Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse.

Background: Knowledge about patient characteristics predicting treatment dropout for post-traumatic stress disorder (PTSD) is scarce, whereas more understanding about this topic may give direction to address this important issue. Method: Data were obtained from a randomized controlled trial in which a phase-based treatment condition (Eye Movement Desensitization and Reprocessing [EMDR] therapy preceded by Skills Training in Affect and Interpersonal Regulation [STAIR]; n = 57) was compared with a direct trauma-focused treatment (EMDR therapy only; n = 64) in people with a PTSD due to childhood abuse. All pre-treatment variables included in the trial were examined as possible predictors for dropout using machine learning techniques. Results: For the dropout prediction, a model was developed using Elastic Net Regularization. The ENR model correctly predicted dropout in 81.6% of all individuals. Males, with a low education level, suicidal thoughts, problems in emotion regulation, high levels of general psychopathology and not using benzodiazepine medication at screening proved to have higher scores on dropout. Conclusion: Our results provide directions for the development of future programs in addition to PTSD treatment or for the adaptation of current treatments, aiming to reduce treatment dropout among patients with PTSD due to childhood abuse.

Does psychological process change during psychotherapy predict long-term depression outcome after successful cognitive therapy or interpersonal psychotherapy? Secondary analysis of a randomized trial.

Objective Psychotherapies for depression are similarly effective, but the processes through which these therapies work have not been identified. We focus on psychological process changes during therapy as predictors of long-term depression outcome in treatment responders. Method: Secondary analysis of a randomized trial comparing cognitive therapy (CT) and interpersonal psychotherapy (IPT) that focuses on 85 treatment responders. Using mixed-effects models, changes during therapy (0-7 months) on nine process variables were associated with depression severity (BDI-II) at follow-up (7-24 months). Results: A decrease in dysfunctional attitudes was associated with a decrease in depression scores over time. Improved self-esteem was associated with less depression at follow-up (borderline significant). More improvement in both work and social functioning and interpersonal problems was associated with better depression outcomes in IPT relative to CT, while less improvement in work and social functioning and interpersonal problems was associated with better outcomes in CT relative to IPT. Conclusions: Less negative thinking during therapy is associated with lower depression severity in time, while changes during therapy in work and social functioning and interpersonal problems appear to predict different long-term outcomes in CT vs. IPT. If replicated, these findings can be used to guide clinical decision-making during psychotherapy.

Individual differences in response to once versus twice weekly sessions of CBT and IPT for depression.

OBJECTIVE: The Personalized Advantage Index (PAI) is a method to guide treatment selection by investigating which of two or more treatments is optimal for a given individual. Recently, it was shown that, on average, twice-weekly sessions of psychotherapy for depression lead to better outcomes compared to once-weekly sessions. The present study applied the PAI method to assess if subgroups of patients may have a differential response to psychotherapy frequency. METHOD: Data came from a clinical trial (n = 200) randomizing depressed patients into different session frequencies: weekly sessions versus twice-weekly sessions. Machine-learning techniques were used to select pretreatment variables and develop a multivariable prediction model that calculated each patient's PAI. Differences in observed depression post-treatment scores (Beck Depression Inventory-II [BDI-II]) were tested between patients that received their PAI-indicated versus non-indicated session frequency. Between-group effect sizes (Cohen's d) were reported. RESULTS: We identified prognostic indicators generally associated with lower post-treatment BDI-II regardless of treatment assignment. In addition, we identified specific demographic and psychometric features associated with differential response to weekly- versus twice-weekly therapy sessions. Observed post-treatment BDI-II scores were significantly different between individuals receiving the PAI-indicated versus non-indicated session frequency (d = .37). CONCLUSIONS: Although a higher session frequency is more effective on average, different session frequencies seem beneficial for different patients. Future studies should externally validate these findings before they can be generalized to other settings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Interrogating Associations Between Polygenic Liabilities and Electroconvulsive Therapy Effectiveness.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective treatment for severe major depressive episodes (MDEs). Nonetheless, firmly established associations between ECT outcomes and biological variables are currently lacking. Polygenic risk scores (PRSs) carry clinical potential, but associations with treatment response in psychiatry are seldom reported. Here, we examined whether PRSs for major depressive disorder, schizophrenia (SCZ), cross-disorder, and pharmacological antidepressant response are associated with ECT effectiveness. METHODS: A total of 288 patients with MDE from 3 countries were included. The main outcome was a change in the 17-item Hamilton Depression Rating Scale scores from before to after ECT treatment. Secondary outcomes were response and remission. Regression analyses with PRSs as independent variables and several covariates were performed. Explained variance (R2) at the optimal p-value threshold is reported. RESULTS: In the 266 subjects passing quality control, the PRS-SCZ was positively associated with a larger Hamilton Depression Rating Scale decrease in linear regression (optimal p-value threshold = .05, R2 = 6.94%, p < .0001), which was consistent across countries: Ireland (R2 = 8.18%, p = .0013), Belgium (R2 = 6.83%, p = .016), and the Netherlands (R2 = 7.92%, p = .0077). The PRS-SCZ was also positively associated with remission (R2 = 4.63%, p = .0018). Sensitivity and subgroup analyses, including in MDE without psychotic features (R2 = 4.42%, p = .0024) and unipolar MDE only (R2 = 9.08%, p < .0001), confirmed the results. The other PRSs were not associated with a change in the Hamilton Depression Rating Scale score at the predefined Bonferroni-corrected significance threshold. CONCLUSIONS: A linear association between PRS-SCZ and ECT outcome was uncovered. Although it is too early to adopt PRSs in ECT clinical decision making, these findings strengthen the positioning of PRS-SCZ as relevant to treatment response in psychiatry.

