Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety

Abstract

Abstract Internet-delivered cognitive behavioural therapy (ICBT) is an effective and accessible treatment for mild to moderate depression and anxiety disorders. However, up to 50% of patients do not experience sufficient symptom relief. Identifying patient characteristics predictive of higher post-treatment symptom severity is crucial for devising personalized interventions to avoid treatment failures and reduce healthcare costs. Using the new Swedish multimodal database MULTI-PSYCH, we expand upon established predictors of treatment outcome and assess the added benefit of utilizing polygenic risk scores (PRS) and nationwide register data in a combined sample of 2668 patients treated with ICBT for major depressive disorder (n = 1300), panic disorder (n = 727), and social anxiety disorder (n = 641). We present two linear regression models: a baseline model using six well-established predictors and a full model incorporating six clinic-based, 32 register-based predictors, and PRS for seven psychiatric disorders and traits. First, we assessed predictor importance through bivariate associations and then compared the models based on the proportion of variance explained in post-treatment scores. Our analysis identified several novel predictors of higher post-treatment severity, including comorbid ASD and ADHD, receipt of financial benefits, and prior use of some psychotropic medications. The baseline model explained 27% of the variance in post-treatment symptom scores, while the full model offered a modest improvement, explaining 34%. Developing a machine learning model that can capture complex non-linear associations and interactions between high-quality multimodal input features is a viable next step to improve prediction of symptom severity post ICBT.