Predicting post-treatment symptom severity for adults receiving psychological therapy in routine care for generalised anxiety disorder: a machine learning approach.

Approximately half of generalised anxiety disorder (GAD) patients do not recover from first-line treatments, and no validated prediction models exist to inform individuals or clinicians of potential treatment benefits. This study aimed to develop and validate an accurate and explainable prediction model of post-treatment GAD symptom severity. Data from adults receiving treatment for GAD in eight Improving Access to Psychological Therapies (IAPT) services (n=15,859) were separated into training, validation and holdout datasets. Thirteen machine learning algorithms were compared using 10-fold cross-validation, against two simple clinically relevant comparison models. The best-performing model was tested on the holdout dataset and model-specific explainability measures identified the most important predictors. A Bayesian Additive Regression Trees model out-performed all comparison models (MSE=16.54 [95 % CI=15.58; 17.51]; MAE=3.19; R²=0.33, including a single predictor linear regression model: MSE=20.70 [95 % CI=19.58; 21.82]; MAE=3.94; R²=0.14). The five most important predictors were: PHQ-9 anhedonia, GAD-7 annoyance/irritability, restlessness and fear items, then the referral-assessment waiting time. The best-performing model accurately predicted post-treatment GAD symptom severity using only pre-treatment data, outperforming comparison models that approximated clinical judgement and remaining within the GAD-7 error of measurement and minimal clinically important differences. This model could inform treatment decision-making and provide desired information to clinicians and patients receiving treatment for GAD.

Measurement invariance and differential item functioning of the PHQ-9 and GAD-7 between working age and older adults seeking treatment for common mental disorders.

BACKGROUND: The nine-item Patient Health Questionnaire (PHQ-9) and seven-item Generalised Anxiety Disorder (GAD-7) scale are widely used clinically and within research, and so it is important to determine how the measures, and individual items within the measures, are answered by adults of differing ages. This study sought to evaluate measurement invariance and differential item functioning (DIF) of the PHQ-9 and GAD-7 between working age and older adults seeking routine psychological treatment. METHODS: Data of working age (18-64 years old) and older (≥65) adults in eight Improving Access to Psychological Therapies (IAPT) services were used. Confirmatory factor analysis (CFA) was used to establish unidimensionality of the PHQ-9 and GAD-7, with multiple-group CFA to test measurement invariance and The Multiple Indicators, Multiple Causes Models approach to assess DIF. The employed methods were applied to a propensity score matched (PSM) sample in sensitivity analyses to control for potential confounding. RESULTS: Data from 166,816 patients (159,325 working age, 7491 older) were used to show measurement invariance for the PHQ-9 and GAD-7, with limited evidence of DIF and similar results found with a PSM sample (n = 5868). LIMITATIONS: The localised sample creates an inability to detect geographical variance, and the potential effect of unmeasured confounders cannot be ruled out. CONCLUSIONS: The findings support the use of the PHQ-9 and GAD-7 measures for working age and older adults, both clinically and in research settings. This study validates using the measures for these age groups to assess clinically significant symptom thresholds, and monitor treatment outcomes between them.

Examining bi-directional change in sleep and depression symptoms in individuals receiving routine psychological treatment.

BACKGROUND: Sleep disturbance is a common symptom of depression. There is conflicting evidence whether improvements in sleep might impact depressive symptoms, or whether treating the core depressive symptoms might improve sleep disturbance. This study explored the bi-directional impact of sleep and depressive symptom change among individuals receiving psychological treatment. METHODS: Session-by-session change in sleep disturbance and depressive symptom severity scores were explored in patients receiving psychological therapy for depression from Improving Access to Psychological Therapies services in England. Bi-directional change in sleep disturbance and depressive symptoms was modelled using random-intercept cross-lagged panel models with items from the PHQ-9. RESULTS: The sample included 17,732 adults that had received three or more treatment sessions. Both depressive symptoms and sleep disturbance scores decreased. Between initial timepoints, higher sleep disturbance was associated with lower depression scores, but after this point positive cross-lagged effects were observed for both the impact of sleep disturbance on later depressive symptoms, and depressive symptoms on later sleep disturbance scores. The magnitude of effects suggested depressive symptoms may have more impact on sleep than the reverse, and this effect was larger in sensitivity analyses. CONCLUSIONS: Findings provide evidence that psychological therapy for depression results in improvements in core depressive symptoms and sleep disturbance. There was some evidence that depressive symptoms may have more impact on sleep disturbance scores at the next therapy session, than sleep disturbance does on later depressive symptoms. Targeting the core symptoms of depression initially may optimise outcomes, but further research is needed to elucidate these relationships.

