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Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System

Caitrin W McDonough1, Steven M Smith1, Rhonda M Cooper-DeHoff1,2

  • 1Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.

JMIR Medical Informatics
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

This study developed two antihypertensive drug classification systems using electronic health record data. These systems, one based on RxNorm Concept Unique Identifiers and another on medication names, will improve hypertension computable phenotypes.

Keywords:
RxNormantihypertensive agentsclassificationelectronic health recordsphenotype

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Area of Science:

  • Health Informatics
  • Pharmacology
  • Clinical Research

Background:

  • Electronic Health Records (EHRs) enable computable phenotypes to identify patient cohorts.
  • Accurate classification of antihypertensive medications in EHRs is crucial for hypertension (HTN) computable phenotypes.
  • Existing classification methods require evaluation for optimal use in HTN research.

Purpose of the Study:

  • To develop and evaluate antihypertensive drug classification systems for use with EHR data.
  • To create robust classifications for hypertension-related computable phenotypes.
  • To compare different terminologies and methodologies for drug classification.

Main Methods:

  • Compared four antihypertensive drug classification systems: three RxNorm Concept Unique Identifier (RxCUI)-based and one medication name-based.
  • Utilized Drug Ontology, Medication Reference Terminology, and ATC/DrugBank for RxCUI classifications.
  • Applied classification systems to EHR prescription data from hypertensive patients.

Main Results:

  • Analyzed 13,879,046 prescriptions, identifying 13,627 unique RxCUIs and 8025 medication names.
  • Observed significant overlap (84.1%-95.3%) between classification methods.
  • Identified discrepancies due to multi-ingredient drugs, varying dosage forms, and obsolete RxCUIs.

Conclusions:

  • Developed two effective antihypertensive drug classification systems: one RxCUI-based and one medication name-based.
  • These classifications are suitable for integration into future hypertension computable phenotypes.
  • The study provides valuable tools for EHR-based clinical research in hypertension.