Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1

Frontiers in Psychiatry

Psychiatry

Rafaele J.C. Huntjens¹  Suzanne C. van Bronswijk²  Ad de Jongh³  Noortje I. van Vliet⁴  Susanne Bremer-Hoeve⁴  Maarten K. van Dijk⁴ 

[1] Department of Experimental Psychotherapy and Psychopathology, University of Groningen, Groningen, Netherlands;Department of Psychiatry and Neuropsychology, School for Mental health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands;Department of Psychiatry and Psychology, Maastricht University Medical Center, Maastricht, Netherlands;Department of Social Dentistry and Behavioral Sciences, University of Amsterdam and Vrije Universiteit, Amsterdam, Netherlands;School of Health Sciences, Salford University, Manchester, United Kingdom;Institute of Health and Society, University of Worcester, Worcester, United Kingdom;Dimence Mental Health Group, Deventer, Netherlands;

关键词: PTSD;    childhood abuse;    EMDR;    dropout;    predictors;    machine learning;   

DOI  :  10.3389/fpsyt.2023.1194669

 received in 2023-03-27, accepted in 2023-07-19,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundKnowledge about patient characteristics predicting treatment dropout for post-traumatic stress disorder (PTSD) is scarce, whereas more understanding about this topic may give direction to address this important issue.MethodData were obtained from a randomized controlled trial in which a phase-based treatment condition (Eye Movement Desensitization and Reprocessing [EMDR] therapy preceded by Skills Training in Affect and Interpersonal Regulation [STAIR]; n = 57) was compared with a direct trauma-focused treatment (EMDR therapy only; n = 64) in people with a PTSD due to childhood abuse. All pre-treatment variables included in the trial were examined as possible predictors for dropout using machine learning techniques.ResultsFor the dropout prediction, a model was developed using Elastic Net Regularization. The ENR model correctly predicted dropout in 81.6% of all individuals. Males, with a low education level, suicidal thoughts, problems in emotion regulation, high levels of general psychopathology and not using benzodiazepine medication at screening proved to have higher scores on dropout.ConclusionOur results provide directions for the development of future programs in addition to PTSD treatment or for the adaptation of current treatments, aiming to reduce treatment dropout among patients with PTSD due to childhood abuse.

【 授权许可】

Unknown   
Copyright © 2023 Bremer-Hoeve, van Vliet, van Bronswijk, Huntjens, de Jongh and van Dijk.