People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis.

We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP.

A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance.

Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice.

Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised.

Treatment-resistant schizophrenia (TRS) is associated with high levels of functional impairment, healthcare usage and societal costs. Cross-sectional studies may overestimate TRS rates because of selection bias.

We aimed to quantify TRS rates by using first-episode cohorts to improve resource allocation and clozapine access.

We undertook a systematic review of TRS rates among people with first-episode psychosis and schizophrenia, with a minimum follow-up of 8 weeks. We searched PubMed, PsycINFO, EMBASE, CINAHL and the Cochrane Database of Systematic Reviews, and meta-analysed TRS rates from included studies.

Twelve studies were included, totalling 11 958 participants; six studies were of high quality. The rate of TRS was 22.8% (95% CI 19.1–27.0%, P < 0.001) among all first-episode cohorts and 24.4% (95% CI 19.5–30.0%, P < 0.001) among first-episode schizophrenia cohorts. Subgroup sensitivity analyses by location of recruitment, TRS definition, study quality, time of data collection and retrospective versus prospective data collection did not lead to statistically significant differences in heterogeneity. In a meta-regression, duration of follow-up and percentage drop-out did not significantly affect the overall TRS rate. Men were 1.57 times more likely to develop TRS than women (95% CI 1.11–2.21, P = 0.010).

Almost a quarter of people with first-episode psychosis or schizophrenia will develop TRS in the early stages of treatment. When including people with schizophrenia who relapse despite initial response and continuous treatment, rates of TRS may be as high as a third. These high rates of TRS highlight the need for improved access to clozapine and psychosocial supports.

Borderline personality disorder is associated with impaired quality of life and has a number of untoward public health associations. There is no established first-line pharmacological treatment for borderline personality disorder, and available options are not suitable for all individuals.

To evaluate brexpiprazole, which has effects on the dopaminergic and serotonergic systems, for the reduction of borderline personality disorder symptoms.

Eighty adults with borderline personality disorder were recruited for a randomised, double-blind placebo-controlled study. Participants received 12-week treatment with brexpiprazole (1 mg/day for 1 week, then increasing to 2 mg/day) or placebo in a parallel design. The primary efficacy outcome measure was the clinician-rated Zanarini Rating Scale for Borderline Personality Disorder (ZAN-BPD). Safety data were collected. Effects of active versus placebo treatment were characterised with linear repeated measures models.

There was a significant interaction between treatment and time on the ZAN-BPD scale (P = 0.0031), solely because of differentiation specifically at week 12. Brexpiprazole was generally well tolerated. Secondary measures did not result in statistically significant differences from placebo.

Brexpiprazole appears to have some possible effect on borderline personality disorder symptoms, but further studies are needed because of the significant effects evident, specifically at the final time point. These findings also need to be viewed cautiously, given the small sample size, large drop-out rate and robust placebo response.

Many healthcare workers do not seek help, despite their enormous stress and greater risk for anxiety, depression and post-traumatic stress disorder (PTSD).

This study screened for psychopathology and evaluated the efficacy of a brief, social contact-based video intervention in increasing treatment-seeking intentions among healthcare workers (trial registration: NCT04497415). We anticipated finding high rates of psychopathology and greater treatment-seeking intentions post-intervention.

Healthcare workers (n = 350) were randomised to (a) a brief video-based intervention at day 1, coupled with a booster video at day 14; (b) the video at day 1 only; or (c) a non-intervention control. In the 3 min video, a female nurse described difficulty coping with stress, her anxieties and depression, barriers to care and how therapy helped her. Assessments were conducted pre- and post-intervention and at 14- and 30-day follow-ups.

Of the 350 healthcare workers, 281 (80%) reported probable anxiety, depression and/or PTSD. Participants were principally nurses (n = 237; 68%), physicians (n = 52; 15%) and emergency medical technicians (n = 30; 9%). The brief video-based intervention yielded greater increases in treatment-seeking intentions than the control condition, particularly among participants in the repeat-video group. Exploratory analysis revealed that in both video groups, we found greater effect among nurses than non-nurses.

A brief video-based intervention increased treatment-seeking intention, possibly through identification and emotional engagement with the video protagonist. A booster video magnified that effect. This easily disseminated intervention could increase the likelihood of seeking care and offer employers a proactive approach to encourage employees to search for help if needed.

