Machine Learning-Driven Comparative Analysis of Quetiapine and Olanzapine for Managing Depressive Episodes in Bipolar Disorder

Abstract

Rocco de Filippis and Abdullah Al Foysal

Background: Bipolar disorder (BD) necessitates effective management of both manic and depressive episodes. Quetiapine and olanzapine are antipsychotic medications commonly used in the treatment of BD, but their relative efficacy as antidepressants is a topic of ongoing research.

Objective: This case series aims to evaluate the greater efficacy of quetiapine compared to olanzapine as an antidepressant in the treatment of bipolar disorder.

Methods: Two patients with bipolar disorder were treated with either quetiapine or olanzapine. Clinical assessments were conducted using the Mood Disorder Questionnaire (MDQ), Clinical Global Impression-Severity (CGI-S), Hamilton Depression Rating Scale (HDRS), and Global Assessment of Functioning (GAF) before and after the treatment.

Results: Quetiapine showed greater efficacy in reducing depressive symptoms and improving overall functioning compared to olanzapine. MDQ scores decreased, CGI-S scores improved, HDRS scores reduced, and GAF scores increased more significantly in the patient treated with quetiapine.

Conclusion: Quetiapine appears to be a more effective antidepressant than olanzapine in the treatment of bipolar disorder. These findings suggest that quetiapine could be considered a preferred option for managing depressive episodes in BD patients.

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