Impact of anxiety and depression across childhood and adolescence on adverse outcomes in young adulthood: a UK birth cohort study.

BACKGROUND: Little is still known about the long-term impact of childhood and adolescent persistent depression and anxiety in adulthood. AIMS: To investigate the impact of persistent anxiety, depression, and comorbid anxiety and depression across childhood and adolescence on the development of multiple adverse outcomes in young adulthood. METHOD: This study used data from 8122 participants in the Avon Longitudinal Study of Parents and Children cohort. The Development and Well-Being Assessment (DAWBA) examined child anxiety and depression symptomatology. The DAWBA generalised anxiety and mood subscales at 8, 10 and 13 years were selected, and a measure of comorbid anxiety and depression symptoms was created at each time point. Further, several mental and physical health, substance misuse and education/employment problems were assessed at 24 years. Latent class growth analyses were used to detect trajectories of anxiety, depression and comorbid anxiety and depression; and logistic regression to examine how persistent anxiety, depression or both were associated with adverse outcomes at 24 years. RESULTS: All three classes with persistent anxiety, depression or both were significantly associated with presenting with any mental health problems and any education/employment problem. Persistent high levels of depression and high levels of comorbid anxiety and depression, but not persistent high anxiety, were significantly associated with any physical health problem. High levels of comorbid anxiety and depression was the only DAWBA domain significantly associated with substance misuse; and overall, this was the domain that exerted the greatest negative impact, as it presented the highest odd ratio values. CONCLUSIONS: Children and adolescents with comorbid anxiety and depression are at the highest risk for having more adverse outcomes at 24 years.

Prediction models in first-episode psychosis: systematic review and critical appraisal.

BACKGROUND: 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. AIMS: We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP. METHOD: 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. RESULTS: 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. CONCLUSIONS: 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.

Indicated prevention interventions for anxiety in children and adolescents: a review and meta-analysis of school-based programs.

Anxiety disorders are among the most common youth mental health disorders. Early intervention can reduce elevated anxiety symptoms. School-based interventions exist but it is unclear how effective targeted approaches are for reducing symptoms of anxiety. This review and meta-analysis aimed to determine the effectiveness of school-based indicated interventions for symptomatic children and adolescents. The study was registered with PROSPERO [CRD42018087628]. We searched MEDLINE, EMBASE, PsycINFO, and the Cochrane Library for randomised-controlled trials comparing indicated programs for child and adolescent (5-18 years) anxiety to active or inactive control groups. Data were extracted from papers up to December 2019. The primary outcome was efficacy (mean change in anxiety symptom scores). Sub-group and sensitivity analyses explored intervention intensity and control type. We identified 20 studies with 2076 participants. Eighteen studies were suitable for meta-analysis. A small positive effect was found for indicated programs compared to controls on self-reported anxiety symptoms at post-test (g = - 0.28, CI = - 0.50, - 0.05, k = 18). This benefit was maintained at 6 (g = - 0.35, CI = - 0.58, - 0.13, k = 9) and 12 months (g = - 0.24, CI = - 0.48, 0.00, k = 4). Based on two studies, > 12 month effects were very small (g = - 0.01, CI = - 0.38, 0.36). No differences were found based on intervention intensity or control type. Risk of bias and variability between studies was high (I2 = 78%). Findings show that school-based indicated programs for child and adolescent anxiety can produce small beneficial effects, enduring for up to 12 months. Future studies should include long-term diagnostic assessments.

Treatment of depression in schizophrenia: systematic review and meta-analysis.

BackgroundDepression in schizophrenia predicts poor outcomes, including suicide, yet the effectiveness of antidepressants for its treatment remains uncertain.AimsTo synthesise the evidence of the effectiveness of antidepressants for the treatment of depression in schizophrenia.MethodMultiple databases were searched and inclusion criteria included participants aged over 18 years with schizophrenia or related psychosis with a depressive episode. Papers were quality assessed used the Cochrane risk bias tool. Meta-analyses were performed for risk difference and standardised mean difference of all antidepressants, antidepressant class and individual antidepressant where sufficient studies allowed.ResultsA total of 26 moderate- to low-quality trials met inclusion criteria. In meta-analysis a significant risk difference was found in favour of antidepressant treatment, with a number needed to treat of 5 (95% CI 4-9). Studies using tools specifically designed to assess depression in schizophrenia showed a larger effect size. However, after sensitivity analysis standardised mean difference of all antidepressants did not show a statistically significant improvement in depression score at end-point, neither did any individual antidepressant class.ConclusionsAntidepressants may be effective for the treatment of depression in schizophrenia, however, the evidence is mixed and conclusions must be qualified by the small number of low- or moderate-quality studies. Further sufficiently powered, high-quality studies are needed.

