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

Abstract

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.

Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data

“…The authors found that a composite moderator Barber & Muenz (1996) CBT vs. IPT as per the method suggested by Kraemer (2013) interacted with treatment conditions to predict treatments outcomes, with a large effect (r = 0.72, 95% CI: 0.43 -1.01). Others report much smaller effect sizes (Z. D. Friedl et al, 2020;van Bronswijk et al, 2019 vs. their model-predicted non-optimal treatment suggested that there were medium differences that may be explained by treatment matching (d =0.51). In a subsequent publication (Bruijniks et al, 2020), we used machine-learning methods to conduct a similar analysis, still finding large effects of the PTR on those assigned to their optimal vs. non-optimal treatment (d =0.57).…”

Specifying the mechanisms and targets of emotion regulation: A translational framework from affective science to psychological treatment.

“…One naturalistic study of DBT for BPD found that patients with a history of childhood trauma demonstrated greater symptom improvements than patients without such a history (McFetridge et al, 2015). Similarly, patients with a history of childhood trauma and those who had experienced more recent life stressors were predicted to have better outcomes in cognitive therapy than interpersonal therapy for depression (van Bronswijk et al, 2019). Capturing patients' developmental trajectories may be important moderators for researchers to consider.…”

Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care

“…Furthermore, the current study predicts treatment outcome after 12 weeks. Future studies should predict long-term treatment outcome [83]. Finally, we have followed a data-driven approach instead of a theory-driven approach.…”