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Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit., Competing Interests: Declaration of competing interest To the best of our knowledge, no author has a conflict of interest, financial or otherwise., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Dropout from psychotherapy is common and can have negative effects for patients, providers, and researchers. A better understanding of when and why patients stop treatment early, as well as actionable factors contributing to dropout, has the potential to prevent it. Here, we examined dropout from a large randomized controlled trial of transdiagnostic versus single-diagnosis cognitive-behavioral treatment (CBT) for patients with anxiety disorders (n = 179; Barlow et al., 2017). We aimed to characterize the timing of and reasons for dropout and test whether participants who dropped out had different symptom trajectories than those who completed treatment. Results indicated that overall, the greatest risk of dropout was prior to the first treatment session. In single-diagnosis CBT, dropout risk was particularly elevated before the first session and after other early sessions, whereas in transdiagnostic CBT, dropout risk was low and stable before and during treatment. Participants most often dropped out due to failure to comply with study procedures or dissatisfaction with or desiring alternative treatment. Results from multilevel models showed that trajectories of anxiety symptoms did not significantly differ between dropouts and completers. These findings suggest that there may be specific time windows for targeted and timely interventions to prevent dropout from CBT., (Copyright © 2021. Published by Elsevier Ltd.)

In light of the well-established relationship between posttraumatic stress disorder (PTSD) and suicidal ideation (SI), there has been a push for treatments that simultaneously improve symptoms of PTSD and decrease SI. Using data from a randomized controlled hybrid implementation-effectiveness trial, the current study investigated the effectiveness of Cognitive Processing Therapy (CPT; Resick, Monson, & Chard, 2016) on PTSD and SI. The patient sample (N = 188) was diverse in military and veteran status, gender, and comorbidity, and 73% of the sample endorsed SI at one or more points during CPT. Participants demonstrated significant improvement in SI over the course of CPT. Multilevel growth curve modeling revealed a significant association between PTSD symptom change and change in SI. Results from cross-lagged multilevel regressions indicated that PTSD symptoms predicted SI in the next session, yet SI in a given session did not predict PTSD symptoms in the next session. Potentially relevant clinical factors (i.e., military status, gender, depression diagnosis, baseline SI, study consultation condition) were not associated with the relationship between PTSD symptoms and SI. These results add to the burgeoning literature suggesting that evidence-based treatments for PTSD, like CPT, reduce suicidality in a range of individuals with PTSD, and that this reduction is predicted by improvements in PTSD symptoms., (Copyright © 2021 Association for Behavioral and Cognitive Therapies. Published by Elsevier Ltd. All rights reserved.)

Objective: This study aims at developing a treatment selection algorithm using a combination of machine learning and statistical inference to recommend patients' optimal treatment based on their pre-treatment characteristics. Methods: A disorder-heterogeneous, naturalistic sample of N  = 1,379 outpatients treated with either cognitive behavioral therapy or psychodynamic therapy was analyzed. Based on a combination of random forest and linear regression, differential treatment response was modeled in the training data ( n  = 966) to indicate each individual's optimal treatment. A separate holdout dataset ( n  = 413) was used to evaluate personalized recommendations. Results: The difference in outcomes between patients treated with their optimal vs. non-optimal treatment was significant in the training data, but non-significant in the holdout data ( b  = -0.043, p  = .280). However, for the 50% of patients with the largest predicted benefit of receiving their optimal treatment, the average percentage of change on the BSI in the holdout data was 52.6% for their optimal and 38.4% for their non-optimal treatment ( p  = .017; d  = 0.33 [0.06, 0.61]). Conclusion: A treatment selection algorithm based on a combination of ML and statistical inference might improve treatment outcome for some, but not all outpatients and could support therapists' clinical decision-making.

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.