Predicting treatment resistance from first-episode psychosis using routinely collected clinical information.

Around a quarter of people who experience a first episode of psychosis (FEP) will develop treatment-resistant schizophrenia (TRS), but there are currently no established clinically useful methods to predict this from baseline. We aimed to explore the predictive potential for clozapine use as a proxy for TRS of routinely collected, objective biomedical predictors at FEP onset, and to externally validate the model in a separate clinical sample of people with FEP. We developed and externally validated a forced-entry logistic regression risk prediction Model fOr cloZApine tReaTment, or MOZART, to predict up to 8-year risk of clozapine use from FEP using routinely recorded information including age, sex, ethnicity, triglycerides, alkaline phosphatase levels, and lymphocyte counts. We also produced a least-absolute shrinkage and selection operator (LASSO) based model, additionally including neutrophil count, smoking status, body mass index, and random glucose levels. The models were developed using data from two UK psychosis early intervention services (EIS) and externally validated in another UK EIS. Model performance was assessed via discrimination and calibration. We developed the models in 785 patients, and validated externally in 1,110 patients. Both models predicted clozapine use well at internal validation (MOZART: C 0.70; 95%CI 0.63,0.76; LASSO: 0.69; 95%CI 0.63,0.77). At external validation, discrimination performance reduced (MOZART: 0.63; 0.58,0.69; LASSO: 0.64; 0.58,0.69) but recovered after re-estimation of the lymphocyte predictor (C: 0.67; 0.62,0.73). Calibration plots showed good agreement between observed and predicted risk in the forced-entry model. We also present a decision-curve analysis and an online data visualisation tool. The use of routinely collected clinical information including blood-based biomarkers taken at FEP onset can help to predict the individual risk of clozapine use, and should be considered equally alongside other potentially useful information such as symptom scores in large-scale efforts to predict psychiatric outcomes.

Anxiety disorders across middle childhood and early adolescence in a UK population-based cohort.

Background: Patterns of development and underlying factors explaining anxiety disorders in children and adolescents are under-researched, despite their high prevalence, impact and associations with other mental disorders. We aimed to a] understand the pattern and persistence of specific anxiety disorders; b] examine differing trajectories of symptoms of specific anxiety disorders and; c] examine socio-demographic and health-related predictors of persistent anxiety disorder-specific symptoms, across middle childhood to early adolescence. Methods: The current study used data from 8122 participants in the Avon Longitudinal Study of Parents and Children birth cohort. The Development and Wellbeing Assessment questionnaire was administered to parents to capture child and adolescent anxiety total scores and DAWBA-derived diagnoses. Separation anxiety, specific phobia, social anxiety, acute stress reaction, and generalized anxiety at 8, 10 and 13 years were selected. Further, we included the following socio-demographic and health-related predictors: sex, birth weight, sleep difficulties at 3.5 years, ethnicity, family adversity, maternal age at birth, maternal postnatal anxiety, maternal postnatal depression, maternal bonding, maternal socio-economic status and maternal education. Results: Different anxiety disorders presented different prevalence and patterns of development over time. Further, latent class growth analyses yielded a trajectory characterized by individuals with persistent high levels of anxiety across childhood and adolescence; for specific phobia (high = 5.8%; moderate = 20.5%; low = 73.6%), social anxiety (high = 3.4%; moderate = 12.1%; low = 84.5%), acute stress reaction (high = 1.9%; low = 98.1%) and generalized anxiety (high = 5.4%; moderate = 21.7%; low = 72.9%). Finally, the risk factors associated with each of the persistent high levels of anxiety disorders were child sleeping difficulties and postnatal maternal depression and anxiety. Conclusions: Our findings show that a small group of children and young adolescents continue to suffer from frequent and severe anxiety. When considering treatment strategies for anxiety disorders in this group, children's sleep difficulties and postnatal maternal depression and anxiety need to be assessed as these may predict a more prolonged and severe course of illness.

Persistent Childhood and Adolescent Anxiety and Risk for Psychosis: A Longitudinal Birth Cohort Study.

