BMC psychiatry · 2025
Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety.
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Kravchenko Olly, Bäckman Julia, Mataix-Cols David, Crowley James J, Halvorsen Matthew, Sullivan Patrick F, Wallert John, Rück Christian
Аннотация
BACKGROUND: 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 achieve 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.
METHODS: Using the Swedish multimodal database MULTI-PSYCH, we evaluated novel and established predictors associated with treatment outcome and assessed the added benefit of polygenic risk scores (PRS) and nationwide register data in a sample of 2668 patients treated with ICBT for major depressive disorder, panic disorder, and social anxiety disorder. Two linear regression models were compared: a baseline model employing six well-established predictors and a full model incorporating six clinic-based, 32 register-based predictors, and PRS for seven psychiatric disorders and traits. Predictor importance was assessed through bivariate associations, and models were compared by the variance explained in post-treatment symptom scores.
RESULTS: Our analysis identified several novel predictors of higher post-treatment severity, including comorbid ASD and ADHD, receipt of financial benefits, and prior use of psychotropic medications. The baseline model explained 27%, while the full model accounted for 34% of the variance.
CONCLUSIONS: The findings suggest that a model incorporating a broad array of multimodal data offered a modest improvement in explanatory power compared to one using a limited set of easily accessible measures. Employing machine learning algorithms capable of capturing complex non-linear associations and interactions is a viable next step to improve prediction of post-ICBT symptom severity.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: Using the Swedish multimodal database MULTI-PSYCH, we evaluated novel and established predictors associated with treatment outcome and assessed the added benefit of polygenic risk scores (PRS) and nationwide register data in a sample of 2668 patients treated with ICBT for major depressive disorder, panic disorder, and social anxiety disorder. Two linear regression models were compared: a baseline model employing six well-established predictors and a full model incorporating six clinic-based, 32 register-based predictors, and PRS for seven psychiatric disorders and traits. Predictor importance was assessed through bivariate associations, and models were compared by the variance explained in post-treatment symptom scores.
RESULTS: Our analysis identified several novel predictors of higher post-treatment severity, including comorbid ASD and ADHD, receipt of financial benefits, and prior use of psychotropic medications. The baseline model explained 27%, while the full model accounted for 34% of the variance.
CONCLUSIONS: The findings suggest that a model incorporating a broad array of multimodal data offered a modest improvement in explanatory power compared to one using a limited set of easily accessible measures. Employing machine learning algorithms capable of capturing complex non-linear associations and interactions is a viable next step to improve prediction of post-ICBT symptom severity.
CLINICAL TRIAL NUMBER: Not applicable.