Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety
(2025) In BMC Psychiatry 25(1).- Abstract
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,... (More)
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.
(Less)
- author
- Kravchenko, Olly ; Bäckman, Julia ; Mataix-Cols, David LU ; Crowley, James J. ; Halvorsen, Matthew ; Sullivan, Patrick F. ; Wallert, John and Rück, Christian
- organization
- publishing date
- 2025-12
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Anxiety, Cognitive behavioural therapy, Depression, Treatment outcome
- in
- BMC Psychiatry
- volume
- 25
- issue
- 1
- article number
- 555
- publisher
- BioMed Central (BMC)
- external identifiers
-
- scopus:105006904339
- pmid:40448103
- ISSN
- 1471-244X
- DOI
- 10.1186/s12888-025-07012-x
- language
- English
- LU publication?
- yes
- id
- 9a2f961f-a473-4d1f-8194-2eae9eb93637
- date added to LUP
- 2025-07-15 11:05:16
- date last changed
- 2025-07-16 03:00:11
@article{9a2f961f-a473-4d1f-8194-2eae9eb93637,
abstract = {{<p>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.</p>}},
author = {{Kravchenko, Olly and Bäckman, Julia and Mataix-Cols, David and Crowley, James J. and Halvorsen, Matthew and Sullivan, Patrick F. and Wallert, John and Rück, Christian}},
issn = {{1471-244X}},
keywords = {{Anxiety; Cognitive behavioural therapy; Depression; Treatment outcome}},
language = {{eng}},
number = {{1}},
publisher = {{BioMed Central (BMC)}},
series = {{BMC Psychiatry}},
title = {{Clinical, genetic, and sociodemographic predictors of symptom severity after internet-delivered cognitive behavioural therapy for depression and anxiety}},
url = {{http://dx.doi.org/10.1186/s12888-025-07012-x}},
doi = {{10.1186/s12888-025-07012-x}},
volume = {{25}},
year = {{2025}},
}