Predicting treatment outcome for complex posttraumatic stress disorder using the personalized advantage index

利用个性化优势指数预测复杂性创伤后应激障碍的治疗结果

阅读:1

Abstract

ABSTRACTBackground: Ample studies have demonstrated the effectiveness of psychotherapy for posttraumatic stress disorder (PTSD). However, large individual variation in treatment outcome remains unsolved and treatment options for complex posttraumatic stress disorder (CPTSD) are debated. There is a need for exploring methods for matching patients with treatment they will most likely benefit from.Objective: To develop a personalized advantage index (PAI) based on relevant clinical and demographic predictors of outcome from exposure therapy and skills-training for CPTSD.Method: Data from a previous randomized controlled trial (RCT) in 92 patients with a CPTSD diagnosis was used to compare Prolonged Exposure (PE; n = 32) and Skills Training in Affective and Interpersonal Regulation (STAIR; n = 60). Outcome measures were clinician-assessed and self-reported PTSD symptoms. Predictors of outcome in PE and STAIR were identified separately from sixty-one candidate variables using random forest and bootstrap procedures. Relevant predictors were then used to calculate PAI and retrospectively identify optimal versus suboptimal treatment in a leave-one-out cross-validation approach.Results: In PE, somatoform dissociation, depression, suicidal ideation, and reduced physical health predicted worse outcome. In STAIR, interpersonal problems, total PTSD symptom severity, intrusions, elevated guilt, and psychoticism predicted worse outcome, while being a witness to trauma predicted better outcome. Allocation to optimal treatment according to the PAI was associated with large improvements in clinician-assessed (Cohen's d = 0.83) and moderate improvement in self-rated (Cohen's d = 0.60) PTSD symptoms as compared to allocation to suboptimal teatment.Conclusions: Using the PAI in personalizing psychological treatment for CPTSD is a promising approach to improve treatment benefits. Further research on larger samples and external validation of the PAI is needed.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。