Predicting outcome of daycare cognitive behavioural therapy in a naturalistic sample of patients with PTSD: a machine learning approach. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Stuke, Heiner; Schoofs, Nikola; Johanssen, Helen; Bermpohl, Felix; Ülsmann, Dominik; Schulte-Herbrüggen, Olaf; Priebe, Kathlen
• Is Part Of: European journal of psychotraumatology. Volume 12:Issue 1(2021)
• Publication Date: 2021-01-01
• Subjects: PTSD -- behavioural therapy -- outcome prediction -- individualized treatment; TEPT -- terapia conductual -- predicción de resultados -- tratamiento individualizado; PTSD -- 行为疗法 -- 结果预测 -- 个体化治疗; Post-traumatic stress disorder -- Periodicals; Stress Disorders, Post-Traumatic; Post-traumatic stress disorder; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 616.8521
• DOI: 10.1080/20008198.2021.1958471
• Languages: English
• ISSNs: 2000-8198

Abstract

ABSTRACT: Background: Identifying predictors for treatment outcome in patients with posttraumatic stress disorder (PTSD) is important in order to provide an effective treatment, but robust and replicated treatment outcome predictors are not available up to now. Objectives: We investigated predictors of treatment outcome in a naturalistic sample of patients with PTSD admitted to an 8-week daycare cognitive behavioural therapy programme following a wide range of traumatic events. Method: We used machine learning (linear and non-linear regressors and cross-validation) to predict outcome at discharge for 116 patients and sustained treatment effects 6 months after discharge for 52 patients who had a follow-up assessment. Predictions were based on a wide selection of demographic and clinical assessments including age, gender, comorbid psychiatric disorders, trauma history, posttraumatic symptoms, posttraumatic cognitions, depressive symptoms, general psychopathology and psychosocial functioning. Results: We found that demographic and clinical variables significantly, but only modestly predicted PTSD treatment outcome at discharge (r = 0.21, p = .021 for the best model) and follow-up (r = 0.31, p = .026). Among the included variables, more severe posttraumatic cognitions were negatively associated with treatment outcome. Early response in PTSD symptomatology (percentage change of symptom scores after 4 weeks of treatment) allowed more accurate predictions of outcome at discharge (r = 0.56, p < .001) and follow-up (r = 0.43, p = .001). Conclusion: Our results underscore the importance of early treatment response for short- and long-term treatment success. Nevertheless, it remains an unresolved challenge to identify variables that can robustly predict outcome before the initiation of treatment. HIGHLIGHTS: Psychotherapy can improve PTSD, but many patients do not respond adequately. We found that clinical variables (high posttraumatic cognitions but low re-experiencing symptoms) modestly predicted poor response to CBT. Early therapy response more accurately predicted final outcome.