Prediction of Clinically Meaningful Improvement After Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety Disorders: Machine Learning-Based Predictive Model Development and Temporal Validation Study.
Up to 50% of patients treated with internet-delivered cognitive behavioral therapy (ICBT) for depression and anxiety disorders do not experience clinically significant symptom reduction. Identifying these patients prior to the initiation of ICBT can support treatment planning.
The aim of this study was to enhance baseline prediction of clinically meaningful improvement in patients treated with ICBT for common psychiatric disorders in routine care, which could ultimately inform treatment planning at intake.
We developed multimodal predictive models integrating clinical, sociodemographic, and genetic data available pretreatment to predict clinically meaningful improvement in a sample of 1790 patients treated with ICBT for major depressive disorder, panic disorder, and social anxiety disorder. We applied machine learning algorithms of varying complexity (logistic regression, random forest [RF], extreme gradient boosting, support vector machines, soft voting, and stacking ensemble), with nested cross-validation, elastic net variable selection, multiple imputation, and temporal validation in a 20% holdout test set (n=356). The primary performance measure was the area under the receiver operating characteristic curve (AUC).
All full phenotypic models showed comparable performance (AUCtest 0.732-0.749), with RF achieving the best holdout discrimination (AUCtest 0.749, 95% CI 0.698-0.797). Compared with the benchmark model based on self-reported screening data (AUCtest 0.695, 95% CI 0.637-0.748), RF and both ensemble models incorporating register data showed higher discrimination in paired DeLong tests (P=.04, P=.02, and P=.03, respectively), whereas polygenic scores added no independent predictive value in this cohort and modeling setup (P=.97).
These promising results support the feasibility of baseline prognostic prediction of clinically meaningful improvement after ICBT and provide a basis for the prospective validation of model-informed risk stratification.
The aim of this study was to enhance baseline prediction of clinically meaningful improvement in patients treated with ICBT for common psychiatric disorders in routine care, which could ultimately inform treatment planning at intake.
We developed multimodal predictive models integrating clinical, sociodemographic, and genetic data available pretreatment to predict clinically meaningful improvement in a sample of 1790 patients treated with ICBT for major depressive disorder, panic disorder, and social anxiety disorder. We applied machine learning algorithms of varying complexity (logistic regression, random forest [RF], extreme gradient boosting, support vector machines, soft voting, and stacking ensemble), with nested cross-validation, elastic net variable selection, multiple imputation, and temporal validation in a 20% holdout test set (n=356). The primary performance measure was the area under the receiver operating characteristic curve (AUC).
All full phenotypic models showed comparable performance (AUCtest 0.732-0.749), with RF achieving the best holdout discrimination (AUCtest 0.749, 95% CI 0.698-0.797). Compared with the benchmark model based on self-reported screening data (AUCtest 0.695, 95% CI 0.637-0.748), RF and both ensemble models incorporating register data showed higher discrimination in paired DeLong tests (P=.04, P=.02, and P=.03, respectively), whereas polygenic scores added no independent predictive value in this cohort and modeling setup (P=.97).
These promising results support the feasibility of baseline prognostic prediction of clinically meaningful improvement after ICBT and provide a basis for the prospective validation of model-informed risk stratification.
Authors
Kravchenko Kravchenko, Halvorsen Halvorsen, Bäckman Bäckman, Kaldo Kaldo, Crowley Crowley, Kuja-Halkola Kuja-Halkola, Rück Rück, Wallert Wallert
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