ArticleJournal of the American Medical Informatics Association : JAMIA
An electronic health record driven algorithm to identify incident antidepressant medication users.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- The Association of Antidepressants in Late Pregnancy with Postpartum Hemorrhage: Systematic Review of Controlled Observational Studies.Journal of child and adolescent psychopharmacology · 2024Pooled it
- Poor Neonatal Adaptation After Antidepressant Exposure During the Third Trimester in a Geographically Defined Cohort.Mayo Clinic proceedings. Innovations, quality & outcomes · 2023Article
- Analysis of 'One in a Million' primary care consultation conversations using natural language processing.BMJ health & care informatics · 2023Article
- Unsupervised characterization of Major Depressive Disorder medication treatment pathways.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Detecting moderate or complex congenital heart defects in adults from an electronic health records system.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveWe validated an algorithm designed to identify new or prevalent users of antidepressant medications via population-based drug prescription records. PATIENTS AND
methodsWe obtained population-based drug prescription records for the entire Olmsted County, Minnesota, population from 2011 to 2012 (N=149,629) using the existing electronic medical records linkage infrastructure of the Rochester Epidemiology Project (REP). We selected electronically a random sample of 200 new antidepressant users stratified by age and sex. The algorithm required the exclusion of antidepressant use in the 6 months preceding the date of the first qualifying antidepressant prescription (index date). Medical records were manually reviewed and adjudicated to calculate the positive predictive value (PPV). We also manually reviewed the records of a random sample of 200 antihistamine users who did not meet the case definition of new antidepressant user to estimate the negative predictive value (NPV).
results161 of the 198 subjects electronically identified as new antidepressant users were confirmed by manual record review (PPV 81.3%). Restricting the definition of new users to subjects who were prescribed typical starting doses of each agent for treating major depression in non-geriatric adults resulted in an increase in the PPV (90.9%). Extending the time windows with no antidepressant use preceding the index date resulted in only modest increases in PPV. The manual abstraction of medical records of 200 antihistamine users yielded an NPV of 98.5%.
conclusionsOur study confirms that REP prescription records can be used to identify prevalent and incident users of antidepressants in the Olmsted County, Minnesota, population.
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Registered trials
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