BACKGROUND: Persistent anxiety in childhood and adolescence could represent a novel treatment target for psychosis, potentially targeting activation of stress pathways and secondary nonresolving inflammatory response. Here, we examined the association between persistent anxiety through childhood and adolescence with individuals with psychotic experiences (PEs) or who met criteria for psychotic disorder (PD) at age 24 years. We also investigated whether C-reactive protein mediated any association. METHODS: Data from the Avon Longitudinal Study of Parents and Children (ALSPAC) were available in 8242 children at age 8 years, 7658 at age 10 years, 6906 at age 13 years, and 3889 at age 24 years. The Development and Well-Being Assessment was administered to capture child and adolescent anxiety. We created a composite score of generalized anxiety at ages 8, 10, and 13. PEs and PD were assessed at age 24, derived from the Psychosis-like Symptoms Interview. The mean of C-reactive protein at ages 9 and 15 years was used as a mediator. RESULTS: Individuals with persistent high levels of anxiety were more likely to develop PEs (odds ratio 2.02, 95% CI 1.26-3.23, p = .003) and PD at age 24 (odds ratio 4.23, 95% CI 2.27-7.88, p < .001). The mean of C-reactive protein at ages 9 and 15 mediated the associations of persistent anxiety with PEs (bias-corrected estimate -0.001, p = .013) and PD (bias-corrected estimate 0.001, p = .003). CONCLUSIONS: Persistent high levels of anxiety through childhood and adolescence could be a risk factor for psychosis. Persistent anxiety is potentially related to subsequent psychosis via activation of stress hormones and nonresolving inflammation. These results contribute to the potential for preventive interventions in psychosis, with the novel target of early anxiety.

Trait related aberrant connectivity in clinically stable patients with schizophrenia: A seed based resting state fMRI study.

Aberrant resting-state connectivity within and between the Default Mode Network, the Executive Control Network, and the Salience Network is well-established in schizophrenia. Meta-analyses have identified that bilateral lingual gyrus is as the only region showing hyperactivity in schizophrenia and there are reports of increased connectivity between the lingual gyrus and other brain regions in schizophrenia. It is not clear whether these abnormalities represent state or trait markers of the illness, i.e., if they are only present during the acute phase of the illness (state) or if they reflect a predisposition to schizophrenia (trait). In this study, we used a seed-based functional connectivity analysis to investigate brain networks in schizophrenia patients who are in the stable phase of their illness and assess functional connectivity using seeds in the lingual gyrus, the posterior cingulate, the right dorsolateral prefrontal cortex (dlPFC), the right anterior insula (rAI) and the right orbital frontoinsula. Twenty patients with schizophrenia in a stable phase of their illness (as defined by the course of illness and Signs and Symptoms of Psychotic Illness (SSPI) scores) and 20 age and sex-matched healthy controls underwent resting-state functional Magnetic Resonance Imaging (rs-fMRI). Data was analysed using the Data Processing Assistant for Resting-State fMRI Advanced Edition (DPARSFA) V3.1 ( http://rfmri.org/DPARSF ) and the statistical parametric mapping software 8 (SPM8). Compared with healthy controls, patients with schizophrenia showed increased connectivity between the left lingual gyrus and the middle frontal gyrus, and the cingulate cortex. Lingual gyrus hyper-connectivity may be a stable trait neuroimaging marker for schizophrenia. Our findings suggest that aberrant connectivity in major resting-state networks may not be present after the acute illness has stabilised.

Safeguarding adolescent mental health in India (SAMA): study protocol for codesign and feasibility study of a school systems intervention targeting adolescent anxiety and depression in India.

INTRODUCTION: Symptoms of anxiety and depression in Indian adolescents are common. Schools can be opportune sites for delivery of mental health interventions. India, however, is without a evidence-based and integrated whole-school mental health approach. This article describes the study design for the safeguarding adolescent mental health in India (SAMA) project. The aim of SAMA is to codesign and feasibility test a suite of multicomponent interventions for mental health across the intersecting systems of adolescents, schools, families and their local communities in India. METHODS AND ANALYSIS: Our project will codesign and feasibility test four interventions to run in parallel in eight schools (three assigned to waitlist) in Bengaluru and Kolar in Karnataka, India. The primary aim is to reduce the prevalence of adolescent anxiety and depression. Codesign of interventions will build on existing evidence and resources. Interventions for adolescents at school will be universal, incorporating curriculum and social components. Interventions for parents and teachers will target mental health literacy, and also for teachers, training in positive behaviour practices. Intervention in the school community will target school climate to improve student mental health literacy and care. Intervention for the wider community will be via adolescent-led films and social media. We will generate intervention cost estimates, test outcome measures and identify pathways to increase policy action on the evidence. ETHICS AND DISSEMINATION: Ethical approval has been granted by the National Institute of Mental Health Neurosciences Research Ethics Committee (NIMHANS/26th IEC (Behv Sc Div/2020/2021)) and the University of Leeds School of Psychology Research Ethics Committee (PSYC-221). Certain data will be available on a data sharing site. Findings will be disseminated via peer-reviewed journals and conferences.

