Suicidal behaviors are prevalent public health concerns, and we need to improve our predictive ability to better inform prevention efforts.
Using nationwide longitudinal Swedish registers, we included 344,490 males and 323,177 females born 1982–1990 with information on genetic liability and environmental exposures from birth to age 16: perinatal variables, parental psychopathology (suicide attempt, substance use disorder, major depression), family status, socioeconomic difficulties, peers' psychopathology, and school grades. We conducted sex-specific analysis and developed data-driven predictive models including risk factors that occurred between ages 0 and 16 using structural equation modeling.
In both females and males, the best-fitting models reveal a complex risk pathway to suicide attempt. In females, the model indicates four direct effects on suicide attempt risk: the occurrence of suicide attempt in parents during childhood (β = 0.159, 95% CI: 0.118; 0.199) and adolescence (β = 0.115, 95% CI: 0.077; 0.153), suicide attempt in peers (β = 0.068, 95% CI: 0.057; 0.079), and low academic achievement (β = 0.166, 95% CI: 0.156; 0.175). In males, aggregate genetic liability for suicide attempt (β = 0.130, 95% CI: 0.111; 0.148), suicide attempt in parents during adolescence (β = 0.099, 95% CI: 0.074; 0.124), suicide attempt in peers (β = 0.118, 95% CI: 0.108; 0.129), and low academic achievement (β = 0.61, 95% CI: 0.152; 0.171) were related to later suicide attempt. These factors also acted as mediators to explain the association between environmental exposures in childhood and later suicide attempt.
These findings illustrate sex-specific pathways to suicide attempt by including risk factors that occur during the development. Results highlight the importance of genetic and family environment but also the prominent role of academic achievement.
Machine learning models have shown promising potential in individual-level outcome prediction for patients with psychosis, but also have several limitations. To address some of these limitations, we present a model that predicts multiple outcomes, based on longitudinal patient data, while integrating prediction uncertainty to facilitate more reliable clinical decision-making.
We devised a recurrent neural network architecture incorporating long short-term memory (LSTM) units to facilitate outcome prediction by leveraging multimodal baseline variables and clinical data collected at multiple time points. To account for model uncertainty, we employed a novel fuzzy logic approach to integrate the level of uncertainty into individual predictions. We predicted antipsychotic treatment outcomes in 446 first-episode psychosis patients in the OPTiMiSE study, for six different clinical scenarios. The treatment outcome measures assessed at both week 4 and week 10 encompassed symptomatic remission, clinical global remission, and functional remission.
Using only baseline predictors to predict different outcomes at week 4, leave-one-site-out validation AUC ranged from 0.62 to 0.66; performance improved when clinical data from week 1 was added (AUC = 0.66–0.71). For outcome at week 10, using only baseline variables, the models achieved AUC = 0.56–0.64; using data from more time points (weeks 1, 4, and 6) improved the performance to AUC = 0.72–0.74. After incorporating prediction uncertainties and stratifying the model decisions based on model confidence, we could achieve accuracies above 0.8 for ~50% of patients in five out of the six clinical scenarios.
We constructed prediction models utilizing a recurrent neural network architecture tailored to clinical scenarios derived from a time series dataset. One crucial aspect we incorporated was the consideration of uncertainty in individual predictions, which enhances the reliability of decision-making based on the model's output. We provided evidence showcasing the significance of leveraging time series data for achieving more accurate treatment outcome prediction in the field of psychiatry.
Nonadherence/discontinuation of antipsychotic (AP) medications represents an important clinical issue in patients across psychiatric disorders, including schizophrenia spectrum disorders (SSDs). While antipsychotic-induced weight gain (AIWG) is a reported contributor to nonadherence, a systematic review of the association between AIWG and medication nonadherence/discontinuation has not been explored previously.
A systematic search was conducted in MEDLINE, EMBASE, PsychINFO, CINAHL, and CENTRAL databases, among others, to help identify all studies which explored adherence, study dropouts, AP switching and/or discontinuations attributable to AIWG among individuals with severe mental illness. A meta-analysis was also completed where applicable.
