European Psychiatry

BackgroundExternal validations of existing risk models is an efficient step towards potential implementation, obviating the need to develop new models. However, validation in new clinical settings poses several challenges. ObjectiveTo externally validate the OxSATS tool using data from the Oxford Monitoring System for Self-harm in England. OxSATS is a validated tool to predict suicide after self-harm developed using Swedish population registers. MethodsWe selected episodes of self-harm (ICD-10 codes X60-84; Y10-34) by individuals aged 10-64 years who presented to a large regional hospital between 1 January 2000 and 31 December 2018, and were followed up until 31 December 2019. We applied the OxSATS tool to estimate each individuals suicide risk within 12 months after their index self-harm. We assessed model performance using discrimination (Harrells c-index) and calibration measures (calibration plot and the observed-to-expected events ratio, O:E). We assessed the effects of missing predictors on calibration and subsequently recalibrated the model. FindingsWe identified 16,120 individuals who presented to hospital with self-harm, of whom 101 (0.6%) died by suicide in the 12-month follow-up period. The OxSATS model showed good discrimination in external validation (c-index=0.72, 95% CI=0.67, 0.77). Recalibration was required because initial calibration reflected a lower outcome rate in the new data. After recalibration, calibration performance was excellent (O:E=1.00, 95% CI=0.80, 1.20). ConclusionsDespite differences in clinical services and outcome ascertainment, suicide risk models can maintain good predictive performance in new settings. However, recalibration should be considered when applying prediction models in new settings, and the impact of missing predictors should be assessed using sensitivity analyses. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSSuicide risk is substantially elevated after hospital presentation for self-harm, but most existing risk assessment tools rely on rating scales or binary cut-offs, show limited predictive accuracy, and rarely report calibration. OxSATS is a prognostic model developed using Swedish register data that provides continuous risk estimates and demonstrated good discrimination and calibration in its original setting. External validation in new healthcare systems is essential before implementation, but is often complicated by differences in predictor definitions, missing variables, and outcome prevalence. What this study addsThis study provides the first external validation of OxSATS in an English clinical setting using routinely collected hospital data. The model retained good discrimination but initially overpredicted suicide risk due to a lower baseline event rate and one missing predictor, highlighting the importance of calibration assessment. How this study might affect research, practice or policyFuture research and implementation strategies should routinely incorporate external validation, sensitivity analyses for missing predictors, and local recalibration before clinical or policy adoption.

BackgroundDepression and anxiety are common mental health conditions in the UK. NHS Talking Therapies offers evidence-based therapies and is the largest provider of treatment, yet, only 50% of patients recover. Accurate outcome prediction could identify those at risk of poor outcomes and support more personalised care. This study aimed to develop and internally validate multivariable prediction models using routinely collected data from a large, ethnically diverse sample to enable fair, data-driven treatment decisions. MethodsData included 30,999 adults who completed high-intensity therapy at a single NHS trust between 2018 and mid-2024. Seven NHS post-treatment outcomes were modelled: reliable improvement, recovery, and reliable recovery for both depression and anxiety, and also functional impairment at the end of treatment. Predictors measured at baseline included sociodemographic and clinical characteristics. Models were developed using elastic net logistic regression and internally validated using bootstrap resampling. ResultsThe sample was predominantly female (73%) with a median age of 34; 57% identified as White and 22% as Black. Models showed moderate to good discrimination (AUC 0.63-0.77) and strong calibration. Key predictors aligned with clinical expectations, including baseline symptom severity, unemployment, benefit receipt, reporting a disability or long-term condition, psychotropic medication use among other sociodemographic factors. ConclusionsThis study highlights the potential of data-driven tools to inform clinical decisions and treatment stratification in NHS Talking Therapies. Early identification of patients less likely to benefit from standard care could support timely review, monitoring, or tailored interventions. External validation and implementation research are needed to ensure generalisability and equity in care.

BackgroundThe Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial was designed to give guidance in selecting the best next-step treatment for depressed patients who did not remit during their first, and/or subsequent, antidepressant trial, with up to four trials per patient. Our prior research documented protocol violations which inflated STAR*Ds reported cumulative remission rate by 91.4%. A similar reanalysis of the step-2 drug-switch trial has not been done until now. MethodsWe reanalyzed the patient-level dataset of STAR*Ds drug-switch treatment therapies--with fidelity to the original research protocol and related publications--to determine whether there were clinically-relevant differences in results compared to the original publication. ResultsWhile our reanalysis largely comported with STAR*Ds published findings of no significant differences between drug-switch treatments, we found the following discrepancies: Lower than reported step-2 remission rates ranging from 16.2 to 19.3% (versus 17.6 to 24.8%); A significant increase in treatment-emergent suicidal ideation during the step-2 drug-switch therapies ranging from 11.2 to 15.0% compared to step-1 citalopram treatment (9.0%); A four times greater number of severe suicidal behaviors reported by the treating clinicians compared to the published suicide-related Serious Adverse Events (16 versus 4); and A sustained remission rate of only 3.1 to 8.4%. ConclusionCompared to the original publication, our reanalysis found lower remission rates and more suicidal risk than reported. This adds to the discrepancies found in our prior reanalysis and also to the finding that switching antidepressants is not well supported by the evidence.

BackgroundInternalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early identification of at-risk youth is important for effective intervention, though it remains challenging due to the multifactorial nature of risk. Machine learning (ML) offers opportunities to integrate multiple data sources and improve risk prediction for internalizing disorders. MethodsWe used data from 13,743 adolescents (mean age 14.45 years; 52.7% female) participating in the Norwegian Mother, Father and Child Cohort Study (MoBa), linked to national health registries. Logistic regression with elastic net regularization was applied to predict the risk of an internalizing disorder (mood, anxiety or stress-related) occurring within one to five years after assessment. Nested models of increasing complexity incorporated sociodemographic, clinical, lifestyle, mental health, psychosocial, and genetic predictors. Model performance was evaluated in a hold-out test set. Simplified models combining three questionnaire scales were also evaluated. ResultsTest-set performance increased with model complexity, reaching area under the receiver operating characteristic curve (AUC) of 0.732 for the full model. Mental health self-reported symptoms and psychosocial predictors contributed most to the discrimination. Simplified models using three questionnaire scales, alongside age and sex, achieved AUCs up to 0.715 and effectively stratified adolescents into high- and low-risk groups (OR80/20 ranged 6.39-10.60). ConclusionMultimodal ML models integrating registry information, mental health symptoms, psychosocial factors, and genetic data demonstrated moderate predictive performance. Simplified models with three questionnaire items reached comparable performance, highlighting their potential utility in the early identification of adolescents at elevated internalizing disorder risk.

BackgroundIn response to the Bataclan terrorist attacks, the deadliest on French soil since World War II, the Paris Memoire Vive (PARIS MEM) project was launched to expand the public hospital networks capacity to treat trauma-related disorders. Reconsolidation Therapy (RT), a brief evidence-based intervention for post-traumatic stress disorder (PTSD), was selected for rapid implementation across hospital sites. ObjectiveTo evaluate the feasibility, effectiveness, and cost-utility of implementing RT to enhance public hospital treatment capacity after mass trauma exposure. MethodA two-arm, multicentric, preference-based clinical trial and economic evaluation compared RT to treatment-as-usual (TAU) in 332 adults, mostly with PTSD. RT involves recalling trauma under propranolol during six weekly 25-minute sessions. Feasibility endpoints included the number of hospital staff trained and the proportion of participants choosing RT. The primary effectiveness endpoint was the delta scores in PTSD symptoms from baseline to Week 52. Cost-utility was evaluated using incremental cost-effectiveness ratios and quality-adjusted life years (QALYs). ResultsAcross hospital sites, 160 therapists were trained in two days, and more participants opted for RT (n = 262) over TAU (n = 70), supporting feasibility endpoints. After one year, mean PTSD symptom scores decreased by 38.14 points (SD = 0.10) in RT and 35.02 (SD = 1.68) in TAU (p = .297), indexing significant improvement in both groups. At Week 7, RT showed faster initial recovery (difference = -5.11; p = .041). RT had a 55.4% probability of being cheaper and more effective than TAU (8.4%), with estimated savings of 27150{euro} per QALY. Annual sick-leave costs were lower for RT (4147{euro}; 95% CI = 3 394-5 012) than TAU (7 386{euro}; 95% CI = 5 416-10 340; p = .01). ConclusionsFollowing massive trauma exposure, training mental health staff over two days in providing efficient, evidence-based, cost-effective PTSD treatment is achievable. Findings await replication. Trial RegistrationNCT02789982. HighlightsO_LIIt is feasible to train a large cohort of therapists in the aftermath of mass trauma and enhance the treatment capacities of institutions. C_LIO_LIReconsolidation Therapy worked faster than treatment as usual in the alleviation of traumatic stress symptoms. C_LIO_LIReconsolidation Therapy emerges as a cost-effective treatment, being both cheaper and more effective in 55% of cases. C_LI

