Developing Transdiagnostic Internalizing Disorder Prognostic Indices for Outpatient Cognitive Behavioral Therapy

A growing literature is devoted to understanding and pre-dicting heterogeneity in response to cognitive behavioral therapy (CBT), including using supervised machine learn-ing to develop prognostic models that could be used to inform treatment planning. The current study developed CBT prognostic models using data from a broad dimen-sionally 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 remis-sion 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 dis-tribution 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 out-come, compared to 2.7% in the bottom tertile. Key predic-tors 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 ulti-mately could lead to more effective and efficient treatment of patients with internalizing psychopathology.