Machine Learning-Driven Evaluation of Pramipexole and Aripiprazole as Augmentation Therapies in Treatment-Resistant Depression
Background Treatment-resistant depression (TRD) presents a significant clinical challenge, often requiring augmentation strategies. Pramipexole and aripiprazole are two such agents used to augment antidepressant therapy. However, their comparative efficacy remains under-researched. Objective This case report aims to evaluate the efficacy of pramipexole compared to aripiprazole as augmentation therapy in treatment-resistant depression. Methods Two patients with TRD were treated with pramipexole or aripiprazole augmentation. Clinical assessments were conducted using the Hamilton Depression Rating Scale (HDRS), Clinical Global Impression-Severity (CGI-S), and the Beck Depression Inventory (BDI) before and after the treatment. Results Pramipexole showed greater efficacy in reducing depressive symptoms and improving overall functioning compared to aripiprazole. HDRS, CGI-S, and BDI scores decreased more significantly in the patient treated with pramipexole. Conclusion Pramipexole appears to be a more effective augmentation agent than aripiprazole in the treatment of TRD. These findings suggest pramipexole could be considered a preferred option for managing TRD.
Authors
- Rocco de Filippis (ORCID: https://orcid.org/0000-0001-9101-072X)
- Abdullah Al Foysal (ORCID: https://orcid.org/0000-0002-5102-4999)
Publication Details
- Journal
- Journal of Mathematical Techniques and Computational Mathematics
- Published
- 2024-08-20
- DOI
- https://doi.org/10.33140/jmtcm.03.08.005
- Primary Topic
- Treatment of Major Depression
- Type
- article
- Field-Weighted Citation Impact
- 0.00