BackgroundThe effectiveness of Digital Cognitive Behavioural Therapy (dCBT) smartphone applications for reducing depressive symptoms in adolescents remain unclear. MethodsAn online three-arm, parallel-group randomised controlled trial evaluated the effectiveness of a CBT smartphone application (ClearlyMe(R)) for reducing depressive symptoms in adolescents with outcomes assessed at baseline, post intervention (primary endpoint: 6-weeks post baseline) and follow-up (secondary endpoint: 4-months post baseline). The University of New South Wales Human Research Ethics Committee provided ethical approval. Youth were eligible if they were aged 12 to 17 years, in Australia, had mild to moderate depressive symptoms as measured by the adolescent Patient Health Questionnaire-9 (PHQ-A), were not receiving treatment or experiencing recent or severe suicidality, had access to a smartphone, and parental consent. Participants were randomised to self-directed ClearlyMe(R), ClearlyMe(R) with SMS-guided support, or the attention-matched control. Participants were not directly informed of their allocation. The statistician was blinded for analysis. The primary outcome was PHQ-A change post intervention. Intention-to-treat analyses used mixed models for repeated measures. The trial was prospectively registered on the Australian New Zealand Clinical Trials Registry (ACTRN12622000131752). Outcomes569 adolescents (Mean age: 15.89, SD: 1.26, 74.2% female) were included in the analyses. The self-directed and guided conditions showed significantly greater reductions in depressive symptoms post intervention than the control (self-directed: Cohens d=0.35, mean differential decline 1.77; 95%CI: 0.56 - 2.98; P=.004; guided: d=0.33, mean differential decline: 1.31; 95%CI: 0.12 - 2.49; P=.030). The effects of self-directed and guided were comparable. Effects were also more robust and substantially larger post intervention among adolescents with probable MDD at baseline. Secondary outcomes showed similar patterns of change, although no differential effects for anxiety. There were no differences between the conditions at follow-up for any outcomes. Risk of adverse events was almost double in controls compared to self-directed (IRR: 1.73, 95%CI: 1.15 - 2.62, P=.009) and guided (IRR: 1.98 (95%CI: 1.27 - 3.08, P=.002). InterpretationClearlyMe(R), self-directed or with SMS-guided support, was effective for the short-term reduction of depressive symptoms in adolescents who have mild to moderate depression and are not receiving any other treatment. FundingThe Goodman Foundation and the Australian National Health and Medical Research Council Investigator Grants (MRF1197249, GNT2008839, GNT115614).

BackgroundForensic psychiatric services are expanding in many countries, and discharging patients from secure hospitals relies on accurate estimates of risk of adverse outcomes. Novel evidence-based tools for estimating one key risk, violent reoffending, have been developed in recent years. We aimed to externally validate one new tool, FoVOx, in forensic psychiatric patients sentenced to treatment, and to develop an updated model (FoVOx2), incorporating additional clinical predictors. MethodsUsing Swedish national registers, we conducted a temporal external validation of FoVOx by examining 767 patients discharged between 2014 and 2023. For the FoVOx2 cohort, 906 patients discharged between 2008 and 2023 were followed up, and additional predictors tested. The outcome was violent reconviction within 12 or 24 months. Model performance was evaluated using Harrells C-index, time-dependent AUCs, calibration, and classification metrics at predefined thresholds. ResultsIn temporal validation, FoVOx showed moderate discrimination (AUCs 0.69 and 0.71; C-index = 0.69) and acceptable overall accuracy (Brier <0.11). Calibration was generally good, with mild overestimation at the highest predicted risks (>20%) at 12 months and slight underprediction at 24 months. The updated FoVOx2 model newly incorporated clozapine treatment and additional diagnostic categories. It was associated with improved performance (AUCs 0.77; optimism-corrected C-index = 0.72; Brier 0.06 and 0.09) and achieved good calibration (intercept {approx} 0; slopes 1.03 and 1.05). ConclusionsUpdating risk assessment tools with additional clinical factors can lead to incremental improvement in model performance. Implementing tools should consider clinical utility and impact as next steps.

