Predicting treatment outcome in psychological treatment services by identifying latent profiles of patients

Identifying and understanding cognitive profiles in multiple sclerosis: a role for visuospatial memory functioning

BACKGROUND

The heterogeneous nature of cognitive impairment in people with multiple sclerosis (PwMS) hampers understanding of the underlying mechanisms and developing patient-tailored interventions. We aim to identify and classify cognitive profiles in PwMS, comparing these to cognitive status (preserved versus impaired).

METHODS

We included 1213 PwMS (72% female, age 45.4 ± 10.7 years, 83% relapsing-remitting MS). Cognitive test scores were converted to Z-scores compared to healthy controls for the functions: attention, inhibition, information processing speed (IPS), verbal fluency and verbal/visuospatial memory. Concerning cognitive status, impaired cognition (CI) was defined as performing at Z ≤ - 1.5 SD on ≥ 2 functions. Cognitive profiles were constructed using latent profile analysis on all cognitive functions. Cognitive profiles or status was classified using gradient boosting decision trees, providing the importance of each feature (demographics, clinical, cognitive and psychological functioning) for the overall classification.

RESULTS

Six profiles were identified, showing variations in overall performance and specific deficits (attention, inhibition, IPS, verbal fluency, verbal memory and visuospatial memory). Across the profiles, IPS was the most impaired function (%CI most preserved profile, Profile 1 = 22.4%; %CI most impaired profile, Profile 6 = 76.6%). Cognitive impairment varied from 11.8% in Profile 1 to 95.3% in Profile 6. Of all cognitive functions, visuospatial memory was most important in classifying profiles and IPS the least (area under the curve (AUC) = 0.910). For cognitive status, IPS was the most important classifier (AUC = 0.997).

CONCLUSIONS

This study demonstrated that cognitive heterogeneity in MS reflects a continuum of cognitive severity, distinguishable by distinct cognitive profiles, primarily explained by variations in visuospatial memory functioning.

PTSD, depression, and treatment outcomes: A latent profile analysis among active duty personnel in a residential PTSD program

Depression frequently co-occurs with posttraumatic stress disorder (PTSD), including among active duty service members. However, symptom heterogeneity of this comorbidity is complex and its association with treatment outcomes is poorly understood, particularly among active duty service members in residential treatment. This study used latent profile analysis (LPA) to identify symptom-based subgroups of PTSD and depression among 282 male service members in a 10-week, residential PTSD treatment program with evidence-based PTSD psychotherapies and adjunctive interventions. The PTSD Checklist-Military Version and Patient Health Questionnaire-8 were completed by service members at pre- and posttreatment and weekly during treatment. Multilevel models compared subgroups on PTSD and depression symptom change across treatment. LPA indicated four subgroups provided optimal fit: Depressive (high depression severity, low PTSD avoidance; n = 33, 11.7%), Avoidant (high PTSD avoidance, moderate depression severity; n = 89, 31.6%), Moderate (moderate PTSD and depression severity; n = 27, 9.6%), and Distressed (high PTSD and depression severity; n = 133, 47.2%). Treatment response differed across classes for both PTSD and depression outcomes (time × LPA class interaction ps < 0.001). In PTSD models, post-hoc comparisons indicated the Moderate class was associated with less PTSD symptom improvement relative to the other classes (ps < 0.006). In depression models, symptom reduction was greatest for the Distressed and Depressive subgroups relative to the other two classes (ps < 0.009). Study results provide an initial model for two prevalent, impairing disorders among service members and show how these symptom-based subgroups may differentially respond to residential PTSD treatment.

Prior psychiatric morbidity and differential psychopharmacological treatment patterns: Exploring the heterogeneity of bipolar disorder in a nationwide study of 9594 patients

OBJECTIVES

Individuals with bipolar disorders (BD) have heterogenic pre-onset illness courses and responses to treatment. The pattern of illness preceding the diagnosis of BD may be a marker of future treatment response. Here, we examined associations between psychiatric morbidity preceding the diagnosis of BD and pharmacological treatment patterns in the 2 years following diagnosis.

