Suicide Risk Screening Research Articles

Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk.

Suicide is a leading cause of death that demands cross-disciplinary research efforts to develop and deploy suicide risk screening tools. Such tools, partly informed by influential suicide theories, can help identify individuals at the greatest risk of suicide and should be able to predict the transition from suicidal thoughts to suicide attempts. Advances in artificial intelligence have revolutionized the development of suicide screening tools and suicide risk detection systems. Thus, various types of AI systems, including text-based systems, have been proposed to identify individuals at risk of suicide. Although these systems have shown acceptable performance, most of them have not incorporated suicide theories in their design. Furthermore, directly applying suicide theories may be difficult because of the diversity and complexity of these theories. To address these challenges, we propose an approach to develop speech- and language-based suicide risk detection systems. We highlight the promise of establishing a benchmark textual and vocal dataset using a standardized speech and language assessment procedure, and research designs that distinguish between the risk factors for suicide attempt above and beyond those for suicidal ideation alone. The benchmark dataset could be used to develop trustworthy machine learning or deep learning-based suicide risk detection systems, ultimately constructing a foundation for vocal and textual-based suicide risk detection systems.

Prevalence of suicide risk among a national sample of individuals referred from a primary care subpopulation, 2017-2020.

Over the past decade, the age-adjusted suicide rate has increased by 35.2% in the United States. In primary care, practitioners often interact with patients at risk of dying by suicide, yet little is known about the prevalence of suicide risk in primary care populations. Patient data from 2017-2020, consisting of a national sample of patients referred from primary care and enrolled in collaborative care behavioral health services (n = 37 666), were analyzed. Controlling for demographic characteristics, logistic models were used to compare suicide risk prevalence by behavioral health diagnosis. An estimated 9.96% (95% confidence interval [CI]: 9.65-10.27)-or approximately 3751 individuals-of the total sample screened positively for suicide risk. Compared with individuals diagnosed with generalized anxiety disorder, individuals diagnosed with bipolar disorder had 8.21 times the odds (95% CI: 6.66-10.10) of screening for suicide risk. Practitioners and health care systems may benefit from adding suicide risk screeners as a standard practice for referred patients, which may lead to further development of clinical pathways and provider training. The high rate of suicide risk across the sample suggests that more research is needed to understand suicide risk prevalence across primary care and collaborative care populations.

The Association of Pediatric Emergency Medicine Physicians' Self-Identified Skills in Suicide Risk Assessment and Management With Training in Mental Health.

Because changes to pediatric emergency medicine (PEM) education may help address barriers to youth suicide risk screening programs, this study aimed to understand the impact of formal training in areas that likely include suicide-related practices, developmental-behavioral pediatrics (DBP) and adolescent medicine (AM), on PEM physician-perceived level of training, attitudes, and confidence assessing and managing youth suicide risk. Twenty-seven PEM attendings and trainees completed an online survey and were divided into 2 groups: those who had completed DBP and AM rotations (DBP/AM+; n = 20) and those who had not completed either rotation (DBP/AM-; n = 7). We compared perceived level of training, attitudes, and confidence in assessing and managing suicide risk across groups. We also examined the relationship between perceived level of training and confidence. Finally, we conducted exploratory analyses to evaluate the effect of an additional formal rotation in child psychiatry. The DBP/AM+ and DBP/AM- groups did not differ on perceived level of training or on attitudes and confidence in suicide risk assessment or management. Perceived level of training in assessment and management predicted confidence in both assessing and managing suicide risk. Additional training in child psychiatry was not associated with increased perceived level of training or confidence. The DBP and AM rotations were not associated with higher perceived levels of suicide risk training or greater confidence; however, perceived level of training predicted physician confidence, suggesting continued efforts to enhance formal PEM education in mental health would be beneficial.

