BACKGROUND: Self-injurious thoughts and behaviors (SITBs) are increasing dramatically among children and adolescents. Crisis support is intended to provide immediate mental health care, risk mitigation, and intervention for those experiencing SITBs and acute mental health distress. Digital mental health interventions (DMHIs) have emerged as accessible and effective alternatives to in-person care; however, most do not provide crisis support or ongoing care for children and adolescents with SITBs.
OBJECTIVE: To inform the development of digital crisis support and mental health care for children and adolescents presenting with SITBs, this study aims to (1) characterize children and adolescents with SITBs who participate in a digital crisis response service, (2) compare anxiety and depressive symptoms of children and adolescents presenting with SITBs versus those without SITBs throughout care, and (3) suggest future steps for the implementation of digital crisis support and mental health care for children and adolescents presenting with SITBs.
METHODS: This retrospective study was conducted using data from children and adolescents (aged 1-17 y; N=2161) involved in a pediatric collaborative care DMHI. SITB prevalence was assessed during each live session. For children and adolescents who exhibited SITBs during live sessions, a rapid crisis support team provided evidence-based crisis support services. Assessments were completed approximately once a month to measure anxiety and depressive symptom severity. Demographics, mental health symptoms, and change in the mental health symptoms of children and adolescents presenting with SITBs (group with SITBs) were compared to those of children and adolescents with no SITBs (group without SITBs).
RESULTS: Compared to the group without SITBs (1977/2161, 91.49%), the group with SITBs (184/2161, 8.51%) was mostly made up of adolescents (107/184, 58.2%) and female children and adolescents (118/184, 64.1%). At baseline, compared to the group without SITBs, the group with SITBs had more severe anxiety and depressive symptoms. From before to after mental health care with the DMHI, the 2 groups did not differ in the rate of children and adolescents with anxiety symptom improvement (group with SITBs: 54/70, 77% vs group without SITBs: 367/440, 83.4%; χ21=1.2; P=.32) as well as depressive symptom improvement (group with SITBs: 58/72, 81% vs group without SITBs: 255/313, 81.5%; χ21=0; P=.99). The 2 groups also did not differ in the amount of change in symptom severity during care with the DMHI for anxiety (t80.20=1.37; P=.28) and depressive (t83.75=-0.08; P=.99) symptoms.
CONCLUSIONS: This study demonstrates that participation in a collaborative care DMHI is associated with improved mental health outcomes in children and adolescents experiencing SITBs. These results provide preliminary insights for the use of pediatric DMHIs in crisis support and mental health care for children and adolescents presenting with SITBs, thereby addressing the public health issue of acute mental health crisis in children and adolescents.

BACKGROUND: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent\'s gender.
OBJECTIVE: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide.
METHODS: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning-based large language model.
RESULTS: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause.
CONCLUSIONS: Linguistic differences, by gender, in stigmatizing or glorifying language when reporting suicide may exacerbate suicide disparities.

BACKGROUND: Due to recent advances in artificial intelligence, large language models (LLMs) have emerged as a powerful tool for a variety of language-related tasks, including sentiment analysis, and summarization of provider-patient interactions. However, there is limited research on these models in the area of crisis prediction.
OBJECTIVE: This study aimed to evaluate the performance of LLMs, specifically OpenAI\'s generative pretrained transformer 4 (GPT-4), in predicting current and future mental health crisis episodes using patient-provided information at intake among users of a national telemental health platform.
METHODS: Deidentified patient-provided data were pulled from specific intake questions of the Brightside telehealth platform, including the chief complaint, for 140 patients who indicated suicidal ideation (SI), and another 120 patients who later indicated SI with a plan during the course of treatment. Similar data were pulled for 200 randomly selected patients, treated during the same time period, who never endorsed SI. In total, 6 senior Brightside clinicians (3 psychologists and 3 psychiatrists) were shown patients\' self-reported chief complaint and self-reported suicide attempt history but were blinded to the future course of treatment and other reported symptoms, including SI. They were asked a simple yes or no question regarding their prediction of endorsement of SI with plan, along with their confidence level about the prediction. GPT-4 was provided with similar information and asked to answer the same questions, enabling us to directly compare the performance of artificial intelligence and clinicians.
RESULTS: Overall, the clinicians\' average precision (0.7) was higher than that of GPT-4 (0.6) in identifying the SI with plan at intake (n=140) versus no SI (n=200) when using the chief complaint alone, while sensitivity was higher for the GPT-4 (0.62) than the clinicians\' average (0.53). The addition of suicide attempt history increased the clinicians\' average sensitivity (0.59) and precision (0.77) while increasing the GPT-4 sensitivity (0.59) but decreasing the GPT-4 precision (0.54). Performance decreased comparatively when predicting future SI with plan (n=120) versus no SI (n=200) with a chief complaint only for the clinicians (average sensitivity=0.4; average precision=0.59) and the GPT-4 (sensitivity=0.46; precision=0.48). The addition of suicide attempt history increased performance comparatively for the clinicians (average sensitivity=0.46; average precision=0.69) and the GPT-4 (sensitivity=0.74; precision=0.48).
CONCLUSIONS: GPT-4, with a simple prompt design, produced results on some metrics that approached those of a trained clinician. Additional work must be done before such a model can be piloted in a clinical setting. The model should undergo safety checks for bias, given evidence that LLMs can perpetuate the biases of the underlying data on which they are trained. We believe that LLMs hold promise for augmenting the identification of higher-risk patients at intake and potentially delivering more timely care to patients.

