The development and internal evaluation of a predictive model to identify for whom Mindfulness-Based Cognitive Therapy (MBCT) offers superior relapse prevention for recurrent depression versus maintenance antidepressant medication

Examining what works for whom and how in mindfulness-based cognitive therapy (MBCT) for recurrent depression: moderated-mediation analysis in the PREVENT trial

BACKGROUND

Personalised management of recurrent depression, considering individual patient characteristics, is crucial.

AIMS

This study evaluates the potentially different mediating role of mindfulness skills in managing recurrent depression using mindfulness-based cognitive therapy (MBCT) among people with varying depression severity.

METHOD

Data from the Prevention of Depressive Relapse or Recurrence (PREVENT) trial, comparing MBCT (with antidepressant medication (ADM) tapering support, MBCT-tapering support) versus maintenance-ADM, were used. The study included pre, post, 9-, 12-, 18- and 24-month follow-ups. Adults with ≥3 previous major depressive episodes, in full/partial remission (below threshold for a current episode), on ADM, were assessed for eligibility in primary care practices in the UK. People were randomised (1:1) to MBCT-tapering support or maintenance-ADM. We used the Beck Depression Inventory-II to evaluate depressive symptom changes over the six time points. Pre-post treatment, we employed the Five Facets of Mindfulness Questionnaire to gauge mindfulness skills. Baseline symptom and history variables were used to identify individuals with varying severity profiles. We conducted Latent Profile Moderated-Mediation Growth Mixture Models.

RESULTS

A total of 424 people (mean (s.d.) age = 49.44 (12.31) years; with 325 (76.7%) self-identified as female) were included. A mediating effect of mindfulness skills, between trial arm allocation and the linear rate of depressive symptoms change over 24 months, moderated by depression severity, was observed (moderated-mediation index = -0.27, 95% CI = -0.66, -0.03). Conditional indirect effects were -0.42 (95% CI = -0.78, -0.18) for higher severity (expected mean BDI-II reduction = 10 points), and -0.15 (95% CI = -0.35, -0.02) for lower severity (expected mean BDI-II reduction = 3.5 points).

CONCLUSIONS

Mindfulness skills constitute a unique mechanism driving change in MBCT (versus maintenance-ADM). Individuals with higher depression severity may benefit most from MBCT-tapering support for residual symptoms. It is unclear if these effects apply to those with a current depressive episode. Future research should investigate individuals who are not on medication. This study provides preliminary evidence for personalised management of recurrent depression.

TRIAL REGISTRATION

ISRCTN26666654.

Mindfulness-Based Cognitive Therapy in Clinical Practice: A Systematic Review of Neurocognitive Outcomes and Applications for Mental Health and Well-Being

Background/Objectives: This systematic review outlines the neurocognitive outcomes and mechanisms of mindfulness-based cognitive therapy (MBCT) that influence subjective well-being. MBCT is a clinical intervention that integrates cognitive therapy with mindfulness practices to prevent depression relapses and improve mental health. Methods: The review focuses on the effects of MBCT on brain structure changes, cognitive processes, and emotional regulation, which are related to improvements in subjective well-being. A total of 87 studies were included in the review to assess the effectiveness of MBCT. Results: Evidence from the studies highlights the effectiveness of MBCT in reducing symptoms of depression, anxiety, and stress. MBCT was also shown to enhance cognitive functions and emotional regulation across diverse populations. These findings point to the potential for MBCT to induce neuroplastic changes in the brain and widen the applicability of the treatment for a variety of disorders, calling for further research into long-term benefits and underlying neurobiological mechanisms. Conclusions: The review emphasizes the potential of MBCT to bring about neuroplastic changes, calling for further research into its long-term benefits and the underlying neurobiological mechanisms. This study underlines the need to incorporate multidisciplinary measures by integrating psychology and neuroscience to comprehend comprehensively the effects of MBCT.

Evaluating the performance of personality-based profiling in predicting physical activity

BACKGROUND

Profiling or clustering individuals based on personality and other characteristics is a common statistical approach used in marketing, medicine, and social sciences. This approach improves data simplicity, supports the implementation of a data-driven decision-making process, and guides intervention strategies, such as personalized care. However, the clustering process involves loss of information owing to the discretization of continuous variables. Although any loss of information may be practically or pragmatically acceptable, the amount of information lost and its influence on predicting external outcomes have not yet been systematically investigated.