The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples.

Background: Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors. Methods: To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16-35y without MetS at baseline who had 1-6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered. Findings: We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68-0.79, partial-model C=0.68, 95% C.I., 0.62-0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66-0.78; partial-model C=0.66, 95% C.I., 0.60-0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration. Interpretation: The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality. Funding: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marques de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R).For the Spanish and French translation of the abstract see Supplementary Materials section.

Longitudinal Associations Between Cognitive Deficits in Childhood and Psychopathological Symptoms in Adolescence and Young Adulthood.

Importance: Cognitive deficits are core features of mental disorders and are important in predicting long-term prognosis. However, it is still unknown whether individual patterns of cognitive deficits predate specific mental disorders. Objective: To investigate the specificity of the associations of attention, working memory, and inhibition in childhood with borderline personality disorder (BPD), psychosis, depression, and hypomania in adolescence and young adulthood. Design, Setting, and Participants: This cohort study obtained data from the Avon Longitudinal Study of Parents and Children in the United Kingdom. All pregnant women resident in Avon, United Kingdom, with an expected date of delivery from April 1, 1991, and December 31, 1992, were eligible. Data analysis was conducted from April 1 to September 30, 2020. The sample initially comprised 13 988 participants who were alive at 1 year of age. For this study, data were available for 6333 individuals reporting on any psychopathological measure at ages 11 to 12 years, 4903 individuals at ages 17 to 18 years, and 2963 individuals at 22 to 23 years. Exposures: Sustained attention, selective attention, and attentional control were assessed with the Test of Everyday Attention for Children at age 8 years, and working memory and inhibition were assessed at age 10 years with the Counting Span Task and the stop-signal paradigm, respectively. Main Outcomes and Measures: Symptoms of BPD were assessed at ages 11 to 12 years, psychotic experiences and depression were examined at ages 17 to 18 years, and hypomania was examined at ages 22 to 23 years. Results: Among 5315 individuals included in the statistical analysis, 2551 (48.0%) were male and 2764 (52.0) were female. Higher sustained attention at 8 years was associated with decreased risk of BPD symptoms at ages 11 to 12 years (adjusted odds ratio [aOR], 0.964; 95% CI, 0.933-0.996; P = .03), better performance on inhibition at age 10 years with decreased risk of psychotic experiences at ages 17 to 18 years (aOR, 0.938; 95% CI, 0.890-0.989; P = .02), higher sustained attention at age 8 years with decreased risk of depressive symptoms at ages 17 to 18 years (aOR, 0.969; 95% CI 0.938-0.9997; P = .048), and better performance in working memory at age 10 years with decreased risk of hypomania symptoms at ages 22 to 23 years (aOR, 0.694; 95% CI, 0.529-0.911; P = .008). After controlling for potential psychopathological overlay, all the associations remained, except for working memory and hypomania. Higher sustained attention at age 8 years was associated with decreased risk of BPD symptoms at ages 11 to 12 years (ß = -0.05; P < .001) and of depression at ages 17 to 18 years (ß = -0.03; P = .04), and better performance in inhibition at age 10 years was associated with decreased risk of psychotic experiences at ages 17 to 18 years (ß = -0.03; P = .04). Conclusions and Relevance: These findings suggest that specific cognitive deficits in childhood are distinctively associated with different psychopathological symptoms in young people. Furthermore, these results suggest the potential of early cognitive interventions in childhood as a way of modifying or attenuating risk for subsequent psychopathological symptoms.

Development and external validation of the Psychosis Metabolic Risk Calculator (PsyMetRiC): a cardiometabolic risk prediction algorithm for young people with psychosis.