Predicting prognosis for adults with depression using individual symptom data: a comparison of modelling approaches.

BACKGROUND: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data. METHODS: Individual patient data from all six eligible randomised controlled trials were used to develop (k = 3, n = 1722) and test (k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months. RESULTS: Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact. CONCLUSIONS: Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

Transdiagnostic symptom dynamics during psychotherapy.

Psychotherapy is an effective treatment for many common mental health problems, but the mechanisms of action and processes of change are unclear, perhaps driven by the focus on a single diagnosis which does not reflect the heterogeneous symptom experiences of many patients. The objective of this study was to better understand therapeutic change, by illustrating how symptoms evolve and interact during psychotherapy. Data from 113,608 patients from psychological therapy services who completed depression and anxiety symptom measures across three to six therapy sessions were analysed. A panel graphical vector-autoregression model was estimated in a model development sample (N = 68,165) and generalizability was tested in a confirmatory model, fitted to a separate (hold-out) sample of patients (N = 45,443). The model displayed an excellent fit and replicated in the confirmatory holdout sample. First, we found that nearly all symptoms were statistically related to each other (i.e. dense connectivity), indicating that no one symptom or association drives change. Second, the structure of symptom interrelations which emerged did not change across sessions. These findings provide a dynamic view of the process of symptom change during psychotherapy and give rise to several causal hypotheses relating to structure, mechanism, and process.

Implementing peer support in higher education: A feasibility study.

Background: When experiencing mental distress, many university students seek support from their peers. In schools and mental health services, formalised peer support interventions have demonstrated some success but implementation challenges have been reported. This study aimed to assess the feasibility, acceptability and safety of a novel manualized peer support intervention and associated data collection processes. Methods: A longitudinal mixed methods study was conducted following the pilot of a peer support intervention at a large London university between June 2021 and May 2022. The study utilised data routinely recorded on all students who booked a peer support session, focus groups with nine peer workers and five staff members implementing the intervention, pre-post intervention surveys with 13 students and qualitative interviews with 10 of those students. Results: 169 bookings were made during the pilot, of which 130 (77%) were attended, with November the peak month. Staff and peer workers described strong motivation and commitment to implement the intervention, noting that the peer support model and peer worker role addressed previously unmet needs at the university. However, students described implementation problems relating to the coherence of the intervention and the burden of participation. While students mostly described acceptable experiences, there were examples where acceptability was lower. No adverse events were reported during the pilot. Conclusion: The training and supervision of peer workers, and the provision of one-to-one peer support to students was found to be feasible, mostly acceptable, and safe. However, sustained implementation difficulties were observed. These pose challenges to the scalability of peer support in universities. We make recommendations to improve implementation of peer support including improving reach, greater clarity about the intervention, and fuller involvement of students throughout.

Role of age, gender and marital status in prognosis for adults with depression: An individual patient data meta-analysis.