Suicide Risk Screening in Primary Care

Objective: The purpose of this research was to determine the general risk of suicide in the scope of primary health care services and to evaluate its relationship with hopelessness, depression, and psychological resilience.
 Methods: Seven hundred twenty-five individuals presenting to primary health care services were included in this descriptive, cross-sectional study. The research data were collected using a form including the sociodemographic information form, the Suicide Probability Scale (SPS), the Beck Depression Inventory, the Beck Hopelessness Scale, and the Brief Psychological Resilience Scale, and were analyzed on SPSS software.
 Results: The general SPS score was 69.49±14.65, indicating a moderate likelihood of suicide. Significant predictors of the risk of suicide by effect sizes were depression level, hopelessness, and psychological resilience.
 Conclusion: Screening the risk of suicide in primary health care services is of very great importance. Evaluating the individual’s history of attempted suicide, suicidal ideation, and suicide planning provides important information in assessing the likelihood of suicide.

Predicting Suicide Risk among South African University Students: Optimal Cutoff Score for the Center for Epidemiological Studies Depression Scale

University students have been identified as a particularly vulnerable group for adverse mental health outcomes owing to the distinctive implications of the COVID-19 prevention measures on the educational sector. Even prior to the pandemic, university students were identified as being at increased risk for adverse mental health outcomes and suicidality. Although various factors can enhance the risk of suicide, one of the strongest predictors of suicidality is the presence of depressive symptomology. The current study used receiver operating characteristic (ROC) analysis to determine the optimal cutoff score on the CES-D when screening for suicide risk in university students during COVID-19 in South Africa. Participants (n = 337) were students at a historically disadvantaged university in South Africa. The ROC analysis showed no statistically significant difference between men’s and women's area under the curve (AUC); thus, a single AUC can be used for the whole sample. The study found that a cutoff score of 30 with a sensitivity of 89.30 and a specificity of 62.60 represented an acceptable cutoff point. It is recommended that when the instrument is used among student populations, those with scores of 30 and above need to be followed up with a clinician-administered interview to confirm suicide risk and facilitate timely intervention.

Feasibility of a universal suicidality tool for adolescents.

The suicide rate among adolescents has been increasing rapidly over the past several years. Adequate screening for suicide risk in this population, particularly youth of color, is lacking. The Ask Suicide-Screening Questions (ASQ) tool was implemented at two adolescent-focused health clinics in a large U.S. city. This project followed the Ottawa Model of Research Use. Participating clinicians were surveyed before and after receiving an educational module on suicide risk screening, the ASQ tool, and clinical pathways. Clinicians were also asked about the feasibility and acceptability of the ASQ tool in their practice. An electronic medical records software was used to gather data on patients newly screened for suicide risk using the ASQ tool. Among eligible patients, 40.2% were screened using the ASQ tool during the 4-month duration of the project. Most clinicians reported that using the tool was feasible within their practice (66%) and 100% endorsed its acceptability (i.e., reporting that they were comfortable screening for suicide and that the ASQ was easy to use). The ASQ may be a promising screening tool for clinicians to use to address the mental health needs of at-risk youth. This project supports the universal acceptability and feasibility of its use in inner-city primary care clinics.

Competency of primary care providers to assess and manage suicide risk in Nepal: The role of emotional validation and invalidation techniques

Screening for suicide risk in primary care settings is a recommended strategy worldwide for suicide prevention. However, few studies examine which clinical behaviors on the part of primary care providers are most predictive of competency in suicide risk assessment and management. The current study used ratings on the Enhancing Assessment of Common Therapeutic Factors (ENACT) tool during observations of provider-patient role plays (N = 91) to understand how primary care providers’ use of emotional validation and invalidation strategies associate with competency in assessing for and managing suicide risk. Structural equation modeling revealed that emotional validation by primary care providers (β = 0.63, p < 0.001) was significantly associated with clinical competency for assessing and managing suicide risk. Scoring procedures on the ENACT that capture specific behavioral attributes had an improved model fit, compared to a general Likert scale version of ENACT scoring. This study suggests that training primary care providers on emotional validation strategies may improve their competency in suicide prevention. The findings also support assessing suicide prevention competency by documenting specific behaviors, rather than generic ratings such as “done poorly” or “done well.” Future research rating interactions with actual patients and connecting competency with patient outcomes could help refine clinical training as well as our understanding of processes through which emotional validation and invalidation strategies contribute to more effective suicide prevention.