IntroductionPatients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked population-based registry data from Catalonia, Spain, we developed machine learning-based prediction models for post-discharge intentional self-harm across different follow-up horizons, sex, and age groups, and evaluated their generalizability and robustness with multiple validation strategies. MethodsRetrospective cohort study including 41,827 individuals accounting for 71,865 psychiatric hospitalizations with discharge at age [&ge;]10 years, between January 1, 2015, and December 31, 2018, in Catalonia, Spain, with follow-up until December 31, 2019. Primary outcome was intentional self-harm (fatal or non-fatal) within 7, 30, 90, 180, and 365 days post-discharge. Models incorporated 247 predictors from electronic health records, including sociodemographic characteristics, mental and physical disorder categories, categories of dispensed psychotropic medication, and history of self-harm and psychiatric hospitalization. Model performance was evaluated using the area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR). Predictor importance was assessed using Shapley Additive Explanations (SHAP). ResultsWithin 365 days, 4,901 hospitalizations (6.8%) were followed by intentional self-harm. The 365-day model trained on the full cohort achieved a AUCROC of 0.819, in the test sample with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence. This model generalized well across event horizons and sex-age strata, outperforming subgroup-specific models when data sparsity limited performance. Separate models trained by event horizons, and stratified by sex, and sex-age groups achieved a median AUCROC of 0.775 (IQR 0.764-0.808), with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence (IQR 4.5-6.2). Key predictors included the recency of the last registered diagnosis of depressive episodes, recurrent depression, adjustment disorders, and schizophrenia, as well as recent SSRI dispensation and the number of childhood-onset disorder and musculoskeletal disease diagnoses in the previous five years. Predictor importance varied considerably across sex-age strata, with smaller differences across horizons. Subject-level and temporal split validation strategies reduced performance (AUCROC 0.711-0.746), though estimates remained clinically informative (2.8-3.1-fold improvement over baseline prevalence). ConclusionsMachine learning models using routinely collected health records predicted intentional self-harm after psychiatric hospitalization with good discrimination and clinically meaningful precision-recall performance. A single 365-day model generalized well across horizons and demographic groups, suggesting that one broadly trained model may provide a pragmatic and scalable approach for clinical implementation.

BackgroundIntensive Support Teams (ISTs) are recommended for individuals with intellectual disabilities who display behaviours that challenge. However, there is currently little evidence about the clinical and cost effectiveness of IST models operating in England. AimsTo investigate the clinical and cost effectiveness of IST models. MethodsWe carried out a cohort study to evaluate the clinical and cost-effectiveness of two previously identified IST models (independent and enhanced) in England. Adult participants (n=226) from 21 ISTs (10 independent and 11 enhanced) were enrolled. The primary outcome was change in challenging behaviour between baseline and 9 months measured by the Aberrant Behaviour Checklist-Community 2. ResultsWe found no statistically significant differences between models for the primary outcome (adjusted {beta}: 4.27; 95% CI: -6.34 to 14.87; p=0.430) or any secondary outcomes. Quality Adjusted Life Years (0.0158; 95% CI: -0.0088 to 0.0508) and costs ({pound}3409.95; 95% CI: -{pound}9957.92 to {pound}4039.89) of the two models were comparable. ConclusionsThe study provides evidence that both models were associated with clinical improvement for similar costs at follow-up. We recommend that the choice of service model should rest with local services. Further research should investigate the critical components of IST care to inform the development of fidelity criteria, and policy makers should consider whether roll out of such teams should be mandated. Study registration numberClinicalTrials.gov NCT03586375; IRAS 239820; National Institute for Health Research (NIHR) Central Portfolio Management System (CPMS) 38554.

BackgroundChildren with conduct problems are at high risk of a wide range of mental health problems in later life, making them a priority for early intervention. Group-based parent-training is known to be effective but with a substantial failure rate. Based on evidence on the value of involving children, we developed Reflective Interpersonal Therapy for Children and Parents, (RICAP). We report here, feasibility and outcomes from the first trial of an intervention for children with conduct problems persisting after parent training. In contrast to most other studies, we used both parent and teacher report. MethodsThe sample comprised 105 children and their parents aged 5-10 years referred to UK Child and Adolescent Mental Health Services (CAMHS) with conduct problems. All were offered the Incredible Years (IY) parent training intervention, and parents provided pre- and post-treatment measures (including CBCL, SDQ). Children still above clinical threshold after IY were randomized either to RICAP or to usual CAMHS treatment (CTAU) with follow up 8 months later. Trial Registration Number: ISRCTN25252940. ResultsFeasibility was supported by high retention through the initial IY (102/105) and subsequent RCT phases of the study (58/70 eligible for randomization). The majority of those randomized to RICAP attended for 11/14 or more sessions, reflecting its high acceptability to both children and parents. By parent report RICAP was superior to CTAU on CBCL externalising (d=0.32) and internalising (d=0.42) problems, while by teacher report CTAU was superior on SDQ total problems (d=0.32) and reactive aggression (d = 0.27). ConclusionsWe provide first evidence of the acceptability and effectiveness of a novel intervention for children with persisting conduct problem following parent training. We also find differences between parent and teacher reported outcomes, pointing either to reporter or social context effects, both of which need to be addressed in future research.

