Initial severity of depression and efficacy of cognitive-behavioural therapy: individual-participant data meta-analysis of pill-placebo-controlled trials

BACKGROUND: The influence of baseline severity has been examined for antidepressant medications but has not been studied properly for cognitive-behavioural therapy (CBT) in comparison with pill placebo. AIMS: To synthesise evidence regarding the influence of initial severity on efficacy of CBT from all randomised controlled trials (RCTs) in which CBT, in face-to-face individual or group format, was compared with pill-placebo control in adults with major depression. METHOD: A systematic review and an individual-participant data meta-analysis using mixed models that included trial effects as random effects. We used multiple imputation to handle missing data. RESULTS: We identified five RCTs, and we were given access to individual-level data (n = 509) for all five. The analyses revealed that the difference in changes in Hamilton Rating Scale for Depression between CBT and pill placebo was not influenced by baseline severity (interaction P = 0.43). Removing the non-significant interaction term from the model, the difference between CBT and pill placebo was a standardised mean difference of -0.22 (95% CI -0.42 to -0.02, P = 0.03, I2 = 0%). CONCLUSIONS: Patients suffering from major depression can expect as much benefit from CBT across the wide range of baseline severity. This finding can help inform individualised treatment decisions by patients and their clinicians.R01 MH060998 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; K02 MH001697 - NIMH NIH HHS; R01 MH060713 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

The Development and Internal Evaluation of a Predictive Model to Identify for Whom Mindfulness-Based Cognitive Therapy Offers Superior Relapse Prevention for Recurrent Depression Versus Maintenance Antidepressant Medication

Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to mindfulness-based cognitive therapy (MBCT). Using previously published data ( N = 424), we constructed prognostic models using elastic-net regression that combined demographic, clinical, and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: area under the curve [AUC] = .68) predicted relapse better than baseline depression severity (AUC = .54; one-tailed DeLong’s test: z = 2.8, p = .003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared with individuals who maintained ADM (48% vs. 70% relapse, respectively; superior survival times, z = −2.7, p = .008). For individuals with moderate to good ADM prognoses, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression

Associations between socioeconomic status and psychological therapy outcomes: A systematic review and meta-analysis

Background: Socioeconomic deprivation is associated with higher prevalence of mental health problems; however, the influence of socioeconomic status (SES) on psychological therapy outcomes is as yet unclear. Aim: To review published evidence on the association between indicators of SES (income, education, employment, neighbourhood deprivation, social position) and the outcomes of psychological interventions for depression and anxiety. Methods: Systematic review and meta-analysis of outcomes research studies published in the last 10 years. Results: Seventeen studies including 165,574 patients measured at least one indicator of SES and its relationship with psychological therapy outcomes. Twelve of these studies found significant relationships between SES measures and mental health outcomes. Six studies focusing on employment status offered sufficient quantitative information to conduct meta-analysis. The overall effect of employment was not significant (-0.66, C.I. -1.33, 0.02). A sensitivity analysis (k=5) showed a small effect (-0.22, C.I. -0.36, -0.09) of employment on treatment outcomes. Conclusions: There is some evidence to indicate that socioeconomic deprivation is associated with poorer treatment outcomes, although limitations of the available data warrant treating this as a preliminary conclusion

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

The personalized advantage index: Translating research on prediction into individualized treatment recommendations. A demonstration

Background: Advances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations. Objective: To illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison. Method: Data from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units. Results: For 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their "Optimal" treatment versus those assigned to their "Non-optimal" treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17-1.01). Conclusions: This approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments. © 2014 DeRubeis et al

Cognitive therapy and interpersonal psychotherapy for major depressive disorder:how do they work, how long, and for whom?

BACKGROUND: Although the effectiveness of cognitive therapy (ct) and interpersonal psychotherapy (ipt) for depression has been well established, little is known about how, how long and for whom they work.&lt;br/&gt; AIM: To summarize findings from a large rct to the (differential) effects and mechanisms of change of ct/ipt for depression.&lt;br/&gt; METHOD: 182 adult depressed outpatients were randomized to ct (n = 76), ipt (n = 75), or a two-month wait-list-control condition (n = 31). Primary outcome was depression severity (bdi-ii). Other outcomes were quality of life, social and general psychological functioning and various potential process measures. Interventions were compared at the end of treatment, and up to 17 months follow-up.&lt;br/&gt; RESULTS: Overall, ct and ipt were both superior to the wait-list, but did not differ significantly from one another. However, the pathway through which therapeutic change occurred appeared to be different for ct and ipt, and many patients were predicted to have a clinically meaningful advantage in one of the two interventions. We did not find empirical support for the theoretical models of change.&lt;br/&gt; CONCLUSION: (Long-term) outcomes of ct and ipt appear to not differ significantly. The field would benefit from further refinement of research methods to disentangle mechanisms of change, and from advances in the field of personalized medicine.</p

Life events and treatment prognosis for depression: A systematic review and individual patient data meta-analysis

Objective: To investigate associations between major life events and prognosis independent of treatment type: (1) after adjusting for clinical prognostic factors and socio-demographics; (2) amongst patients with depressive episodes at least six-months long; and (3) patients with a first life-time depressive episode. // Methods: Six RCTs of adults seeking treatment for depression in primary care met eligibility criteria, individual patient data (IPD) were collated from all six (n = 2858). Participants were randomized to any treatment and completed the same baseline assessment of life events, demographics and clinical prognostic factors. Two-stage random effects meta-analyses were conducted. // Results: Reporting any major life events was associated with poorer prognosis regardless of treatment type. Controlling for baseline clinical factors, socio-demographics and social support resulted in minimal residual evidence of associations between life events and treatment prognosis. However, removing factors that might mediate the relationships between life events and outcomes reporting: arguments/disputes, problem debt, violent crime, losing one's job, and three or more life events were associated with considerably worse prognoses (percentage difference in 3–4 months depressive symptoms compared to no reported life events =30.3%(95%CI: 18.4–43.3)). // Conclusions: Assessing for clinical prognostic factors, social support, and socio-demographics is likely to be more informative for prognosis than assessing self-reported recent major life events. However, clinicians might find it useful to ask about such events, and if they are still affecting the patient, consider interventions to tackle problems related to those events (e.g. employment support, mediation, or debt advice). Further investigations of the efficacy of such interventions will be important