Background: Trauma-focused cognitive behavioral therapy (Tf-CBT) and eye movement desensitization and reprocessing (EMDR) are two highly effective treatment options for posttraumatic stress disorder (PTSD). Yet, on an individual level, PTSD patients vary substantially in treatment response. The aim of the paper is to test the application of a treatment selection method based on a personalized advantage index (PAI).
Method: The study used clinical data for patients accessing treatment for PTSD in a primary care mental health service in the north of England. PTSD patients received either EMDR (N = 75) or Tf-CBT (N = 242). The Patient Health Questionnaire (PHQ-9) was used as an outcome measure for depressive symptoms associated with PTSD. Variables predicting differential treatment response were identified using an automated variable selection approach (genetic algorithm) and afterwards included in regression models, allowing the calculation of each patient's PAI.
Results: Age, employment status, gender, and functional impairment were identified as relevant variables for Tf-CBT. For EMDR, baseline depressive symptoms as well as prescribed antidepressant medication were selected as predictor variables. Fifty-six percent of the patients (n = 125) had a PAI equal or higher than one standard deviation. From those patients, 62 (50%) did not receive their model-predicted treatment and could have benefited from a treatment assignment based on the PAI.
Conclusions: Using a PAI-based algorithm has the potential to improve clinical decision making and to enhance individual patient outcomes, although further replication is necessary before such an approach can be implemented in prospective studies.
Keywords: automated variable selection; eye movement desensitization and reprocessing; personalized advantage index; personalized medicine; post-traumatic stress disorder; trauma focused cognitive behavioral therapy.
© 2018 Wiley Periodicals, Inc.