About 30% of patients drop out of cognitive–behavioural therapy (CBT), which has implications for psychiatric and psychological treatment. Findings concerning drop out remain heterogeneous.

This paper aims to compare different machine-learning algorithms using nested cross-validation, evaluate their benefit in naturalistic settings, and identify the best model as well as the most important variables.

The data-set consisted of 2543 out-patients treated with CBT. Assessment took place before session one. Twenty-one algorithms and ensembles were compared. Two parameters (Brier score, area under the curve (AUC)) were used for evaluation.

The best model was an ensemble that used Random Forest and nearest-neighbour modelling. During the training process, it was significantly better than generalised linear modelling (GLM) (Brier score: d = –2.93, 95% CI (−3.95, −1.90)); AUC: d = 0.59, 95% CI (0.11 to 1.06)). In the holdout sample, the ensemble was able to correctly identify 63.4% of cases of patients, whereas the GLM only identified 46.2% correctly. The most important predictors were lower education, lower scores on the Personality Style and Disorder Inventory (PSSI) compulsive scale, younger age, higher scores on the PSSI negativistic and PSSI antisocial scale as well as on the Brief Symptom Inventory (BSI) additional scale (mean of the four additional items) and BSI overall scale.

Machine learning improves drop-out predictions. However, not all algorithms are suited to naturalistic data-sets and binary events. Tree-based and boosted algorithms including a variable selection process seem well-suited, whereas more advanced algorithms such as neural networks do not.

Victims of violence, accidents and threats are at risk for mental health problems. Lower coping self-efficacy and social support levels increase this risk. Although highly relevant, it is unknown if the coronavirus disease 2019 (COVID-19) pandemic amplifies these risks.

To examine if the prevalence, incidence and/or mean scores for post-traumatic stress disorder (PTSD), anxiety and depression symptoms, general mental health problems, coping self-efficacy, lack of emotional support and social acknowledgement are higher among adults victimised in the year after the COVID-19 outbreak compared with adults victimised in a similar period before the outbreak. Also, to compare symptoms, problems and support within non-victims during the same period.

Data was extracted from four surveys of the VICTIMS study (March 2018, 2019, 2020, 2021), based on a random sample of the Dutch population. Multivariate logistic regression analyses and mixed-effects models were used to examine differences between the two victim groups (2019: n = 421, 2021: n = 319) and non-victims (n = 3245).

Adults victimised after the outbreak more often had PTSD, anxiety and depression symptoms, general mental health problems and lower coping self-efficacy than those victimised before. They did not differ in lack of support and acknowledgement. Both victim groups differed from non-victims, where mental health problems and lack of support levels were much lower and almost stable.

The COVID-19 pandemic had a negative impact on the mental health and coping self-efficacy levels of victims, whereas mental health problems among non-victims remained virtually stable. Mental healthcare workers, general practitioners and victim services should take this impact into account.

We have developed the bispectral electroencephalography (BSEEG) method for detection of delirium and prediction of poor outcomes.

To improve the BSEEG method by introducing a new EEG device.

In a prospective cohort study, EEG data were obtained and BSEEG scores were calculated. BSEEG scores were filtered on the basis of standard deviation (s.d.) values to exclude signals with high noise. Both non-filtered and s.d.-filtered BSEEG scores were analysed. BSEEG scores were compared with the results of three delirium screening scales: the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU), the Delirium Rating Scale-Revised-98 (DRS) and the Delirium Observation Screening Scale (DOSS). Additionally, the 365-day mortalities and the length of stay (LOS) in the hospital were analysed.

We enrolled 279 elderly participants and obtained 620 BSEEG recordings; 142 participants were categorised as BSEEG-positive, reflecting slower EEG activity. BSEEG scores were higher in the CAM-ICU-positive group than in the CAM-ICU-negative group. There were significant correlations between BSEEG scores and scores on the DRS and the DOSS. The mortality rate of the BSEEG-positive group was significantly higher than that of the BSEEG-negative group. The LOS of the BSEEG-positive group was longer compared with that of the BSEEG-negative group. BSEEG scores after s.d. filtering showed stronger correlations with delirium screening scores and more significant prediction of mortality.

We confirmed the usefulness of the BSEEG method for detection of delirium and of delirium severity, and prediction of patient outcomes with a new EEG device.

Little is known about methylphenidate (MPH) use and mortality outcomes.

