Predictors Of Suicide Attempts Research Articles

Is serum homocysteine level a biomarker of suicide attempts: A preliminary study

Suicide is a global public health concern, and understanding its multifaceted determinants is crucial for effective prevention. This study was designed to find an answer to the question of whether serum homocysteine level can be a biomarker of suicide attempts. This preliminary study involving 90 participants (45 suicide attempt cases and 45 controls) was conducted at Elazig Fethi Sekin City Hospital. Biochemical analyses were performed to assess serum homocysteine, vitamin B12, and folic acid levels. Statistical analyses, including t-tests, Mann-Whitney U tests, and ROC analysis, were employed to explore differences between groups and assess the diagnostic potential of homocysteine. Elevated homocysteine levels were found in individuals who attempted suicide compared to the control group (p= <0.001). Additionally, lower levels of vitamin B12 (p=<0.001) and folic acid (p=<0.001) were observed in the suicide attempt group. ROC analysis indicated a significant diagnostic potential for homocysteine in predicting suicide attempts (AUC = 0.845, sensitivity = 91%, specificity = 71%). This study establishes a significant association between high homocysteine levels and suicide attempts, accompanied by lower vitamin B12 and folic acid levels. The findings suggest a potential link between disturbances in homocysteine metabolism and suicidal tendencies, urging further research to establish causation and explore therapeutic implications. Consideration of the study's limitations and directions for future research are warranted.

Intentions to use mental health and suicide prevention resources among individuals with symptoms of the suicide crisis syndrome and/or suicidal ideation.

The suicide crisis syndrome (SCS) has demonstrated efficacy in predicting suicide attempts, showing potential utility in detecting at-risk individuals who may not be willing to disclose suicidal ideation (SI). The present international study examined differences in intentions to utilize mental health and suicide prevention resources among community-based adults with varying suicide risk (i.e., presence/absence of SCS and/or SI). A sample of 16,934 community-based adults from 13 countries completed measures about the SCS and SI. Mental health and suicide prevention resources were provided to all participants, who indicated their intentions to use these resources. Individuals with SCS (55.7%) were just as likely as those with SI alone (54.0%), and more likely than those with no suicide-related symptoms (45.7%), to report willingness to utilize mental health resources. Those with SI (both with and without SCS) were more likely to seek suicide prevention resources (52.6% and 50.5%, respectively) than those without SI (41.7% and 41.8%); however, when examining endorsements for personal use, those with SCS (21.6%) were more likely to use resources than individuals not at risk (15.1%). These findings provide insight into individuals' willingness to use resources across configurations of explicitly disclosed (SI) and indirect (SCS) suicide risk.

Prediction of Suicide Attempt based on Psychological Pain, Interpersonal Needs, and Peer Pressure in Students

Aim: The aim of this study was to predict suicide attempt based on psychological pains, interpersonal needs, and peer pressure in students. Methods: The research method was descriptive-correlational. The statistical population of the study included all students in city of Rasht (Iran), from which 250 people were selected through random sampling. Research instruments included Schneidmann (1993) Psychache Scale, Joiner et al. (2006) Interpersonal Needs, Dehingra, Budoshk, &amp; O'Connor (2016) Suicide Attempt, and a peer pressure researcher-made questionnaire through which data was collected and analyzed using Pearson correlation and regression. Finding: The results showed that there was a significant relationship between psychological pain (psychache), perceived burdensomeness, and peer pressure with suicide attempts (P &lt;0.01). And the results of regression analysis showed that psych ache and neutral belongingness can predict suicide in students (P &lt;0.05). But the role of psychological pain has a higher power of predictability in suicide attempts among students. Conclusion: It can be concluded that psychological painss and neutral belonging are important factors that can predict suicide in students.

Inhibition and Activation Systems of Behavior and Suicide Mediating Role of Cognitive Emotion Regulation

Objective: This study aimed to predict suicide attempts based on behavioral activation and inhibition systems (BAS/BIS) with the mediating role of cognitive emotion regulation (CER) strategies among adolescents of Abyek City. Materials and methods: The research methodology was descriptive-correlational. The cluster sampling method was used, and the resulting sample included 194 adolescents who responded to the BAS/BIS questionnaire (Carver &amp; White, 1994), CER questionnaire, and Beck scale for suicide ideation. Path analysis was employed to evaluate the proposed model using AMOS 24.0 and SPSS 27 softwares. Results: The findings indicate that the proposed model is fitted with the data well. The results of path analysis showed that the BAS/BIS systems explain 27% of the variance of maladaptive CER and 61% of the variance of adaptive CER. In addition, maladaptive CER, BAS, and BIS explain 65% of the variance of suicide attempts. Conclusion: Low levels of BAS, high levels of BIS, and the use of maladaptive CER skills can be considered risk factors for suicide attempts in adolescents.