BackgroundSelf-harm and suicide are relatively overrepresented in incarcerated populations, especially in female prisons. Identifying those most at risk of significant self-harm could provide opportunities for effective, targeted interventions. AimsTo develop and validate a machine learning-based algorithm capable of achieving a clinically useful level of accuracy when predicting the risk of self-harm in female prisoners. MethodData were available on 31 variables for 286 female prisoners from a single UK-based prison. This included sociodemographic factors, nature of the index offence, and responses to several psychometric assessment tools used at baseline. At 12-month follow-up any self-harm incidents were reported. A machine learning algorithm (CatBoost) to predict self-harm at one-year was developed and tested. To quantify uncertainty about the accuracy of the algorithm, the model building and evaluation process was repeated 2000 times and the distribution of results summarised. ResultsThe mean Area Under the Curve (AUC) for the model on unseen (validation) data was 0.92 (SD 0.04). Sensitivity was 0.83 (SD 0.07), specificity 0.94 (SD 0.03), positive predictive value 0.78 (SD 0.08) and the negative predictive value 0.95 (0.02). If the algorithm was used in this population, for every 100 women screened, this would equate to approximately 17 true positives and five false positives. ConclusionsThe accuracy of the algorithm was superior to those previously reported for predicting future self-harm in general and prison populations and likely to provide clinically useful levels of prediction. Research is needed to evaluate the feasibility of implementing this approach in a prison setting.

Even before the pandemic, the treatment gaps in depression care were substantial, with issues ranging from rates of depression detection and intervention to a lack of follow-up after treatment initiation and access to secondary care services. The COVID-19 pandemic, which has had major effects on global healthcare systems, is almost certain to have impacted the MDD care pathway, though it is unclear what changes have manifested and what opportunities have arisen in response to COVID-19. The extent to which patients receive best-practice care is likely closely linked to clinical outcomes (and therefore disability burden) and as such, it is important to examine treatment gaps on the MDD care pathway during the pandemic. Here, we outline a protocol for a scoping review that investigates this broad topic, focusing on continuity of care and novel methods (e.g. digital approaches) used to mitigate care disruption. This scoping review protocol was designed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) standards and will culminate in a narrative synthesis of evidence.

BackgroundProlonged waiting times for Child and Adolescent Mental Health Services (CAMHS) leave many young people without structured support while awaiting specialist treatment. Social prescribing has been proposed as a community-based adjunct within CAMHS pathways; however, evidence regarding its safety and clinical impact remains limited. MethodsWellbeing While Waiting was a multi-site non-randomised controlled trial embedded within a hybrid type II implementation-effectiveness evaluation conducted across 11 CAMHS in England. The protocol was prospectively published prior to recruitment (BMC Psychiatry; 10.1186/s12888-023-04758-0). Between May 2023 and March 2025, 558 young people aged 11-18 years referred to CAMHS were enrolled (225 usual care; 333 social prescribing). Primary outcomes were anxiety and depression symptoms, total emotional and behavioural difficulties, and perceived stress. Secondary outcomes included resilience and wellbeing. ResultsNo intervention-related adverse events were observed. On average, participants had 5 sessions with a Link Worker. Compared with usual care, no significant differences were observed in anxiety or depression symptoms. However, participants receiving social prescribing demonstrated significant improvements in total emotional and behavioural difficulties over six months, driven by reductions in conduct difficulties, hyperactivity and peer problems. Significant improvements for those receiving social prescribing were also found for prosocial behaviour and resilience. ConclusionsWithin routine CAMHS pathways, no intervention-related adverse events were observed for social prescribing, and social prescribing was associated with improvements in behavioural and resilience-related outcomes, although not in anxiety or depressive symptoms. Findings suggest social prescribing may offer a valuable adjunct during delayed access to specialist treatment, with effects distinct from symptom-focused clinical therapies.

