Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study.

BACKGROUND: Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems. METHODS: This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203). RESULTS: Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. LIMITATIONS: Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. CONCLUSIONS: Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

Predicting optimal treatment outcomes in phase-based treatment and direct trauma-focused treatment among patients with posttraumatic stress disorder stemming from childhood abuse.

Research over the last few decades has demonstrated the effectiveness of various treatments for posttraumatic stress disorder (PTSD). However, the question of which treatment works best remains, especially for patients with PTSD stemming from childhood abuse. Using the Personalized Advantage Index (PAI), we explored which patients benefit more from phase-based treatment and which benefit more from direct trauma-focused treatment. Data were obtained from a multicenter randomized controlled trial (RCT) comparing a phase-based treatment condition (i.e., eye-movement desensitization and reprocessing [EMDR] therapy preceded by Skills Training in Affect and Interpersonal Regulation [STAIR]; n = 57) and a direct trauma-focused treatment (EMDR only; n = 64) among individuals with PTSD related to childhood abuse. Machine learning techniques were used to examine all pretreatment variables included in the trial as potential predictors and moderators, with selected variables combined to build the PAI model. The utility of the PAI was tested by comparing actual posttreatment outcomes of individuals who received PAI-indicated treatment with those allocated to a non-PAI-indicated treatment. Although eight pretreatment variables between PTSD treatment outcome and treatment condition were selected as moderators, there was no significant difference between participants assigned to their PAI-indicated treatment and those randomized to a non-PAI-indicated treatment, d = 0.25, p = .213. Hence, the results of this study do not support the need for personalized medicine for patients with PTSD and a history of childhood abuse. Further research with larger sample sizes and external validation is warranted.

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.

The association between sample and treatment characteristics and the efficacy of repetitive transcranial magnetic stimulation in depression: A meta-analysis and meta-regression of sham-controlled trials.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a form of non-invasive neuromodulation that is increasingly used to treat major depressive disorder (MDD). However, treatment with rTMS could be optimized by identifying optimal treatment parameters or characteristics of patients that are most likely to benefit. This meta-analysis and meta-regression aims to identify sample and treatment characteristics that are associated with change in depressive symptom level, treatment response and remission. METHODS: The databases PubMed, Embase, Web of Science and Cochrane library were searched for randomized controlled trials (RCTs) reporting on the therapeutic efficacy of high-frequent, low-frequent, or bilateral rTMS for MDD compared to sham. Study and sample characteristics as well as rTMS parameters and outcome variables were extracted. Effect sizes were calculated for change in depression score and risk ratios for response and remission. RESULTS: Sixty-five RCTs with a total of 2982 subjects were included in this meta-analysis. Active rTMS resulted in a larger depressive symptom reduction than sham protocol (Hedges' g = -0.791 95% CI -0.977; -0.605). Risk ratios for response and remission were 2.378 (95% CI 1.882; 3.005) and 2.450 (95% CI 1.779; 3.375), respectively. We found no significant association between sample and treatment parameters and rTMS efficacy. CONCLUSIONS: rTMS is an efficacious treatment for MDD. No associations between sample or treatment characteristics and efficacy were found, for which we caution that publication bias, heterogeneity and lack of consistency in the definition of remission might bias these latter null findings. Our results are clinically relevant and support the use of rTMS as a non-invasive and effective treatment option for depression.

Efficacy and acceptability of next step treatment strategies in adults with treatment-resistant major depressive disorder: protocol for systematic review and network meta-analysis.

INTRODUCTION: For major depression, a one-size-fits-all treatment does not exist. Patients enter a 'trial-and-change' algorithm in which effective therapies are subsequently applied. Unfortunately, an empirically based order of treatments has not yet been determined. There is a magnitude of different treatment strategies while clinical trials only compare a small number of these. Network meta-analyses (NMA) might offer a solution, but so far have been limited in scope and did not account for possible differences in population characteristics that arise with increasing levels of treatment-resistance, potentially violating the transitivity assumption. We; therefore, present a protocol for a systematic review and NMA aiming at summarising and ranking treatments for treatment-resistant depression (TRD) while covering a broad range of therapeutic options and accounting for possible differences in population characteristics at increasing levels of treatment-resistance. METHODS AND ANALYSIS: Randomised controlled trials will be included that compared next-step pharmacological, neuromodulation or psychological treatments for treatment-resistant depression (TRD; ie, failure to respond to ≥1 adequate antidepressant drug trial(s) in the current episode) to each other or to a control condition. Primary outcomes will be the proportion of patients who responded to (efficacy) and dropped out of (acceptability) the allocated treatment. A random effects NMA will be conducted, synthesising the evidence for each outcome and determining the differential efficacy of treatments. Heterogeneity in treatment nodes will be reduced by considering alternative geometries of the network structure and by conducting a meta-regression examining different levels of TRD. Local and global methods will be applied to evaluate consistency. The Cochrane Risk of Bias 2 tool, Confidence in Network Meta-Analysis and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework will be used to assess risk of bias and certainty. ETHICS AND DISSEMINATION: This review does not require ethical approval.

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.

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).