Impact of Comorbid Personality Disorders on Depression Treatment in Routine Outpatient Care.

OBJECTIVE: The impact of personality disorder on treatment effectiveness for depression has been debated, and study results have been inconsistent. However, studies that report a negative impact of personality disorders on depression treatment outcomes are often characterized by uncontrolled treatment designs. Within such contexts, individuals with depression and personality disorders are at risk to receive suboptimal treatment. The aim of this retrospective observational study was to investigate whether and to what extent comorbid personality disorders were associated with the type and amount of depression treatment received in routine outpatient care. METHODS: Retrospectively extracted data from electronic records of 1,455 outpatients treated for depression at several sites of a nationwide mental health provider in the Netherlands were included. The type and number of treatment sessions and visits were analyzed by using regression models. RESULTS: Individuals with depression and comorbid personality disorders received more psychotherapy sessions than individuals without personality disorders, irrespective of depression severity. The number of pharmacotherapy sessions and supportive and crisis visits did not differ between individuals with and without comorbid personality disorders. CONCLUSIONS: Individuals with depression and personality disorders received more intensive treatment than individuals without comorbid personality disorders. These results conflict with treatment guidelines and recommendations from high-quality studies and may be indicative of overtreatment among this large group of patients.

Patient Choice in Depression Psychotherapy: Outcomes of Patient-Preferred Therapy Versus Randomly Allocated Therapy.

OBJECTIVE: Patient choice is recognized as a factor that influences clinical outcomes and treatment evaluation in mental health care. However, research on how having a choice affects patients with depression has been rare. This Dutch study examined whether patients randomly selected to choose between two types of depression psychotherapy benefited more from treatment than patients randomly assigned to an intervention. METHODS: Data were derived from a trial of outpatients with depression who were randomly assigned to cognitive therapy (CT), interpersonal psychotherapy (IPT), or a 2-month waitlist control condition followed by the patient's choice of CT or IPT. Treatment groups were combined into a no-choice condition (N=151), with the waitlist as the choice condition (N=31). Multilevel regression was used to compare depression severity (measured with the Beck Depression Inventory-II [BDI-II]) and general psychological distress (measured with the Brief Symptom Inventory [BSI]) posttreatment and at the 5-month follow-up. Differences in patients' pretreatment expectations, beliefs about treatment credibility, and posttreatment evaluation were examined. RESULTS: No significant differences in clinical outcomes were found between the choice and no-choice conditions (mean difference: BDI-II posttreatment=-0.55, 95% confidence interval [CI]=-5.25 to 4.15; follow-up=2.10, 95% CI=-4.01 to 8.20; BSI posttreatment=-1.89, 95% CI=-15.35 to 11.58; follow-up=3.13, 95% CI=-12.32 to 18.57). Patients in both groups reported comparable scores on pretreatment expectations, credibility beliefs, and posttreatment evaluation. Neither expectations nor credibility beliefs were predictive of clinical outcomes. CONCLUSIONS: Our findings did not support the value of patient choice. Considering the exploratory nature of the trial, future studies designed to examine the effects of choice in depression treatment are recommended.

