in Japan, were incorporated into a suicide risk prediction machine-learning model, trained using 65 items related to trauma and stress. The study included 3,090 and 2,163 survey respondents >18 years old at Time 1 and Time 2, respectively. The mean (standard deviation, SD) age was 44.9 (10.9) years at Time 1 and 46.0 (10.7) years at Time 2. We analyzed the participants with increased suicide risk at Time 2 survey. Model performance, including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, were also analyzed.ResultsThe model showed a good performance (AUC = 0.830, 95% confidence interval = 0.795–0.866). Overall, the model achieved an accuracy of 78.8%, sensitivity of 75.4%, specificity of 80.4%, positive predictive value of 63.4%, and negative predictive value of 87.9%. The most important risk factor for suicide risk was the participants' Suicidal Ideation Attributes Scale score, followed by the Sheehan Disability Scale score, Patient Health Questionnaire-9 scores, Cross-Cutting Symptom Measure (CCSM-suicidal ideation domain, Dissociation Experience Scale score, history of self-harm, Generalized Anxiety Disorder-7 score, Post-Traumatic Stress Disorder check list-5 score, CCSM-dissociation domain, and Impact of Event Scale-Revised scores at Time 1.ConclusionsThis prognostic study suggests the ability to identify patients at a high risk of suicide using an online survey method. In addition to confirming several well-known risk factors of suicide, new risk measures related to trauma and trauma-related experiences were also identified, which may help guide future clinical assessments and early intervention approaches.</p

Image_1_Associations Between PTSD Symptom Custers and Longitudinal Changes in Suicidal Ideation: Comparison Between 4-Factor and 7-Factor Models of DSM-5 PTSD Symptoms.TIF

Objective: The association between posttraumatic stress disorder (PTSD) and suicidal ideation (SI) is well-known. However, a few studies have investigated the associations between PTSD symptom clusters based on the fifth edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) and changes in suicide risk longitudinally.Methods: We adopted a longitudinal study design using data from the National Survey for Stress and Health of 3,090 of the Japanese population. The first and second surveys were conducted on November 2016 and March 2017, respectively. The suicidal ideation attributes scale was applied to assess the severity of suicidal ideation at baseline and the follow-up period. A multivariate linear regression model was conducted to examine the associations between the 4- or 7-factor model of PTSD symptom clusters at baseline and longitudinal changes in SI.Results: Overall, 3,090 subjects were analyzed (mean age, 44.9 ± 10.9 years; 48.8% female) at Baseline, and 2,163 completed the second survey. In the 4-factor model, we found that the severity of negative alternations in cognition and mood were significantly associated with increased SI after 4 months. In the 7-factor model, we found that the severity of anhedonia and externalizing behavior at baseline was significantly associated with increased SI during the follow-up period.Conclusions: We found that the seven-factor model of DSM-5 PTSD symptoms may provide greater specificity in predicting longitudinal SI change in the general population. Closely monitoring specific PTSD core symptoms may be more effective in mitigating key clinical and functional outcomes.</p