METHODS

In this register-based study, we included all patients with a diagnosis of BD attending Danish Psychiatric Services between January 1, 2012 and December 31, 2016. We examined the association between a diagnosis of substance use disorder, psychosis (other than schizophrenia or schizoaffective disorder), unipolar depression, anxiety/OCD, PTSD, personality disorder, or ADHD preceding BD and pharmacological treatment patterns following the diagnosis of BD (lithium, valproate, lamotrigine, antidepressants, olanzapine, risperidone, and quetiapine) via multivariable Cox proportional hazards regression adjusted for age, sex, and year of BD diagnosis.

RESULTS

We included 9594 patients with a median age of 39 years, 58% of whom were female. Antidepressants, quetiapine, and lamotrigine were the most commonly used medications in BD and were all linked to prior depressive illness and female sex. Lithium was used among patients with less diagnostic heterogeneity preceding BD, while valproate was more likely to be used for patients with prior substance use disorder or ADHD.

CONCLUSION

The pharmacological treatment of BD is linked to psychiatric morbidity preceding its diagnosis. Assuming that these associations reflect well-informed clinical decisions, this knowledge may inform future clinical trials by taking participants' prior morbidity into account in treatment allocation.

The addition of employment support alongside psychological therapy enhances the chance of recovery for clients most at risk of poor clinical outcomes

BACKGROUND

Many people achieve positive outcomes from psychological therapies for anxiety and depression. However, not everyone benefits and some may require additional support. Previous studies have examined the demographic and clinical characteristics of people starting treatment and identified a patient profile that is associated with poor clinical outcomes.

AIMS

To examine whether the addition of employment-related support alongside psychological therapy was associated with a greater chance of recovery for clients belonging to this patient profile.

METHOD

We analysed 302 clients across three services, who were offered employment-related support alongside psychological therapy. The rate of clinical recovery (falling below clinical thresholds on measures of both anxiety and depression) was compared between individuals who accepted the offer and those who declined, while adjusting for potential confounders.

RESULTS

Logistic regression showed that receiving employment support was significantly associated with clinical recovery after controlling for baseline anxiety and depression scores, the number of psychological treatment sessions, and other clinical and demographic variables. The odds of recovery were 2.54 times greater if clients received employment support; 47% of clients who received employment support alongside psychological therapy were classified as recovered, compared with 27% of those receiving psychological therapy only.

CONCLUSIONS

Providing employment support alongside therapy may be particularly helpful for clients belonging to this patient profile, who represent approximately 10% of referrals to NHS Talking Therapies for Anxiety and Depression services. Services could consider how to increase the provision and uptake of employment-focused support to enhance clients' clinical outcomes.

Comparing clinical decision-making of AI technology to a multi-professional care team in an electronic cognitive behavioural therapy program for depression: protocol

Introduction

Depression is a leading cause of disability worldwide, affecting up to 300 million people globally. Despite its high prevalence and debilitating effects, only one-third of patients newly diagnosed with depression initiate treatment. Electronic cognitive behavioural therapy (e-CBT) is an effective treatment for depression and is a feasible solution to make mental health care more accessible. Due to its online format, e-CBT can be combined with variable therapist engagement to address different care needs. Typically, a multi-professional care team determines which combination therapy most benefits the patient. However, this process can add to the costs of these programs. Artificial intelligence (AI) has been proposed to offset these costs.

Methods

This study is a double-blinded randomized controlled trial recruiting individuals experiencing depression. The degree of care intensity a participant will receive will be randomly decided by either: (1) a machine learning algorithm, or (2) an assessment made by a group of healthcare professionals. Subsequently, participants will receive depression-specific e-CBT treatment through the secure online platform. There will be three available intensities of therapist interaction: (1) e-CBT; (2) e-CBT with a 15-20-min phone/video call; and (3) e-CBT with pharmacotherapy. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources.

Discussion

Artificial intelligence and providing patients with varying intensities of care can increase the efficiency of mental health care services. This study aims to determine a cost-effective method to decrease depressive symptoms and increase treatment adherence to online psychotherapy by allocating the correct intensity of therapist care for individuals diagnosed with depression. This will be done by comparing a decision-making machine learning algorithm to a multi-professional care team. This approach aims to accurately allocate care tailored to each patient's needs, allowing for more efficient use of resources with the convergence of technologies and healthcare.

Ethics

The study received ethics approval and began participant recruitment in December 2022. Participant recruitment has been conducted through targeted advertisements and physician referrals. Complete data collection and analysis are expected to conclude by August 2024.