Impact of Comorbid Personality Disorders on Depression Treatment in Routine Outpatient Care.

OBJECTIVE: The impact of personality disorder on treatment effectiveness for depression has been debated, and study results have been inconsistent. However, studies that report a negative impact of personality disorders on depression treatment outcomes are often characterized by uncontrolled treatment designs. Within such contexts, individuals with depression and personality disorders are at risk to receive suboptimal treatment. The aim of this retrospective observational study was to investigate whether and to what extent comorbid personality disorders were associated with the type and amount of depression treatment received in routine outpatient care. METHODS: Retrospectively extracted data from electronic records of 1,455 outpatients treated for depression at several sites of a nationwide mental health provider in the Netherlands were included. The type and number of treatment sessions and visits were analyzed by using regression models. RESULTS: Individuals with depression and comorbid personality disorders received more psychotherapy sessions than individuals without personality disorders, irrespective of depression severity. The number of pharmacotherapy sessions and supportive and crisis visits did not differ between individuals with and without comorbid personality disorders. CONCLUSIONS: Individuals with depression and personality disorders received more intensive treatment than individuals without comorbid personality disorders. These results conflict with treatment guidelines and recommendations from high-quality studies and may be indicative of overtreatment among this large group of patients.

Patient Choice in Depression Psychotherapy: Outcomes of Patient-Preferred Therapy Versus Randomly Allocated Therapy.

OBJECTIVE: Patient choice is recognized as a factor that influences clinical outcomes and treatment evaluation in mental health care. However, research on how having a choice affects patients with depression has been rare. This Dutch study examined whether patients randomly selected to choose between two types of depression psychotherapy benefited more from treatment than patients randomly assigned to an intervention. METHODS: Data were derived from a trial of outpatients with depression who were randomly assigned to cognitive therapy (CT), interpersonal psychotherapy (IPT), or a 2-month waitlist control condition followed by the patient's choice of CT or IPT. Treatment groups were combined into a no-choice condition (N=151), with the waitlist as the choice condition (N=31). Multilevel regression was used to compare depression severity (measured with the Beck Depression Inventory-II [BDI-II]) and general psychological distress (measured with the Brief Symptom Inventory [BSI]) posttreatment and at the 5-month follow-up. Differences in patients' pretreatment expectations, beliefs about treatment credibility, and posttreatment evaluation were examined. RESULTS: No significant differences in clinical outcomes were found between the choice and no-choice conditions (mean difference: BDI-II posttreatment=-0.55, 95% confidence interval [CI]=-5.25 to 4.15; follow-up=2.10, 95% CI=-4.01 to 8.20; BSI posttreatment=-1.89, 95% CI=-15.35 to 11.58; follow-up=3.13, 95% CI=-12.32 to 18.57). Patients in both groups reported comparable scores on pretreatment expectations, credibility beliefs, and posttreatment evaluation. Neither expectations nor credibility beliefs were predictive of clinical outcomes. CONCLUSIONS: Our findings did not support the value of patient choice. Considering the exploratory nature of the trial, future studies designed to examine the effects of choice in depression treatment are recommended.

Cognitive therapy and interpersonal psychotherapy reduce suicidal ideation independent from their effect on depression.

BACKGROUND: Clinical guidelines suggest that psychological interventions specifically aimed at reducing suicidality may be beneficial. We examined the impact of two depression treatments, cognitive therapy (CT) and interpersonal psychotherapy (IPT) on suicidal ideation (SI) and explored the temporal associations between depression and SI over the course of therapy. METHODS: Ninety-one adult (18-65) depressed outpatients from a large randomized controlled trial who were treated with CT (n = 37) and IPT (n = 54) and scored at least ≥1 on the Beck Depression Inventory II (BDI-II) suicide item were included. Linear (two-level) mixed effects models were used to evaluate the impact of depression treatments on SI. Mixed-effects time-lagged models were applied to examine temporal relations between the change in depressive symptoms and the change in SI. RESULTS: SI decreased significantly during treatment and there were no differential effects between the two intervention groups (B = -0.007, p = .35). Depressive symptoms at the previous session did not predict higher levels of SI at the current session (B = 0.016, p = .16). However, SI measured at the previous session significantly predicted depressive symptoms at the current session (B = 2.06, p < .001). CONCLUSIONS: Both depression treatments seemed to have a direct association with SI. The temporal association between SI and depression was unidirectional with SI predicting future depressive symptoms during treatment. Our findings suggest that it may be most beneficial to treat SI first.

