Patients' judgments of the importance of treatment-induced reductions in symptoms of depression: The role of specific symptoms, magnitudes of change, and post-treatment levels.

Objective: An implicit assumption in the use of depressive severity measures to assess change during treatment, such as the Hamilton Rating Scale for Depression (HRSD), is that reductions from pre- to post-treatment that are equal to each other are of equal value. However, stakeholders' valuations of changes might depart substantially from this assumption. Method: Vignettes were constructed that reflected the six possible 1, 2, and 3-point reductions on five cognitive and four somatic symptoms derived from the HRSD. Former or currently depressed patients provided judgments of the importance of the symptom reductions. Mean importance ratings were modeled using symptom category and the pre/post-treatment combination. Differences were explored using the Tukey method. Results: Results indicated that mean ratings, from most to least important, were: Anxiety, Suicide, Depressed Mood, Work, and Guilt (the cognitive symptoms) followed by Somatic, Sleep, Appetite & Weight, and Retardation (the somatic symptoms). Participants valued reductions that resulted in posttreatment scores of zero more than expected, given the magnitude of the reductions. Conclusions: The value of reductions in symptoms captured by the HRSD, as judged by patients, appears to differ as a function of symptom category and the post-treatment score. Similar patterns might characterize other measures of depression severity.

Treatment selection in borderline personality disorder between dialectical behavior therapy and psychodynamic psychiatric management.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Precision medicine for long-term depression outcomes using the Personalized Advantage Index approach: cognitive therapy or interpersonal psychotherapy?

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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 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.

Smartphone apps for eating disorders: A systematic review of evidence-based content and application of user-adjusted analyses.

OBJECTIVE: To examine the frequency of evidence-based treatment elements in popular smartphone apps for eating disorders (EDs), and to characterize the extent to which real-world users encounter different elements. METHOD: We searched the Apple App Store and Google Play Store for apps offering treatment or support to individuals with EDs. Then, we created a codebook of 47 elements found in evidence-based treatments for EDs. We examined the presence or absence of each element within each ED app. We also acquired estimates of the monthly active users (MAU) of each app. RESULTS: The ED apps (n = 28) included a median of nine elements of empirically supported treatments (mean = 9.46, SD = 6.28). Four apps accounted for 96% of all MAU. MAU-adjusted analyses revealed that several elements are reaching more users than raw frequency tallies would suggest. For example, assessments were included in 32% of apps, but 84% of users used an app with assessments. Similar trends were found for cognitive restructuring (21% of apps, 56% of MAU), activity scheduling (39%, 57%), and self-monitoring (14%, 46%). Problem solving, exposure, and relapse prevention strategies, elements that are prominent in face-to-face empirically supported treatments, were rarely included in the apps. DISCUSSION: Evidence-based content is commonly included in ED apps, with certain elements reaching more users than others. Additionally, the top four apps are responsible for nearly all active users. We recommend that ED clinicians and researchers familiarize themselves with these apps-those that patients are most likely to encounter.

Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression.

Objective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n = 151 and FreqMech: n = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment (d = .57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment (d = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment (d = .16 and d = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.

Interpersonal Psychotherapy Versus Cognitive Therapy for Depression: How They Work, How Long, and for Whom-Key Findings From an RCT.

OBJECTIVE: Although the effectiveness of interpersonal psychotherapy (IPT) and cognitive therapy (CT) for major depression has been established, little is known about how and for whom they work and how they compare in the long term. The latter is especially relevant for IPT because research on its long-term effects has been limited. This overview paper summarizes findings from a Dutch randomized controlled trial on the effects and mechanisms of change of IPT versus CT for major depression. METHODS: Adult outpatients with depression (N=182) were randomly assigned to CT (N=76), IPT (N=75), or a 2-month waitlist control group followed by patient's treatment of choice (N=31). The primary outcome was depression severity. Other outcomes were quality of life, social and general psychological functioning, and scores on various mechanism measures. Interventions were compared at the end of treatment and up to 17 months follow-up. RESULTS: On average, IPT and CT were both superior to waitlist, and their outcomes did not differ significantly from one another. However, the pathway through which change occurred appeared to differ. For a majority of participants, one of the interventions was predicted to be more beneficial than the other. No support for the theoretical models of change was found. CONCLUSIONS: Outcomes of IPT and CT did not appear to differ significantly. IPT may have an enduring effect not different from that of CT. The field would benefit from further refinement of study methods to disentangle mechanisms of change and from advances in the field of personalized medicine (i.e., person-specific analyses and treatment selection methods).