Intimate Partner Violence and Suicide Attempt Among Women in the Philippines

Intimate partner violence is among the leading women’s health issues globally. Hence, the primary aim of this study was to determine if intimate partner violence (IPV) against women is a significant risk factor in suicide attempts, and whether such an association is moderated by help-seeking behavior. A predictive correlational design was employed to determine associations between the variables of interest. The study sample consisted of 1,413 currently married or cohabiting Filipino women with exposure to IPV. In the direct effects model, physical IPV, psychological IPV, and sexual IPV emerged as significant risk factors for suicide attempts. In the moderation analysis, help-seeking behavior did not demonstrate a significant interaction with IPV in predicting suicide attempts. The current data highlight the importance of identifying and screening IPV-abused women in communities who may be at risk of attempting suicide. Additionally, suicide prevention strategies must consider the potential role of help-seeking behavior in increasing the risk of suicide among IPV-abused women.

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample

Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

Transdiagnostic inflammatory and oxidative biomarkers with predictive capacity of self-injurious behavior in impulsive and unstable disorders

IntroductionAlterations in inflammatory processes have previously been reported in impulsive and unstable disorders, as well as in other psychiatric conditions. In order to investigate transdiagnostic biomarkers associated with various phenotypic features of these disorders, this study is designed to identify biomarkers of inflammatory and oxidative endophenotypes related to autolytic behavior. MethodsPeripheral blood mononuclear cells were collected from 35 patients with borderline personality disorder (BPD), 29 patients with restrictive eating disorder (rED), 21 patients with purging eating disorder (pED) and 23 control subjects. Plasma levels of different inflammatory and oxidative factors were measured by ELISA and the expression of selected proteins was by Western Blot. Principal component analysis (PCA) was performed to categorize the different inflammatory factors. Additionally, Ancova was performed to observe the differences in the principal components among the different groups and logistic regression analysis was conducted to assess the predictive capacity of these components for autolytic behaviors. ResultsWe found two inflammatory/oxidative components were associated with BPD, characterized by high levels of JNK and ERK and low levels of GPx, SOD and Keap1; and two other inflammatory/oxidative components were linked to pED, associated with more JNK, TBARS and TNF-α and less GPx and SOD. Two components, with more JNK and ERK and less GPx, SOD and Keap1, predicted non-suicidal self-injury and three components, with higher JNK, TBARS and TNF-α levels and lower GPx, SOD and iNOS levels, predicted suicide attempts. ConclusionsThese results strongly support the endophenotypic characterization of impulsivity and the identification of transdiagnostic inflammatory/oxidative biomarkers relevant to autolytic behavior in impulsive and unstable disorders. These dates lay the groundwork for developing of screening tests for these biomarker components to rapidly detect biological risk factors for specific impulse control disorders and future self-injurious behaviors.

Machine Learning-Based Prediction of Suicidality in Adolescents With Allergic Rhinitis: Derivation and Validation in 2 Independent Nationwide Cohorts.