Clinical trial registration

ClinicalTrials.Gov, identifier NCT04747873.

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

Objectives

Anxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.

Methods

In the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.

Results

ML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models' performance (AUC range: 0.68-0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58-0.62 vs. 0.58-0.63) and models with early change scores (AUC: 0.69-0.73 vs. 0.68-0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.

Conclusion

Accurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress.

Predicting Youth and Young Adult Treatment Engagement in a Transdiagnostic Remote Intensive Outpatient Program: Latent Profile Analysis

BACKGROUND

The youth mental health crisis in the United States continues to worsen, and research has shown poor mental health treatment engagement. Despite the need for personalized engagement strategies, there is a lack of research involving youth. Due to complex youth developmental milestones, there is a need to better understand clinical presentation and factors associated with treatment engagement to effectively identify and tailor beneficial treatments.

OBJECTIVE

This quality improvement investigation sought to identify subgroups of clients attending a remote intensive outpatient program (IOP) based on clinical acuity data at intake, to determine the factors associated with engagement outcomes for clients who present in complex developmental periods and with cooccurring conditions. The identification of these subgroups was used to inform programmatic decisions within this remote IOP system.

METHODS

Data were collected as part of ongoing quality improvement initiatives at a remote IOP for youth and young adults. Participants included clients (N=2924) discharged between July 2021 and February 2023. A latent profile analysis was conducted using 5 indicators of clinical acuity at treatment entry, and the resulting profiles were assessed for associations with demographic factors and treatment engagement outcomes.

RESULTS

Among the 2924 participants, 4 profiles of clinical acuity were identified: a low-acuity profile (n=943, 32.25%), characterized by minimal anxiety, depression, and self-harm, and 3 high-acuity profiles defined by moderately severe depression and anxiety but differentiated by rates of self-harm (high acuity+low self-harm: n=1452, 49.66%; high acuity+moderate self-harm: n=203, 6.94%; high acuity+high self-harm: n=326, 11.15%). Age, gender, transgender identity, and sexual orientation were significantly associated with profile membership. Clients identified as sexually and gender-marginalized populations were more likely to be classified into high-acuity profiles than into the low-acuity profile (eg, for clients who identified as transgender, high acuity+low self-harm: odds ratio [OR] 2.07, 95% CI 1.35-3.18; P<.001; high acuity+moderate self-harm: OR 2.85, 95% CI 1.66-4.90; P<.001; high acuity+high self-harm: OR 3.67, 95% CI 2.45-5.51; P<.001). Race was unrelated to the profile membership. Profile membership was significantly associated with treatment engagement: youth and young adults in the low-acuity and high-acuity+low-self-harm profiles attended an average of 4 fewer treatment sessions compared with youth in the high-acuity+moderate-self-harm and high-acuity+high-self-harm profiles (ꭓ23=27.6, P<.001). Individuals in the high-acuity+low-self-harm profile completed treatment at a significantly lower rate relative to the other 2 high-acuity profiles (ꭓ23=13.4, P=.004). Finally, those in the high-acuity+high-self-harm profile were significantly less likely to disengage early relative to youth in all other profiles (ꭓ23=71.12, P<.001).

CONCLUSIONS

This investigation represents a novel application for identifying subgroups of adolescents and young adults based on clinical acuity data at intake to identify patterns in treatment engagement outcomes. Identifying subgroups that differentially engage in treatment is a critical first step toward targeting engagement strategies for complex populations.

A randomized controlled trial on using predictive algorithm to adapt level of psychological care for community college students: STAND triaging and adapting to level of care study protocol

BACKGROUND

There is growing interest in using personalized mental health care to treat disorders like depression and anxiety to improve treatment engagement and efficacy. This randomized controlled trial will compare a traditional symptom severity decision-making algorithm to a novel multivariate decision-making algorithm for triage to and adaptation of mental health care. The stratified levels of care include a self-guided online wellness program, coach-guided online cognitive behavioral therapy, and clinician-delivered psychotherapy with or without pharmacotherapy. The novel multivariate algorithm will be comprised of baseline (for triage and adaptation) and time-varying variables (for adaptation) in four areas: social determinants of mental health, early adversity and life stressors, predisposing, enabling, and need influences on health service use, and comprehensive mental health status. The overarching goal is to evaluate whether the multivariate algorithm improves adherence to treatment, symptoms, and functioning above and beyond the symptom-based algorithm.