BACKGROUND: Young people with psychosis are at high risk of developing cardiometabolic disorders; however, there is no suitable cardiometabolic risk prediction algorithm for this group. We aimed to develop and externally validate a cardiometabolic risk prediction algorithm for young people with psychosis. METHODS: We developed the Psychosis Metabolic Risk Calculator (PsyMetRiC) to predict up to 6-year risk of incident metabolic syndrome in young people (aged 16-35 years) with psychosis from commonly recorded information at baseline. We developed two PsyMetRiC versions using the forced entry method: a full model (including age, sex, ethnicity, body-mass index, smoking status, prescription of a metabolically active antipsychotic medication, HDL concentration, and triglyceride concentration) and a partial model excluding biochemical results. PsyMetRiC was developed using data from two UK psychosis early intervention services (Jan 1, 2013, to Nov 4, 2020) and externally validated in another UK early intervention service (Jan 1, 2012, to June 3, 2020). A sensitivity analysis was done in UK birth cohort participants (aged 18 years) who were at risk of developing psychosis. Algorithm performance was assessed primarily via discrimination (C statistic) and calibration (calibration plots). We did a decision curve analysis and produced an online data-visualisation app. FINDINGS: 651 patients were included in the development samples, 510 in the validation sample, and 505 in the sensitivity analysis sample. PsyMetRiC performed well at internal (full model: C 0·80, 95% CI 0·74-0·86; partial model: 0·79, 0·73-0·84) and external validation (full model: 0·75, 0·69-0·80; and partial model: 0·74, 0·67-0·79). Calibration of the full model was good, but there was evidence of slight miscalibration of the partial model. At a cutoff score of 0·18, in the full model PsyMetRiC improved net benefit by 7·95% (sensitivity 75%, 95% CI 66-82; specificity 74%, 71-78), equivalent to detecting an additional 47% of metabolic syndrome cases. INTERPRETATION: We have developed an age-appropriate algorithm to predict the risk of incident metabolic syndrome, a precursor of cardiometabolic morbidity and mortality, in young people with psychosis. PsyMetRiC has the potential to become a valuable resource for early intervention service clinicians and could enable personalised, informed health-care decisions regarding choice of antipsychotic medication and lifestyle interventions. FUNDING: National Institute for Health Research and Wellcome Trust.

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach.

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery.

Development and Validation of a Nonremission Risk Prediction Model in First-Episode Psychosis: An Analysis of 2 Longitudinal Studies.

Psychosis is a major mental illness with first onset in young adults. The prognosis is poor in around half of the people affected, and difficult to predict. The few tools available to predict prognosis have major weaknesses which limit their use in clinical practice. We aimed to develop and validate a risk prediction model of symptom nonremission in first-episode psychosis. Our development cohort consisted of 1027 patients with first-episode psychosis recruited between 2005 and 2010 from 14 early intervention services across the National Health Service in England. Our validation cohort consisted of 399 patients with first-episode psychosis recruited between 2006 and 2009 from a further 11 English early intervention services. The one-year nonremission rate was 52% and 54% in the development and validation cohorts, respectively. Multivariable logistic regression was used to develop a risk prediction model for nonremission, which was externally validated. The prediction model showed good discrimination C-statistic of 0.73 (0.71, 0.75) and adequate calibration with intercept alpha of 0.12 (0.02, 0.22) and slope beta of 0.98 (0.85, 1.11). Our model improved the net-benefit by 15% at a risk threshold of 50% compared to the strategy of treating all, equivalent to 15 more detected nonremitted first-episode psychosis individuals per 100 without incorrectly classifying remitted cases. Once prospectively validated, our first episode psychosis prediction model could help identify patients at increased risk of nonremission at initial clinical contact.

The Psychopathology and Neuroanatomical Markers of Depression in Early Psychosis.

Depression frequently occurs in first-episode psychosis (FEP) and predicts longer-term negative outcomes. It is possible that this depression is seen primarily in a distinct subgroup, which if identified could allow targeted treatments. We hypothesize that patients with recent-onset psychosis (ROP) and comorbid depression would be identifiable by symptoms and neuroanatomical features similar to those seen in recent-onset depression (ROD). Data were extracted from the multisite PRONIA study: 154 ROP patients (FEP within 3 months of treatment onset), of whom 83 were depressed (ROP+D) and 71 who were not depressed (ROP-D), 146 ROD patients, and 265 healthy controls (HC). Analyses included a (1) principal component analysis that established the similar symptom structure of depression in ROD and ROP+D, (2) supervised machine learning (ML) classification with repeated nested cross-validation based on depressive symptoms separating ROD vs ROP+D, which achieved a balanced accuracy (BAC) of 51%, and (3) neuroanatomical ML-based classification, using regions of interest generated from ROD subjects, which identified BAC of 50% (no better than chance) for separation of ROP+D vs ROP-D. We conclude that depression at a symptom level is broadly similar with or without psychosis status in recent-onset disorders; however, this is not driven by a separable depressed subgroup in FEP. Depression may be intrinsic to early stages of psychotic disorder, and thus treating depression could produce widespread benefit.

