Background: Treatment-resistant depression (TRD) poses significant clinical challenges, with many patients inadequately responding to augmentation strategies like aripiprazole. Pramipexole, a dopamine agonist, has emerged as a promising alternative, though predictors of response remain unclear. This study applies machine learning (ML) to identify predictors and subgroups influencing pramipexole augmentation (PA) effectiveness in TRD, especially among patients previously failing aripiprazole augmentation (FAA). Methods: A synthetic dataset (n = 500), based on real-world data, comprised FAA (n = 150) and aripiprazole-untreated (UAA, n = 350) groups. Four ML algorithms (Random Forest, Gradient Boosting, Logistic Regression, SVM) predicted treatment response. Model accuracy, ROC curves, calibration, feature importance (via SHAP), and patient clustering (k-means) were evaluated. Results: Response rates were higher in UAA (76.9%) versus FAA (66.2%). SVM had the highest accuracy (73.3%), while Logistic Regression showed the best discrimination (ROC AUC = 0.612) and calibration. Key predictors included baseline depression severity, episode duration, pramipexole dosage, and patient age, with significant age-dose interactions. Clustering revealed younger FAA patients with prolonged depressive episodes as a high-risk subgroup with notably lower remission rates (49%). Conclusions: ML analysis highlights baseline depression severity, age, episode duration, and pramipexole dosage as crucial predictors of PA response. Younger FAA patients with extended depressive episodes represent a high-risk subgroup needing tailored therapeutic strategies, reinforcing precision psychiatry for managing complex TRD cases.
Cite this paper
Filippis, R. D. and Foysal, A. A. (2025). Machine Learning Analysis of Pramipexole Augmentation in Treatment-Resistant Depression: Identifying Predictors of Response. Open Access Library Journal, 12, e3515. doi: http://dx.doi.org/10.4236/oalib.1113515.
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