BackgroundAdvances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations.ObjectiveTo illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison.MethodData from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units.ResultsFor 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01).ConclusionsThis approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

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Objective Little is known about the variability of the alliance-outcome correlation across identifiable client subsets. This question was explored in a sample of 60 clients receiving cognitive therapy for depression, from which an overall correlation of .23 was observed between alliance ratings and subsequent symptom change. Methods We examined interactions between the observer-rated version of the Working Alliance Inventory (WAI-O) and client demographics, features of depression, personality and other clinical features in predicting subsequent symptom change. Results After correcting for multiple comparisons, interactions between the WAI-O and the number of prior depressive episodes, as well as the severity of baseline anxiety symptoms, were significant predictors of symptom change. When both interactions were controlled for, number of prior depressive episodes emerged as a statistically significant moderator. The alliance predicted outcome in the subgroup of clients with 0–2 prior episodes (r = .52), but not in those with 3 or more prior episodes (r = −.02). These findings were obtained despite similar univariate distributions on the alliance and symptom change in the two subgroups. Discussion Differences that were observed in the predictive relation of alliance to outcome as a function of number of prior episodes suggests that different therapy processes may account for change in these subgroups. If the pattern observed in the present study is replicated, it would suggest that the alliance-outcome association has been both under- and over-estimated.

Depression is a leading cause of disability worldwide, but is often underdiagnosed and undertreated. Cognitive behavioural therapy holds that individuals with depression exhibit distorted modes of thinking, that is, cognitive distortions, that can negatively affect their emotions and motivation. Here, we show that the language of individuals with a self-reported diagnosis of depression on social media is characterized by higher levels of distorted thinking compared with a random sample. This effect is specific to the distorted nature of the expression and cannot be explained by the presence of specific topics, sentiment or first-person pronouns. This study identifies online language patterns that are indicative of depression-related distorted thinking. We caution that any future applications of this research should carefully consider ethical and data privacy issues.

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In this conceptual paper, we outline the many challenges on the road to personalized psychotherapy, using the example of cognitive behavior therapy (CBT) for depression. To optimize psychotherapy for the individual patient, we need to find out how therapy works (identification of mechanisms of change) and for whom it works (identification of moderators). To date, psychotherapy research has not resulted in compelling evidence for or against common or specific factors that have been proposed as mechanisms of change. Our central proposition is that we need to combine the “how does it work?”-question with the “for whom does it work?”-question in order to advance the field. We introduce the personalized causal pathway hypothesis that emphasizes the links and distinction between individual patient differences, therapeutic procedures and therapy processes as a paradigm to facilitate und understand the concept of personalized psychotherapy. We review the mechanism of change literature for CBT for depression to see what we have learned so far, and describe preliminary observational evidence supporting the personalized causal pathway hypothesis. We then propose a research agenda to push the ball forward: exploratory studies into the links between individual differences, therapeutic procedures, therapy processes and outcome that constitute a potential causal pathway, making use of experience sampling, network theory, observer ratings of therapy sessions, and moderated mediation analysis; testing and isolation of CBT procedures in experiments; and testing identified causal pathways of change as part of a personalized CBT package against regular CBT, in order to advance the application of personalized psychotherapy.

Cognitive-behavioral therapies (CBTs) are the most widely studied form of psychotherapy for disorders like depression and anxiety. Nonetheless, there is heterogeneity in response to CBTs vs. other treatments. Researchers have become increasingly interested in using pre-treatment individual differences (i.e., moderators) to match patients to the most effective treatments for them. Several methods to combine multiple variables to create precision treatment rules (PTRs) that identify subgroups have been proposed. We review the rationale behind multivariable PTRs as well as the findings of studies that have used different PTRs. We identify conceptual and methodological issues in the literature. Multivariable treatment assignment is a promising avenue of research. Nonetheless, effect sizes appear to be small and most of the samples that have been used to study these questions have been grossly underpowered to detect small effects. We recommend researchers explore multivariable treatment selection strategies, particularly those resembling risk-stratification, in heterogeneous samples of patients undergoing low-intensity CBTs vs. realistic minimal controls.

Objective Compare the safety and effectiveness of continuation antidepressant versus mood stabilizer monotherapy for preventing depressive relapse in bipolar II disorder. Methods Subjects ≥18 years old with bipolar II depression (n=129) were randomized to double-blind venlafaxine or lithium monotherapy for 12 weeks. Responders with a ≥50% reduction in depression score were continued for an additional 6 months of relapse-prevention monotherapy. Primary outcome was depressive relapse during continuation monotherapy. Secondary outcomes included sustained response rate from initiation of treatment to study end-point, relapse hazard, time to relapse, change in mania ratings, and frequency of treatment-emergent sub-syndromal hypomania and/or depressive episodes. Results Venlafaxine produced greater sustained response rate versus lithium (p<0.0001); however, there was no difference in relapse rate for venlafaxine (7.5%) versus lithium (26.7%) (p=0.079); relapse hazard (p=0.073), or time to relapse (p=0.090) between treatment conditions during continuation monotherapy. There were no group differences in mania rating scores over time and no difference in frequency or duration of syndromal or sub-syndromal hypomanic episodes. There were more sub-syndromal depressive episodes during lithium monotherapy (p=0.03). Limitations Sample size was limited by the lower sustained response rate for lithium versus venlafaxine; study was not specifically powered to detect differences in treatment-emergent hypomanic or depressive episodes between groups. Conclusion Results suggest that continuation venlafaxine monotherapy may provide similar prophylactic effectiveness relative to lithium, with no difference in treatment-emergent hypomanic episodes and without the need for frequent serum lithium level and metabolic monitoring. Larger, prospective trials are needed to confirm these observations.