METHODS/DESIGN

This trial will recruit a total of 1000 participants over the course of 5 years in the greater Los Angeles Metropolitan Area. Participants will be recruited from a highly diverse sample of community college students. For the symptom severity approach, initial triaging to level of care will be based on symptom severity, whereas for the multivariate approach, the triaging will be based on a comprehensive set of baseline measures. After the initial triaging, level of care will be adapted throughout the duration of the treatment, utilizing either symptom severity or multivariate statistical approaches. Participants will complete computerized assessments and self-report questionnaires at baseline and up to 40 weeks. The multivariate decision-making algorithm will be updated annually to improve predictive outcomes.

DISCUSSION

Results will provide a comparison on the traditional symptom severity decision-making and the novel multivariate decision-making with respect to treatment adherence, symptom improvement, and functional recovery. Moreover, the developed multivariate decision-making algorithms may be used as a template in other community college settings. Ultimately, findings will inform the practice of level of care triage and adaptation in psychological treatments, as well as the use of personalized mental health care broadly.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05591937, submitted August 2022, published October 2022.

Why is it so hard to identify (consistent) predictors of treatment outcome in psychotherapy? - clinical and research perspectives

BACKGROUND

Anxiety and depression are two of the most debilitating psychological disorders worldwide today. Fortunately, effective treatments exist. However, a large proportion of patients do not recover from treatment, and many still have symptoms after completing treatment. Numerous studies have tried to identify predictors of treatment outcome. So far, researchers have found few or no consistent predictors applicable to allocate patients to relevant treatment.

METHODS

We set out to investigate why it is so hard to identify (consistent) predictors of treatment outcome for psychotherapy in anxiety and depression by reviewing relevant literature.

RESULTS

Four challenges stand out; a) the complexity of human lives, b) sample size and statistical power, c) the complexity of therapist-patient relationships, and d) the lack of consistency in study designs. Together these challenges imply there are a countless number of possible predictors. We also consider ethical implications of predictor research in psychotherapy. Finally, we consider possible solutions, including the use of machine learning, larger samples and more realistic complex predictor models.

CONCLUSIONS

Our paper sheds light on why it is so hard to identify consistent predictors of treatment outcome in psychotherapy and suggest ethical implications as well as possible solutions to this problem.

Riuscire a ottenere un beneficio di massa tramite l'offerta di terapie psicologiche: l'esperienza del programma inglese

Viene pubblicata la trascrizione della relazione tenuta al Convegno "Le terapie psicologiche per l'ansia e la depressione: nuove forme di integrazione clinica e organizzativa" (Isola di San Servolo, Venezia, 26 ottobre 2022). Dopo la descrizione delle principali caratteristiche del programma inglese Improving Access to Psychological Therapies (IAPT), viene sottolineata l'importanza di alcuni aspetti tra cui i seguenti: l'affidabilità delle linee-guida; un programma nazionale di training in cui si valuta accuratamente anche la competenza raggiunta dai terapeuti; la raccolta di tutti i risultati e la loro pubblicazione, che ha permesso di capire molti problemi (come ridurre le differenze nei risultati di gruppi etnici diversi, come migliorare i Servizi in aree povere, etc.); il benessere e la motivazione degli operatori (circa 10.000 terapeuti), che devono avere figure di leadership innovative che non danno solo importanza al risultato ma che sanno anche creare un ambiente di lavoro stimolante in cui si lavora volentieri e col piacere di apprendere sempre nuove cose.

Patient characteristics associated with retrospectively self-reported treatment outcomes following psychological therapy for anxiety or depressive disorders - a cohort of GLAD study participants

BACKGROUND

Progress towards stratified care for anxiety and depression will require the identification of new predictors. We collected data on retrospectively self-reported therapeutic outcomes in adults who received psychological therapy in the UK in the past ten years. We aimed to replicate factors associated with traditional treatment outcome measures from the literature.

METHODS

Participants were from the Genetic Links to Anxiety and Depression (GLAD) Study, a UK-based volunteer cohort study. We investigated associations between retrospectively self-reported outcomes following therapy, on a five-point scale (global rating of change; GRC) and a range of sociodemographic, clinical and therapy-related factors, using ordinal logistic regression models (n = 2890).