Personalized prediction of antidepressant v. placebo response: evidence from the EMBARC study.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition in terms of symptom presentation and, likely, underlying pathophysiology. Accordingly, it is possible that only certain individuals with MDD are well-suited to antidepressants. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes of depression, such as neuroticism, anhedonia, and cognitive control deficits. METHODS: Within an 8-week multisite trial of sertraline v. placebo for depressed adults (n = 216), we examined whether the combination of machine learning with a Personalized Advantage Index (PAI) can generate individualized treatment recommendations on the basis of endophenotype profiles coupled with clinical and demographic characteristics. RESULTS: Five pre-treatment variables moderated treatment response. Higher depression severity and neuroticism, older age, less impairment in cognitive control, and being employed were each associated with better outcomes to sertraline than placebo. Across 1000 iterations of a 10-fold cross-validation, the PAI model predicted that 31% of the sample would exhibit a clinically meaningful advantage [post-treatment Hamilton Rating Scale for Depression (HRSD) difference ⩾3] with sertraline relative to placebo. Although there were no overall outcome differences between treatment groups (d = 0.15), those identified as optimally suited to sertraline at pre-treatment had better week 8 HRSD scores if randomized to sertraline (10.7) than placebo (14.7) (d = 0.58). CONCLUSIONS: A subset of MDD patients optimally suited to sertraline can be identified on the basis of pre-treatment characteristics. This model must be tested prospectively before it can be used to inform treatment selection. However, findings demonstrate the potential to improve individual outcomes through algorithm-guided treatment recommendations.

Predictive modeling for response to lithium and quetiapine in bipolar disorder.

OBJECTIVES: Lithium and quetiapine are known to be effective treatments for bipolar disorder. However, little information is available to inform prediction of response to these medications. Machine-learning methods can identify predictors of response by examining variables simultaneously. Further evaluation of models on a test sample can estimate how well these models would generalize to other samples. METHODS: Data (N = 482) were drawn from a randomized clinical trial of outpatients with bipolar I or II disorder who received adjunctive personalized treatment plus either lithium or quetiapine. Elastic net regularization (ENR) was used to generate models for lithium and quetiapine; these models were evaluated on a test set. RESULTS: Predictions from the lithium model explained 17.4% of the variance in actual observed scores of patients who received lithium in the test set, while predictions from the quetiapine model explained 32.1% of the variance of patients that received quetiapine. Of the baseline variables selected, those with the largest parameter estimates were: severity of mania; attention-deficit/hyperactivity disorder (ADHD) comorbidity; nonsuicidal self-injurious behavior; employment; and comorbidity with each of two anxiety disorders (social phobia/society anxiety and agoraphobia). Predictive accuracy of the ENR model outperformed the simple and basic theoretical models. CONCLUSION: ENR is an effective approach for building optimal and generalizable models. Variables identified through this methodology can inform future research on predictors of response to lithium and quetiapine, as well as future modeling efforts of treatment choice in bipolar disorder.

A prognostic index for long-term outcome after successful acute phase cognitive therapy and interpersonal psychotherapy for major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) has a highly recurrent nature. After successful treatment, it is important to identify individuals who are at risk of an unfavorable long-term course. Despite extensive research, there is no consensus yet on the clinically relevant predictors of long-term outcome in MDD, and no prediction models are implemented in clinical practice. The aim of this study was to create a prognostic index (PI) to estimate long-term depression severity after successful and high quality acute treatment for MDD. METHODS: Data come from responders to cognitive therapy (CT) and interpersonal psychotherapy (IPT) in a randomized clinical trial (n = 85; CT = 45, IPT = 40). Primary outcome was depression severity, assessed with the Beck Depression Inventory II, measured throughout a 17-month follow-up phase. We examined 29 variables as potential predictors, using a model-based recursive partitioning method and bootstrap resampling in conjunction with backwards elimination. The selected predictors were combined into a PI. Individual PI scores were estimated using a cross-validation approach. RESULTS: A total of three post-treatment predictors were identified: depression severity, hopelessness, and self-esteem. Cross-validated PI scores evidenced a strong correlation (r = 0.60) with follow-up depression severity. CONCLUSION: Long-term predictions of MDD are multifactorial, involving a combination of variables that each has a small prognostic effect. If replicated and validated, the PI can be implemented to predict follow-up depression severity for each individual after acute treatment response, and to personalize long-term treatment strategies.

