Who will respond to intensive PTSD treatment? A machine learning approach to predicting response prior to starting treatment

• Published in: Journal of psychiatric research 2022-07, Vol.151, p.78-85
• Main Authors: Held, Philip, Schubert, Ryan A., Pridgen, Sarah, Kovacevic, Merdijana, Montes, Mauricio, Christ, Nicole M., Banerjee, Uddyalok, Smith, Dale L.
• Format: Article
• Language: English
• Subjects: Machine learning; Massed treatment; Precision medicine; PTSD; Trajectory modeling; Treatment response; Veterans
• ISSN: 0022-3956; 1879-1379
• DOI: 10.1016/j.jpsychires.2022.03.066

Despite the established effectiveness of evidence-based PTSD treatments, not everyone responds the same. Specifically, some individuals respond early while others respond minimally throughout treatment. Our ability to predict these trajectories at baseline has been limited. Predicting which individuals will respond to a certain type of treatment can significantly reduce short- and long-term costs and increase the ability to preemptively match individuals with treatments to which they are most likely to respond. In the present study, we examined whether veterans’ responses to a 3-week Cognitive Processing Therapy-based intensive PTSD treatment program could be accurately predicted prior to the first session. Using a sample of 432 veterans, and a wide range of demographic and clinical data collected during intake, we assessed six machine learning and statistical methods and their ability to predict fast and minimal responders prior to treatment initiation. For fast response classification, gradient boosted models (GBM) had the highest AUC-PR (0.466). For minimal response classification, elastic net (EN) had the highest mean CV AUC-PR (0.628). Using the best performing classifiers, we were able to predict both fast and minimal responders prior to starting treatment with relatively high AUC-ROC of 0.765 (GBM) and 0.826 (EN), respectively. These results may inform treatment modifications, although the accuracy may not be sufficient for clinicians to base inclusion/exclusion decisions entirely on the classifiers. Future research should evaluate whether these classifiers can be expanded to predict to which treatment type(s) an individual is most likely to respond based on various clinical, circumstantial, and biological features.