Given the additional risk of suicide-related behaviors in adolescents with allergic rhinitis (AR), it is important to use the growing field of machine learning (ML) to evaluate this risk. This study aims to evaluate the validity and usefulness of an ML model for predicting suicide risk in patients with AR. We used data from 2 independent survey studies, Korea Youth Risk Behavior Web-based Survey (KYRBS; n=299,468) for the original data set and Korea National Health and Nutrition Examination Survey (KNHANES; n=833) for the external validation data set, to predict suicide risks of AR in adolescents aged 13 to 18 years, with 3.45% (10,341/299,468) and 1.4% (12/833) of the patients attempting suicide in the KYRBS and KNHANES studies, respectively. The outcome of interest was the suicide attempt risks. We selected various ML-based models with hyperparameter tuning in the discovery and performed an area under the receiver operating characteristic curve (AUROC) analysis in the train, test, and external validation data. The study data set included 299,468 (KYRBS; original data set) and 833 (KNHANES; external validation data set) patients with AR recruited between 2005 and 2022. The best-performing ML model was the random forest model with a mean AUROC of 84.12% (95% CI 83.98%-84.27%) in the original data set. Applying this result to the external validation data set revealed the best performance among the models, with an AUROC of 89.87% (sensitivity 83.33%, specificity 82.58%, accuracy 82.59%, and balanced accuracy 82.96%). While looking at feature importance, the 5 most important features in predicting suicide attempts in adolescent patients with AR are depression, stress status, academic achievement, age, and alcohol consumption. This study emphasizes the potential of ML models in predicting suicide risks in patients with AR, encouraging further application of these models in other conditions to enhance adolescent health and decrease suicide rates.

Prediction models of suicide and non-fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers.

To develop machine learning models capable of predicting suicide and non-fatal suicide attempt as separate outcomes in the first 30 days after discharge from a psychiatric inpatient stay. Prospective cohort study using nationwide Danish registry data. We included individuals who were 18 years or older, and all discharges from psychiatric hospitalizations in Denmark from 1995 to 2018. We trained predictive models using 10-fold cross validation on 80% of the data and did testing on the remaining 20%. The best model for predicting non-fatal suicide attempt was an ensemble of predictions from gradient boosting (XGBoost) and categorical boosting (catBoost). The ROC-AUC for predicting suicide attempt was 0.85 (95% CI: 0.84-0.85). At a risk threshold of 4.36%, positive predictive value (PPV) was 11.0% and sensitivity was 47.2%. The best model for predicting suicide was an ensemble of predictions from random forest, XGBoost and catBoost. For suicide, the ROC-AUC was 0.71 (95% CI: 0.70-0.73). At a risk threshold of 0.15%, PPV was 0.34% and sensitivity was 56.0%. The most contributing predictors differed when predicting suicideand suicide attempt, indicating that separate models are needed. The ensemble model was fair across sex and age, and more so than the penalized logistic regression model. We achieved good performance for predicting suicide attempts and demonstrated a clinical application of ensemble models. Our results indicate a difference in predictive performance for models predicting suicide and suicide attempt, respectively. Thus, we recommend that suicide and suicide attempt are treated as two separate endpoints, in particular for clinical application. We demonstrated that the ensemble model is fairer across sex and age compared with a penalized logistic regression, and therefore we recommend the use of well-tested ensembles despite a more complex explainability.

Comparison of Suicide Attempts and Associated Risk Factors During the COVID-19 Pandemic and One Year Before

Background: Many studies have investigated the rate of suicide attempts and their risk factors during the coronavirus disease 2019 (COVID-19) pandemic; however, the results are still challenging. Objectives: This study investigated and compared the risk factors effective in suicide attempts during the pandemic period and one year before. Methods: This case-control study was conducted at Loghman Hakim hospital, Tehran, Iran. Random sampling included all patients in the inpatient ward who were over 12 years of age and committed suicide during the pandemic within October 1, 2021, to August 30, 2022 (case group; n = 160) or one year before within March 11, 2019, to March 11, 2020 (control group; n = 160). Demographic information, mental-psychological status, and socioeconomic status (SES) of the patients in the case group, before and after the COVID-19 pandemic, were recorded during face-to-face interviews using a predesigned semi-structured questionnaire by study staff trained and supervised by a clinical psychiatrist. Additionally, the data of the patients in the control group were retrospectively recorded during the review of medical records and telephone interviews. The Wilcoxon signed-rank test was used to compare the variables in the case group before and after the COVID-19 pandemic. The Student’s t-test and chi-square test were used to compare the variables between the case and control groups. Additionally, to determine the risk factors predicting suicide attempts during the pandemic, a multivariable logistic regression test was used. The data were analyzed using PASW18 software. Results: The results showed that the patients in the case and control groups had significant differences in terms of age (P = 0.01), underlying mental disorders (P = 0.032), and parental relationship (P = 0.001). Moreover, the variables of suicidal ideation (P = 0.002), feeling lonely (P = 0.023), sleep disorder (P = 0.32), and domestic violence (P = 0.004) in the case group were significantly higher than the control group. In addition, the comparison of these variables in the patients of the case group, before and after the COVID-19 pandemic, showed that the start of the pandemic triggered these factors and led to an increase in suicide attempts in this population. Finally, the logistic regression analysis showed that the variables of SES, having a close friend, parental relationship, previous planning for suicide, domestic violence, and job loss could be considered predictive risk factors for suicide attempts during the pandemic period. Conclusions: According to the present study’s findings, the psychological and socioeconomic conditions triggered by the COVID-19 pandemic led to an increased likelihood of suicidal ideation or attempts in vulnerable individuals.

