Currently submitted to: JMIR Mental Health
Date Submitted: Aug 12, 2026
Open Peer Review Period: Aug 12, 2026 - Oct 7, 2026
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Prediction Models for Treatment Response in Stress-Related Disorders
ABSTRACT
Background:
Stress-related disorders, including adjustment disorder and exhaustion disorder, are associated with disability, sickness absence, and heterogeneous treatment response. Internet-delivered interventions can improve stress-related symptoms, but tools for individualized prognosis are lacking.
Objective:
This study aimed to identify predictors of treatment response in patients with stress-related disorders and evaluate whether machine learning models could provide clinically useful individual-level prediction.
Methods:
We conducted a predictive modeling study using data from a randomized controlled trial of internet-delivered cognitive behavioral therapy versus general health promotion. Treatment arms were pooled because the parent trial found no between-group differences in efficacy. The outcome was responder status on the Perceived Stress Scale-10 at 12 weeks, defined using the reliable change index. Multivariable logistic regression examined prespecified predictors. Elastic net logistic regression, random forest, support vector machine, and AdaBoost models were trained using a 70/30 train-test split with 5-fold cross-validation and evaluated in an unseen hold-out test set. Clinical utility was prespecified as balanced accuracy of at least 67%.
Results:
Of 300 randomized participants, 282 had outcome data and 146 (51.8%) were responders. Higher baseline perceived stress (adjusted odds ratio [aOR] 2.03, 95% CI 1.50-2.74), higher quality of life (aOR 1.60, 95% CI 1.18-2.16), and higher educational attainment (aOR 1.57, 95% CI 1.14-2.15) predicted higher odds of response, whereas death of a close relative predicted lower odds (aOR 0.44, 95% CI 0.24-0.83). Elastic net logistic regression performed best in the per-protocol machine learning analysis (balanced accuracy 64.8%, 95% CI 54%-75%; area under the curve 0.74), but no per-protocol model met the prespecified clinical utility threshold. In a post hoc expanded-feature analysis, elastic net logistic regression reached balanced accuracy of 68.2% (95% CI 58%-78%; area under the curve 0.72).
Conclusions:
Baseline clinical variables showed group-level prognostic value, but individual-level prediction remained modest. These findings support further development and external validation in larger samples before prediction models are used to guide routine care for stress-related disorders. Clinical Trial: ClinicalTrials.gov NCT04797273
Citation
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