Predictors of treatment attendance in patients with posttraumatic stress disorder and comorbid personality disorders.

INTRODUCTION

High dropout and low treatment attendance rates among patients with posttraumatic stress disorder (PTSD) and personality disorders (PDs) continue to pose a significant challenge. Despite numerous studies focusing on enhancing treatment attendance, the identification of consistent and reliable predictors in patients with PTSD and comorbid PDs remains limited.

OBJECTIVES

This study aims to investigate a wide range of potential predictors of treatment attendance, encompassing demographic, patient-severity, treatment, and therapist-related variables in patients with PTSD and comorbid borderline and/or cluster C PDs.

METHODS

Utilizing data from 255 patients participating in two randomized controlled trials comparing trauma-focused treatment with or without concurrent PD treatment, candidate predictors were individually analyzed in univariate regression models. Significant predictors were then combined in a multiple ordinal regression model.

RESULTS

In total, 40% of patients attended fewer trauma-focused treatment sessions than the minimum recommended in treatment guidelines. Out of the 38 candidate predictors examined, five significant, independent predictors of treatment attendance emerged in a multiple ordinal regression model. Higher baseline PTSD severity (OR = 1.04, p = .036), higher education level (OR = 1.22, p = .009) and a stronger patient-rated working alliance (OR = 1.72, p = .047) with the therapist predicted higher treatment attendance. Conversely, inadequate social support from friends (OR = 0.90, p = .042) and concurrent PD treatment and trauma-focused treatment (OR = 0.52, p = .022) were associated with lower treatment attendance.

CONCLUSIONS

In conclusion, this constitutes the first study investigating predictors of treatment attendance in patients with PTSD and comorbid PDs. The results highlight the complexity of pinpointing reliable predictors. Nevertheless, the identification of five predictors provides valuable insights, aiding clinicians in customizing treatment strategies for individual patients and enhancing overall treatment attendance.