Cognitive Behavioral Therapy for Depression, Anxiety, and Stress in Caregivers of Dementia Patients: A Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: There is limited evidence for the efficacy of cognitive behavioral therapy (CBT) in managing psychological morbidities in caregivers of dementia patients. To evaluate changes in dementia caregivers' depression, anxiety, and stress following CBT. Also to assess quality of life, intervention adherence/satisfaction and therapy effectiveness using different formats, frequencies, and delivery methods. RESEARCH DESIGN AND METHODS: Studies were identified through electronic bibliographic searches (MEDLINE, EMBASE, CINAHL, PsycINFO, and the Cochrane Library) and from gray literature (Conference Proceedings Citation Index and clinicaltrials.gov). Data were pooled for meta-analysis. RESULTS: Twenty-five studies were included. Depression (standardized mean difference [SMD] = -0.34; 95% confidence interval [CI] -0.47 to -0.21; p < .001) and stress (SMD = -0.36; 95% CI: -0.52 to -0.20; p < .001) were significantly reduced after CBT, relative to comparator groups, while anxiety was not (SMD = 0.10; 95% CI: -0.18 to 0.39; p = .47). A subgroup analysis demonstrated that statistically significant reductions in depression and stress were limited to group, but not individual, formats. An additional subgroup analysis revealed that eight CBT sessions or fewer were equally effective as more than eight sessions at significantly reducing depression and stress, relative to comparator groups. Furthermore, analysis with independent samples t-tests demonstrated no statistically significant differences between mean changes in depression (MD = 0.79; 95% CI: -0.45 to 2.03; p = .21) and stress (MD = 0.21; 95% CI: -1.43 to 1.85; p = .80) when directly comparing CBT groups of ≤8 and >8 sessions. DISCUSSION AND IMPLICATIONS: Group CBT provides small but significant benefits to caregivers' depression and stress. Therapy cost-effectiveness may be improved by limiting therapy to group formats and eight sessions.

Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis. METHODS: In this machine learning approach, we applied supervised machine learning, using regularised regression and nested leave-one-site-out cross-validation, to baseline clinical data from the English Evaluating the Development and Impact of Early Intervention Services (EDEN) study (n=1027), to develop and internally validate prediction models at 1-year follow-up. We assessed four binary outcomes that were recorded at 1 year: symptom remission, social recovery, vocational recovery, and quality of life (QoL). We externally validated the prediction models by selecting from the top predictor variables identified in the internal validation models the variables shared with the external validation datasets comprised of two Scottish longitudinal cohort studies (n=162) and the OPUS trial, a randomised controlled trial of specialised assertive intervention versus standard treatment (n=578). FINDINGS: The performance of prediction models was robust for the four 1-year outcomes of symptom remission (area under the receiver operating characteristic curve [AUC] 0·703, 95% CI 0·664-0·742), social recovery (0·731, 0·697-0·765), vocational recovery (0·736, 0·702-0·771), and QoL (0·704, 0·667-0·742; p<0·0001 for all outcomes), on internal validation. We externally validated the outcomes of symptom remission (AUC 0·680, 95% CI 0·587-0·773), vocational recovery (0·867, 0·805-0·930), and QoL (0·679, 0·522-0·836) in the Scottish datasets, and symptom remission (0·616, 0·553-0·679), social recovery (0·573, 0·504-0·643), vocational recovery (0·660, 0·610-0·710), and QoL (0·556, 0·481-0·631) in the OPUS dataset. INTERPRETATION: In our machine learning analysis, we showed that prediction models can reliably and prospectively identify poor remission and recovery outcomes at 1 year for patients with first-episode psychosis using baseline clinical variables at first clinical contact. FUNDING: Lundbeck Foundation.

The relapse rate and predictors of relapse in patients with first-episode psychosis following discontinuation of antipsychotic medication.