Exploring differences in quality of life in clinical populations of depressed outpatients with and without personality disorders.

BACKGROUND: Although depression and personality disorders (PDs) often co-occur, less is known about the impact of PDs on health-related quality of life (HRQOL) in patients with depression. This study explores the differences in HRQOL of depressed patients with and without PD. METHODS: Baseline data of 397 patients with depression from two randomised controlled trials were used for this analysis. HRQOL was measured with the EuroQol-5D (EQ-5D). Differences were examined between three groups: patients with 1) depression-only, 2) depression and comorbid PD and 3) PD and comorbid depression. The EQ-5D scores of the groups were compared with linear regression. RESULTS: HRQOL scores were lower in the depression-only group than the depression + PD group, even though depression severity was higher in patients with PD. HRQOL in the PD ± depression group did not differ from the other groups. In addition, no associations were found between the type or severity of PD and HRQOL. DISCUSSION: These findings could indicate that patients with PD are less affected by the impact of depression on HRQOL. In addition, the EQ-5D might not adequately capture the impact of PD on quality of life. Further research is needed to compare the EQ-5D with quality of life instruments that include more life domains. LIMITATIONS: Two study samples are combined, and therefore not designed to compare the three groups directly. Generalisation of the results should be done with caution. CONCLUSION: Depressed patients with PD report higher HRQOL than depression-only patients. Although higher HRQOL, patients with PD report more severe depressions than depressed-only patients.

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.

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.

Selecting the optimal treatment for a depressed individual: Clinical judgment or statistical prediction?

BACKGROUND: Optimizing treatment selection is a way to enhance treatment success in major depressive disorder (MDD). In clinical practice, treatment selection heavily depends on clinical judgment. However, research has consistently shown that statistical prediction is as accurate - or more accurate - than predictions based on clinical judgment. In the context of new technological developments, the current aim was to compare the accuracy of clinical judgment versus statistical predictions in selecting cognitive therapy (CT) or interpersonal psychotherapy (IPT) for MDD. METHODS: Data came from a randomized trial comparing CT (n=76) with IPT (n=75) for MDD. Prior to randomization, therapists' recommendations were formulated during multidisciplinary staff meetings. Statistical predictions were based on Personalized Advantage Index models. Primary outcomes were post-treatment and 17-month follow-up depression severity. Secondary outcome was treatment dropout. RESULTS: Individuals receiving treatment according to their statistical prediction were less depressed at post-treatment and follow-up compared to those receiving their predicted non-indicated treatment. This difference was not found for recommended versus non-recommended treatments based on clinical judgment. Moreover, for individuals with an IPT recommendation by therapists, higher post-treatment and follow-up depression severity was found for those that actually received IPT compared to those that received CT. Recommendations based on statistical prediction and clinical judgment were not associated with differences in treatment dropout. LIMITATIONS: Information on the clinical reasoning behind therapist recommendations was not collected, and statistical predictions were not externally validated. CONCLUSIONS: Statistical prediction outperforms clinical judgment in treatment selection for MDD and has the potential to personalize treatment strategies.

Personalized predictions of treatment outcome in patients with post-stroke depressive symptoms.