RESULTS

Four factors were associated with therapy outcomes (adjusted odds ratios, OR). One sociodemographic factor, having university-level education, was associated with favourable outcomes (OR = 1.37, 95%CI: 1.18, 1.59). Two clinical factors, greater number of reported episodes of illness (OR = 0.95, 95%CI: 0.92, 0.97) and higher levels of personality disorder symptoms (OR = 0.89, 95%CI: 0.87, 0.91), were associated with less favourable outcomes. Finally, reported regular use of additional therapeutic activities was associated with favourable outcomes (OR = 1.39, 95%CI: 1.19, 1.63). There were no statistically significant differences between fully adjusted multivariable and unadjusted univariable odds ratios.

CONCLUSION

Therapy outcome data can be collected quickly and inexpensively using retrospectively self-reported measures in large observational cohorts. Retrospectively self-reported therapy outcomes were associated with four factors previously reported in the literature. Similar data collected in larger observational cohorts may enable detection of novel associations with therapy outcomes, to generate new hypotheses, which can be followed up in prospective studies.

Understanding differences in mental health service use by men: an intersectional analysis of routine data

PURPOSE

Rates of help-seeking for common mental health problems are lower for men, but less is known about patterns of engagement once they are in contact with services. Previous research has been limited in its ability to understand the intersection between service user characteristics and engagement. This study compared analytic approaches to investigate intersectional associations between sociodemographic and socioeconomic indicators and use of psychological treatment services by men.

METHOD

Data from 9,904 male service users attending two psychological treatment services in London were analysed. The association between ethnicity, sexual orientation, religious affiliation and employment status of service users and service use outcomes was explored using multinomial logistic regression and latent class analysis (LCA).

RESULTS

Being from a minoritised ethnic background, of Muslim faith, being unemployed, and living in the most deprived neighbourhoods were associated with greater risk of not commencing or completing treatment. Seven classes were identified in LCA, with men predominately differentiated by self-reported ethnicity and religion. Compared with the 'White British, non-religious' class, the 'Asian Muslim' class and the 'minoritised ethnic, non-religious' class were at higher risk of disengagement, whilst the 'Asian, other religion' class were at higher risk of being referred elsewhere rather than completing initiated treatment.

CONCLUSIONS

There were significant inequalities in engagement by men associated with ethnicity, religion and socioeconomic status. Compared with the regression models, further nuance was apparent in LCA regarding the intersection of gender, religion and ethnicity. Identifying groups at greater risk of discontinuation of treatment could inform more personalised pathways through care.

Examining the dimensionality of anxiety and depression: A latent profile approach to modeling transdiagnostic features

Depression and anxiety are highly prevalent psychological disorders; our understanding of these conditions remains limited. Efforts to explain anxiety and depression have been constrained in part by binary classification systems. Dimensional approaches to understanding psychopathology may be more effective. The present study used latent profile analysis (LPA) to assess whether unique subgroups exist within a tri-level model of anxiety and depression. Participants (N=627) completed self-report questionnaires from which tri-level model factors were derived. LPA was conducted on those factors. A 4-profile model offered optimal fit to the data at baseline. This model was replicated at a second time point. Models derived included profiles labelled 'Mixed Fears,' 'Anxious Arousal,' 'Low Mood/Anhedonia,' and 'Sub-Clinical.' Profiles were validated at Time 1 using diagnostic status and clinical severity ratings associated with mood and anxiety presentations. Profiles demonstrated flexibility in accommodating breadth in clinical presentations and common comorbidities. Latent variable models may offer more ecologically valid approaches to understanding psychopathology.

Stratified Care vs Stepped Care for Depression: A Cluster Randomized Clinical Trial

IMPORTANCE

Depression is a major cause of disability worldwide. Although empirically supported treatments are available, there is scarce evidence on how to effectively personalize psychological treatment selection.

OBJECTIVE

To compare the clinical effectiveness and cost-effectiveness of 2 treatment selection strategies: stepped care and stratified care.

DESIGN, SETTING, AND PARTICIPANTS

This multisite, cluster randomized clinical trial recruited participants from the English National Health Service from July 5, 2018, to February 1, 2019. Thirty clinicians working across 4 psychological therapy services were randomly assigned to provide stratified (n = 15) or stepped (n = 15) care. In stepped care, patients sequentially access low-intensity guided self-help followed by high-intensity psychotherapy. In stratified care, patients are matched with either low- or high-intensity treatments at initial assessment. Data were analyzed from May 18, 2020, to October 13, 2021, using intention-to-treat principles.

