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 ac...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-025-02886-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571628499763200 |
|---|---|
| author | Hua Min Farrokh Alemi |
| author_facet | Hua Min Farrokh Alemi |
| author_sort | Hua Min |
| collection | DOAJ |
| description | 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 fluoxetine and sertraline were more common in younger individuals. Condition-specific factors included trazodone for insomnia, and amitriptyline or nortriptyline for headaches. Paroxetine, venlafaxine, and sertraline more often prescribed to females, while bupropion and doxepin were commonly prescribed for patients with tobacco use disorder and opioid dependence. Predictive factors per medicine ranged from 51 (doxepin) to 168 (citalopram), with cross-validated AROC scores averaging 76.3%. Conclusions Our findings provide valuable insights into the nuanced factors that shape evidence-based antidepressant prescribing practices, offering a foundation for more personalized, effective depression treatment. Further research is needed to validate these models in other extant databases. These findings contribute to a more comprehensive understanding of antidepressant prescribing practices and have the potential to improve patient outcomes in the management of depression. |
| format | Article |
| id | doaj-art-ffc31fa00d8a492eab0e9f4befb1621d |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-ffc31fa00d8a492eab0e9f4befb1621d2025-02-02T12:27:49ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-012511710.1186/s12911-025-02886-zInsights into prescribing patterns for antidepressants: an evidence-based analysisHua Min0Farrokh Alemi1Department of Health Administration and Policy, College of Public Health, George Mason UniversityDepartment of Health Administration and Policy, College of Public Health, George Mason UniversityAbstract 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 fluoxetine and sertraline were more common in younger individuals. Condition-specific factors included trazodone for insomnia, and amitriptyline or nortriptyline for headaches. Paroxetine, venlafaxine, and sertraline more often prescribed to females, while bupropion and doxepin were commonly prescribed for patients with tobacco use disorder and opioid dependence. Predictive factors per medicine ranged from 51 (doxepin) to 168 (citalopram), with cross-validated AROC scores averaging 76.3%. Conclusions Our findings provide valuable insights into the nuanced factors that shape evidence-based antidepressant prescribing practices, offering a foundation for more personalized, effective depression treatment. Further research is needed to validate these models in other extant databases. These findings contribute to a more comprehensive understanding of antidepressant prescribing practices and have the potential to improve patient outcomes in the management of depression.https://doi.org/10.1186/s12911-025-02886-zAntidepressantsPrescribing behaviorsMachine learningClaimsEvidence-based analysis |
| spellingShingle | Hua Min Farrokh Alemi Insights into prescribing patterns for antidepressants: an evidence-based analysis BMC Medical Informatics and Decision Making Antidepressants Prescribing behaviors Machine learning Claims Evidence-based analysis |
| title | Insights into prescribing patterns for antidepressants: an evidence-based analysis |
| title_full | Insights into prescribing patterns for antidepressants: an evidence-based analysis |
| title_fullStr | Insights into prescribing patterns for antidepressants: an evidence-based analysis |
| title_full_unstemmed | Insights into prescribing patterns for antidepressants: an evidence-based analysis |
| title_short | Insights into prescribing patterns for antidepressants: an evidence-based analysis |
| title_sort | insights into prescribing patterns for antidepressants an evidence based analysis |
| topic | Antidepressants Prescribing behaviors Machine learning Claims Evidence-based analysis |
| url | https://doi.org/10.1186/s12911-025-02886-z |
| work_keys_str_mv | AT huamin insightsintoprescribingpatternsforantidepressantsanevidencebasedanalysis AT farrokhalemi insightsintoprescribingpatternsforantidepressantsanevidencebasedanalysis |