BACKGROUND: To determine the "real world" relapse rate in patients with first-episode psychosis (FEP) who had discontinued antipsychotic medication and identify socio-demographic and clinical factors associated with the risk of relapse. METHODS: Quantitative data were obtained via case-note review on 63 patients with FEP who had discontinued antipsychotic medication from Birmingham Early Intervention Service between 2012 and 2015. The follow-up period was until either: an occurrence of a relapse; end of 12-month study period; end of patient's case-note record. Relapse was defined as a return of symptoms requiring one of the following: home treatment, hospital admission or was based on clinical teams' decision as having a relapse. A pro-forma targeted pre-defined socio-demographic and clinical factors. Survival analysis was undertaken to estimate the 12-month relapse rate following discontinuation of antipsychotics and Cox regression performed to identify relapse predictors. RESULTS: The Kaplan-Meier 12-month relapse estimate was 67% (95% confidence interval, 54%, 80%). Significant factors (P < .05) independently associated with an increased risk of relapse following discontinuation of antipsychotic medication were: male gender, not being in education, employment or training (NEET) and number of previous psychiatric hospital admissions. CONCLUSIONS: Relapse is common after discontinuation of antipsychotic medication following recovery from a FEP. It is important that patients who wish to discontinue their medication are informed of the high relapse rates and the associated risks. Furthermore, male patients, patients with NEET status and those who have had previous hospital admissions may require closer monitoring.

Aberrant salience network functional connectivity in auditory verbal hallucinations: a first episode psychosis sample.

Auditory verbal hallucinations (AVH) often lead to distress and functional disability, and are frequently associated with psychotic illness. Previously both state and trait magnetic resonance imaging (MRI) studies of AVH have identified activity in brain regions involving auditory processing, language, memory and areas of default mode network (DMN) and salience network (SN). Current evidence is clouded by research mainly in participants on long-term medication, with chronic illness and by choice of seed regions made 'a priori'. Thus, the aim of this study was to elucidate the intrinsic functional connectivity in patients presenting with first episode psychosis (FEP). Resting state functional MRI data were available from 18 FEP patients, 9 of whom also experienced AVH of sufficient duration in the scanner and had symptom capture functional MRI (sc fMRI), together with 18 healthy controls. Symptom capture results were used to accurately identify specific brain regions active during AVH; including the superior temporal cortex, insula, precuneus, posterior cingulate and parahippocampal complex. Using these as seed regions, patients with FEP and AVH showed increased resting sb-FC between parts of the SN and the DMN and between the SN and the cerebellum, but reduced sb-FC between the claustrum and the insula, compared to healthy controls.It is possible that aberrant activity within the DMN and SN complex may be directly linked to impaired salience appraisal of internal activity and AVH generation. Furthermore, decreased intrinsic functional connectivity between the claustrum and the insula may lead to compensatory over activity in parts of the auditory network including areas involved in DMN, auditory processing, language and memory, potentially related to the complex and individual content of AVH when they occur.

Prevention of postnatal depression.

Postnatal depression is the most frequent psychiatric disorder seen after childbirth, with a prevalence rate of 10% to 15%. The women at risk need to be identified by a valid and reliable method, either using a screening instrument or an interview schedule. The preventive strategies need to have enough power to detect a clinically worthwhile effect to be considered useful in clinical practice. Many of the risk factors for developing postnatal depression are present during the pregnancy and immediate post-partum period. The risk factors for postnatal depression include depression or anxiety during pregnancy, experiencing stressful life events during pregnancy or the early puerperium, maternity blues, low levels of social support, past history of depression and poor marital adjustment. The antenatal and postnatal period provides an ideal opportunity to screen women for these risk factors. The women identified to be at risk can be identified, and preventive interventions can be implemented. Routine clinical practice can be improved to identify some of the women at risk by better communication between health professionals. There are no antenatal screening tools that have been shown to be of benefit in predicting postnatal depression. Edinburgh Postnatal Depression Scale is widely used in the postnatal period to screen for depression. The psychosocial interventions to prevent postnatal depression have not been shown to be beneficial and there is a dearth of psychopharmacological trials to make firm conclusions about their efficacy in preventing postnatal depression. Individualised psychosocial interventions aimed at the at-risk populations and initiated in the postnatal period appear to have some benefit in preventing postnatal depression. The focus of this article will be the risk factors associated with postnatal depression, screening methods and tools to identify those at risk of developing the disorder and the psychosocial, psychological and psychopharmacological interventions to prevent postnatal depression.