Psychological Medicine最新文献

Background: The identification of predictors of treatment response is crucial for improving treatment outcome for children with anxiety disorders. Machine learning methods provide opportunities to identify combinations of factors that contribute to risk prediction models.

Methods: A machine learning approach was applied to predict anxiety disorder remission in a large sample of 2114 anxious youth (5-18 years). Potential predictors included demographic, clinical, parental, and treatment variables with data obtained pre-treatment, post-treatment, and at least one follow-up.

Results: All machine learning models performed similarly for remission outcomes, with AUC between 0.67 and 0.69. There was significant alignment between the factors that contributed to the models predicting two target outcomes: remission of all anxiety disorders and the primary anxiety disorder. Children who were older, had multiple anxiety disorders, comorbid depression, comorbid externalising disorders, received group treatment and therapy delivered by a more experienced therapist, and who had a parent with higher anxiety and depression symptoms, were more likely than other children to still meet criteria for anxiety disorders at the completion of therapy. In both models, the absence of a social anxiety disorder and being treated by a therapist with less experience contributed to the model predicting a higher likelihood of remission.

Conclusions: These findings underscore the utility of prediction models that may indicate which children are more likely to remit or are more at risk of non-remission following CBT for childhood anxiety.

Objective: To refine the knowledge on familial transmission, we examined the (shared) familial components among neurodevelopmental problems (i.e. two attention-deficit/hyperactivity-impulsivity disorder [ADHD] and six autism spectrum disorder [ASD] subdomains) and with aggressive behavior, depression, anxiety, and substance use.

Methods: Data were obtained from a cross-sectional study encompassing 37 688 participants across three generations from the general population. ADHD subdomains, ASD subdomains, aggressive behavior, depression, anxiety, and substance use were assessed. To evaluate familial (co-)aggregation, recurrence risk ratios (λ_R) were estimated using Cox proportional hazards models. The (shared) familiality (f²), which is closely related to (shared) heritability, was assessed using residual maximum likelihood-based variance decomposition methods. All analyses were adjusted for sex, age, and age².

Results: The familial aggregation and familiality of neurodevelopmental problems were moderate (λ_R = 2.40-4.04; f² = 0.22-0.39). The familial co-aggregation and shared familiality among neurodevelopmental problems (λ_R = 1.39-2.56; r_F = 0.52-0.94), and with aggressive behavior (λ_R = 1.79-2.56; r_F = 0.60-0.78), depression (λ_R = 1.45-2.29; r_F = 0.43-0.76), and anxiety (λ_R = 1.44-2.31; r_F = 0.62-0.84) were substantial. The familial co-aggregation and shared familiality between all neurodevelopmental problems and all types of substance use were weak (λ_R = 0.53-1.57; r_F = -0.06-0.35).

Conclusions: Neurodevelopmental problems belonging to the same disorder were more akin than cross-disorder problems. That said, there is a clear (shared) familial component to neurodevelopmental problems, in part shared with other psychiatric problems (except for substance use). This suggests that neurodevelopmental disorders, disruptive behavior disorders, and internalizing disorders share genetic and environmental risk factors.

Background: Determining whether the incidence of suicidal behavior during the COVID-19 pandemic changed for those with severe mental disorders is essential to ensure the provision of suicide preventive initiatives in the case of future health crises.

Methods: Using population-based registers, quarterly cohorts from the first quarter of 2018 (2018Q1) to 2021Q4 were formed including all Swedish-residents >10 years old. Interrupted time series and generalized estimating equations analyses were used to evaluate changes in Incidence Rates (IR) of specialised healthcare use for suicide attempt and death by suicide per 10 000 person-years for individuals with or without specific severe mental disorders (SMDs) during, compared to before the pandemic.