OBJECTIVE: Post-stroke depressive symptoms have a vast individual and societal impact. However, research into interventions for such symptoms show contradictory results; it is unclear what works for which patients. In addition, clinical prediction tools are lacking. This study aimed to develop a prognostic index model for treatment outcome in patients with post-stroke depressive symptoms. METHODS: Data from a randomized controlled trial (n = 61) evaluating 2 interventions for post-stroke depressive symptoms were used to predict post-treatment post-stroke depressive symptoms and participation. From 18 pre-treatment variables of patients and caregivers, predictors were selected using elastic net regression. Based on this selection, prognostic index scores (i.e. predictions) for both out-comes were computed for each individual patient. RESULTS: The depression model included all pre-treatment variables, explaining 44% of the variance. The strongest predictors were: lesion location, employment, participation, comorbidities, mobility, sex, and pre-treatment depression. Six predictors of post-treatment participation were identified, explaining 51% of the variance: mobility, pre-treatment participation, age, satisfaction with participation, caregiver strain, and psychological distress of the spouse. The cross-validated prognostic index scores correlated highly with the actual outcome scores (depression: correlation = 0.672; participation: correlation = 0.718). CONCLUSION: Post-stroke depressive symptoms form a complex and multifactorial problem. Treatment outcome is influenced by the characteristics of the stroke, the patients, and their spouses. The results show that psychological distress is probably no obstacle to attempting to improve participation. The personalized predictions (prognostic index scores) of treatment outcome show promising results, which, after further replication and validation, could aid clinicians with treatment selection.

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).

Long-term outcomes of acute treatment with cognitive therapy v. interpersonal psychotherapy for adult depression: follow-up of a randomized controlled trial.

BACKGROUND: Although equally efficacious in the acute phase, it is not known how cognitive therapy (CT) and interpersonal psychotherapy (IPT) for major depressive disorder (MDD) compare in the long run. This study examined the long-term outcomes of CT v. IPT for MDD. METHODS: One hundred thirty-four adult (18-65) depressed outpatients who were treated with CT (n = 69) or IPT (n = 65) in a large open-label randomized controlled trial (parallel group design; computer-generated block randomization) were monitored across a 17-month follow-up phase. Mixed regression was used to determine the course of self-reported depressive symptom severity (Beck Depression Inventory II; BDI-II) after treatment termination, and to test whether CT and IPT differed throughout the follow-up phase. Analyses were conducted for the total sample (n = 134) and for the subsample of treatment responders (n = 85). Furthermore, for treatment responders, rates of relapse and sustained response were examined for self-reported (BDI-II) and clinician-rated (Longitudinal Interval Follow-up Evaluation; LIFE) depression using Cox regression. RESULTS: On average, the symptom reduction achieved during the 7-month treatment phase was maintained across follow-up (7-24 months) for CT and IPT, both in the total sample and in the responder sample. Two-thirds (67%) of the treatment responders did not relapse across the follow-up period on the BDI-II. Relapse rates assessed with the LIFE were somewhat lower. No differential effects between conditions were found. CONCLUSIONS: Patients who responded to IPT were no more likely to relapse following treatment termination than patients who responded to CT. Given that CT appears to have a prophylactic effect following successful treatment, our findings suggest that IPT might have a prophylactic effect as well.

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.

The influence of comorbid anxiety on the effectiveness of Cognitive Therapy and Interpersonal Psychotherapy for Major Depressive Disorder.

BACKGROUND: Anxious depression is an important subtype of Major Depressive Disorder (MDD) defined by both syndromal (anxiety disorders) and dimensional (anxiety symptoms) criteria. A debated question is how anxiety affects MDD treatment. This study examined the impact of comorbid anxiety disorders and symptoms on the effectiveness of and dropout during Cognitive Therapy (CT) and Interpersonal Psychotherapy (IPT) for MDD. METHODS: Depressed individuals were randomized to CT (n = 76) or IPT (n = 75). Outcome was depression severity measured with the Beck Depression Inventory-II (BDI-II) at the start of each therapy session, post treatment, and monthly up to five months follow-up. Anxiety disorders were assessed with the Structured Clinical Interview for DSM-IV Axis I disorders, (phobic) anxiety symptoms were assessed with Brief Symptom Inventory subscales. RESULTS: Approximately one third of participants had a comorbid anxiety disorder. Comorbid anxiety disorders and anxiety symptoms were associated with less favorable depression change during IPT as compared to CT in the treatment phase, but not in the trial follow-up phase. Individuals with a comorbid anxiety disorder had significantly higher treatment dropout during both treatments. LIMITATIONS: Not all therapists and participants were blind to the assessment of comorbid anxiety disorders and the assessments were performed by one rater. CONCLUSIONS: A preference for CT over IPT for MDD is justifiable when comorbid anxiety is present, although long-term differences are not established and replication of this finding is needed. Clinicians should be aware of the risk of dropout for depressed individuals with an anxiety disorder.