64-LB: Cognitive and Somatic Symptoms of Depression Are Uniquely Related to HbA1c and Suicide Attempt History

Screening for depression and suicide risk is an essential part of care for adolescents and young adults (AYA) with type 1 diabetes (T1D), as increased depression symptoms and suicide attempt history are independently associated with elevated Hba1c. As symptoms of depression and prolonged hyperglycemia may coexist, further insight may be gained from an understanding of how somatic versus cognitive symptoms may uniquely relate to clinical outcomes in AYA with T1D. This study examined the independent relationships between HbA1c and suicide attempt history with cognitive and somatic symptoms on the Patient Health Questionnaire-9 Adolescent Version (PHQ-9) for AYA with T1D. The PHQ-9 was electronically administered (n = 2,491) at routine outpatient medical visits, and PHQ-9 scores, relevant background variables (age, sex, race, ethnicity, diabetes duration), and HbA1c values were obtained via chart review. AYA with T1D were 15.7 years old and had diabetes for 6.2 years, on average. After adjusting for covariates, somatic (B = .08, p &amp;lt;0.001), but not cognitive symptoms (B = .02, p = 0.3), predicted HbA1c levels. Follow up analyses showed this relationship varied by sex, with somatic symptoms and HbA1c related in females (B = .10, p = .002) but not males (B = .05, p = .14), and by race, with somatic symptoms and HbA1c related in those who identify as African American (B = .13, p = .009) but not White (B = .03, p = .32). Logistic regression analyses controlling for relevant background variables showed cognitive symptoms (B = .46, p &amp;lt; 0.001), but not somatic symptoms (B = .06, p = 0.39), were related to suicide attempt history. Findings demonstrate the value of independently monitoring cognitive and somatic symptoms of depression for AYA with T1D. Somatic symptoms are more strongly related to glycemic control, particularly for AYA with T1D who are female and identify as African American, respectively, whereas cognitive symptoms are significantly related to suicide attempt history. Disclosure J. M. Gettings: None. E. J. Pantesco: None. N. Andorko: None. S. M. Willi: Advisory Panel; Boehringer Ingelheim International GmbH, Medtronic.

Effect of an Emergency Department Process Improvement Package on Suicide Prevention

Suicide is a leading cause of deaths in the US. Although the emergency department (ED) is an opportune setting, ED-initiated interventions remain underdeveloped and understudied. To determine if an ED process improvement package, with a subfocus on improving the implementation of collaborative safety planning, reduces subsequent suicide-related behaviors. The Emergency Department Safety Assessment and Follow-up Evaluation 2 (ED-SAFE 2) trial, a stepped-wedge cluster randomized clinical trial conducted in 8 EDs across the US, used an interrupted time series design with three 12-month sequential phases: baseline, implementation, and maintenance. A random sample of 25 patients per month per site 18 years and older who screened positive on the Patient Safety Screener, a validated suicide risk screener, were included. The primary analyses focused on those who were discharged from the ED, while secondary analyses focused on all patients who screened positive, regardless of disposition. Data were collected on patients who presented for care from January 2014 to April 2018, and data were analyzed from April to December 2022. Each site received lean training and built a continuous quality improvement (CQI) team to evaluate the current suicide-related workflow in the ED, identify areas of improvement, and implement efforts to improve. Each site was expected to increase their universal suicide risk screening and implement collaborative safety planning for patients at risk of suicide who were discharged home from the ED. Site teams were centrally coached by engineers experienced in lean CQI and suicide prevention specialists. The primary outcome was a composite comprising death by suicide or suicide-related acute health care visits, measured over a 6-month follow-up window. Across 3 phases, 2761 patient encounters were included in the analyses. Of these, 1391 (50.4%) were male, and the mean (SD) age was 37.4 (14.5) years. A total of 546 patients (19.8%) exhibited the suicide composite during the 6-month follow-up (9 [0.3%] died by suicide and 538 [19.5%] of a suicide-related acute health care visit). A significant difference was observed for the suicide composite outcome between the 3 phases (baseline, 216 of 1030 [21%]; implementation, 213 of 967 [22%]; maintenance, 117 of 764 [15.3%]; P = .001). The adjusted odds ratios of risk of the suicide composite during the maintenance phase was 0.57 (95% CI, 0.43-0.74) compared with baseline and 0.61 (0.46-0.79) compared with the implementation phase, which reflect a 43% and 39% reduction, respectively. In this multisite randomized clinical trial, using CQI methods to implement a department-wide change in suicide-related practices, including the implementation of a safety plan intervention, yielded a significant decrease in suicide behaviors in the maintenance period of the study. ClinicalTrials.gov Identifier: NCT02453243.