BackgroundDepression is estimated to be the leading cause of disability in South Africa, yet data on depression prevalence and antidepressant use are inconsistent and fragmentary. We present a system dynamics modelling approach to integrate these data and assess trends and inequalities in depression prevalence and treatment access. MethodsWe developed a deterministic model of the South African population aged 15 and older, stratified by age, sex, HIV status/stage and susceptibility to depression. Individual transitions between depressed/healthy and treated/untreated states were simulated over time, from 1985. The model was calibrated to depression prevalence data from nine nationally representative household surveys (2002-2024) and ten smaller studies reporting prevalence of antidepressant use, using a Bayesian approach. ResultsThe model estimated a slight decline in depression point prevalence over time, from 5.1% (95% CI: 4.5-5.6%) in 2002 to 4.5% (95% CI: 4.0-5.0%) in 2024, although with a transient rise in depression prevalence during the COVID-19 period. In 2024, depression prevalence was higher in women (5.3%, 95% CI: 4.7-5.9%) than men (3.6%, 95% CI: 3.2-4.0%), and highest at ages 60 and older. The lifetime prevalence of depression was 70.6% (95% CI: 67.8-73.6); alternative model settings with a more concentrated distribution of depression risk were inconsistent with longitudinal data. The proportion of adults using antidepressants increased from 1.0% (95% CI: 0.8-1.2%) in 2008 to 2.8% (95% CI: 2.2-3.4%) in 2024. In 2024, antidepressant use was 11.0% (95% CI: 8.8-13.5%) in the private sector, compared to only 0.9% (95% CI: 0.7-1.1%) in the rest of the population, and the ratio of new antidepressant initiations to new cases of depression was 0.12 nationally. ConclusionThe prevalence of depression in South Africa has been relatively stable over the last two decades. Although antidepressant use has increased, overall use remains low, and substantial inequality remains in access to treatment in the public and private health sectors.

ImportanceNo study with available data from birth into late childhood has explored how prenatal antidepressant exposure affects offspring body mass index (BMI) throughout childhood. ObjectiveTo determine the association between prenatal antidepressant exposure and longitudinal differences in child BMI up to age 8 years. Design, Setting, and ParticipantsWe used data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) linked to the Medical Birth Registry of Norway and the MoBa Genetics. We included 6,084 pregnancy-child dyads (singleton, liveborn) with available parent-reported data on child BMI from birth up to 8 years of age, born to women with depression/anxiety prior to pregnancy. Analysis was performed between January 2023 and April 2024. ExposuresPrenatal antidepressant exposure was categorized as i) continued antidepressants in pregnancy (n=626); ii) discontinued antidepressants proximal to pregnancy (n=412); or iii) unexposed to antidepressants both before and during pregnancy (n=5,046). Main outcomes and measuresChild BMI up to 8 years of age. Mean BMI differences over time across antidepressant exposure groups were compared using multilevel mixed-effect linear models. ResultsChildren born to mothers who continued antidepressant into pregnancy had comparable childhood BMIs with those born to unexposed mothers or mothers who discontinued antidepressant proximal to pregnancy. Higher BMI was observed up to 3 years of age among male offspring born to antidepressant continuers compared to discontinuers, especially in those exposed to selective-serotonin-reuptake-inhibitor before pregnancy (mean difference in BMI, {beta}=0.334; 95% CI: 0.081 to 0.588 at baseline). Lower BMI was seen among female offspring born to continued vs. discontinued mothers and the gap became larger over time, especially between low-moderate use of antidepressant vs. discontinuation during pregnancy. Analyses integrating parental genetic liability for depression, BMI, and antidepressant response using polygenic risk scores in a sub-population (n=1,913) suggests potential influence of the genetic component on the differences in BMI across antidepressant trajectory groups in some strata. Conclusion and relevanceThe longitudinal childhood BMI of children born to mothers with pre-pregnancy depression/anxiety did not differ across prenatal antidepressant exposure trajectories. Exploratory analyses revealed differences at specific time frames which might be sex-specific and potentially influenced by genetic liability profiles. KEY POINTSO_ST_ABSQuestionC_ST_ABSDoes prenatal antidepressant exposure affect longitudinal childhood BMI? FindingsIn this cohort study of 6084 pregnancy-child dyads in mothers with pre-pregnancy depressive/anxiety disorders, no difference in longitudinal childhood BMI across prenatal antidepressant exposure groups were observed. Exploratory analyses revealed differences at specific time frames which might be sex-specific and potentially influenced by parental genetic liability profiles. MeaningLongitudinal BMI throughout childhood of children born to mothers with pre-pregnancy depression/anxiety did not differ across prenatal antidepressant exposure trajectories. Further research is needed to investigate the time-dependent, sex-specific, and genetic-related aspects of some strata of antidepressant exposure on BMI differences.