Sex differences in prevalence and clinical correlates of suicide attempts in first-episode and drug-naïve patients with anxious depression in a Chinese Han population: A large-scale cross-sectional study

BackgroundsAnxious depression (AD) has been extensively studied. However, fewer studies have examined sex differences in the prevalence of suicide attempts among AD patients. This study aimed to explore sex differences in suicide attempts and risk factors in patients with AD. Methods1380 first episode drug-naïve patients with AD were recruited. Sociodemographic and clinical characteristics were measured using a self-administered demographic questionnaire. The Hamilton Depression Scale (HAMD), Hamilton Anxiety Inventory (HAMA), and positive subscale of the Positive and Negative Syndrome Scale (PANSS) were used to assess patients' clinical symptoms. We also measured the patient's blood glucose, lipids, and thyroid axis hormone levels. ResultsThere were no sex differences in the prevalence of suicide attempts in patients with FEDN anxious depression. In addition, binary logistic regression analysis showed that HAMA score, TSH levels, and TPOAb levels significantly predicted suicide attempts in both male and female patients with AD, while HAMD score significantly predicted suicide attempts in female patients with AD only. ConclusionsThe severity of anxiety and higher levels of TSH and TPOAb were associated with an increased risk for suicide attempts in both male and female patients with AD, whereas the severity of depression was only associated with suicide attempts in females.

3.79 Predictors of Suicide Attempts Among Adolescents With Suicidal Ideations and Plan

3.79 Predictors of Suicide Attempts Among Adolescents With Suicidal Ideations and Plan

A Risk Calculator to Predict Suicide Attempts Among Individuals With Early-Onset Bipolar Disorder.

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder. Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC. There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history. This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.Reprinted from Bipolar Disord 2022; 24:749-757, with permission from John Wiley and Sons. Copyright © 2022.

Deep sequential neural network models improve stratification of suicide attempt risk among US veterans.

To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimately improving outreach and intervention efforts. The DNNs fused demographic information with diagnostic, prescription, and procedure codes. Models were trained and tested on EHR data of approximately 500000 US veterans: all veterans with recorded suicide attempts from April 1, 2005, through January 1, 2016, each paired with 5 veterans of the same age who did not attempt suicide. Shapley Additive Explanation (SHAP) values were calculated to provide explanations of DNN predictions. The DNNs outperformed logistic and linear regression models in predicting suicide attempts. After adjusting for the sampling technique, the convolutional neural network (CNN) model achieved a positive predictive value (PPV) of 0.54 for suicide attempts within 12 months by veterans in the top 0.1% risk tier. Explainability methods identified meaningful subgroups of high-risk veterans as well as key determinants of suicide attempt risk at both the group and individual level. The deep learning methods employed in the present study have the potential to significantly enhance existing suicide risk models for veterans. These methods can also provide important clues to explore the relative value of long-term and short-term intervention strategies. Furthermore, the explainability methods utilized here could also be used to communicate to clinicians the key features which increase specific veterans' risk for attempting suicide.

Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach.

Research on the classification models of suicide attempts has predominantly depended on the collection of sensitive data related to suicide. Gathering this type of information at the population level can be challenging, especially when it pertains to adolescents. We addressed two main objectives: (1) the feasibility of classifying adolescents at high risk of attempting suicide without relying on specific suicide-related survey items such as history of suicide attempts, suicide plan, or suicide ideation, and (2) identifying the most important predictors of suicide attempts among adolescents. Nationwide survey data from 173,664 Norwegian adolescents (ages 13-18) were utilized to train a binary classification model, using 169 questionnaire items. The Extreme Gradient Boosting (XGBoost) algorithm was fine-tuned to classify adolescent suicide attempts, and the most important predictors were identified. XGBoost achieved a sensitivity of 77% with a specificity of 90%, and an AUC of 92.1% and an AUPRC of 47.1%. A coherent set of predictors in the domains of internalizing problems, substance use, interpersonal relationships, and victimization were pinpointed as the most important items related to recent suicide attempts. This study underscores the potential of machine learning for screening adolescent suicide attempts on a population scale without requiring sensitive suicide-related survey items. Future research investigating the etiology of suicidal behavior may direct particular attention to internalizing problems, interpersonal relationships, victimization, and substance use.