INTERVENTIONS

All clinicians used the same interview schedule to conduct initial assessments with patients seeking psychological treatment for common mental disorders, but those in the stratified care group received a personalized treatment recommendation for each patient generated by a machine learning algorithm. Eligible patients received either stratified or stepped care (ie, treatment as usual).

MAIN OUTCOMES AND MEASURES

The preregistered outcome was posttreatment reliable and clinically significant improvement (RCSI) of depression symptoms (measured using the 9-item Patient Health Questionnaire). The RCSI outcome was compared between groups using logistic regression adjusted for baseline severity. Cost-effectiveness analyses compared incremental costs and health outcomes of the 2 treatment pathways.

RESULTS

A total of 951 patients were included (618 women among 950 with data available [65.1%]; mean [SD] age, 38.27 [14.53] years). The proportion of cases of RCSI was significantly higher in the stratified care arm compared with the stepped care arm (264 of 505 [52.3%] vs 134 of 297 [45.1%]; odds ratio, 1.40 [95% CI, 1.04-1.87]; P = .03). Stratified care was associated with a higher mean additional cost per patient (£104.5 [95% CI, £67.5-£141.6] [$139.83 (95% CI, $90.32-$189.48)]; P < .001) because more patients accessed high-intensity treatments (332 of 583 [56.9%] vs 107 of 368 [29.1%]; χ2 = 70.51; P < .001), but this additional cost resulted in an approximately 7% increase in the probability of RCSI.

CONCLUSIONS AND RELEVANCE

In this cluster randomized clinical trial of adults with common mental disorders, stratified care was efficacious and cost-effective for the treatment of depression symptoms compared with stepped care. Stratified care can improve depression treatment outcomes at a modest additional cost.

TRIAL REGISTRATION

isrctn.org Identifier: ISRCTN11106183.

The impact of mental health support for the chronically ill on hospital utilisation: Evidence from the UK

Individuals with common mental disorders (CMDs) such as depression and anxiety frequently have co-occurring long-term physical health conditions (LTCs) and this co-occurrence is associated with higher hospital utilisation. Psychological treatment for CMDs may reduce healthcare utilisation through better management of the LTC, but there is little previous research. We examined the impact of psychological treatment delivered under the nationwide Improving Access to Psychological Therapies (IAPT) programme in England on hospital utilisation 12-months after the end of IAPT treatment. We examined three types of hospital utilisation: Inpatient treatment, Outpatient treatment and Emergency Room attendance. We examined individuals with Chronic Obstructive Pulmonary Disease (COPD) (n = 816), Diabetes (n = 2813) or Cardiovascular Disease (CVD) (n = 4115) who received psychological treatment between April 2014 and March 2016. IAPT episode data was linked to hospital utilisation data which went up to March 2017. Changes in the probability of hospital utilisation were compared to a matched control sample for each LTC. Individuals in the control sample received IAPT treatment between April 2017 and March 2018. Compared to the control sample, the treated sample had significant reductions in the probability of all three types of hospital utilisation, for all three LTCs 12-months after the end of IAPT treatment. Reductions in utilisation of Emergency Room, Outpatient and non-elective Inpatient treatment were also observed immediately following the end of psychological treatment, and 6-months after, for individuals with diabetes and CVD, compared to the matched sample. These findings suggest that psychological interventions for CMDs delivered to individuals with co-occurring long-term chronic conditions may reduce the probability of utilisation of hospital services. Our results support the roll-out of psychological treatment aimed at individuals who have co-occurring CMDs and long-term chronic conditions.

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A Patient Stratification Approach to Identifying the Likelihood of Continued Chronic Depression and Relapse Following Treatment for Depression

Background: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment. Method: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care. Results: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24–6.87), chronic course = 2.27 (1.27–4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16–5.40), chronic course = 1.98 (1.16–3.37)). Conclusions: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments.

Treatment selection using prototyping in latent-space with application to depression treatment

Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.

The contribution of depressive 'disorder characteristics' to determinations of prognosis for adults with depression: an individual patient data meta-analysis

BACKGROUND

This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care.

METHODS

We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted.

RESULTS

Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3-4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3-4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions.