Two core features of depression include depressed mood (heightened distress) and anhedonia (reduced pleasure). Despite their centrality to depression, studies have not examined their contribution to treatment outcomes in a randomized clinical trial providing mainstream treatments like antidepressant medications (ADM) and cognitive therapy (CT). We used baseline distress and anhedonia derived from a factor analysis of the Mood and Anxiety Symptom Questionnaire to predict remission and recovery in 433 individuals with recurrent/chronic major depressive disorder. Patients were provided with only ADM or both ADM and CT. Overall, higher baseline distress and anhedonia predicted longer times to remission within one year and recovery within three years. When controlling for treatment condition, distress improved prediction of outcomes over and above anhedonia, while anhedonia did not improve prediction of outcomes over and above distress. Interactions with treatment condition demonstrated that individuals with higher distress and anhedonia benefited from receiving CT in addition to ADM, whereas there was no added benefit of CT for individuals with lower distress and anhedonia. Assessing distress and anhedonia prior to treatment may help select patients who will benefit most from CT in addition to ADM. For the treatments and outcome measures tested, utilizing distress to guide treatment planning may yield the greatest benefit. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00057577., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objective : We use a new variable selection procedure for treatment selection which generates treatment recommendations based on pre-treatment characteristics for adults with mild-to-moderate depression deciding between cognitive behavioral (CBT) versus psychodynamic therapy (PDT). Method : Data are drawn from a randomized comparison of CBT versus PDT for depression ( N  = 167, 71% female, mean-age = 39.6). The approach combines four different statistical techniques to identify patient characteristics associated consistently with differential treatment response. Variables are combined to generate predictions indicating each individual's optimal-treatment. The average outcomes for patients who received their indicated treatment versus those who did not were compared retrospectively to estimate model utility. Results : Of 49 predictors examined, depression severity, anxiety sensitivity, extraversion, and psychological treatment-needs were included in the final model. The average post-treatment Hamilton-Depression-Rating-Scale score was 1.6 points lower (95%CI = [0.5:2.8]; d  = 0.21) for those who received their indicated-treatment compared to non-indicated. Among the 60% of patients with the strongest treatment recommendations, that advantage grew to 2.6 (95%CI = [1.4:3.7]; d  = 0.37). Conclusions : Variable selection procedures differ in their characterization of the importance of predictive variables. Attending to consistently-indicated predictors may be sensible when constructing treatment selection models. The small N and lack of separate validation sample indicate a need for prospective tests before this model is used.

Objective: Research on predictors of treatment outcome in depression has largely derived from randomized clinical trials involving strict standardization of treatments, stringent patient exclusion criteria, and careful selection and supervision of study clinicians. The extent to which findings from such studies generalize to naturalistic psychiatric settings is unclear. This study sought to predict depression outcomes for patients seeking treatment within an intensive psychiatric hospital setting and while comparing the performance of a range of machine learning approaches., Method: Depressed patients (N = 484; ages 18-72; 89% White) receiving treatment within a psychiatric partial hospital program delivering pharmacotherapy and cognitive behavioral therapy were split into a training sample and holdout sample. First, within the training sample, 51 pretreatment variables were submitted to 13 machine learning algorithms to predict, via cross-validation, posttreatment Patient Health Questionnaire-9 depression scores. Second, the best performing modeling approach (lowest mean squared error; MSE) from the training sample was selected to predict outcome in the holdout sample., Results: The best performing model in the training sample was elastic net regularization (ENR; MSE = 20.49, R2 = .28), which had comparable performance in the holdout sample (MSE = 11.26; R2 = .38). There were 14 pretreatment variables that predicted outcome. To demonstrate the translation of an ENR model to personalized prediction of treatment outcome, a patient-specific prognosis calculator is presented., Conclusions: Informed by pretreatment patient characteristics, such predictive models could be used to communicate prognosis to clinicians and to guide treatment planning. Identified predictors of poor prognosis may suggest important targets for intervention. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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., (© 2018 Wiley Periodicals, Inc.)

Introduction: Although psychotherapies for depression produce equivalent outcomes, individual patients respond differently to different therapies. Predictors of outcome have been identified in the context of randomized trials, but this information has not been used to predict which treatment works best for the depressed individual. In this paper, we aim to replicate a recently developed treatment selection method, using data from an RCT comparing the effects of cognitive therapy (CT) and interpersonal psychotherapy (IPT)., Methods: 134 depressed patients completed the pre- and post-treatment BDI-II assessment. First, we identified baseline predictors and moderators. Second, individual treatment recommendations were generated by combining the identified predictors and moderators in an algorithm that produces the Personalized Advantage Index (PAI), a measure of the predicted advantage in one therapy compared to the other, using standard regression analyses and the leave-one-out cross-validation approach., Results: We found five predictors (gender, employment status, anxiety, personality disorder and quality of life) and six moderators (somatic complaints, cognitive problems, paranoid symptoms, interpersonal self-sacrificing, attributional style and number of life events) of treatment outcome. The mean average PAI value was 8.9 BDI points, and 63% of the sample was predicted to have a clinically meaningful advantage in one of the therapies. Those who were randomized to their predicted optimal treatment (either CT or IPT) had an observed mean end-BDI of 11.8, while those who received their predicted non-optimal treatment had an end-BDI of 17.8 (effect size for the difference = 0.51)., Discussion: Depressed patients who were randomized to their predicted optimal treatment fared much better than those randomized to their predicted non-optimal treatment. The PAI provides a great opportunity for formal decision-making to improve individual patient outcomes in depression. Although the utility of the PAI approach will need to be evaluated in prospective research, this study promotes the development of a treatment selection approach that can be used in regular mental health care, advancing the goals of personalized medicine.