To investigate the association between MPH use and mortality among children with an attention-deficit hyperactivity disorder (ADHD) diagnosis.

This population-based cohort study analysed data from Taiwan's National Health Insurance Research Database (NHIRD). A total of 68 096 children and adolescents aged 4–17 years with an ADHD diagnosis and prescribed MPH between 2000 and 2010 were compared with 68 096 without an MPH prescription, matched on age, gender and year of first ADHD diagnosis. All participants were followed to death, migration, withdrawal from the National Health Insurance programme or 31 December 2013. MPH prescriptions were measured on a yearly basis during the study period, and the association between MPH use and mortality was analysed using a repeated-measures time-dependent Cox regression model. The outcome measures included all-cause, unnatural-cause (including suicide, accident and homicide) and natural-cause mortality, obtained from linkage to the National Mortality Register in Taiwan.

The MPH group had lower unadjusted all-cause, natural-, unnatural- and accident-cause mortality than the comparison group. After controlling for potential confounders, MPH use was associated with a significantly lower all-cause mortality (adjusted hazard ratio AHR = 0.81, 95% CI 0.67–0.98, P = 0.027), delayed use of MPH was associated with higher mortality (AHR = 1.05, 95% CI 1.01–1.09) and longer MPH use was associated with lower mortality (AHR = 0.83, 95% CI 0.70–0.98).

MPH use is associated with a reduced overall mortality in children with ADHD in this cohort study, but unmeasured confounding cannot be excluded absolutely.

Exposure to parental suicide has been associated with increased risk for suicide and suicide attempts, although the strength of this association is unclear as evidence remains inconsistent.

To quantify this risk using meta-analysis and identify potential effect modifiers.

A systematic search in PubMed, PsycInfo and Embase databases to 2020 netted 3614 articles. Inclusion criteria were: observation of history of parental death by suicide, comparison with non-exposed populations and definition of suicide and suicide attempt according to standardised criteria. We focused on population-based studies. The primary outcome was the pooled relative risk (RR) for incidence of suicide attempt and suicide in offspring of a parent who died by suicide compared with offspring of two living parents. Additionally, we compared the RR for attempted and completed suicide after parental suicide with the RR for attempted and completed suicide after parental death by other causes.

Twenty studies met our inclusion criteria. Offspring exposed to parental suicide were more likely to die by suicide (RR = 2.97, 95% CI 2.50–3.53) and attempt suicide (RR = 1.76, 95% CI 1.58–1.96) than offspring of two living parents. Furthermore, their risk of dying by or attempting suicide was significantly higher compared with offspring bereaved by other causes of death.

The experience of losing a parent to suicide is a strong and independent risk factor for suicidal behaviour in offspring. Our findings highlight the need for prevention strategies, outreach programmes and support interventions that target suicide-related outcomes in the exposed population.

The COVID-19 pandemic has disrupted lives and livelihoods, and people already experiencing mental ill health may have been especially vulnerable.

Quantify mental health inequalities in disruptions to healthcare, economic activity and housing.

We examined data from 59 482 participants in 12 UK longitudinal studies with data collected before and during the COVID-19 pandemic. Within each study, we estimated the association between psychological distress assessed pre-pandemic and disruptions since the start of the pandemic to healthcare (medication access, procedures or appointments), economic activity (employment, income or working hours) and housing (change of address or household composition). Estimates were pooled across studies.

Across the analysed data-sets, 28% to 77% of participants experienced at least one disruption, with 2.3–33.2% experiencing disruptions in two or more domains. We found 1 s.d. higher pre-pandemic psychological distress was associated with (a) increased odds of any healthcare disruptions (odds ratio (OR) 1.30, 95% CI 1.20–1.40), with fully adjusted odds ratios ranging from 1.24 (95% CI 1.09–1.41) for disruption to procedures to 1.33 (95% CI 1.20–1.49) for disruptions to prescriptions or medication access; (b) loss of employment (odds ratio 1.13, 95% CI 1.06–1.21) and income (OR 1.12, 95% CI 1.06 –1.19), and reductions in working hours/furlough (odds ratio 1.05, 95% CI 1.00–1.09) and (c) increased likelihood of experiencing a disruption in at least two domains (OR 1.25, 95% CI 1.18–1.32) or in one domain (OR 1.11, 95% CI 1.07–1.16), relative to no disruption. There were no associations with housing disruptions (OR 1.00, 95% CI 0.97–1.03).