IntroductionUse of coercive measures in psychiatric hospitals is clinically and ethically challenging. Aiming to support prevention, we developed and evaluated machine learning models to predict both mechanical restraint and a broader composite outcome that includes related coercive measures. MethodsThe dataset comprised electronic health records (EHR) from adults ([&ge;]18 years) who had at least one admission to the Psychiatric Services in the Central Denmark Region between 2015 and 2021. For each inpatient day, XGBoost machine learning models were trained to predict mechanical restraint or composite (mechanical, chemical, or manual) restraint within 48 hours. Hyperparameters were optimised for the area under the receiver operating characteristic curve (AUROC) using five-fold cross validation on 85% of the data, with performance validated on a held-out 15% test set. ResultsThe cohort included 16,834 patients with 45,179 inpatient stays, covering 687,388 prediction days. Of these, 2,736 days were followed by a restraint episode within 48 hours, including 983 episodes of mechanical restraint. The final models were trained on 2,389 EHR-based predictors, derived from demographics, diagnoses, medications, and clinical notes. The mechanical restraint model achieved an AUROC of 0.921 (95% CI: [0.918-0.922]) and a positive predictive value (PPV) of 4.9% when classifying the top 1% of risk scores as positive. The composite model achieved an AUROC of 0.912 (95% CI: [0.909-0.913]) and a PPV of 4.2% when predicting mechanical restraint, and 0.900 (95% CI: [0.898-0.900]) with a PPV of 10.4% when predicting composite restraint. ConclusionThe results indicate that incorporating related coercive measures into model training did not improve discrimination (AUROC) for predicting mechanical restraint but did increase PPV when predicting composite restraint, reflecting the higher outcome prevalence. This suggests that leveraging related outcomes can inform prediction of rare events, emphasising the importance of problem framing in clinical prediction modelling. Future work should include external validation across temporal, geographic, and demographic contexts. Significant Outcomes- A machine learning model trained solely for predicting mechanical restraint achieved strong performance (AUROC 0.92), identifying nearly one-third of restraint cases at high specificity. - Training on a broader composite outcome yielded similar discriminatory performance when predicting mechanical restraint, while the higher base rate resulted in a higher positive predictive value for predicting composite restraint. - Broadening the outcome to include multiple restraint types increased the number of at-risk patients detected due to the higher prevalence, without compromising accuracy for mechanical restraint, supporting shared underlying risk factors. Limitations- The model requires more extensive external validation to assess generalisability across time, demographic groups, and settings, which may be limited by regional/national differences in legislation and clinical documentation. - Prediction performance was highest near the restraint event, limiting early forecasting and suggesting that limiting predictions to the early phase of hospitalisation, where most restraint occurs, could elevate the base rate and improve model performance.

ObjectiveThe pandemic of the coronavirus disease 2019 (COVID-19) has led to an increased burden on mental health. This study therefore investigated the development of major depressive disorder (MDD), generalized anxiety disorder (GAD), and suicidal ideation in the Netherlands during the first fifteen months of the pandemic and three nation-wide lockdowns. MethodsParticipants of the Lifelines Cohort Study -a Dutch population-based sample-reported current symptoms of MDD and GAD, including suicidal ideation, according to DSM-IV criteria using a digital structured questionnaire. Between March 2020 and June 2021, 36,106 participants (aged 18-96) filled out a total of 629,811 questionnaires across 23 time points. Trajectories over time were estimated using generalized additive models and analyzed in relation to age, sex, and lifetime history of MDD/GAD to identify groups at risk. ResultsWe found non-linear trajectories for MDD and GAD with a higher number of symptoms and prevalence rates during periods of lockdown. The point prevalence of MDD and GAD peaked during the third hard lockdown at 2.88% (95% CI: 2.71%-3.06%) and 2.92% (95% CI: 2.76%-3.08%), respectively, in March 2021. Women, younger adults, and participants with a history of MDD/GAD reported significantly more symptoms. For suicidal ideation, we found a linear increase over time in younger participants which continued even after the lockdowns ended. For example, 4.63% (95% CI: 3.09%-6.96%) of 20-year-old participants reported suicidal ideation at our last measured time point in June 2021, which represents a 4.14x increase since the start of the pandemic. ConclusionsOur study showed greater prevalence of MDD and GAD during COVID-19 lockdowns suggesting that the pandemic and government enacted restrictions impacted mental health in the population. We furthermore found a continuing increase in suicidal ideation in young adults. This warrants for alertness in clinical practice and prioritization of mental health in public health policy.