BACKGROUND: In an age when telehealth services are increasingly being used for forward triage, there is a need for accurate suicide risk detection. Vocal characteristics analyzed using artificial intelligence are now proving capable of detecting suicide risk with accuracies superior to traditional survey-based approaches, suggesting an efficient and economical approach to ensuring ongoing patient safety.
OBJECTIVE: This systematic review aimed to identify which vocal characteristics perform best at differentiating between patients with an elevated risk of suicide in comparison with other cohorts and identify the methodological specifications of the systems used to derive each feature and the accuracies of classification that result.
METHODS: A search of MEDLINE via Ovid, Scopus, Computers and Applied Science Complete, CADTH, Web of Science, ProQuest Dissertations and Theses A&I, Australian Policy Online, and Mednar was conducted between 1995 and 2020 and updated in 2021. The inclusion criteria were human participants with no language, age, or setting restrictions applied; randomized controlled studies, observational cohort studies, and theses; studies that used some measure of vocal quality; and individuals assessed as being at high risk of suicide compared with other individuals at lower risk using a validated measure of suicide risk. Risk of bias was assessed using the Risk of Bias in Non-randomized Studies tool. A random-effects model meta-analysis was used wherever mean measures of vocal quality were reported.
RESULTS: The search yielded 1074 unique citations, of which 30 (2.79%) were screened via full text. A total of 21 studies involving 1734 participants met all inclusion criteria. Most studies (15/21, 71%) sourced participants via either the Vanderbilt II database of recordings (8/21, 38%) or the Silverman and Silverman perceptual study recording database (7/21, 33%). Candidate vocal characteristics that performed best at differentiating between high risk of suicide and comparison cohorts included timing patterns of speech (median accuracy 95%), power spectral density sub-bands (median accuracy 90.3%), and mel-frequency cepstral coefficients (median accuracy 80%). A random-effects meta-analysis was used to compare 22 characteristics nested within 14% (3/21) of the studies, which demonstrated significant standardized mean differences for frequencies within the first and second formants (standardized mean difference ranged between -1.07 and -2.56) and jitter values (standardized mean difference=1.47). In 43% (9/21) of the studies, risk of bias was assessed as moderate, whereas in the remaining studies (12/21, 57%), the risk of bias was assessed as high.
CONCLUSIONS: Although several key methodological issues prevailed among the studies reviewed, there is promise in the use of vocal characteristics to detect elevations in suicide risk, particularly in novel settings such as telehealth or conversational agents.
BACKGROUND: PROSPERO International Prospective Register of Systematic Reviews CRD420200167413; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020167413.

UNASSIGNED: There is growing concern around the use of sodium nitrite (SN) as an emerging means of suicide, particularly among younger people. Given the limited information on the topic from traditional public health surveillance sources, we studied posts made to an online suicide discussion forum, \"Sanctioned Suicide,\" which is a primary source of information on the use and procurement of SN.
UNASSIGNED: This study aims to determine the trends in SN purchase and use, as obtained via data mining from subscriber posts on the forum. We also aim to determine the substances and topics commonly co-occurring with SN, as well as the geographical distribution of users and sources of SN.
UNASSIGNED: We collected all publicly available from the site\'s inception in March 2018 to October 2022. Using data-driven methods, including natural language processing and machine learning, we analyzed the trends in SN mentions over time, including the locations of SN consumers and the sources from which SN is procured. We developed a transformer-based source and location classifier to determine the geographical distribution of the sources of SN.
UNASSIGNED: Posts pertaining to SN show a rise in popularity, and there were statistically significant correlations between real-life use of SN and suicidal intent when compared to data from the Centers for Disease Control and Prevention (CDC) Wide-Ranging Online Data for Epidemiologic Research (⍴=0.727; P<.001) and the National Poison Data System (⍴=0.866; P=.001). We observed frequent co-mentions of antiemetics, benzodiazepines, and acid regulators with SN. Our proposed machine learning-based source and location classifier can detect potential sources of SN with an accuracy of 72.92% and showed consumption in the United States and elsewhere.
UNASSIGNED: Vital information about SN and other emerging mechanisms of suicide can be obtained from online forums.

