Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1
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 ph...
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1194669/full |
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| author | Susanne Bremer-Hoeve Noortje I. van Vliet Suzanne C. van Bronswijk Suzanne C. van Bronswijk Rafaele J.C. Huntjens Ad de Jongh Ad de Jongh Ad de Jongh Maarten K. van Dijk |
| author_facet | Susanne Bremer-Hoeve Noortje I. van Vliet Suzanne C. van Bronswijk Suzanne C. van Bronswijk Rafaele J.C. Huntjens Ad de Jongh Ad de Jongh Ad de Jongh Maarten K. van Dijk |
| author_sort | Susanne Bremer-Hoeve |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T17:35:31Z |
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| id | doaj.art-5fd0a349915e4b7e9c8c47cf3b793064 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-03-12T17:35:31Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-5fd0a349915e4b7e9c8c47cf3b7930642023-08-04T11:34:47ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402023-08-011410.3389/fpsyt.2023.11946691194669Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1Susanne Bremer-Hoeve0Noortje I. van Vliet1Suzanne C. van Bronswijk2Suzanne C. van Bronswijk3Rafaele J.C. Huntjens4Ad de Jongh5Ad de Jongh6Ad de Jongh7Maarten K. van Dijk8Dimence Mental Health Group, Deventer, NetherlandsDimence Mental Health Group, Deventer, NetherlandsDepartment of Psychiatry and Neuropsychology, School for Mental health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, NetherlandsDepartment of Psychiatry and Psychology, Maastricht University Medical Center, Maastricht, NetherlandsDepartment of Experimental Psychotherapy and Psychopathology, University of Groningen, Groningen, NetherlandsDepartment of Social Dentistry and Behavioral Sciences, University of Amsterdam and Vrije Universiteit, Amsterdam, NetherlandsSchool of Health Sciences, Salford University, Manchester, United KingdomInstitute of Health and Society, University of Worcester, Worcester, United KingdomDimence Mental Health Group, Deventer, NetherlandsBackgroundKnowledge 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1194669/fullPTSDchildhood abuseEMDRdropoutpredictorsmachine learning |
| spellingShingle | Susanne Bremer-Hoeve Noortje I. van Vliet Suzanne C. van Bronswijk Suzanne C. van Bronswijk Rafaele J.C. Huntjens Ad de Jongh Ad de Jongh Ad de Jongh Maarten K. van Dijk Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 Frontiers in Psychiatry PTSD childhood abuse EMDR dropout predictors machine learning |
| title | Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 |
| title_full | Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 |
| title_fullStr | Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 |
| title_full_unstemmed | Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 |
| title_short | Predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 |
| title_sort | predictors of treatment dropout in patients with posttraumatic stress disorder due to childhood abuse1 |
| topic | PTSD childhood abuse EMDR dropout predictors machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1194669/full |
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