No evidence-based therapy for borderline personality disorder (BPD) exhibits a clear superiority. However, BPD is highly heterogeneous, and different patients may specifically benefit from the interventions of a particular treatment.

From a randomized trial comparing a year of dialectical behavior therapy (DBT) to general psychiatric management (GPM) for BPD, long-term (2-year-post) outcome data and patient baseline variables (n = 156) were used to examine individual and combined patient-level moderators of differential treatment response. A two-step bootstrapped and partially cross-validated moderator identification process was employed for 20 baseline variables. For identified moderators, 10-fold bootstrapped cross-validated models estimated response to each therapy, and long-term outcomes were compared for patients randomized to their model-predicted optimal v. non-optimal treatment.

Significant moderators surviving the two-step process included psychiatric symptom severity, BPD impulsivity symptoms (both GPM > DBT), dependent personality traits, childhood emotional abuse, and social adjustment (all DBT > GPM). Patients randomized to their model-predicted optimal treatment had significantly better long-term outcomes (d = 0.36, p = 0.028), especially if the model had a relatively stronger (top 60%) prediction for that patient (d = 0.61, p = 0.004). Among patients with a stronger prediction, this advantage held even when applying a conservative statistical check (d = 0.46, p = 0.043).

Patient characteristics influence the degree to which they respond to two treatments for BPD. Combining information from multiple moderators may help inform providers and patients as to which treatment is the most likely to lead to long-term symptom relief. Further research on personalized medicine in BPD is needed.

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.

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.

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.

If replicated, long-term PAI predictions could enhance precision medicine by selecting the optimal treatment for a given depressed individual over the long term.