Research Landscape on Student Suicide: A Bibliometric and Visual Analysis of 62 Years (1959-2021).

To guide and provide orientation to research on student suicide, this study aimed to analyze the research status on student suicide with the help of bibliometric analysis. Utilising bibliometric methodology, this study complements existing literature by giving a comprehensive, reliable thematic representation of this topic. A Scopus search was done and all published research papers were reviewed. A total of 187 papers published between 1959 and 2021 were analyzed. We identified the most influential journals in this field, the most prolific nations and institutions, as well as the years with the most publications, the most cited papers, notable authors, and most researched areas, It was followed by use of the VOSviewer software (version 1.6.18) to construct and visualize the bibliometric networks like co-authorship network map, inter-country co-authorship network map, and keywords co-occurrences network maps. The analysis reveals the current research trend where the number of publications on 'student suicide' reflects an overall upward trend over the years, suggesting that research on student suicidology is fast evolving but the pace is slow. The maximum publications were in the year 2020. The United States made the largest contribution (n = 85). The most productive institution was the University of Rochester, New York (n = 10). Allan J Schwartz was the most prolific author, with nine research papers. Journal of College Student Psychotherapy (n = 13) was the journal and Medicine (n = 118) was the discipline with the highest number of publications. The study potentially has important implications for designing more comprehensive screening and assessment tools for suicide risk. Future work should pay more attention to developing effective intervention programs for students. The results also highlight the need for more research work on student suicidology. More research in this area is needed to create awareness among students. Consequently, this study can be a big critique and torch bearer at the same time.

Multimodal region-based behavioral modeling for suicide risk screening

IntroductionSuicide is a leading cause of death around the world, interpolating a huge suffering to the families and communities of the individuals. Such pain and suffering are preventable with early screening and monitoring. However, current suicide risk identification relies on self-disclosure and/or the clinician's judgment.Research question/statmentTherefore, we investigate acoustic and nonverbal behavioral markers that are associated with different levels of suicide risks through a multimodal approach for suicide risk detection.Given the differences in the behavioral dynamics between subregions of facial expressions and body gestures in terms of timespans, we propose a novel region-based multimodal fusion.MethodsWe used a newly collected video interview dataset of young Japanese who are at risk of suicide to extract engineered features and deep representations from the speech, regions of the face (i.e., eyes, nose, mouth), regions of the body (i.e., shoulders, arms, legs), as well as the overall combined regions of face and body.ResultsThe results confirmed that behavioral dynamics differs between regions, where some regions benefit from a shorter timespans, while other regions benefit from longer ones. Therefore, a region-based multimodal approach is more informative in terms of behavioral markers and accounts for both subtle and strong behaviors. Our region-based multimodal results outperformed the single modality, reaching a sample-level accuracy of 96% compared with the highest single modality that reached sample-level accuracy of 80%. Interpretation of the behavioral markers, showed the higher the suicide risk levels, the lower the expressivity, movement and energy observed from the subject. Moreover, the high-risk suicide group express more disgust and contact avoidance, while the low-risk suicide group express self-soothing and anxiety behaviors.DiscussionEven though multimodal analysis is a powerful tool to enhance the model performance and its reliability, it is important to ensure through a careful selection that a strong behavioral modality (e.g., body movement) does not dominate another subtle modality (e.g., eye blink). Despite the small sample size, our unique dataset and the current results adds a new cultural dimension to the research on nonverbal markers of suicidal risks. Given a larger dataset, future work on this method can be useful in helping psychiatrists with the assessment of suicide risk and could have several applications to identify those at risk.