UNASSIGNED: This study aimed to evaluate oxidative stress parameters in individuals with depression and schizophrenia, considering gender differences, and manifesting suicidal behavior, encompassing thoughts without a tendency to be realized, thoughts with a tendency to be realized, and suicide attempts.
UNASSIGNED: From among the patients from Department of Psychiatry 120 individuals were selected who met the inclusion criteria and did not meet the exclusion criteria for the study. In the initial phase of the project, patients eligible for the study underwent the M.I.N.I 7.0.2 questionnaire (Mini International Neuropsychiatric Interview). Subsequently, in the second phase of the research, venous blood samples were collected from the patients for the purpose of conducting biochemical assessments, focusing on oxidative stress parameters.
UNASSIGNED: The obtained results suggest that redox biomarkers, namely TOS (total oxidation state) and OSI (TOS/TAC ratio), in the blood plasma of women increase in tandem with the severity of suicidal behavior. No notable alterations in SOD (Cu-Zn-superoxide dismutase), GPx (glutathione peroxidase), and GSH (reduced glutathione) concentrations and activity were noted between groups exhibiting suicidal behavior. The observed variations in the concentrations and activity of antioxidant parameters were significant solely in comparison to the control group.
UNASSIGNED: Redox biomarkers TOS and OSI could prove valuable in diagnosing women at a genuine risk of committing suicide. On the other hand, antioxidant parameters - SOD, GPx, and GSH may be instrumental in identifying patients with suicidal behaviors, without specifying their intensity.

BACKGROUND: Digital phenotyping has seen a broad increase in application across clinical research; however, little research has implemented passive assessment approaches for suicide risk detection. There is a significant potential for a novel form of digital phenotyping, termed screenomics, which captures smartphone activity via screenshots.
OBJECTIVE: This paper focuses on a comprehensive case review of 2 participants who reported past 1-month active suicidal ideation, detailing their passive (ie, obtained via screenomics screenshot capture) and active (ie, obtained via ecological momentary assessment [EMA]) risk profiles that culminated in suicidal crises and subsequent psychiatric hospitalizations. Through this analysis, we shed light on the timescale of risk processes as they unfold before hospitalization, as well as introduce the novel application of screenomics within the field of suicide research.
METHODS: To underscore the potential benefits of screenomics in comprehending suicide risk, the analysis concentrates on a specific type of data gleaned from screenshots-text-captured prior to hospitalization, alongside self-reported EMA responses. Following a comprehensive baseline assessment, participants completed an intensive time sampling period. During this period, screenshots were collected every 5 seconds while one\'s phone was in use for 35 days, and EMA data were collected 6 times a day for 28 days. In our analysis, we focus on the following: suicide-related content (obtained via screenshots and EMA), risk factors theoretically and empirically relevant to suicide risk (obtained via screenshots and EMA), and social content (obtained via screenshots).
RESULTS: Our analysis revealed several key findings. First, there was a notable decrease in EMA compliance during suicidal crises, with both participants completing fewer EMAs in the days prior to hospitalization. This contrasted with an overall increase in phone usage leading up to hospitalization, which was particularly marked by heightened social use. Screenomics also captured prominent precipitating factors in each instance of suicidal crisis that were not well detected via self-report, specifically physical pain and loneliness.
CONCLUSIONS: Our preliminary findings underscore the potential of passively collected data in understanding and predicting suicidal crises. The vast number of screenshots from each participant offers a granular look into their daily digital interactions, shedding light on novel risks not captured via self-report alone. When combined with EMA assessments, screenomics provides a more comprehensive view of an individual\'s psychological processes in the time leading up to a suicidal crisis.