Changing character: A narrative review of personality change in psychotherapies for personality disorder.

Objective: Personality disorder (PD) is a negative prognostic indicator for treatment, and absolute improvements in functioning among these patients are often modest. This may be because personality features that give rise to dysfunction in PD are not targeted optimally during most treatments. Method: Attachment, mentalization, core beliefs, and personality organization/defense use were identified as personality constructs that have been pursued in treatment studies and that are proposed to underlie PD. Results: All constructs correlate with psychiatric symptoms, PD diagnosis, and functioning. Defense mechanisms and core beliefs further distinguish specific PDs, whereas personality organization separates more versus less severe PDs. Evidence from treatment and naturalistic studies indicate that maturation of defense mechanisms temporally precedes improvements in symptoms and functioning. Changes in attachment and mentalization correlate with some outcomes, but mediation of improvement has not been established. In psychodynamic therapy, transference interpretations may promote amelioration of personality dysfunction. With the exception of attachment, the experimental literature is lacking that could explicate the mechanisms by which these personality constructs maintain psychosocial dysfunction. Conclusions: Future research should aim to identify changes in these mechanisms that mediate positive outcomes in PD, as well as the specific therapeutic procedures that best promote positive change in PD.

Focus is key: Panic-focused interpretations are associated with symptomatic improvement in panic-focused psychodynamic psychotherapy.

Objective: This study examines whether, in panic-focused psychodynamic psychotherapy (PFPP), interpretations of conflicts that underlie anxiety (panic-focused or PF-interpretations) are specifically associated with subsequent panic disorder (PD) symptom improvement, over and above the provision of non-symptom-focused interpretations. Method: Technique use in Sessions 2 and 10 of a 24-session PFPP protocol was assessed for the 65 patients with complete outcome data randomized to PFPP in a two-site trial of psychotherapies for PD. Sessions were rated in 15-min segments for therapists' use of PF-interpretations, non-PF-interpretations, and PF-clarifications. Robust regressions were conducted to examine the relationship between these interventions and symptom change subsequent to the sampled session. Interpersonal problems were examined as a moderator of the relationship of PF-interpretations to symptom change. Results: At Session 10, but not at Session 2, patients who received a higher degree of PF-interpretations experienced greater subsequent improvement in panic symptoms. Non-PF-interpretations were not predictive. Patients with more interpersonal distress benefitted particularly from the use of PF-interpretations at Session 10. Conclusions: By the middle phase of PFPP, panic-focused interpretations may drive subsequent improvements in panic symptoms, especially among patients with higher interpersonal distress. Interpretations of conflict absent a panic focus may not be especially helpful.

In rape trauma PTSD, patient characteristics indicate which trauma-focused treatment they are most likely to complete.

BACKGROUND: Dropout rates for effective therapies for posttraumatic stress disorder (PTSD) can be high, especially in practice settings. Although clinicians have intuitions regarding what treatment patients may complete, there are few systematic data to drive those judgments. METHODS: A multivariable model of dropout risk was constructed with randomized clinical trial data (n = 160) comparing prolonged exposure (PE) and cognitive processing therapy (CPT) for rape-induced PTSD. A two-step bootstrapped variable selection algorithm was applied to identify moderators of dropout as a function of treatment condition. Employing identified moderators in a model, fivefold cross-validation yielded estimates of dropout probability for each patient in each condition. Dropout rates between patients who did and did not receive their model-indicated treatment were compared. RESULTS: Despite equivalent dropout rates across treatments, patients assigned to their model-indicated treatment were significantly less likely to drop out relative to patients who did not (relative risk = 0.49 [95% CI: 0.29-0.82]). Moderators included in the model were: childhood physical abuse, current relationship conflict, anger, and being a racial minority, all of which were associated with higher likelihood of dropout in PE than CPT. CONCLUSIONS: Individual differences among patients affect the likelihood they will complete a particular treatment, and clinicians can consider these moderators in treatment planning. In the future, treatment selection models could be used to increase the percentage of patients who will receive a full course of treatment, but replication and extension of such models, and consideration of how best to integrate them into routine practice, are needed.

