PREACT

Although cognitive-behavioral therapy (CBT) is a first-line treatment for internalizing disorders, a substantial proportion of patients fails to benefit - with severe consequences for patients and increasing costs for societies. The paucity of standard clinical features that allow for single-case predictions, limited methodological approaches, and fragmented data levels serve as an impetus to implement state-of-the-art predictive analytics to search for the best predictors of (non-) response. The present project (SP1, TIKI study) aims to set up a prospective-longitudinal observational cohort of n = 500 patients with mental disorders from the internalizing spectrum (specific phobia, social anxiety disorder, panic disorder, agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, unipolar depressive disorders) who are treated with CBT. Embedded within the Research Unit, patients will be deeply phenotyped for several layers of bio-behavioral markers (SP3-SP6). It will be supported by our clinical (SP1), methods (SP2), and neuroimaging platform (SP3). As such, SP1 contains both a research and a service component. Research-wise, we will test the hypothesis if treatment (non-)response can be predicted with sufficient accuracy based on clinical routine data when state-of-the-art machine learning methods are applied. Service-wise, SP1 will coordinate the clinical trial by serving as a recruitment hub for the other Subprojects, implementing diagnostic assessments, documenting and quality-controlling treatment contents, and calculating primary and secondary outcomes. As an observational trial in the academic outpatient sector, the study design exerts a high degree of external validity as a prerequisite for translating predictive analytics into practice. No randomization applies - all patients receive active treatment. In line with the idea of precision medicine, our overall aim is to enable the prediction of treatment outcomes prior to therapy onset for the early identification and optimized treatment of patients at risk for non-response towards standard treatment.