CONCLUSIONS

When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

Subgroups of comorbid PTSD and AUD in U.S. military veterans predict differential responsiveness to two integrated treatments: A latent class analysis

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) frequently co-occur. Integrated treatments are effective, but not all patients respond and predicting outcome remains difficult. In this study, latent class analysis (LCA) identified symptom-based subgroups of comorbid PTSD/AUD among 119 veterans with PTSD/AUD from a randomized controlled trial of integrated exposure therapy (I-PE) versus integrated coping skills therapy (I-CS). Multilevel models compared subgroups on PTSD severity and percentage of heavy drinking days at post-treatment and 3- and 6-month follow-up. LCA revealed three subgroups best fit the data: Moderate PTSD/Low AUD Impairment (21%), High PTSD/High AUD Impairment (48%), and Low PTSD/High AUD Impairment (31%). There was a three-way interaction between time, treatment condition, and subgroup in predicting PTSD outcomes (p < .05). For the Moderate PTSD/Low AUD Impairment class, outcomes at post-treatment and 3-months were similar (ds = 0.17, 0.55), however I-PE showed greater reductions at 6-months (d = 1.36). For the High PTSD/High AUD Impairment class, I-PE demonstrated better post-treatment (d = 0.83) but comparable follow-up (ds = -0.18, 0.49) outcomes. For the Low PTSD/High AUD Impairment class, I-PE demonstrated stronger outcomes at every timepoint (ds = 0.82-1.15). Heavy drinking days declined significantly through follow-up, with an effect of subgroup, but not treatment, on timing of response. This was the first study modeling how PTSD and AUD symptoms might cluster together in a treatment sample of veterans with PTSD/AUD. Symptom-based subgroups show promise in helping understand variability in treatment response among patients with PTSD/AUD and deserve further study.

Factors Contributing to Parents' Psychological and Medical Help Seeking During the COVID-19 Global Pandemic

The COVID-19 pandemic and related quarantine orders will impact the mental health of millions of individuals in the United States. Mental health difficulties, including depression, anxiety, traumatic stress, and other negative mental health sequelae are likely and likely to persist. These challenges will require response from the psychotherapeutic and medical community that addresses the mental health needs of the population. Using binary logistic regression (n = 322 at time 1, and n = 189 at time 2), researchers in the present study examined promotive factors related to having sought medical or behavioral health treatment during a 30-day period in the midst of the COVID-19 pandemic in the United States. Approximately 10% of the sample indicated having sought either type of help. Results from the binary logistic regressions indicated those who sought counseling or medical help were those who reported increased depression symptoms at time 1. The likelihood of help seeking was heightened for those who reported greater caregiving burden, highlighting the need to consider the availability of services for those caring for children during this community-wide crisis.

Improvement in IAPT outcomes over time: are they driven by changes in clinical practice?

Treatment outcomes across Improving Access to Psychological Therapies (IAPT) services in England have improved year-on-year, with the national average proportion of patients in recovery at the end of treatment now exceeding the 50% target. This is despite the number of referrals and numbers of treated patients also increasing year-on-year, suggesting that services have evolved local practices and treatment delivery to meet needs whilst improving performance. This study explores whether there have been changes in clinical practice with regard to: (1) the number of sessions and length of treatments; (2) the number of cancellations and non-attendance; and (3) the recording of problem descriptor information, and the association with treatment outcomes in IAPT. Routinely collected data from seven IAPT services involved in the North and Central East London (NCEL) IAPT Service Improvement and Research Network (SIRN) were brought together to form a dataset of nearly 88,000 patients who completed a course of IAPT treatment. Results showed that there was a slight increase in the average number of sessions, and decreases in the length of time in treatment, as well as decreases in both the number of non-attended appointments and the use of inappropriate problem descriptors. These findings highlight a number of areas where potentially small changes to clinical practice may have had positive effects on patient outcomes. The value of using IAPT data to inform service improvement evaluations is discussed.

Evidence-based statistical analysis and methods in biomedical research (SAMBR) checklists according to design features

BACKGROUND

Statistical analysis according to design features and objectives is essential to ensure the validity and reliability of the study findings and conclusions in biomedical research. Heterogeneity in reporting study design elements and conducting statistical analyses is often observed for the same study design and study objective in medical literatures. Sometimes, researchers face a lot of predicaments using appropriate statistical approaches highlighted by methodologists for a specific study design either due to lack of accessibility or understanding of statistical methods or unavailability of checklists related to design and analysis in a concise format. The purpose of this review is to provide the checklist of statistical analysis and methods in biomedical research (SAMBR) to applied researchers.