METHODS

We assessed the accuracy of predicting physical activity using the clustering approach and compared it with the dimensional approach, where variables are used as continuous regressors. This analysis is based on survey data from a sample of 20,573 individuals regarding physical activity and psychological traits, including the Big-Five personality traits.

RESULTS

A four-cluster solution, supported by the standard criterion for determining the number of clusters, achieved no more than 60-70% prediction accuracy of the dimensional approach employing the raw dimensional scale as explanatory variables.

CONCLUSION

The cluster solution suggested by conventional statistical criteria may not be optimal when clusters are used to predict external outcomes.

Initial evaluation of a personalized advantage index to determine which individuals may benefit from mindfulness-based cognitive therapy for suicide prevention

OBJECTIVE

Develop and evaluate a treatment matching algorithm to predict differential treatment response to Mindfulness-Based Cognitive Therapy for suicide prevention (MBCT-S) versus enhanced treatment-as-usual (eTAU).

METHODS

Analyses used data from Veterans at high-risk for suicide assigned to either MBCT-S (n = 71) or eTAU (n = 69) in a randomized clinical trial. Potential predictors (n = 55) included available demographic, clinical, and neurocognitive variables. Random forest models were used to predict risk of suicidal event (suicidal behaviors, or ideation resulting in hospitalization or emergency department visit) within 12 months following randomization, characterize the prediction, and develop a Personalized Advantage Index (PAI).

RESULTS

A slightly better prediction model emerged for MBCT-S (AUC = 0.70) than eTAU (AUC = 0.63). Important outcome predictors for participants in the MBCT-S arm included PTSD diagnosis, decisional efficiency on a neurocognitive task (Go/No-Go), prior-year mental health residential treatment, and non-suicidal self-injury. Significant predictors for participants in the eTAU arm included past-year acute psychiatric hospitalizations, past-year outpatient psychotherapy visits, past-year suicidal ideation severity, and attentional control (indexed by Stroop task). A moderation analysis showed that fewer suicidal events occurred among those randomized to their PAI-indicated optimal treatment.

CONCLUSIONS

PAI-guided treatment assignment may enhance suicide prevention outcomes. However, prior to real-world application, additional research is required to improve model accuracy and evaluate model generalization.

A prognostic index to predict symptom and functional outcomes of a coached, web-based intervention for trauma-exposed veterans

Researchers at the Department of Veterans Affairs (VA) have studied interventions for posttraumatic stress disorder and co-occurring conditions in both traditional and digital formats. One such empirically supported intervention is web skills training in affective and interpersonal regulation (webSTAIR), a coached, 10-module web program based on STAIR. To understand which patient characteristics were predictive of webSTAIR outcomes in a sample of trauma-exposed veterans (N = 189), we used machine learning (ML) to develop a prognostic index from among 18 baseline characteristics (i.e., demographic, military, trauma history, and clinical) to predict posttreatment posttraumatic stress disorder severity, depression severity, and psychosocial functioning impairment. We compared the ML models to a benchmark of linear regression models in which the only predictor was the baseline severity score of the outcome measure. The ML and "severity-only" models performed similarly, explaining 39%-45% of the variance in outcomes. This suggests that baseline symptom severity and functioning are strong indicators for webSTAIR outcomes in veterans, with higher severity indicating worse prognosis, and that the other variables examined did not contribute significant added predictive signal. Findings also highlight the importance of comparing ML models to an appropriate benchmark. Future research with larger samples could potentially detect smaller patient-level effects as well as effects driven by other types of variables (e.g., therapeutic process variables). As a transdiagnostic, digital intervention, webSTAIR can potentially serve a diverse veteran population with varying trauma histories and may be best conceptualized as a beneficial first step of a stepped care model for those with heightened symptoms or impairment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Precision Mental Health and Data-Informed Decision Support in Psychological Therapy: An Example

Outcome measurement including data-informed decision support for therapists in psychological therapy has developed impressively over the past two decades. New technological developments such as computerized data assessment, and feedback tools have facilitated advanced implementation in several seetings. Recent developments try to improve the clinical decision-making process by connecting clinical practice better with empirical data. For example, psychometric data can be used by clinicians to personalize the selection of therapeutic programs, strategies or modules and to monitor a patient's response to therapy in real time. Furthermore, clinical support tools can be used to improve the treatment for patients at risk for a negative outcome. Therefore, measurement-based care can be seen as an important and integral part of clinical competence, practice, and training. This is comparable to many other areas in the healthcare system, where continuous monitoring of health indicators is common in day-to-day clinical practice (e.g., fever, blood pressure). In this paper, we present the basic concepts of a data-informed decision support system for tailoring individual psychological interventions to specific patient needs, and discuss the implications for implementing this form of precision mental health in clinical practice.

Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Identifying who benefits most from supportive versus expressive techniques in psychotherapy for depression: Moderators of within- versus between-individual effects

OBJECTIVE

A recent randomized controlled trial (RCT) indicated that individuals with higher levels of attachment anxiety exhibited better treatment outcomes in supportive-expressive therapy (SET) relative to supportive therapy (ST). But to gain insight into within-patient therapeutic changes, a within-individual design is required. The present study contrasts previous findings based on theory-driven between-patient moderators with data-driven moderators of within-patient processes to investigate whether findings converge or diverge across these two approaches.

METHOD

We used data of 118 patients from the pilot and active phases of a recent RCT for patients with major depressive disorder, comparing ST with SET, a time-limited psychodynamic therapy. The predefined primary outcome measure was the Hamilton Rating Scale for Depression. Supportive versus expressive techniques were rated based on patients' end-of-session perspective. We compared previous findings based on moderators of between-patient effects with a data-driven approach for identifying moderators of within-patient effects of techniques on subsequent outcome.

RESULTS

After false discovery rate corrections, of 10 preselected moderators, patients' attachment anxiety and domineering style remained significant. Of these, bootstrap resampling revealed significant differences between ST and SET techniques for the attachment anxiety moderator: Those with higher attachment anxiety benefited more from greater use of ST than SET techniques in a particular session, as evidenced by lower levels of symptoms at the subsequent session.

CONCLUSIONS

Our within-individual findings diverge from previously published between-individual analyses. This proof-of-concept study demonstrates the importance of complementing between-individuals with within-individual analyses to achieve better understanding of who benefits most from specific treatment techniques. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Identifying relapse predictors in individual participant data with decision trees

BACKGROUND

Depression is a highly common and recurrent condition. Predicting who is at most risk of relapse or recurrence can inform clinical practice. Applying machine-learning methods to Individual Participant Data (IPD) can be promising to improve the accuracy of risk predictions.

METHODS

Individual data of four Randomized Controlled Trials (RCTs) evaluating antidepressant treatment compared to psychological interventions with tapering ([Formula: see text]) were used to identify predictors of relapse and/or recurrence. Ten baseline predictors were assessed. Decision trees with and without gradient boosting were applied. To study the robustness of decision-tree classifications, we also performed a complementary logistic regression analysis.

RESULTS

The combination of age, age of onset of depression, and depression severity significantly enhances the prediction of relapse risk when compared to classifiers solely based on depression severity. The studied decision trees can (i) identify relapse patients at intake with an accuracy, specificity, and sensitivity of about 55% (without gradient boosting) and 58% (with gradient boosting), and (ii) slightly outperform classifiers that are based on logistic regression.

CONCLUSIONS

Decision tree classifiers based on multiple-rather than single-risk indicators may be useful for developing treatment stratification strategies. These classification models have the potential to contribute to the development of methods aimed at effectively prioritizing treatment for those individuals who require it the most. Our results also underline the existing gaps in understanding how to accurately predict depressive relapse.

A common factors perspective on mindfulness-based interventions

Mindfulness-based interventions (MBIs) have entered mainstream Western culture in the past four decades. There are now dozens of MBIs with varying degrees of empirical support and a variety of mindfulness-specific psychological mechanisms have been proposed to account for the beneficial effects of MBIs. Although it has long been acknowledged that non-specific or common factors might contribute to MBI efficacy, relatively little empirical work has directly investigated these aspects. In this Perspective, I suggest that situating MBIs within the broader psychotherapy research literature and emphasizing the commonalities rather than differences between MBIs and other treatments might help guide future MBI research. To that end, I summarize the evidence for MBI efficacy and several MBI-specific psychological mechanisms, contextualize MBI findings within the broader psychotherapy literature from a common factors perspective, and propose suggestions for future research based on innovations and challenges occurring within psychotherapy research.