Background: Monoamine oxidase inhibitors (MAOIs) were among the earliest antidepressants developed and remain a viable treatment option, particularly for patients with treatment-resistant depression (TRD). This report presents two cases of TRD successfully treated with MAOIs after failure of multiple antidepressants. Objective: To evaluate the efficacy of MAOIs in patients with major depressive disorder (MDD) who did not respond to selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine reuptake inhibitors (SNRIs). Methods: Two patients diagnosed with MDD, resistant to at least three classes of antidepressants, were treated with MAOI augmentation (Phenelzine 45 mg/day and Tranylcypromine 30 mg/day, respectively) over a 6-month period. Clinical outcomes were assessed using the Hamilton Depression Rating Scale (HDRS) and the Beck Depression Inventory (BDI). Machine learning was employed to identify predictors of treatment success, focusing on baseline scores and demographic factors. Results: Both patients showed substantial improvements. Patient 1’s HDRS score decreased from 26 to 9 (65% reduction) and BDI score from 35 to 12 (66% reduction). Patient 2’s HDRS score dropped from 28 to 11 (61% reduction) and BDI from 33 to 14 (58% reduction). Machine learning analysis identified post-treatment HDRS and BDI scores as the most significant predictors of success. No severe side effects were observed, though mild insomnia and headaches were managed with supportive therapy. Conclusion: MAOIs, particularly Phenelzine and Tranylcypromine, can be an effective treatment option for patients with TRD where other medications have failed. Despite their historical decline in use due to dietary restrictions and potential side effects, modern machine-learning insights can help refine patient selection and enhance treatment outcomes. These findings suggest that MAOIs deserve continued consideration in contemporary psychiatric practice for resistant cases, though larger studies are needed to confirm their efficacy.
Cite this paper
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