BackgroundGenetic liability of depressive disorder can be captured by psychopathology in relatives (family history). Various methods summarize family history in a single score, differing in included information as well as underlying model. We systematically compared the performance of family history indicators, including promising new indicators based on the liability threshold model, in predicting depressive disorder. MethodsWe calculated selected family history indicators for depression (dichotomous, proportion, novel genetically-informed method PAFGRS) in 1339 participants of the Netherlands Study of Depression and Anxiety (Ncase= 1086). Polygenic scores were computed from the most recent GWAS for major depression. We assessed correlations between genetic liability indicators, as well as their prediction of lifetime depressive disorder diagnosis. ResultsCorrelations of family history indicators with each other were high (r = 0.71 - 0.99), and much lower with the PGS (r = 0.15). There was a suggested increase in predictive accuracy for more elaborately computed scores, ranging from proportion (AUC = 0.66, OR = 2.26, 95%CI = 1.88-2.71) to PAFGRS (AUC = 0.70, OR =17.06, 95%CI = 9.46 - 30.77). The best-performing family history indicator and the PGS were independently associated with depressive disorder (PAFGRS: OR = 15.17, 95%CI = 8.36-27.51, p = 3.59x10-19; PGS: OR = 1.30, 95%CI = 1.12-1.50, p = 0.0004). ConclusionsOur analysis shows that more elaborate family history indicators, including family size, prevalence, heritability and based on genetic theory, would be preferrable over simpler methods. Family history and PGS were complementary in prediction, showing the added value of including both in future studies.

BackgroundEvidence for longer-term use of antidepressant medications (ADM) in major depressive disorder (MDD) derives from maintenance/discontinuation (M/D) trials showing that approximately 20% of patients who remitted on ADMs relapse by 1 year when treatment is maintained vs. approximately 40% if ADMs are discontinued. This treatment effect is larger than that demonstrated in acute-phase trials and has been criticized as "too good to be true." The enrichment design of M/D trials has been proposed as one explanation for this larger effect size, but it is unclear whether enrichment design alone is a sufficient explanation, raising questions about the internal validity of M/D trials. ObjectiveTo test whether the enrichment design of M/D trials is sufficient to account for the larger-than-expected effect of maintenance treatment. MethodsWe simulated M/D trials by applying the study characteristics including the enrichment phase of 9 real M/D trials to depression trajectories derived from the STAR-D pragmatic trial and depression efficacy data derived from acute-phase trial and compared the resulting relapse rates by arm between the simulated data and real data. ResultsThe simulated ADM average treatment effect increased 1.7-fold after selection on remitters from the enrichment phase from 1.75 Hamilton Depression Rating Scale points in unselected participants to 2.91 HAM-D points in just the participants who pass the enrichment filter from phase 1 to phase 2. Simulated relapse rates and the relative risks between arms had excellent fit on the withheld post-randomization aggregated real trial data. ConclusionsOur simulations indicate that the enrichment design of M/D trials is sufficient to explain their larger effect sizes. These results support the internal validity of M/D studies in characterizing the benefits of ADM maintenance treatment.

BackgroundHealthcare workers experience disproportionately high rates of depression, anxiety, and post-traumatic stress compared with the general population. Within the NHS, work-related stress and mental health-related sickness absence has increased over the past decade, a trend intensified by COVID-19. Mental health support offers are patchy across the UK, and the evidence base around interventions is scarce. The Staff Mental Health Service (SMHS) provides rapid, confidential support for NHS staff across Cambridgeshire and Peterborough. In this study, we report an economic evaluation of this dedicated service. AimsTo assess costs and patient outcomes associated with SMHS treatment, compared with local NHS Talking Therapies (TT) support. MethodA model-based cost-consequence analysis comparing two treatment pathways: SMHS or TT, versus TT only. Routinely collected service data and survey responses informed a decision-tree model estimating costs (2022/23 {pound}GBP), clinical outcomes (PHQ-9 and GAD-7 scores), and quality-adjusted life years (QALYs). Additional analyses examined service waiting times and productivity losses. ResultsCosts per patient were slightly higher for SMHS or TT ({pound}614 versus {pound}553), resulting in an incremental cost-effectiveness ratio of {pound}7,126/QALY. Treatment at either SMHS or TT yielded greater improvements in mental health outcomes than TT alone, with mean score reductions of 4.2 versus 2.8 (PHQ-9), and 4.6 versus 2.7 (GAD-7). Median waiting times were substantially shorter at SMHS versus TT from referral to assessment (14 versus 17 days), referral to treatment (22 versus 51 days), and assessment to first treatment (7 versus 30 days; all p<0.001). Productivity losses during waiting periods were lower for SMHS, with an estimated value of {pound}2,018 per patient. ConclusionsThe SMHS offers a clinically effective and cost-effective model of support for NHS staff, delivering greater improvements in mental health symptoms, substantially shorter waiting times, and reduced productivity losses at only modest additional cost compared with TT. These findings provide early evidence that specialist services for healthcare workers represent good value for money and support continued investment in specialist staff mental health provision within the NHS.