Insights into prescribing patterns for antidepressants: an evidence-based analysis
Abstract
Background: Antidepressants are a primary treatment for depression, yet prescribing them poses significant challenges due to the absence of clear guidelines for selecting the most suitable option for individual patients. This study aimed to analyze prescribing patterns for antidepressants across healthcare providers, including physicians, physician assistants, nurse practitioners, and pharmacists, to better understand the complex factors influencing these patterns in the management of depression.
Methods: Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to identify variables that explained the variation in the prescribed antidepressants, utilizing a large number of claims. Models were created to identify the prescription patterns of the 14 most common antidepressants, including amitriptyline, bupropion, citalopram, desvenlafaxine, doxepin, duloxetine, escitalopram, fluoxetine, mirtazapine, nortriptyline, paroxetine, sertraline, trazodone, and venlafaxine. The accuracy of predictions was measured through the Area under the Receiver Operating Curve (AROC).
Results: Our analysis revealed several key factors influencing prescribing patterns, including patients' comorbidities, previous medications, age, and gender. A history of high antidepressant use (four or more prior medications) was the most common factor across antidepressants. Age influenced prescribing patterns, with mirtazapine and trazodone more frequent among older patients, while…
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