We identified two categories of studies for the meta-analysis. Category 1 included three studies, which compared measures of AP adherence or discontinuation across BMI classes/degrees of self-reported weight gain. When compared to normal weight individuals receiving APs or those who did not report AIWG, individuals who were either overweight or obese or reported weight gain in relation to AP use had an increased odds of AP nonadherence (OR 2.37; 95% CI 1.51–3.73; p = 0.0002). Category 2 had 14 studies which compared measures of discontinuation related to weight gain reported as an adverse effect across different APs. Olanzapine was associated with a 3.32 times (95% CI 2.32–4.74; p < 0.00001) increased likelihood of nonadherence or discontinuation when compared to other APs with lower weight gain liabilities. Similarly, APs with moderate weight gain liability (paliperidone, risperidone, and quetiapine) increased the odds of nonadherence or discontinuation by 2.25 (95% CI 1.31–3.87; p = 0.003) when compared to APs considered to have lower weight gain liability (i.e. haloperidol and aripiprazole). The qualitative summary also confirmed these findings.
This review and meta-analysis suggests that AIWG influences medication nonadherence/discontinuation, whereby APs with higher weight gain liability are associated with nonadherence/discontinuation. Additional studies are needed to confirm these findings.
Youth mental health (YMH) services have been established internationally to provide timely, age-appropriate, mental health treatment and improve long-term outcomes. However, YMH services face challenges including long waiting times, limited continuity of care, and time-bound support. To bridge this gap, MOST was developed as a scalable, blended, multi-modal digital platform integrating real-time and asynchronous clinician-delivered counselling; interactive psychotherapeutic content; vocational support; peer support, and a youth-focused online community. The implementation of MOST within Australian YMH services has been publicly funded.
The primary aim of this study was to evaluate the real-world engagement, outcomes, and experience of MOST during the first 32 months of implementation.
Young people from participating YMH services were referred into MOST. Engagement metrics were derived from platform usage. Symptom and satisfaction measures were collected at baseline, 6, and 12 (primary endpoint) weeks. Effect sizes were calculated for the primary outcomes of depression and anxiety and secondary outcomes of psychological distress and wellbeing.
Five thousand seven hundred and two young people from 262 clinics signed up and used MOST at least once. Young people had an average of 19 login sessions totalling 129 min over the first 12 weeks of use, with 71.7% using MOST for at least 14 days, 40.1% for 12 weeks, and 18.8% for 24 weeks. There was a statistically significant, moderate improvement in depression and anxiety at 12 weeks as measured by the PHQ4 across all users irrespective of treatment stage (d = 0.41, 95% CI 0.35–0.46). Satisfaction levels were high, with 93% recommending MOST to a friend. One thousand one hundred and eighteen young people provided written feedback, of which 68% was positive and 31% suggested improvement.
MOST is a highly promising blended digital intervention with potential to address the limitations and enhance the impact of YMH services.
Many patients with eating disorders (EDs) engage in excessive and compulsive physical activity (pathological exercise, PE) to regulate negative mood or to “burn calories.” PE can lead to negative health consequences. Non-exercise activity (NEA) bears the potential to serve as intervention target to counteract PE and problematic eating behaviors since it has been associated with positive mood effects. However, to date, there is no investigation on whether the positive link between NEA and mood seen in the healthy translates to patients with ED.
To study potential associations of NEA and mood in ED, we subjected 29 ED-patients and 35 healthy controls (HCs) to an ambulatory assessment study across 7 days. We measured NEA via accelerometers and repeatedly assessed mood on electronic smartphone diaries via a mixed sampling strategy based on events, activity and time. Within- and between-subject effects of NEA on mood, PE as moderator, and the temporal course of effects were analyzed via multilevel modeling.
NEA increased valence (β = 2.12, p < 0.001) and energetic arousal (β = 4.02, p < 0.001) but showed no significant effect on calmness. The effects of NEA on energetic arousal where significantly stronger for HCs (β HC = 6.26, p < 0.001) than for EDs (β ED = 4.02, p < 0.001; β interaction = 2.24, p = 0.0135). Effects of NEA were robust across most timeframes of NEA and significantly moderated by PE, that is, Lower PE levels exhibited stronger NEA effects on energetic arousal.
Patients with ED and HC show an affective benefit from NEA, partly depending on the level of PE. If replicated in experimental daily life studies, this evidence may pave the way towards expedient NEA interventions to cope with negative mood. Interventions could be especially promising if delivered as Just-in-time adaptive interventions (JITAIs) and should be tailored according to the PE level.