AIMS: To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care. METHODS: Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from their general practitioners, that used the Revised Clinical Interview Schedule so that there was uniformity in the measurement of clinical prognostic factors, and that reported on age, gender and marital status. Individual participant data were gathered from all nine eligible RCTs (N = 4864). Two-stage random-effects meta-analyses were conducted to ascertain the independent association between: (i) age, (ii) gender and (iii) marital status, and depressive symptoms at 3-4, 6-8, and 9-12 months post-baseline and remission at 3-4 months. Risk of bias was evaluated using QUIPS and quality was assessed using GRADE. PROSPERO registration: CRD42019129512. Pre-registered protocol https://osf.io/e5zup/. RESULTS: There was no evidence of an association between age and prognosis before or after adjusting for depressive 'disorder characteristics' that are associated with prognosis (symptom severity, durations of depression and anxiety, comorbid panic disorderand a history of antidepressant treatment). Difference in mean depressive symptom score at 3-4 months post-baseline per-5-year increase in age = 0(95% CI: -0.02 to 0.02). There was no evidence for a difference in prognoses for men and women at 3-4 months or 9-12 months post-baseline, but men had worse prognoses at 6-8 months (percentage difference in depressive symptoms for men compared to women: 15.08% (95% CI: 4.82 to 26.35)). However, this was largely driven by a single study that contributed data at 6-8 months and not the other time points. Further, there was little evidence for an association after adjusting for depressive 'disorder characteristics' and employment status (12.23% (-1.69 to 28.12)). Participants that were either single (percentage difference in depressive symptoms for single participants: 9.25% (95% CI: 2.78 to 16.13) or no longer married (8.02% (95% CI: 1.31 to 15.18)) had worse prognoses than those that were married, even after adjusting for depressive 'disorder characteristics' and all available confounders. CONCLUSION: Clinicians and researchers will continue to routinely record age and gender, but despite their importance for incidence and prevalence of depression, they appear to offer little information regarding prognosis. Patients that are single or no longer married may be expected to have slightly worse prognoses than those that are married. Ensuring this is recorded routinely alongside depressive 'disorder characteristics' in clinic may be important.

The importance of transdiagnostic symptom level assessment to understanding prognosis for depressed adults: analysis of data from six randomised control trials.

BACKGROUND: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes. METHOD: Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators. RESULTS: Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms. CONCLUSION: The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.

Adapting IAPT services to support frontline NHS staff during the Covid-19 pandemic: the Homerton Covid Psychological Support (HCPS) pathway.

The Coronavirus (Covid-19) pandemic is exerting unprecedented pressure on NHS Health and Social Care provisions, with frontline staff, such as those of critical care units, encountering vast practical and emotional challenges on a daily basis. Although staff are being supported through organisational provisions, facilitated by those in leadership roles, the emergence of mental health difficulties or the exacerbation of existing ones amongst these members of staff is a cause for concern. Acknowledging this, academics and healthcare professionals alike are calling for psychological support for frontline staff, which not only addresses distress during the initial phases of the outbreak but also over the months, if not years, that follow. Fortunately, mental health services and psychology professional bodies across the United Kingdom have issued guidance to meet these needs. An attempt has been made to translate these sets of guidance into clinical provisions via the recently established Homerton Covid Psychological Support (HCPS) pathway delivered by Talk Changes (Hackney & City IAPT). This article describes the phased, stepped-care and evidence-based approach that has been adopted by the service to support local frontline NHS staff. We wish to share our service design and pathway of care with other Improving Access to Psychological Therapies (IAPT) services who may also seek to support hospital frontline staff within their associated NHS Trusts and in doing so, lay the foundations of a coordinated response. KEY LEARNING AIMS: (1)To understand the ways staff can be psychologically and emotionally impacted by working on the frontline of disease outbreaks.(2)To understand the ways in which IAPT services have previously supported populations exposed to crises.(3)To learn ways of delivering psychological support and interventions during a pandemic context based on existing guidance and research.

Attentional Control as a Predictor of Response to Psychological Treatment for Depression and Relapse up to 1 year After Treatment: A Pilot Cohort Study.

