To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care.

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,<Vinod: Please carry out the deletion of serial commas throughout the article> 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/.

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.

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.

This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data.

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.

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.

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.

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

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.

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.

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.

No evidence-based therapy for borderline personality disorder (BPD) exhibits a clear superiority. However, BPD is highly heterogeneous, and different patients may specifically benefit from the interventions of a particular treatment.

From a randomized trial comparing a year of dialectical behavior therapy (DBT) to general psychiatric management (GPM) for BPD, long-term (2-year-post) outcome data and patient baseline variables (n = 156) were used to examine individual and combined patient-level moderators of differential treatment response. A two-step bootstrapped and partially cross-validated moderator identification process was employed for 20 baseline variables. For identified moderators, 10-fold bootstrapped cross-validated models estimated response to each therapy, and long-term outcomes were compared for patients randomized to their model-predicted optimal v. non-optimal treatment.

Significant moderators surviving the two-step process included psychiatric symptom severity, BPD impulsivity symptoms (both GPM > DBT), dependent personality traits, childhood emotional abuse, and social adjustment (all DBT > GPM). Patients randomized to their model-predicted optimal treatment had significantly better long-term outcomes (d = 0.36, p = 0.028), especially if the model had a relatively stronger (top 60%) prediction for that patient (d = 0.61, p = 0.004). Among patients with a stronger prediction, this advantage held even when applying a conservative statistical check (d = 0.46, p = 0.043).

Patient characteristics influence the degree to which they respond to two treatments for BPD. Combining information from multiple moderators may help inform providers and patients as to which treatment is the most likely to lead to long-term symptom relief. Further research on personalized medicine in BPD is needed.

Psychotherapies for depression are equally effective on average, but individual responses vary widely. Outcomes can be improved by optimizing treatment selection using multivariate prediction models. A promising approach is the Personalized Advantage Index (PAI) that predicts the optimal treatment for a given individual and the magnitude of the advantage. The current study aimed to extend the PAI to long-term depression outcomes after acute-phase psychotherapy.

Data come from a randomized trial comparing cognitive therapy (CT, n = 76) and interpersonal psychotherapy (IPT, n = 75) for major depressive disorder (MDD). Primary outcome was depression severity, as assessed by the BDI-II, during 17-month follow-up. First, predictors and moderators were selected from 38 pre-treatment variables using a two-step machine learning approach. Second, predictors and moderators were combined into a final model, from which PAI predictions were computed with cross-validation. Long-term PAI predictions were then compared to actual follow-up outcomes and post-treatment PAI predictions.

One predictor (parental alcohol abuse) and two moderators (recent life events; childhood maltreatment) were identified. Individuals assigned to their PAI-indicated treatment had lower follow-up depression severity compared to those assigned to their PAI-non-indicated treatment. This difference was significant in two subsets of the overall sample: those whose PAI score was in the upper 60%, and those whose PAI indicated CT, irrespective of magnitude. Long-term predictions did not overlap substantially with predictions for acute benefit.

If replicated, long-term PAI predictions could enhance precision medicine by selecting the optimal treatment for a given depressed individual over the long term.

The Hamilton Depression Rating Scale (HAMD) and the Beck Depression Inventory (BDI) are the most frequently used observer-rated and self-report scales of depression, respectively. It is important to know what a given total score or a change score from baseline on one scale means in relation to the other scale.

We obtained individual participant data from the randomised controlled trials of psychological and pharmacological treatments for major depressive disorders. We then identified corresponding scores of the HAMD and the BDI (369 patients from seven trials) or the BDI-II (683 patients from another seven trials) using the equipercentile linking method.

The HAMD total scores of 10, 20 and 30 corresponded approximately with the BDI scores of 10, 27 and 42 or with the BDI-II scores of 13, 32 and 50. The HAMD change scores of −20 and −10 with the BDI of −29 and −15 and with the BDI-II of −35 and −16.

The results can help clinicians interpret the HAMD or BDI scores of their patients in a more versatile manner and also help clinicians and researchers evaluate such scores reported in the literature or the database, when scores on only one of these scales are provided. We present a conversion table for future research.

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.

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.

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.

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, I 2 = 0%).

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.

This study examines the structure of the Personality Belief Questionnaire (PBQ), a self-report instrument designed to assess dysfunctional beliefs associated with personality pathology, as proposed by the cognitive theory of personality dysfunction.

The PBQ was examined using exploratory factor analysis (EFA) with responses from 438 depressed out-patients, and confirmatory factor analysis (CFA) with responses from 683 treatment-seeking psychiatric out-patients. All participants were assessed for personality disorder (PD) using a standard clinical interview. The validity of the resulting factor structure was assessed in the combined sample (n=1121) by examining PBQ scores for patients with and without PD diagnoses.

Exploratory and confirmatory analyses converged to indicate that the PBQ is best described by seven empirically identified factors: six assess dysfunctional beliefs associated with forms of personality pathology recognized in DSM-IV. Validity analyses revealed that those diagnosed with a PD evidenced a higher average score on all factors, relative to those without these disorders. Subsets of patients diagnosed with specific DSM-IV PDs scored higher, on average, on the factor associated with their respective diagnosis, relative to all other factors.

The pattern of results has implications for the conceptualization of personality pathology. To our knowledge, no formal diagnostic or assessment system has yet systematically incorporated the role of dysfunctional beliefs into its description of personality pathology. The identification of dysfunctional beliefs may not only aid in case conceptualization but also may provide unique targets for psychological treatment. Recommendations for future personality pathology assessment systems are provided.

This study examined therapist–patient interactions during clinical management with antidepressant medication and pill-placebo.

The sample consisted of 80 patients on active medication and 40 patients in a pill-placebo condition from a randomized controlled trial for moderate to severe depression. Pharmacotherapist–patient interactions were characterized using observer ratings of the therapeutic alliance, pharmacotherapist-offered facilitative conditions, pharmacotherapist adherence to clinical management treatment guidelines and pharmacotherapist competence. Patients, therapists and raters were blind to treatment condition and outcome.

Provision of greater non-specific support (facilitative conditions) in early sessions predicted less subsequent improvement in depressive symptoms for patients receiving pill-placebo but not those receiving active medications, for which none of the process ratings predicted subsequent change. Early symptom change predicted later alliance and adherence in both conditions and therapist competence in the active condition.

Higher levels of support in early sessions predict poorer subsequent response among placebo patients. It remains unclear whether patients who are likely to be refractory elicit greater non-specific support or whether the provision of such support has a deleterious effect in unmedicated patients. Differences in treatment process variables between conditions late in treatment are likely to be largely a consequence of symptom relief produced by active medications.