Functional recovery remains a core clinical objective for patients with bipolar disorder (BD). Sociodemographic, clinical, and neurocognitive variables are associated with long-term functional impairment, yet the impact of sex differences is unclear. Functional remediation (FR) is a validated intervention aimed at achieving functional recovery in BD. The present study assessed the effect of sex differences of FR on psychosocial functioning at post-treatment (6-months) and 12-month follow-up (FUP). To the best of our knowledge, this is the first study to explore the role of sex as a factor in the efficacy of FR.
157 participants with BD were randomly assigned to either FR (N = 77) or treatment as usual group (80). Clinical, sociodemographic, neuropsychological, and functional data were obtained using a comprehensive assessment battery. Sex differences were explored via a general linear model (GLM) for repeated measures to compare the effect of sex on the intervention over time (6 months and FUP).
Results demonstrated that FR benefits both sexes, males (p = 0.001; d’ = 0.88) and females (p = 0.04; d’ = 0.57), at 6 months suggesting a generalized functional improvement. Conversely, at 12-month FUP sex differences were observed only in males (p = 0.005; d’ = 0.68).
FR is a beneficial intervention for males and females after treatment, suggesting that there are no relevant distinct needs. Females may benefit from ongoing psychosocial functioning booster sessions after the intervention to maintain original improvements. Future research exploring sex differences could help to identify strategies to offer personalized FR intervention approaches in individuals with BD.
Childhood maltreatment is associated with less favourable treatment outcomes with pharmacotherapy and psychotherapy for depression. It is unknown whether this increased risk of treatment resistance in maltreated individuals extends to electroconvulsive therapy (ECT).
This retrospective cohort study included 501 consecutive adult referrals for an acute course of twice-weekly ECT for unipolar or bipolar depression at an academic inpatient centre in Ireland between 2016 and 2024. Retrospectively reported physical and sexual childhood maltreatment were assessed on hospital admission. Response was defined as a score of 1 or 2 and remission was defined as a score of 1 on the Clinical Global Impression – Improvement scale 1–3 days after final ECT session. Logistic regression analyses were used to examine the associations between childhood maltreatment and ECT nonresponse and nonremission, adjusting for covariates. Mediation analyses were conducted to explore the role of psychiatric comorbidities, persistent depressive symptoms lasting 2 years or more in the current episode, and baseline depression severity.
Compared to the group with no childhood maltreatment, the childhood maltreatment group had similar odds of ECT nonresponse (adjusted odds ratio = 1.47, 95% CI = 0.85–2.53) but significantly elevated odds of ECT nonremission (adjusted odds ratio = 3.75, 95% CI = 1.80–7.81). In a mediation analysis, presence of persistent depressive symptoms mediated 7.4% of the total effect of childhood maltreatment on ECT nonremission.
Individuals with exposure to childhood maltreatment may be less likely to achieve full remission following a course of ECT.
To evaluate the association between exposure to atypical antipsychotics during pregnancy and risk of miscarriage.
This nested case–control study used a large Japanese administrative database. Pregnancy onset and outcomes were estimated using previously reported algorithms, classifying cases as women becoming pregnant between 2013 and 2022 and ending in a miscarriage. Controls were randomly selected from the entire pregnancy cohort by risk-set sampling with replacement and were individually matched to the cases (3:1). The association between exposure to atypical antipsychotics and risk of miscarriage was assessed using conditional logistic regression adjusted for confounders. The association between benzodiazepine exposure and the risk of miscarriage was assessed as a positive control.
In the cohort, 44,118 patients were matched with 132,317 controls. The mean ages (standard deviations) of the case and control groups were 33.3 (5.7) and 33.2 (5.5) years, respectively. The prevalence of atypical antipsychotics was 0.5% in both groups. Aripiprazole is an individual antipsychotic with the highest prescription prevalence. The adjusted odds ratios (aOR) for miscarriage were 0.966 (95% confidence interval [CI], 0.796–1.173) for atypical antipsychotics and 0.998 (0.784–1.269) for aripiprazole. A higher aOR (1.431, 95% CI 1.303–1.573) suggested an association with benzodiazepines. A sensitivity analysis that limited the population to women diagnosed with schizophrenia alone did not suggest an association between atypical antipsychotics and the risk of miscarriage.
The results of this study do not suggest an association between exposure to atypical antipsychotics during pregnancy and the risk of miscarriage.
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