Treatment Selection in Depression.

Mental health researchers and clinicians have long sought answers to the question "What works for whom?" The goal of precision medicine is to provide evidence-based answers to this question. Treatment selection in depression aims to help each individual receive the treatment, among the available options, that is most likely to lead to a positive outcome for them. Although patient variables that are predictive of response to treatment have been identified, this knowledge has not yet translated into real-world treatment recommendations. The Personalized Advantage Index (PAI) and related approaches combine information obtained prior to the initiation of treatment into multivariable prediction models that can generate individualized predictions to help clinicians and patients select the right treatment. With increasing availability of advanced statistical modeling approaches, as well as novel predictive variables and big data, treatment selection models promise to contribute to improved outcomes in depression.

Cross-sectional networks of depressive symptoms before and after antidepressant medication treatment.

PURPOSE: Recent reviews have questioned the efficacy of selective serotonin reuptake inhibitors (SSRIs) above placebo response, and their working mechanisms remain unclear. New approaches to understanding the effects of SSRIs are necessary to enhance their efficacy. The aim of this study was to explore the possibilities of using cross-sectional network analysis to increase our understanding of symptom connectivity before and after SSRI treatment. METHODS: In two randomized controlled trials (total N = 178), we estimated Gaussian graphical models among 20 symptoms of the Beck Depression Inventory-II before and after 8 weeks of treatment with the SSRI paroxetine. Networks were compared on connectivity, community structure, predictability (proportion explained variance), and strength centrality (i.e., connectedness to other symptoms in the network). RESULTS: Symptom severity for all individual BDI-II symptoms significantly decreased over 8 weeks of SSRI treatment, whereas interconnectivity and predictability of the symptoms significantly increased. At baseline, three communities were detected; five communities were detected at week 8. CONCLUSIONS: Findings suggest the effects of SSRIs can be studied using the network approach. The increased connectivity, predictability, and communities at week 8 may be explained by the decrease in depressive symptoms rather than specific effects of SSRIs. Future studies with larger samples and placebo controls are needed to offer insight into the effects of SSRIs. TRIAL REGISTRATION: The trials described in this manuscript were funded by the NIMH. Pennsylvania/Vanderbilt study: 5 R10 MH55877 ( https://projectreporter.nih.gov/project_info_description.cfm?aid=6186633&icde=28344168&ddparam=&ddvalue=&ddsub=&cr=1&csb=default&cs=ASC&MMOpt= ). Washington study: R01 MH55502 ( https://projectreporter.nih.gov/project_info_description.cfm?aid=2034618&icde=28344217&ddparam=&ddvalue=&ddsub=&cr=5&csb=default&cs=ASC ).

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

BackgroundThe 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.AimsTo 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.MethodA 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.ResultsWe 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%).ConclusionsPatients 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.

Comparison of treatment outcome using two definitions of rapid cycling in subjects with bipolar II disorder.

OBJECTIVES: We examined differences in treatment outcome between Diagnostic and Statistical Manual Fourth Edition (DSM-IV)-defined rapid cycling and average lifetime-defined rapid cycling in subjects with bipolar II disorder. We hypothesized that, compared with the DSM-IV definition, the average lifetime definition of rapid cycling may better identify subjects with a history of more mood lability and a greater likelihood of hypomanic symptom induction during long-term treatment. METHODS: Subjects ≥18 years old with a bipolar II major depressive episode (n=129) were categorized into DSM-IV- and average lifetime-defined rapid cycling and prospectively treated with either venlafaxine or lithium monotherapy for 12 weeks. Responders (n=59) received continuation monotherapy for six additional months. RESULTS: These exploratory analyses found moderate agreement between the two rapid-cycling definitions (κ=0.56). The lifetime definition captured subjects with more chronic courses of bipolar II depression, whereas the DSM-IV definition captured subjects with more acute symptoms of hypomania. There was no difference between rapid-cycling definitions with respect to the response to acute venlafaxine or lithium monotherapy. However, the lifetime definition was slightly superior to the DSM-IV definition in identifying subjects who went on to experience hypomanic symptoms during continuation therapy. CONCLUSIONS: Although sample sizes were limited, the findings suggest that the lifetime definition of rapid cycling may identify individuals with a chronic rapid-cycling course and may also be slightly superior to the DSM-IV definition in identifying individuals with hypomania during relapse-prevention therapy. These findings are preliminary in nature and need replication in larger, prospective, bipolar II studies.

