Further revamping VA's NDF-RT drug terminology for clinical research

Biomedical terminology and vocabulary standards (for the purposes of this correspondence, we use the terms ‘terminology,’ ‘vocabulary’ and ‘ontology’ interchangeably.) play an important role in enabling consistent, comparable, and meaningful sharing of data within and across institutional boundaries, as well as ensuring semantic interoperability. An important domain for developing standardized vocabularies is medications, where existing standards structure and organize approved drug products and ingredients by various characteristics or properties to support a multitude of clinical and epidemiological research questions across the spectrum of health and disease. Veteran Affairs' National Drug File-Reference Terminology (NDF-RT; see figure 1) is a Federal Medication-recommended standardized terminology resource encompassing medications, ingredients, and high-level drug classes for Chemical Structure (eg, Acetanilides), Mechanism of Action (eg, Prostaglandin Receptor Antagonists), Physiological Effect (eg, Decreased Prostaglandin Production), drug–disease relationship describing the Therapeutic Intent (eg, Pain), and Pharmacokinetics describing the mechanisms of absorption and distribution of an administered drug within a body (eg, Hepatic Metabolism). Additionally, NDF-RT contains two independent lists of drug classes: Legacy VA classes and External Pharmacologic classes, where the former simply provides a shallow hierarchy of ‘clinically oriented’ classes (eg, β blockers), and the latter focuses on classifying drugs based on their chemical properties and functional groups (eg, H1 Receptor Antagonist).

Figure 1.

National Drug File-Reference Terminology drug-class hierarchy organization (adapted from Carter et al⁴).

In the recent past, several research reports have highlighted important issues and challenges in using NDF-RT for clinical research and interoperability. Bodenreider et al¹ focused on determining anticoagulation status of patients based on a list of medications prescribed using NDF-RT as the underlying drug-class terminology. In particular, this work concentrated on leveraging description logics (DL)-based representation of NDF-RT to infer additional information about drug-class relationships using the Legacy VA classes and External Pharmacologic classes. During this process, the authors not only had to make significant modifications and re-engineering to NDF-RT's DL representation, but also encountered several missing pieces of drug-class membership information in NDF-RT.

In another study, Palchuk et al² constructed a hierarchy of NDF-RT drug classes with drug and medication information from another standardized drug terminology, RxNorm, using data from the patient's electronic medical record. Similar to Bodenreider et al,¹ here authors had to perform significant re-engineering to map, and subsequently classify, RxNorm drug products using Legacy VA classes from NDF-RT. The authors found this process to be extremely onerous, and proposed the evolution of RxNorm toward an interface terminology with hierarchical and categorical organization.

In our own work published in JAMIA,³ we investigated similar issues in mapping and classifying drugs and medication products from RxNorm using NDF-RT's multiaxial classification. We found several issues where the mappings were incomplete and, in many occasions, semantically and clinically inconsistent.

Based on these recent findings, it has become abundantly evident that to leverage NDF-RT continually for clinical and epidemiological research, it is vital to address the existing issues. In particular, we highlight the following issues for consideration:

1. Alignment with RxNorm: Palchuk et al² and our previous work³ illustrated several problematic examples where the relationships between NDF-RT and RxNorm drug concept entities were either missing or misrepresented due to curation problems. Given that RxNorm does not currently provide hierarchical classification of drug products, the mappings between drug concepts in RxNorm and the corresponding classes in NDF-RT are vital for research projects that use RxNorm for coding their medication data. Missing and inconsistent mappings can lead to incorrect conclusions.

2. Relationships between drug products and Legacy VA classes: The Legacy VA classes that were derived from the VA-NDF,⁴ while deprecated, still continue to provide significant value and merit with respect to ‘clinically relevant’ drug classification. However, in its current formalism, NDF-RT only allows assignment of a single Legacy VA class to a particular drug product. For example, even though it is clinically appropriate to classify a drug as both an antihypertensive and a β-blocker, in reality a majority of drug products in NDF-RT are assigned a single Legacy VA class. We believe that this limitation needs to be addressed, since the Legacy VA classes, although derived from the legacy VA-NDF, have significant clinical implications for drug classifications.

3. Relationships between drug products and External Pharmacologic classes: As illustrated by Bodenreider et al,¹ NDF-RT in its current form does not contain any relationships between ingredients and External Pharmacologic classes. As an example, in NDF-RT, Clopidogrel and Platelet Aggregation Inhibitor are not related via any relationship—either direct or indirect. Arguably, this is a significant limitation and has implications with respect to drug classifications and querying.

4. Metadata annotations: Finally, we believe that NDF-RT should follow best practices for vocabulary and terminology development.⁵ In particular, what is notably missing from NDF-RT are appropriate metadata annotations for different drug, ingredient, and drug-class entities. As an example, the Legacy VA class ‘Loop Diuretics’ and External Pharmacologic class ‘Loop Diuretic’ are distinguished only by a slight difference in the label name, without additional annotation indicating their differences, similarities, etc. Consequently, someone unfamiliar with NDF-RT multiaxial classification runs the risk of using the incorrect classification for her application.

In summary, we hope that, via this correspondence, we have highlighted some of the important issues with NDF-RT, which arguably is emerging as one of the most important standardized public drug-classification terminologies. Our expectation is that addressing the above problems in future NDF-RT releases will significantly benefit the clinical research informatics community.