People experiencing psychological distress pre-pandemic were more likely to experience healthcare and economic disruptions, and clusters of disruptions across multiple domains during the pandemic. Failing to address these disruptions risks further widening mental health inequalities.

Exposure to maternal major depressive disorder (MDD) bears long-term negative consequences for children's well-being; to date, no research has examined how exposure at different stages of development differentially affects brain functioning.

Utilising a unique cohort followed from birth to preadolescence, we examined the effects of early versus later maternal MDD on default mode network (DMN) connectivity.

Maternal depression was assessed at birth and ages 6 months, 9 months, 6 years and 10 years, to form three groups: children of mothers with consistent depression from birth to 6 years of age, which resolved by 10 years of age; children of mothers without depression; and children of mothers who were diagnosed with MDD in late childhood. In preadolescence, we used magnetoencephalography and focused on theta rhythms, which characterise the developing brain.

Maternal MDD was associated with disrupted DMN connectivity in an exposure-specific manner. Early maternal MDD decreased child connectivity, presenting a profile typical of early trauma or chronic adversity. In contrast, later maternal MDD was linked with tighter connectivity, a pattern characteristic of adult depression. Aberrant DMN connectivity was predicted by intrusive mothering in infancy and lower mother–child reciprocity and child empathy in late childhood, highlighting the role of deficient caregiving and compromised socio-emotional competencies in DMN dysfunction.

The findings pinpoint the distinct effects of early versus later maternal MDD on the DMN, a core network sustaining self-related processes. Results emphasise that research on the influence of early adversity on the developing brain should consider the developmental stage in which the adversity occured.

Eating disorders are stigmatised. Little is known about whether stigma has decreased over time and which groups hold more stigmatising beliefs.

To explore whether stigma towards eating disorders has changed between 1998 and 2008 and whether it varies by sociodemographic characteristics.

We used the Office for National Statistics Omnibus surveys 1998 and 2008. As outcomes, we selected four questions eliciting participants’ views on issues of blame and ability to recover, and compared their mean scores across eating disorders, depression and alcohol dependence in both years. We used multivariable linear regressions to investigate associations between sociodemographic characteristics and each stigma domain.

In total, 2720 participants had data on all variables of interest. Compared with 1998, in 2008 stigmatising views towards eating disorders improved. In both years, participants believed it was easier to recover from eating disorders than depression or alcohol dependence. Respondents believed people with eating disorders were more to blame for their condition than those with depression, but less than those with alcohol dependence. Men, those with less formal education, and those from ethnic minority backgrounds were more likely to place greater blame on individuals for their mental illness. Men were more likely than women to think it was possible to recover from an eating disorder.

Stigmatising attitudes towards people with eating disorders have improved over time, but are still greater than those observed for other mental illnesses. Improving eating disorder mental health literacy could help to reduce these negative views and lead to improved quality of life, greater help-seeking and better prognosis.

Autism spectrum disorder (ASD) is a highly heterogeneous disorder that affects nearly 1 in 189 females and 1 in 42 males. However, the neurobiological basis of gender differences in ASD is poorly understood, as most studies have neglected females and used methods ill-suited to capture such differences.

To identify robust functional brain organisation markers that distinguish between females and males with ASD and predict symptom severity.

We leveraged multiple neuroimaging cohorts (ASD n = 773) and developed a novel spatiotemporal deep neural network (stDNN), which uses spatiotemporal convolution on functional magnetic resonance imaging data to distinguish between groups.

stDNN achieved consistently high classification accuracy in distinguishing between females and males with ASD. Notably, stDNN trained to distinguish between females and males with ASD could not distinguish between neurotypical females and males, suggesting that there are gender differences in the functional brain organisation in ASD that differ from normative gender differences. Brain features associated with motor, language and visuospatial attentional systems reliably distinguished between females and males with ASD. Crucially, these results were observed in a large multisite cohort and replicated in a fully independent cohort. Furthermore, brain features associated with the motor network's primary motor cortex node predicted the severity of restricted/repetitive behaviours in females but not in males with ASD.

Our replicable findings reveal that the brains of females and males with ASD are functionally organised differently, contributing to their clinical symptoms in distinct ways. They inform the development of gender-specific diagnoses and treatment strategies for ASD, and ultimately advance precision psychiatry.