Developing Transdiagnostic Internalizing Disorder Prognostic Indices for Outpatient Cognitive Behavioral Therapy. Issue 3 (May 2023)

• Record Type: Journal Article
• Authors: Rosellini, Anthony J.; Andrea, Alexandra M.; Galiano, Christina S.; Hwang, Irving; Brown, Timothy A.; Luedtke, Alex; Kessler, Ronald C.
• Is Part Of: Behavior therapy. Volume 54:Issue 3(2023)
• Page Start: 461
• Page End: 475
• Publication Date: 2023-05
• Subjects: cognitive-behavioral therapy -- machine learning -- psychotherapy -- treatment response; Behavior therapy -- Periodicals; 616.8914205
• DOI: 10.1016/j.beth.2022.11.004
• Languages: English
• ISSNs: 0005-7894

Abstract

Highlights: Super learning was used to develop prognostic indices for outpatient CBT. Models for principal diagnosis and transdiagnostic remission achieved acceptable AUROC. Top predictors included diagnostic severity and symptom and temperament dimensions. With sufficient validation, prognostic models may improve treatment planning. Abstract: A growing literature is devoted to understanding and predicting heterogeneity in response to cognitive behavioral therapy (CBT), including using supervised machine learning to develop prognostic models that could be used to inform treatment planning. The current study developed CBT prognostic models using data from a broad dimensionally oriented pretreatment assessment (324 predictors) of 1, 210 outpatients with internalizing psychopathology. Super learning was implemented to develop prognostic indices for three outcomes assessed at 12-month follow-up: principal diagnosis improvement (attained by 65.8% of patients), principal diagnosis remission (56.8%), and transdiagnostic full remission (14.3%). The models for principal diagnosis remission and transdiagnostic remission performed best (AUROCs = 0.71–0.73). Calibration was modest for all three models. Three-quarters (77.3%) of patients in the top tertile of the predicted probability distribution achieved principal diagnosis remission, compared to 35.0% in the bottom tertile. One-third (35.3%) of patients in the top two deciles of predicted probabilities for transdiagnostic complete remission achieved this outcome, compared to 2.7% in the bottom tertile. Key predictors included principal diagnosis severity, social anxiety diagnosis/severity, hopelessness, temperament, and global impairment. While additional work is needed to improve performance, integration of CBT prognostic models ultimately could lead to more effective and efficient treatment of patients with internalizing psychopathology.