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.

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.

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.

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.

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.

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.

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 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.