ArticleJMIR medical informatics2020
Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Characterizing Alzheimer's Disease and Related Dementia in a Hypertension Population Within the State of Florida Using Electronic Health Record-Based Data.Clinical pharmacology and therapeutics · 2026Article
- Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks.BMC cardiovascular disorders · 2024Article
- Anti-inflammatory activity of benidipine hydrochloride in LPS-activated mammalian macrophages.Naunyn-Schmiedeberg's archives of pharmacology · 2024Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Rurality, Cardiovascular Risk Factors, and Early Cardiovascular Disease among Childhood, Adolescent, and Young Adult Cancer Survivors.Research square · 2024Article
- Characteristics and Predictors of Apparent Treatment-Resistant Hypertension in Real-World Populations Using Electronic Health Record-Based Data.American journal of hypertension · 2024Article
- Causal Deep Neural Network-Based Model for First-Line Hypertension Management.Mayo Clinic proceedings. Digital health · 2023Article
- Article
- Long-Term Results of a Digital Hypertension Self-Management Program: Retrospective Cohort Study.JMIR cardio · 2023Article
- Antihypertensive Class and Cardiovascular Outcomes in Patients With HIV and Hypertension.Hypertension (Dallas, Tex. : 1979) · 2021Article
- Optimizing identification of resistant hypertension: Computable phenotype development and validation.Pharmacoepidemiology and drug safety · 2020Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundComputable phenotypes have the ability to utilize data within the electronic health record (EHR) to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information. Hypertension (HTN)-related computable phenotypes are particularly dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information.
objectiveThis study aimed to create an antihypertensive drug classification system to be utilized with EHR-based data as part of HTN-related computable phenotypes.
methodsWe compared 4 different antihypertensive drug classification systems based off of 4 different methodologies and terminologies, including 3 RxNorm Concept Unique Identifier (RxCUI)-based classifications and 1 medication name-based classification. The RxCUI-based classifications utilized data from (1) the Drug Ontology, (2) the new Medication Reference Terminology, and (3) the Anatomical Therapeutic Chemical Classification System and DrugBank, whereas the medication name-based classification relied on antihypertensive drug names. Each classification system was applied to EHR-based prescription drug data from hypertensive patients in the OneFlorida Data Trust.
resultsThere were 13,627 unique RxCUIs and 8025 unique medication names from the 13,879,046 prescriptions. We observed a broad overlap between the 4 methods, with 84.1% (691/822) to 95.3% (695/729) of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms, drug products with an indication of benign prostatic hyperplasia, drug products that contain more than 1 ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs.
conclusionsIn total, 2 antihypertensive drug classifications were constructed, one based on RxCUIs and one based on medication name, that can be used in future computable phenotypes that require antihypertensive drug classifications.
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