VA's implementation of universal screening and evaluation for the suicide risk identification program in November 2020 -Implications for Veterans with prior mental health needs.

United States Veterans are at higher risk for suicide than non-Veterans. Veterans in rural areas are at higher risk than their urban counterparts. The coronavirus pandemic intensified risk factors for suicide, especially in rural areas. To examine associations between Veterans Health Administration's (VA's) universal suicide risk screening, implemented November 2020, and likelihood of Veterans being screened, and receiving follow-up evaluations, as well as post-screening suicidal behavior among patients who used VA mental health services in 2019. VA's Suicide Risk Identification Strategy (Risk ID), implemented October 2018, is a national, standardized process for suicide risk screening and evaluation. In November 2020, VA expanded Risk ID, requiring annual universal suicide screening. As such, we are evaluating outcomes of interest before and after the start of the policy among Veterans who had ≥1 VA mental health care visit in 2019 (n = 1,654,180; rural n = 485,592, urban n = 1,168,588). Regression-adjusted outcomes were compared 6 months pre-universal screening and 6, 12 and 13 months post-universal screening implementation. Item-9 on the Patient Health Questionnaire (I-9, VA's historic suicide screener), Columbia- Suicide Severity Risk Scale (C-SSRS) Screener, VA's Comprehensive Suicide Risk Evaluation (CSRE), and Suicide Behavior and Overdose Report (SBOR). 12 months post-universal screening implementation, 1.3 million Veterans (80% of the study cohort) were screened or evaluated for suicide risk, with 91% the sub-cohort who had at least one mental health visit in the 12 months post-universal screening implementation period were screened or evaluated. At least 20% of the study cohort was screened outside of mental health care settings. Among Veterans with positive screens, 80% received follow-up CSREs. Covariate-adjusted models indicated that an additional 89,160 Veterans were screened per month via the C-SSRS and an additional 30,106 Veterans/month screened via either C-SSRS or I-9 post-universal screening implementation. Compared to their urban counterparts, 7,720 additional rural Veterans/month were screened via the C-SSRS and 9,226 additional rural Veterans/month were screened via either the C-SSRS or I-9. VA's universal screening requirement via VA's Risk ID program increased screening for suicide risk among Veterans with mental health care needs. A universal approach to screening may be particularly advantageous for rural Veterans, who are typically at higher risk for suicide but have fewer interactions with the health care system, particularly within specialty care settings, due to higher barriers to accessing care. Insights from this program offer valuable insights for health systems nationwide.

Assessment of Selective and Universal Screening for Suicide Risk in a Pediatric Emergency Department.