Sex-differential association of suicide attempts with thyroid dysfunction in first-episode and drug-naïve young major depressive disorder patients with comorbid anxiety

This study aimed to explore sex differences in the relationship between thyroid function indicators and suicide attempts in first-episode and drug-naïve young major depressive disorder (MDD) patients with comorbid anxiety (MDA). A total of 917 MDD patients (aged 18–35 years) were recruited. The Hamilton depression rating scale (HAMD-17), Hamilton anxiety rating scale (HAMA), positive and negative syndrome scale (PANSS) positive subscale and clinical global impression of severity scale (CGI-S) were used. 467 patients were classified as MDA. The prevalence of suicide attempts was 31.3% in MDA patients, which was significantly higher than that (7.3%) in MDD patients without anxiety. Compared with MDA patients without suicide attempts, MDA patients with suicide attempts were older, had a later age of onset, higher HAMD-17, HAMA, and PANSS positive symptom subscale scores, as well as higher TSH, TgAb and TPOAb levels. For male patients, TSH and TPOAb levels were independently associated with suicide attempts. For female patients, HAMA, PANSS positive symptom scores, CGI-S score and TPOAb levels were independently associated with suicide attempts. Our results suggest that the indicators of thyroid function which can predict suicide attempts in male and female MDA patients have sex differences.

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts.

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

The amplitude of low-frequency fluctuation characteristics in depressed adolescents with suicide attempts: a resting-state fMRI study.

The amplitude of low-frequency fluctuation (ALFF) is a measure of spontaneous brain activity derived from resting-state functional magnetic resonance imaging (rs-fMRI). Previous research has suggested that abnormal ALFF values may be associated with major depressive disorder (MDD) and suicide attempts in adolescents. In this study, our aim was to investigate the differences in ALFF values between adolescent MDD patients with and without a history of suicide attempts, and to explore the potential utility of ALFF as a neuroimaging biomarker for aiding in the diagnosis and prediction of suicide attempts in this population. The study included 34 adolescent depression patients with suicide attempts (SU group), 43 depression patients without suicide attempts (NSU group), and 36 healthy controls (HC group). Depression was diagnosed using a threshold score greater than 17 on the Hamilton Depression Rating Scale (HDRS). The rs-fMRI was employed to calculate zALFF values and compare differences among the groups. Associations between zALFF values in specific brain regions and clinical variables such as emotion regulation difficulties were explored using Pearson partial correlation analysis. Receiver-Operating Characteristics (ROC) analysis assessed the ability of mean zALFF values to differentiate between SU and NSU groups. Significant differences in zALFF values were observed in the left and right inferior temporal gyrus (l-ITG, r-ITG) and right fusiform gyrus (r-FG) among the three groups (GRF corrected). Both SU and NSU groups exhibited increased zALFF values in the inferior temporal gyrus compared to the HC group. Furthermore, the SU group showed significantly higher zALFF values in the l-ITG and r-FG compared to both the NSU group and the HC group. Partial correlation analysis revealed a negative correlation between zALFF values in the left superior and middle frontal gyrus (l-SFG, l-MFG) and the degree of emotional dysregulation in the SU group (R = -0.496, p = 0.003; R = -0.484, p = 0.005). Combining zALFF values from the l-ITG and r-FG achieved successful discrimination between depressed adolescents with and without suicide attempts (AUC = 0.855) with high sensitivity (86%) and specificity (71%). Depressed adolescents with suicidal behavior exhibit unique neural activity patterns in the inferior temporal gyrus and fusiform gyrus. These findings highlight the potential utility of these specific brain regions as biomarkers for identifying suicide risk in depressed adolescents. Furthermore, associations between emotion dysregulation and activity in their frontal gyrus regions were observed. These findings provide preliminary yet pertinent insights into the pathophysiology of suicide in depressed adolescents.