BACKGROUND: Given that signage, messaging, and advertisements (ads) are the gateway to many interventions in suicide prevention, it is important that we understand what type of messaging works best for whom.
OBJECTIVE: We investigated whether explicitly mentioning suicide increases engagement using internet ads by investigating engagement with campaigns with different categories of keywords searched, which may reflect different cognitive states.
METHODS: We ran a 2-arm study Australia-wide, with or without ads featuring explicit suicide wording. We analyzed whether there were differences in engagement for campaigns with explicit and nonexplicit ads for low-risk (distressed but not explicitly suicidal), high-risk (explicitly suicidal), and help-seeking for suicide keywords.
RESULTS: Our analyses revealed that having explicit wording has opposite effects, depending on the search terms used: explicit wording reduced the engagement rate for individuals searching for low-risk keywords but increased engagement for those using high-risk keywords.
CONCLUSIONS: The findings suggest that individuals who are aware of their suicidality respond better to campaigns that explicitly use the word \"suicide.\" We found that individuals who search for low-risk keywords also respond to explicit ads, suggesting that some individuals who are experiencing suicidality search for low-risk keywords.

BACKGROUND: Drug-induced suicide (DIS) is a severe adverse drug reaction (ADR). Although clinical trials have provided evidence on DIS, limited investigations have been performed on rare ADRs, such as suicide.
OBJECTIVE: We aimed to systematically review case reports on DIS to provide evidence-based drug information.
METHODS: We searched PubMed to obtain case reports regarding DIS published until July 2021. Cases resulting from drugs that are no longer used or are nonapproved, substance use, and suicidal intentions were excluded. The quality of each case report was assessed using the CASE (Case Reports) checklist. We extracted data regarding demographics, medication history, suicide symptoms, and symptom improvement and evaluated the causality of DIS using the Naranjo score. Furthermore, to identify the potential suicidal risk of the unknown drugs, we compared the results of the causality assessment with those of the approved drug labels.
RESULTS: In 83 articles, we identified 152 cases involving 61 drugs. Antidepressants were reported as the most frequent causative drugs of DIS followed by immunostimulants. The causality assessment revealed 61 cases having possible, 89 cases having probable, and 2 cases having definite relationships with DIS. For approximately 85% of suspected drugs, the risk of suicidal ADRs was indicated on the approved label; however, the approved labels for 9 drugs, including lumacaftor/ivacaftor, doxycycline, clozapine, dextromethorphan, adalimumab, infliximab, piroxicam, paclitaxel, and formoterol, did not provide information about these risks.
CONCLUSIONS: We found several case reports involving drugs without suicide risk information on the drug label. Our findings might provide valuable insights into drugs that may cause suicidal ADRs.

BACKGROUND: Prior suicide attempts are a relatively strong risk factor for future suicide attempts. There is growing interest in using longitudinal electronic health record (EHR) data to derive statistical risk prediction models for future suicide attempts and other suicidal behavior outcomes. However, model performance may be inflated by a largely unrecognized form of \"data leakage\" during model training: diagnostic codes for suicide attempt outcomes may refer to prior attempts that are also included in the model as predictors.
OBJECTIVE: We aimed to develop an automated rule for determining when documented suicide attempt diagnostic codes identify distinct suicide attempt events.
METHODS: From a large health care system\'s EHR, we randomly sampled suicide attempt codes for 300 patients with at least one pair of suicide attempt codes documented at least one but no more than 90 days apart. Supervised chart reviewers assigned the clinical settings (ie, emergency department [ED] versus non-ED), methods of suicide attempt, and intercode interval (number of days). The probability (or positive predictive value) that the second suicide attempt code in a given pair of codes referred to a distinct suicide attempt event from its preceding suicide attempt code was calculated by clinical setting, method, and intercode interval.
RESULTS: Of 1015 code pairs reviewed, 835 (82.3%) were nonindependent (ie, the 2 codes referred to the same suicide attempt event). When the second code in a pair was documented in a clinical setting other than the ED, it represented a distinct suicide attempt 3.3% of the time. The more time elapsed between codes, the more likely the second code in a pair referred to a distinct suicide attempt event from its preceding code. Code pairs in which the second suicide attempt code was assigned in an ED at least 5 days after its preceding suicide attempt code had a positive predictive value of 0.90.
CONCLUSIONS: EHR-based suicide risk prediction models that include International Classification of Diseases codes for prior suicide attempts as a predictor may be highly susceptible to bias due to data leakage in model training. We derived a simple rule to distinguish codes that reflect new, independent suicide attempts: suicide attempt codes documented in an ED setting at least 5 days after a preceding suicide attempt code can be confidently treated as new events in EHR-based suicide risk prediction models. This rule has the potential to minimize upward bias in model performance when prior suicide attempts are included as predictors in EHR-based suicide risk prediction models.