According to National Patient Safety Goal 15.01.01, all individuals being treated or evaluated for behavioral health conditions as their primary reason for care in hospitals and behavioral health care organizations accredited by The Joint Commission should be screened for suicide risk using a validated tool. Existing suicide risk screens have minimal or no high-quality evidence of association with future suicide-related outcomes. To test the association between results of the Ask Suicide-Screening Questions (ASQ) instrument in a pediatric emergency department (ED), implemented through selective and universal screening approaches, and subsequent suicide-related outcomes. In this retrospective cohort study at an urban pediatric ED in the United States, the ASQ was administered to youths aged 8 to 18 years with behavioral and psychiatric presenting problems from March 18, 2013, to December 31, 2016 (selective condition), and then to youths aged 10 to 18 years with medical presenting problems (in addition to those aged 8-18 years with behavioral and psychiatric presenting problems) from January 1, 2017, to December 31, 2018 (universal condition). Positive ASQ screen at baseline ED visit. The main outcomes were subsequent ED visits with suicide-related presenting problems (ie, ideation or attempts) based on electronic health records and death by suicide identified through state medical examiner records. Association with suicide-related outcomes was calculated over the entire study period using survival analyses and at 3-month follow-up for both conditions using relative risk. The complete sample was 15003 youths (7044 47.0%] male; 10209 [68.0%] black; mean [SD] age, 14.5 [3.1] years at baseline). The follow-up for the selective condition was a mean (SD) of 1133.7 (433.3) days; for the universal condition, it was 366.2 (209.2) days. In the selective condition, there were 275 suicide-related ED visits and 3 deaths by suicide. In the universal condition, there were 118 suicide-related ED visits and no deaths during the follow-up period. Adjusting for demographic characteristics and baseline presenting problem, positive ASQ screens were associated with greater risk of suicide-related outcomes among both the universal sample (hazard ratio, 6.8 [95% CI, 4.2-11.1]) and the selective sample (hazard ratio, 4.8 [95% CI, 3.5-6.5]). Positive results of both selective and universal screening for suicide risk in pediatric EDs appear to be associated with subsequent suicidal behavior. Screening may be a particularly effective way to detect suicide risk among those who did not present with ideation or attempt. Future studies should examine the impact of screening in combination with other policies and procedures aimed at reducing suicide risk.Appeared originally in JAMA Netw Open 2019; 2:e1914070.

Screening and Assessing Suicide Risk in Medical Settings: Feasible Strategies for Early Detection.

Early detection of risk is a key suicide prevention strategy. Given that most individuals who die by suicide visit a health care provider in the year leading up to their death, medical settings are ideal venues for identifying those at elevated risk and bridging them to life-saving care. Clinicians are presented with an opportunity to engage in proactive suicide prevention efforts through practical and adaptable suicide risk screening, assessment, and management processes. Psychiatrists and mental health clinicians are well positioned to assist nonpsychiatric clinicians on the frontlines of this public health problem. This article discusses the importance of identifying people at elevated suicide risk through screening, differentiates screening from assessment procedures, and presents practical strategies for implementing evidence-based screening and assessment tools into practice as part of a three-tiered clinical pathway. Specifically, this article discusses key components that guide embedding suicide prevention strategies into the workflows of busy medical settings.

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality

Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

Veteran Perspectives on Population-Based Suicide Risk Screening in VHA Primary Care: Mixed-Methods Study.

In late 2018, VHA implemented a multi-stage suicide risk screening and evaluation initiative, Suicide Risk Identification Strategy, or "Risk ID," in primary care settings. The main objective of this study was to characterize VHA primary care patient perspectives regarding population-based suicide risk screening through the Risk ID program. Mixed methods; survey and qualitative interviews. Veterans screened for suicide risk using Risk ID in primary care (n = 868) participated in a survey of veteran attitudes about screening (45% response rate); thirty additionally participated in follow-up qualitative interviews. The quantitative survey consisted of three questions on attitudes about screening for suicidal thoughts in primary care. In qualitative interviews, veterans were asked about their experiences with the Risk ID processes and recommendations for improving Risk ID. Over 90% of veterans reported that it is appropriate for primary care providers or nurses/medical assistants to ask veterans about thoughts of suicide during primary care visits. Approximately half of veterans indicated that veterans should be asked about suicidal thoughts at every visit. Qualitative findings revealed that while most veterans were generally supportive and appreciated VHA screening for suicidal thoughts, they also expressed concern for the potential for inadvertent harm. Participants expressed conflicting preferences for how screening should be handled and delivered. Findings suggest that most veterans support the integration of standardized suicide risk assessment into routine primary care visits. However, findings also suggest that population-based suicide risk assessment should further consider patient experiences and preferences. Specifically, additional guidance or training for staff conducting suicide risk screening may be warranted to ensure patients feel heard (e.g., eye contact, expressing empathy) and increase patient understanding of the purpose of the screening and potential outcomes. These patient-centered approaches may improve patient experience and facilitate disclosure of suicidal thoughts.