RECENT FINDINGS

We initially identified the important steps of reporting design features that may influence the choice of statistical analysis in biomedical research and essential steps of data analysis of common studies. We subsequently searched for statistical approaches employed for each study design/study objective available in publications and other resources. Compilation of these steps produced SAMBR guidance document, which includes three parts. Applied researchers can use part (A) and part (B) of SAMBR to describe or evaluate research design features and quality of statistical analysis, respectively, in reviewing studies or designing protocols. Part (C) of SAMBR can be used to perform essential and preferred evidence-based data analysis specific to study design and objective.

CONCLUSIONS

We believe that the statistical methods checklists may improve reporting of research design, standardize methodological practices, and promote consistent application of statistical approaches, thus improving the quality of research studies. The checklists do not enforce the use of suggested statistical methods but rather highlight and encourage to conduct the best statistical practices. There is a need to develop an interactive web-based application of the checklists for users for its wide applications.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

Latent variable mixture modelling and individual treatment prediction

Understanding which groups of patients are more or less likely to benefit from specific treatments has important implications for healthcare. Many personalised medicine approaches in mental health employ variable-centred approaches to predicting treatment response, yet person-centred approaches that identify clinical profiles of patients can provide information on the likelihood of a range of important outcomes. In this paper, we discuss the use of latent variable mixture modelling and demonstrate its use in the application of a patient profiling algorithm using routinely collected patient data to predict outcomes from psychological treatments. This validation study analysed data from two services, which included n = 44,905 patients entering treatment. There were different patterns of reliable recovery, improvement and clinical deterioration from therapy, across the eight profiles which were consistent over time. Outcomes varied between different types of therapy within the profiles: there were significantly higher odds of reliable recovery with High Intensity therapies in two profiles (32.5% of patients) and of reliable improvement in three profiles (32.2% of patients) compared with Low Intensity treatments. In three profiles (37.4% of patients) reliable recovery was significantly more likely if patients had CBT vs Counselling. The developments and potential application of latent variable mixture approaches are further discussed.

Trajectories of change of youth depressive symptoms in routine care: shape, predictors, and service-use implications

Depression is one of the main reasons for youth accessing mental health services, yet we know little about how symptoms change once youth are in routine care. This study used multilevel modeling to examine the average trajectory of change and the factors associated with change in depressive symptoms in a large sample of youth seen in routine mental health care services in England. Participants were 2336 youth aged 8-18 (mean age 14.52; 77% females; 88% white ethnic background) who tracked depressive symptoms over a period of up to 32 weeks while in contact with mental health services. Explanatory variables were age, gender, whether the case was closed, total length of contact with services, and baseline severity in depression scores. Faster rates of improvement were found in older adolescents, males, those with shorter time in contact with services, closed cases, and those with more severe symptoms at baseline. This study demonstrates that when youth self-report their depressive symptoms during psychotherapy, symptoms decrease in a linear trajectory. Attention should be paid to younger people, females, and those with lower than average baseline scores, as their symptoms decrease at a slower pace compared to others.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

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.

What factors indicate prognosis for adults with depression in primary care? A protocol for meta-analyses of individual patient data using the Dep-GP database

Background: Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims: 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods: Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions: In total 13 RCTs were found to meet inclusion criteria. The Dep-GP database was formed from the 6271 participants. This protocol outlines how these data will be analysed. Registration: PROSPERO CRD42019129512 (01/04/2019).

How data science can advance mental health research

Accessibility of powerful computers and availability of so-called big data from a variety of sources means that data science approaches are becoming pervasive. However, their application in mental health research is often considered to be at an earlier stage than in other areas despite the complexity of mental health and illness making such a sophisticated approach particularly suitable. In this Perspective, we discuss current and potential applications of data science in mental health research using the UK Clinical Research Collaboration classification: underpinning research; aetiology; detection and diagnosis; treatment development; treatment evaluation; disease management; and health services research. We demonstrate that data science is already being widely applied in mental health research, but there is much more to be done now and in the future. The possibilities for data science in mental health research are substantial.