Melancholic and atypical depression as predictor and moderator of outcome in cognitive behavior therapy and pharmacotherapy for adult depression.

BACKGROUND: Melancholic and atypical depression are widely thought to moderate or predict outcome of pharmacological and psychological treatments of adult depression, but that has not yet been established. This study uses the data from four earlier trials comparing cognitive behavior therapy (CBT) versus antidepressant medications (ADMs; and pill placebo when available) to examine the extent to which melancholic and atypical depression moderate or predict outcome in an "individual patient data" meta-analysis. METHODS: We conducted a systematic search for studies directly comparing CBT versus ADM, contacted the researchers, integrated the resulting datasets from these studies into one big dataset, and selected the studies that included melancholic or atypical depressive subtyping according to DSM-IV criteria at baseline (n = 4, with 805 patients). After multiple imputation of missing data at posttest, mixed models were used to conduct the main analyses. RESULTS: In none of the analyses was melancholic or atypical depression found to significantly moderate outcome (indicating a better or worse outcome of these patients in CBT compared to ADM; i.e., an interaction), predict outcome independent of treatment group (i.e., a main effect), or predict outcome within a given modality. The outcome differences between patients with melancholia or atypical depression versus those without were consistently very small (all effect sizes g < 0.10). CONCLUSIONS: We found no indication that melancholic or atypical depressions are significant or relevant moderators or predictors of outcome of CBT and ADM.

Psychometric Properties of the Reconstructed Hamilton Depression and Anxiety Scales.

Although widely used, the Hamilton Rating Scale for Depression (HRSD) and Hamilton Anxiety Rating Scale (HARS) discriminate poorly between depression and anxiety. To address this problem, Riskind, Beck, Brown, and Steer (J Nerv Ment Dis. 175:474-479, 1987) created the Reconstructed Hamilton Scales by reconfiguring HRSD and HARS items into modified scales. To further analyze the reconstructed scales, we examined their factor structure and criterion-related validity in a sample of patients with major depressive disorder and no comorbid anxiety disorders (n = 215) or with panic disorder and no comorbid mood disorders (n = 149). Factor analysis results were largely consistent with those of Riskind et al. The correlation between the new reconstructed scales was small. Compared with the original scales, the new reconstructed scales correlated more strongly with diagnosis in the expected direction. The findings recommend the use of the reconstructed HRSD over the original HRSD but highlight problems with the criterion-related validity of the original and reconstructed HARS.

Short-term venlafaxine v. lithium monotherapy for bipolar type II major depressive episodes: effectiveness and mood conversion rate.

BACKGROUND: Controversy exists over antidepressant use in bipolar II depression. AIMS: To compare the safety and effectiveness of antidepressantv.mood stabiliser monotherapy for bipolar type II major depressive episodes. METHOD: Randomised, double-blind, parallel-group, 12-week comparison of venlafaxine (n= 65)v.lithium (n= 64) monotherapy in adult out-patients (trial registration numberNCT00602537). RESULTS: Primary outcome - venlafaxine produced a greater response rate (67.7%)v lithium (34.4%,P<0.001). Secondary outcomes - venlafaxine produced a greater remission rate (58.5%v 28.1%,P<0.001); greater decline in depression symptom scores over time (ß = -5.32, s.e. = 1.16, χ(2)= 21.19,P<0.001); greater reduction in global severity scores over time (ß = -1.05, s.e. = 0.22, w(2)= 22.33,P<0.001); and greater improvement in global change scores (ß = -1.31, s.e. = 0.32, χ(2)= 16.95,P<0.001) relative to lithium. No statistically significant or clinically meaningful differences in hypomanic symptoms were observed between treatments. CONCLUSIONS: These findings suggest that short-term venlafaxine monotherapy may provide effective antidepressant treatment for bipolar II depression without a statistically significant increase in hypomanic symptoms relative to lithium.