Results: The IR (95% Confidence interval, CI) of suicide in individuals with SMDs decreased from 16.0 (15.0-17.1) in 2018Q1 to 11.6 (10.8-12.5) in 2020Q1 (i.e. the quarter before the start of the pandemic), after which it dropped further to 6.7 (6.3-7.2) in 2021Q2. In contrast, IRs of suicide attempt in SMDs showed more stable trends, as did the trends regarding suicide and suicide attempt for individuals without SMD. These discrepancies were most evident for individuals with substance use disorder and ASD/ADHD. Changes in IRs of suicide v. suicide attempt for one quarter during the pandemic for substance misuse were 11.2% v. 3.6% respectively. These changes for ASD/ADHD were 10.7% v. 3.6%.

Conclusions: The study shows pronounced decreases in suicide rates in individuals with SMDs during the pandemic. Further studies aiming to understand mechanisms behind these trends are warranted to consult future suicide prevention strategies.

While there is ample evidence for the efficacy of IPT, confirmed through the results of the efficacy review, on the ground implementation factors are less well understood. We compiled a book on the global reach of IPT by requesting contributions from local authors through word-of-mouth methods. This approach resulted in reports from 31 countries across six continents and 15 diverse populations within the US that spanned the age range and types of usage. In this paper, our aim was to collate and summarize book contributors' descriptions of barriers and facilitators as related to their experiences of implementing IPT across the 31 countries. We conducted a conceptual content analysis and then applied the updated Consolidated Framework of Implementation Research (CFIR) to deductively organize the barriers and facilitators into its five domains. Most found IPT to be relevant and acceptable and described minor variations needed for tailoring to context. National level policies and mental health stigma were highlighted in the outer setting. Availability of specialists and general and mental health infrastructure were considerations relevant to the inner setting. Many sites had successfully implemented IPT through delivery by nonspecialized providers, although provider workload and burnout were common. Clients faced numerous practical challenges in accessing weekly care. Primary strategies to mitigate these challenges were use of telehealth delivery and shortening of the intervention duration. Most programs ensured competency through a combination of didactic training and case supervision. The latter was identified as time-intensive and costly.

Background: The relationship between adolescent alcohol use and emotional problems remains unclear and contradictory. These inconsistencies may in part be due to differences in the measurement and operationalization of alcohol use and emotional problems across studies, as well as confounder selection and missing data decisions. This study explores the associations between common specifications of adolescent alcohol use and emotional problems in a large sample of adolescents.

Methods: A multiverse analysis (also known as specification curve analysis or vibration of effects) was done with 7680 unique model specifications in a large longitudinal sample of 6639 Australian adolescents (aged ~14.7-15.7, 2021-2022).

Results: While alcohol use and emotional problems nearly universally co-occurred in minimally adjusted cross-sectional models (98-99%), the operationalization of emotional problems, temporality of prospective relationships, and choice of confounders substantially impacted findings. Emotional problems appeared to predict later alcohol use more-so than the reverse, depression-focused measures yielded more consistent associations with alcohol use than anxiety-focused measures, and certain confounders (i.e. conduct, ADHD, smoking) explained most of the associations between adolescent alcohol use and emotional problems. Missing data decisions and whether outcomes were modelled continuously v. dichotomously had minimal impact on findings.

Conclusions: While adolescent alcohol use and emotional problems commonly co-occur, inconsistencies in the magnitude, direction, and significance of effects are closely tied to researcher decisions that are often made arbitrarily.

Background: Executive dysfunction, including working memory deficits, is prominent in posttraumatic stress disorder (PTSD) and can impede treatment effectiveness. Intervention approaches that target executive dysfunction alongside standard PTSD treatments could boost clinical response. The current study reports secondary analyses from a randomized controlled trial testing combined PTSD treatment with a computerized training program to improve executive dysfunction. We assessed if pre-treatment neurocognitive substrates of executive functioning predicted clinical response to this novel intervention.

Methods: Treatment-seeking veterans with PTSD (N = 60) completed a working memory task during functional magnetic resonance imaging prior to being randomized to six weeks of computerized executive function training (five 30-minute sessions each week) plus twelve 50-minute sessions of cognitive processing therapy (CEFT + CPT) or placebo training plus CPT (PT + CPT). Using linear mixed effects models, we examined the extent to which the neurocognitive substrates of executive functioning predicted PTSD treatment response.