Predicting suicidal events: A comparison of the Concise Health Risk Tracking Self-Report (CHRT-SR) and the Columbia Suicide Severity Rating Scale (C-SSRS)

This report examines the predictive capabilities of two scales of suicidality in high-risk adolescents. Charts of adolescents with severe suicidality participating in an intensive outpatient program were reviewed. Self-report data from the 9-item Concise Health Risk Tracking Self-Report (CHRT-SR9) and clinician-completed data from the Columbia Suicide Severity Risk Scale (C-SSRS) were obtained at entry. Scales’ performances in predicting suicide attempts and suicidal events were evaluated using logistic regression models and ROC analyses. Of 539 adolescents, 53 had events of which 19 were attempts. The CHRT-SR9 total score predicted events (OR=1.05) and attempts (OR=1.09), as did the C-SSRS Suicide Ideation (SI) Intensity Composite for events (OR=1.10) and attempts (OR=1.16). The CHRT-SR9 AUC was 0.70 (84.2% sensitivity; 41.7% specificity; PPV=5.0%; NPV=98.6%) for attempts. The C-SSRS Intensity Composite AUC was 0.62 (89.5% sensitivity; 24.1% specificity; PPV=4.2%; NPV=98.4%) for attempts. Both the CHRT-SR9 and C-SSRS capture important parameters related to suicidal events or attempts that can help assess suicidal risk in adolescents.

Predictors of suicide attempt within 30 days of first medically documented major depression diagnosis in U.S. army soldiers with no prior suicidal ideation

BackgroundUnderstanding mental health predictors of imminent suicide attempt (SA; within 30 days) among soldiers with depression and no prior suicide ideation (SI) can inform prevention and treatment. The current study aimed to identify sociodemographic and service-related characteristics and mental disorder predictors associated with imminent SA among U.S. Army soldiers following first documented major depression diagnosis (MDD) with no history of SI.MethodsIn this case-control study using Army Study to Assess Risk and Resilience in Servicemembers (STARRS) administrative data, we identified 101,046 active-duty Regular Army enlisted soldiers (2010–2016) with medically-documented MDD and no prior SI (MDD/No-SI). We examined risk factors for SA within 30 days of first MDD/No-SI using logistic regression analyses, including socio-demographic/service-related characteristics and psychiatric diagnoses.ResultsThe 101,046 soldiers with documented MDD/No-SI were primarily male (78.0%), < 29 years old (63.9%), White (58.1%), high school-educated (74.5%), currently married (62.0%) and < 21 when first entering the Army (56.9%). Among soldiers with MDD/No-SI, 2,600 (2.6%) subsequently attempted suicide, 16.2% (n = 421) within 30 days (rate: 416.6/100,000). Our final multivariable model identified: Soldiers with less than high school education (χ23 = 11.21, OR = 1.5[95%CI = 1.2–1.9]); combat medics (χ22 = 8.95, OR = 1.5[95%CI = 1.1–2.2]); bipolar disorder (OR = 3.1[95%CI = 1.5–6.3]), traumatic stress (i.e., acute reaction to stress/not PTSD; OR = 2.6[95%CI = 1.4–4.8]), and “other” diagnosis (e.g., unspecified mental disorder: OR = 5.5[95%CI = 3.8-8.0]) diagnosed same day as MDD; and those with alcohol use disorder (OR = 1.4[95%CI = 1.0-1.8]) and somatoform/dissociative disorders (OR = 1.7[95%CI = 1.0-2.8]) diagnosed before MDD were more likely to attempt suicide within 30 days. Currently married soldiers (χ22 = 6.68, OR = 0.7[95%CI = 0.6–0.9]), those in service 10 + years (χ23 = 10.06, OR = 0.4[95%CI = 0.2–0.7]), and a sleep disorder diagnosed same day as MDD (OR = 0.3[95%CI = 0.1–0.9]) were less likely.ConclusionsSA risk within 30 days following first MDD is more likely among soldiers with less education, combat medics, and bipolar disorder, traumatic stress, and “other” disorder the same day as MDD, and alcohol use disorder and somatoform/dissociative disorders before MDD. These factors identify imminent SA risk and can be indicators for early intervention.