Gretchen J. Diefenbach’s research while affiliated with Yale University and other places

Purpose The Death-Implicit Association Test (D-IAT) was developed to assess implicit cognitions that may indicate a bias towards suicide or death. Although the D-IAT is widely used, little is known about its prospective validity. The current study assessed the prospective validity and treatment sensitivity of the D-IAT in a large sample of inpatients at high risk of suicide. We hypothesized that baseline D-IAT scores would (a) improve from pre- to post- inpatient admission, reflecting a stronger association between self and life; and (b) predict suicidal thoughts and behaviors across a 6-month follow-up period post-discharge. Methods Participants were 200 individuals admitted to the psychiatric inpatient unit for suicide risk. They were randomly assigned to receive inpatient treatment as usual (TAU) or brief cognitive-behavioral therapy for suicide prevention (BCBT) and completed the D-IAT before and after treatment and monthly for six months after discharge. Results Contrary to our hypotheses, D-IAT scores did not significantly change from pre- to post-admission and did not predict suicidal thoughts or behaviors across the follow-up period, with the exception of aborted attempts. Conclusions The findings largely do not support the prospective validity of the D-IAT nor its sensitivity to detect treatment effects, although replication in samples with greater frequency of suicidal behaviors across the follow-up period will be needed.

Objective: Psychiatric multimorbidity is a well-documented risk factor for suicide. However, diagnostic heterogeneity and patterns of comorbidity likely exists within the population of those who attempt suicide. Person-centered statistical approaches, such as latent class analysis (LCA), extract distinguishable groups differentiated by prevalence and comorbidity of psychiatric disorders. Method: The present study used LCA to identify typologies of psychiatric heterogeneity in a sample of 213 inpatients (M age = 33.04 [SD = 12.67]; 57.3% female; 62.4% White; 23.9% Hispanic/Latino) with a history of suicide attempt who were recruited for a suicide prevention clinical trial. Class differences in suicide history characteristics; demographic characteristics; and cognitive-affective and behavioral risk factors, obtained from an initial evaluation involving the administration of a semi-structured diagnostic interview, suicide risk assessment, and battery of self-report measures, were explored. Results: LCA identified three classes in the best-fitting solution: Depressive-High Comorbidity (n = 68), Depressive-Low Comorbidity (n = 86), and Bipolar (n = 59). The Depressive-Low Comorbidity class reported less severe suicidal ideation (p < .001), anxiety (p < .001), stress (p < .001), unlovability beliefs (p = .006), and impulsivity (p < .001). The Depressive-Low Comorbidity class also reported fewer actual attempts than the Bipolar class (p = .001) and fewer interrupted attempts than the Depressive-High Comorbidity class (p = .004). Conclusions: The Depressive-High Comorbidity and Bipolar classes consistently endorsed higher levels of suicide risk factors. These findings may help to illuminate typologies of suicide attempters with unique clinical needs, which is an essential step toward personalized medicine.

Importance Suicide risk is elevated after discharge from inpatient level of care. Empirically supported inpatient suicide prevention treatments are needed. Objective To determine whether adding an inpatient version of brief cognitive behavioral therapy for suicide prevention to treatment as usual reduces postdischarge suicide attempts, suicidal ideation, and psychiatric readmissions and to determine whether substance use disorder moderates treatment effects. Design, Setting, and Participants This randomized clinical trial compared treatment as usual (n = 106) to treatment as usual plus brief cognitive behavioral therapy for inpatients (n = 94) at a private psychiatric hospital in Connecticut. Follow-up assessments were completed monthly for 6 months postdischarge. Participants were enrolled from January 2020 through February 2023. Inpatients admitted following a suicidal crisis (past-week suicide attempt or ideation with plan on admission and attempt within previous 2 years) were included. Medical records of consecutive admissions (n = 4137) were screened, 213 were study eligible and randomized, and 200 were analyzed. A total of 114 participants (57.0%) completed 6-month follow-up assessments. Data from medical records were also obtained through 6-month follow-up. Intervention Up to 4 individual sessions of brief cognitive behavioral therapy for suicide prevention designed for inpatients. Main Outcomes and Measures Suicide attempts and readmissions were assessed via blind interviews and medical record review. Suicidal ideation was assessed via self-report. Results The mean (SD) age among 200 analyzed participants was 32.8 (12.6) years; 117 participants were female and 83 were male. Brief cognitive behavioral therapy–inpatient reduced the occurrence of suicide attempt over 6 months postdischarge by 60% (odds ratio, 0.40; 95% CI, 0.20-0.80; number needed to treat, 7) in the entire patient group, and the rate of psychiatric readmissions by 71% (rate ratio, 0.29; 95% CI, 0.09-0.90) in those without a substance use disorder. The effect of treatment condition on suicidal ideation was less clear, although post hoc analyses indicated less severe suicidal ideation following brief cognitive behavioral therapy–inpatient vs treatment as usual at 1 and 2 months postdischarge. Conclusions and Relevance Brief cognitive behavioral therapy–inpatient reduced 6-month postdischarge suicide reattempts and rate of readmissions when added to treatment as usual. Substance use disorder moderated the treatment’s effect on readmission rates. Treatment effects on suicidal ideation were less clear. Implementation research is needed to facilitate dissemination. Additional research is also needed to optimize outcomes for individuals with substance use disorders. Trial Registration ClinicalTrials.gov Identifier: NCT04168645