Rapid intensification of suicide risk preceding suicidal behavior among primary care patients.

Approximately half of those who attempt suicide report experiencing suicidal ideation and suicidal planning in advance; others deny these experiences. Some researchers have hypothesized that rapid intensification is due to past suicidal ideation and/or behaviors that are "mentally shelved" but remain available for rapid access later. To evaluate this hypothesis, we examined (a) temporal sequencing of suicidal ideation, suicidal planning, and suicidal behavior, and (b) speed of emergence of suicidal behavior in a prospective cohort study of 2744 primary care patients. Of 52 patients reporting suicidal behavior during follow-up, 20 (38.5%) reported suicidal ideation and planning prior to their suicidal behavior, 23 (44.2%) reported suicidal ideation but not planning, and nine (17.3%) denied both suicidal ideation and planning. Over half (n = 30, 57.7%) reported the onset of suicidal ideation and/or planning on the same day as or after their suicidal behavior (i.e., rapid intensification). Rapid intensification was not associated with increased likelihood of reporting recent or past suicidal ideation, planning, or behaviors, suggesting rapid intensification does not depend on prior experience with suicidal ideation and/or behaviors. Detecting primary care patients at risk for this form of suicidal behavior may be limited even with universal suicide risk screening.

Suicide Risk Screening and Assessment Practices in A Multi-Center Outcome Study Following Inpatient TBI Rehabilitation

Suicide Risk Screening and Assessment Practices in A Multi-Center Outcome Study Following Inpatient TBI Rehabilitation

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department.

Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is still no better than 50%. Traditional screening instruments have helped identify risk factors for suicide, but they have not provided accurate predictive power for reducing death rates. Over the past decade, natural language processing (NLP), a form of machine learning (ML), has been used to identify suicide risk by analyzing language data. Recent work has demonstrated the successful integration of a suicide risk screening interview to collect language data for NLP analysis from patients in two emergency departments (ED) of a large healthcare system. Results indicated that ML/NLP models performed well identifying patients that came to the ED for suicide risk. However, little is known about the clinician's perspective of how a qualitative brief interview suicide risk screening tool to collect language data for NLP integrates into an ED workflow. This report highlights the feedback and observations of patient experiences obtained from clinicians using brief suicide screening interviews. The investigator used an open-ended, narrative interview approach to inquire about the qualitative interview process. Three overarching themes were identified: behavioral health workflow, clinical implications of interview probes, and integration of an application into provider patient experience. Results suggest a brief, qualitative interview method was feasible, person-centered, and useful as a suicide risk detection approach.

Multimodal brain connectome-based prediction of suicide risk in people with late-life depression

Suicidal ideation, plans and behavior are particularly serious health issues among the older population, resulting in a higher likelihood of deaths than in any other age group. The increasing prevalence of depression in late life reflects the urgent need for efficient screening of suicide risk in people with late-life depression. Employing a cross-sectional design, we performed connectome-based predictive modelling using whole-brain resting-state functional connectivity and white matter structural connectivity data to predict suicide risk in late-life depression patients (N = 37 non-suicidal patients, N = 24 patients with suicidal ideation/plan, N = 30 patients who attempted suicide). Suicide risk was measured using three standardized questionnaires. Brain connectivity profiles were used to classify three groups in our dataset and two independent datasets using machine learning. We found that brain patterns could predict suicide risk in the late-life depression population, with the explained variance up to 30.34%. The functional and structural connectivity profiles improved the classification-prediction accuracy compared with using questionnaire scores alone and could be applied to identify depressed patients who had higher suicide risk in two independent datasets. Our findings suggest that multimodal brain connectivity could capture individual differences in suicide risk among late-life depression patients. Our predictive models might be further tested to help clinicians identify patients who need detailed assessments and interventions. The trial registration number for this study is ChiCTR2200066356.