Investigating predictors of treatment response in Dialectical Behavior Therapy for borderline personality disorder using LASSO regression

Maladaptive defense mechanisms moderate treatment outcome in 6 months versus 12 months dialectical-behavior therapy for borderline personality disorder

OBJECTIVE

We investigated whether defense mechanisms in patients with borderline personality disorder (BPD) predict treatment response of dialectical behavior therapy (DBT) and whether they moderate outcome in different treatment lengths.

METHOD

We analyzed a subsample of 60 outpatients with BPD, randomized into either 6 (n = 30) or 12 (n = 30) months of DBT. The average level of defensive adaptiveness, assessed with observer-rated overall defensive functioning (ODF) and "immature" (i.e., maladaptive) defenses were used as predictors and moderators of self-reported frequency of self-harm. We conducted a Generalized Linear Mixed Model (GLMM).

RESULTS

A lower ODF at treatment onset predicted smaller reductions in self-harm, irrespective of treatment length (IRR = 0.92, 95% CI = [0.86, 0.99], p = .020). Lower order "immature" ("major image distorting") defenses showed significantly smaller (IRR = 1.13, 95% CI = [1.06, 1.21], p < .001) and higher order "immature" ("minor image distorting") defenses showed significantly larger (IRR = .91, 95% CI = [.85, .97], p = .006) reductions in self harm in the 6-month but not in the 12-month treatment.

CONCLUSION

Even though the results have to be regarded as preliminary due to the small sample size, findings might indicate that patients with BPD and lower average defensive adaptiveness may benefit from individualized treatment plans including specific interventions targeting defense function.

Shame, suicidal ideation, and urges for non-suicidal self-injury among individuals with borderline personality disorder receiving dialectical behavior therapy: The mediating role of anger

INTRODUCTION

Borderline personality disorder (BPD) is a disorder of pervasive emotion dysregulation associated with high rates of self-injurious thoughts and behaviors (SITB). Understanding specific emotion states in relation to SITB is important for effective intervention.

METHODS

The current study examined whether, and how, the specific emotion of shame contributes to suicide ideation and urges to engage in non-suicidal self-injury (NSSI) both directly, and indirectly via anger, among individuals with BPD. Participants (N = 100) were enrolled in a 6-month comprehensive dialectical behavior therapy (DBT) program and provided daily ratings of shame, anger, suicide ideation, and urges for NSSI.

RESULTS

We found that higher daily ratings of shame and anger were directly associated with higher same-day ratings of both suicidal ideation and urges for NSSI. Furthermore, anger partially mediated the relationships between shame and both suicidal ideation and urges for NSSI.

CONCLUSION

These findings highlight shame and anger as potential antecedents of SITB among individuals with BPD. Clinical approaches, such as DBT, that include personalized, ongoing, clinical assessment of these specific affective states may be particularly important for treatment of SITB.

Integrating exploration and prediction in computational psychotherapy science: proof of concept

Introduction

Psychotherapy research has long preferred explanatory over predictive models. As a result, psychotherapy research is currently limited in the variability that can be accounted for in the process and outcome of treatment. The present study is a proof-of-concept approach to psychotherapy science that uses a datadriven approach to achieve robust predictions of the process and outcome of treatment.

Methods

A trial including 65 therapeutic dyads was designed to enable an adequate level of variability in therapist characteristics, overcoming the common problem of restricted range. A mixed-model, data-driven approach with cross-validation machine learning algorithms was used to predict treatment outcome and alliance (within- and between-clients; client- and therapist-rated alliance).

Results and discussion

Based on baseline predictors only, the models explained 52.8% of the variance for out-of-sample prediction in treatment outcome, and 24.1-52.8% in therapeutic alliance. The identified predictors were consistent with previous findings and point to directions for future investigation. Although limited by its sample size, this study serves as proof of the great potential of the presented approach to produce robust predictions regarding the process and outcome of treatment, offering a potential solution to problems such as p-hacking and lack of replicability. Findings should be replicated using larger samples and distinct populations and settings.