Abstract Background and Hypothesis Sleep disturbance is a well-established risk factor for suicide, though few studies to date have examined whether sleep disturbance contributes to suicide risk among individuals at clinical high risk for psychosis (CHR). The current study addressed this gap in the literature. We hypothesized that sleep disturbance would have a unique relationship with suicidal ideation/attempts when accounting for other variables in the model. We also hypothesized that the interaction between sleep disturbance/attenuated positive symptoms and sleep disturbance/stress would be related to suicidal ideation/attempts in CHR. Study Design The current study used data generated by the Accelerating Medicines Partnership® Schizophrenia Observational Study. The total sample included 1,048 participants (827 CHR and 221 community controls). Participants completed measures of suicidal ideation/attempts, attenuated positive symptoms, depressive symptoms, perceived stress, and sleep disturbance. Study Results Results supported a relationship between sleep disturbance and suicidal ideation/attempts in CHR, with participants who had lifetime ideation and attempts experiencing more sleep disturbance than those with no ideation or attempts. We also found small, but significant positive correlations between sleep disturbance and suicide risk in CHR. When accounting for other variables in the model, the effect of sleep disturbance remained significant for past month ideation, but not lifetime ideation or attempts. Both interaction models were non-significant. Conclusions Our findings highlight the potential value of sleep measures in early identification and treatment of suicide risk in CHR. Further research in this area is warranted.
H. Wastler, Aubrey M. Moe, Alexandra M Blouin et al.· Schizophrenia Bulletin Open· 0 citations
Automated analysis of speech and language provides a critical opportunity for developing a scalable tool for general medical settings to aid in psychiatric diagnosis and triage. In a cross-diagnostic highly comorbid sample, we evaluated the contribution of speech and language features to diagnostic classification through a hierarchical, dichotomous approach: first distinguishing healthy volunteers (HV) vs. participants with any psychiatric disorder (Split 1), then identifying those with serious mental illness (SMI) vs. other psychiatric disorders (Split 2). Speech was collected from 266 participants via picture description, verbal fluency, paragraph reading, and open-ended verbal journaling tasks. We extracted 640 interpretable features spanning acoustic, temporal, lexical, syntactic, discourse, and coherence domains. LightGBM models were trained with 5-fold cross-validation comparing all combinations of speech tasks, and SHAP values were plotted for feature importance. For Split 1, the best-performing model achieved F1=0.865 combining picture description and journaling tasks, with picture description alone reaching F1=0.830. For Split 2, performance was moderate (F1=0.626), with picture description also as the best single task. Top features for Split 1 included amplitude instability (shimmer) and restricted pitch variance; Split 2 was influenced by articulation rate and filled pauses. This study represents a novel classification approach in a naturalistic, clinically complex sample. Findings suggest that a brief, explainable speech-based assessment may be able to identify individuals who need further evaluation for psychiatric disorders. External validation, bias auditing, and deployment studies are warranted to assess clinical impact.