Predicting response to Cognitive Processing Therapy for PTSD: A machine-learning approach.

Abstract

Cognitive Processing Therapy (CPT) is an effective treatment for posttraumatic stress disorder (PTSD); however, not every client achieves optimal outcomes. Data were pooled from four randomized trials in which female interpersonal trauma survivors completed CPT (N\xa0=\xa0179). Random forests of classification trees were used to investigate the role of both baseline (e.g., demographics, trauma history, comorbid disorders) and session PTSD and depressive symptom scores on predicting trajectory and outcome. Of particular focus was whether those on track for poor outcome (e.g., non-response, partial treatment response) could be identified early in therapy. Results demonstrated inconsistent findings for discrimination between delayed responders (no early change but full response after 12 weeks of therapy) and those who either showed a partial response to treatment or did not respond at all; level of discrimination depended on the assessment point under study and the chosen comparison group. Those defined as clear and early responders, however, could be reliably differentiated from the other groups by session 4. Although it is possible to identify clients who will recover from PTSD by the middle of the CPT protocol, further work is needed to accurately identify those who will ultimately not recover from PTSD during a course of CPT.