Results: Results indicated that veterans with greater activation of working memory regions (e.g. lateral prefrontal and cingulate cortex) had better PTSD symptom improvement trajectories in CEFT + CPT v. PT + CPT. Those with less neural activation during working memory showed similar trajectories of PTSD symptom change regardless of treatment condition.

Conclusions: Greater activity of frontal regions implicated in working memory may serve as a biomarker of response to a novel treatment in veterans with PTSD. Individuals with greater regional responsiveness benefited more from treatment that targeted cognitive dysfunction than treatment that did not include active cognitive training. Clinically, findings could inform our understanding of treatment mechanisms and may contribute to better personalization of treatment.

Background: England's primary care service for psychological therapy (Improving Access to Psychological Therapies [IAPT]) treats anxiety and depression, with a target recovery rate of 50%. Identifying the characteristics of patients who achieve recovery may assist in optimizing future treatment. This naturalistic cohort study investigated pre-therapy characteristics as predictors of recovery and improvement after IAPT therapy.

Methods: In a cohort of patients attending an IAPT service in South London, we recruited 263 participants and conducted a baseline interview to gather extensive pre-therapy characteristics. Bayesian prediction models and variable selection were used to identify baseline variables prognostic of good clinical outcomes. Recovery (primary outcome) was defined using (IAPT) service-defined score thresholds for both depression (Patient Health Questionnaire [PHQ-9]) and anxiety (Generalized Anxiety Disorder [GAD-7]). Depression and anxiety outcomes were also evaluated as standalone (PHQ-9/GAD-7) scores after therapy. Prediction model performance metrics were estimated using cross-validation.

Results: Predictor variables explained 26% (recovery), 37% (depression), and 31% (anxiety) of the variance in outcomes, respectively. Variables prognostic of recovery were lower pre-treatment depression severity and not meeting criteria for obsessive compulsive disorder. Post-therapy depression and anxiety severity scores were predicted by lower symptom severity and higher ratings of health-related quality of life (EuroQol questionnaire [EQ5D]) at baseline.

Conclusion: Almost a third of the variance in clinical outcomes was explained by pre-treatment symptom severity scores. These constructs benefit from being rapidly accessible in healthcare services. If replicated in external samples, the early identification of patients who are less likely to recover may facilitate earlier triage to alternative interventions.

Background: The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.

Methods: Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).

Results: The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.

Conclusion: Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.

Background: Pediatric bipolar disorder (PBD) is characterized by abnormal functional connectivity among distributed brain regions. Increasing evidence suggests a role for the limbic network (LN) and the triple network model in the pathophysiology of bipolar disorder (BD). However, the specific relationship between the LN and the triple network in PBD remains unclear. This study aimed to investigate the aberrant causal connections among these four core networks in PBD.

Method: Resting-state functional MRI scans from 92 PBD patients and 40 healthy controls (HCs) were analyzed. Dynamic Causal Modeling (DCM) was employed to assess effective connectivity (EC) among the four core networks. Parametric empirical Bayes (PEB) analysis was conducted to identify ECs associated with group differences, as well as depression and mania severity. Leave-one-out cross-validation (LOOCV) was used to test predictive accuracy.

Result: Compared to HCs, PBD patients exhibited primarily excitatory bottom-up connections from the LN to the salience network (SN) and bidirectional excitatory connections between the default mode network (DMN) and SN. In PBD, top-down connectivity from the triple network to the LN was excitatory in individuals with higher depression severity but inhibitory in those with higher mania severity. LOOCV identified dysconnectivity circuits involving the caudate and hippocampus as being associated with mania and depression severity, respectively.

Conclusions: Disrupted bottom-up connections from the LN to the triple network distinguish PBD patients from healthy controls, while top-down disruptions from the triple network to LN relate to mood state differences. These findings offer insight into the neural mechanisms of PBD.