Adults with clinically significant borderline personality disorder traits (BPTs) are at high risk of experiencing suicidal thoughts and behaviors (STBs). STBs among those with BPTs have been associated with suicidal beliefs (e.g., that one is unlovable or that distress is intolerable). However, the extent to which suicidal beliefs uniquely mediate the relationship between emotional distress and STBs among individuals with BPTs is not known. Individuals admitted to an inpatient unit (N = 198) with recent STBs completed assessments of BPTs, depression, suicidal beliefs, suicidal ideation, and suicide attempt history. Moderated mediation models were used to explore whether suicidal beliefs mediated the relationship between depression and STBs conditional on BPTs. Suicidal patients with versus without BPTs reported stronger suicidal beliefs and more severe STBs (i.e., suicidal ideation, lifetime attempts). Exploratory moderated mediation analysis demonstrated that suicidal beliefs mediated the relationship between depression and suicidal ideation as well as suicide attempts. The mediation effect of suicidal beliefs on the depression-ideation and depression-attempt relationship was not significantly moderated by BPTs. This study was cross-sectional and therefore the estimated mediation models must be considered exploratory. Longitudinal research will be needed to assess the potential causal mediation of suicidal beliefs on the relationship between depression and STBs. The results of this study suggest that suicidal beliefs may play a significant role in the relationship between depression and STBs for inpatients with a history of suicidality regardless of BPTs. This suggests suicidal beliefs may be an important treatment target for adults with a history of STBs.

Purpose Research suggests that performance on the Death-Implicit Association Test (D-IAT) converges with explicit indicators of suicide risk (e.g., suicidal ideation, suicidal behaviors). However, the utility of the D-IAT in acute care settings has been questioned given limited studies and inconsistent findings. Additionally, it remains unclear whether the D-IAT incrementally improves the assessment of suicide risk beyond explicit measures. Method The present study evaluated the validity of the D-IAT in a large sample of psychiatric inpatients who attempted suicide within the past two years (N = 203; M age = 32.93 [SD = 12.61]; 50.7% female; 63.5% White; 22.7% Hispanic/Latino). The D-IAT was scored three ways: (1) conventional D scoring where positive scores indicate stronger implicit associations with death, (2) dichotomized D scores where participants were categorized based on positive or negative D score, and (3) DD scoring, which is a novel procedure that differentiates self-identification with death (Me-DD) from a lack of identification with life (Not Me-DD). Results D and Not Me-DD were weakly associated with suicide cognitions, hopelessness, suicidal ideation, and wishes to live/die and there were corresponding mean differences based on dichotomized D scores. D and Not Me-DD were also weakly correlated with number of lifetime suicide attempts. Me-DD evidenced fewer significant associations than Not Me-DD suggesting that a lack of association with life may be the central component of D scores. Conclusions Findings suggest that the D-IAT may not be a useful standalone measure of suicide risk in high-risk populations.

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain-based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto-striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data.

Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate, small in effect size and have limited clinical relevance. These concerns have prompted a paradigm shift toward highly powered (that is, big data) individual-level inferences, which are data driven, transdiagnostic and neurobiologically informed. Here we built and validated supervised neuroanatomical machine learning models for individual-level inferences, using a case–control design and the largest known neuroimaging database on youth anxiety disorders: the ENIGMA-Anxiety Consortium (N = 3,343; age = 10–25 years; global sites = 32). Modest, yet robust, brain- based classifications were achieved for specific anxiety disorders (panic disorder), but also transdiagnostically for all anxiety disorders when patients were subgrouped according to their sex, medication status and symptom severity (area under the receiver operating characteristic curve, 0.59–0.63). Classifications were driven by neuroanatomical features (cortical thickness, cortical surface area and subcortical volumes) in fronto- striato-limbic and temporoparietal regions. This benchmark study within a large, heterogeneous and multisite sample of youth with anxiety disorders reveals that only modest classification performances can be realistically achieved with machine learning using neuroanatomical data.

Introduction Substance use is an established risk factor for suicide attempt. Clarifying the role of substance use in suicide attempts may identify modifiable treatment targets. This study used mixed methods to associate substance use with suicide attempt history and identify pathways through which substance use contributes to attempts. Methods Study 1 included 213 adult inpatients ( n = 127 with substance use disorder [SUD]), who completed assessments of suicide attempt history as well as demographic and clinical suicide risk factors. Study 2 was a narrative analysis of suicide attempt stories described by 20 inpatients diagnosed with SUD. Results In Study 1, patients with co‐occurring alcohol and drug use disorders reported more actual lifetime suicide attempts than did those without SUD. In addition, alcohol and drug use disorders were independently associated with lifetime suicide attempts after controlling for demographic and clinical confounders. In Study 2, substance use played a role in all suicide attempts through at least one pathway before, during, or after a triggering stressor, or as suicide attempt method. Conclusions Substances play a role in suicide attempt baseline risk, acute risk and as means. It is important to target chronic and acute substance use in suicide prevention treatment plans.