BACKGROUND: Identifying depressed patients unlikely to reach remission and those likely to relapse after reaching remission is of great importance, but there are few pre-treatment factors that can help clinicians predict prognosis and together these explain relatively little variance in treatment outcomes. Attentional control has shown promise in studies to date, but has not been investigated prospectively in routine clinical settings with depressed patients. AIMS: This study aimed to pilot the use of a brief self-report measure of attentional control in routine care and investigate the associations between attentional control, psychological treatment response and relapse to depression up to 1 year post-treatment. METHOD: Depressed patients were recruited from two primary care psychological treatment (IAPT) services and completed the Attentional Control Scale (ACS) alongside routine symptom measures at every therapy session. Participants were tracked and followed up for 1 year post-treatment. RESULTS: Baseline ACS scores were associated with remission and residual depressive symptoms post-treatment, and relapse within 12 months of ending treatment, all independent of pre-treatment depressive symptom severity, and the latter also independent of residual symptoms. CONCLUSION: A self-report measure of attentional control can potentially be used to predict levels of depressive symptoms post-treatment and can contribute to predicting risk of relapse to depression in IAPT services, without affecting rates of therapy completion/drop-out or data completion of standard IAPT measures. However, this pilot study had a small overall sample size and a very small number of observed relapses, so replication in a larger study is needed before firm conclusions can be made.

Risk factors for relapse and recurrence of depression in adults and how they operate: A four-phase systematic review and meta-synthesis.

PURPOSE: To review and synthesise prognostic indices that predict subsequent risk, prescriptive indices that moderate treatment response, and mechanisms that underlie each with respect to relapse and recurrence of depression in adults. RESULTS AND CONCLUSIONS: Childhood maltreatment, post-treatment residual symptoms, and a history of recurrence emerged as strong prognostic indicators of risk and each could be used prescriptively to indicate who benefits most from continued or prophylactic treatment. Targeting prognostic indices or their "down-stream" consequences will be particularly beneficial because each is either a cause or a consequence of the causal mechanisms underlying risk of recurrence. The cognitive and neural mechanisms that underlie the prognostic indices are likely addressed by the effects of treatments that are moderated by the prescriptive factors. For example, psychosocial interventions that target the consequences of childhood maltreatment, extending pharmacotherapy or adapting psychological therapies to deal with residual symptoms, or using cognitive or mindfulness-based therapies for those with prior histories of recurrence. Future research that focuses on understanding causal pathways that link childhood maltreatment, or cognitive diatheses, to dysfunction in the neocortical and limbic pathways that process affective information and facilitate cognitive control, might result in more enduring effects of treatments for depression.

The Impact of Alcohol Use on Drop-out and Psychological Treatment Outcomes in Improving Access to Psychological Therapies Services: an Audit.

BACKGROUND: The impact of alcohol use disorders (AUD) on psychological treatments for depression or anxiety in primary care psychological treatment services is unknown. AIMS: To establish levels of alcohol misuse in an Improving Access to Psychological Therapies (IAPT) service, examine the impact of higher risk drinking on IAPT treatment outcomes and drop-out, and to inform good practice in working with alcohol misuse in IAPT services. METHOD: 3643 patients completed a brief questionnaire on alcohol use pre-treatment in addition to measures of depression, anxiety and functioning. Symptom and functioning measures were re-administered at all treatment sessions. RESULTS: Severity of alcohol misuse was not associated with treatment outcomes, although those scoring eight or more on the AUDIT-C were more likely to drop out from treatment. CONCLUSIONS: IAPT services may be well placed to offer psychological therapies to patients with common mental disorders and comorbid AUD. Patients with AUD can have equivalent treatment outcomes to those without AUD, but some higher risk drinkers may find accessing IAPT treatment more difficult as they are more likely to drop out. Alcohol misuse on its own should not be used as an exclusion criterion from IAPT services. Recommendations are given as to how clinicians can: adjust their assessments to consider the appropriateness of IAPT treatment for patients that misuse alcohol, consider the potential impact of alcohol misuse on treatment, and improve engagement in treatment for higher risk drinkers.