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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: May 21, 2019
Open Peer Review Period: May 24, 2019 - Jul 19, 2019
Date Accepted: Dec 15, 2019
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison

McDonough CW, Smith SM, Cooper-DeHoff RM, Hogan WR

Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison

JMIR Med Inform 2020;8(2):e14777

DOI: 10.2196/14777

PMID: 32130152

PMCID: 7068459

The development of a drug classification system for antihypertensive medications utilizing electronic health record based data: A methodology comparison

  • Caitrin W McDonough; 
  • Steven M Smith; 
  • Rhonda M Cooper-DeHoff; 
  • William R Hogan

ABSTRACT

Background:

Computable phenotypes have the ability to utilize data within the electronic health record (EHR) in order to identify patients with certain characteristics. Many computable phenotypes rely on multiple types of data within the EHR including prescription drug information, which is the case for resistant hypertension (RHTN). RHTN is a phenotype that is dependent on the correct classification of antihypertensive prescription drug information, as well as corresponding diagnoses and blood pressure information.

Objective:

We sought to create an antihypertensive drug classification system to be utilized with EHR based data as part of our RHTN computable phenotype.

Methods:

We compared four different antihypertensive drug classification systems based off of four different methodologies and terminologies, including three RxNorm Concept Unique Identifier (RxCUI) based classifications, and one medication name based classification. The RxCUI based classifications utilized data from 1) the Drug Ontology (DrOn), 2) the new Medication Reference Terminology (MED-RT), and 3) the Anatomical Therapeutic Chemical (ATC) Classification System and DrugBank, while the medication name 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.

Results:

We observed broad overlap between the four methods, with 84-95% of terms overlapping pairwise between the different classification methods. Key differences arose from drug products with multiple dosage forms (e.g. oral and ophthalmic, oral and topical), drug products with an indication of benign prostatic hyperplasia, drug products that contain more than one ingredient (combination products), and terms within the classification systems corresponding to retired or obsolete RxCUIs.

Conclusions:

We have constructed two antihypertensive drug classifications, one based on RxCUIs, and one based on medication name that can be used in future computable phenotypes that require anti-hypertensive drug classifications.


 Citation

Please cite as:

McDonough CW, Smith SM, Cooper-DeHoff RM, Hogan WR

Optimizing Antihypertensive Medication Classification in Electronic Health Record-Based Data: Classification System Development and Methodological Comparison

JMIR Med Inform 2020;8(2):e14777

DOI: 10.2196/14777

PMID: 32130152

PMCID: 7068459

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