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

Abstract

Background Computable 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. Objective This study aimed to create an antihypertensive drug classification system to be utilized with EHR-based data as part of HTN-related computable phenotypes. Methods We 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. Results There 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. Conclusions In 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.

Optimizing identification of resistant hypertension: Computable phenotype development and validation

“…We relied on our prior work on antihypertensive medication classification to ensure proper grouping and counting of the prescription data. 35 The final algorithms for RHTN and stable controlled HTN still classify a small number (5/250 = 2%) of patients as either RHTN or stable controlled HTN when they should be other HTN patients. From our work, four out of five of the patients who were misclassified during the final round appeared to be due to our method of handling medication start and end dates for historical medications and/or discontinued medications.…”

“…Particular care must be taken to ensure that drug classes combine and count correctly, regardless of preparation (single drug product vs combination drug product). We relied on our prior work on antihypertensive medication classification to ensure proper grouping and counting of the prescription data 35 …”

“…Patients were included in the cohort if they had prescription information available from the Prescribing table, and blood pressure (BP) readings available from the Vital table. For patients with all necessary data, the following data preparation steps were conducted: (a) all medications were mapped to antihypertensive drug classes based on RxNorm Concept Unique Identifiers (RxCUI) utilizing our previously developed map and methodology, 35 (b) a drug exposure variable was created to count the number of antihypertensive drug classes prescribed at any point during the study period (by day), and (c) BP data were considered to determine systolic and diastolic BP control (defined as <140/ <90 mmHg) during the study time period when antihypertensive medications were prescribed. Our data preparation steps assume patients are exposed to all antihypertensive medications they are prescribed.…”

Automated identification of unstandardized medication data: A scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data

“…Existing standardization methods for medication data are often inflexible, providing limited support for situations where standardized medication identifiers have low coverage or inconsistent formats [3][4][5][6] . They typically do not account for free-text drug fields that may contain abbreviations, spelling variations, or additional information that obscures simple parsing [3][4][5][6] .…”

Medication Guideline Adherence Among Patients with Markedly Elevated Blood Pressure in A Real-World Setting

“…To properly classify EHR-based medication prescription data into antihypertensive therapeutic indication and antihypertensive drug classes, we used a previously developed antihypertensive drug classification system based on RxNorm Concept Unique Identifier (RxCUI). 14 We included only oral formulations to focus on outpatient medications, with the exception of transdermal clonidine patches, as some medications have ophthalmic dosage forms that are not indicated for hypertension.…”