=Paper=
{{Paper
|id=None
|storemode=property
|title=Comparing pharmacologic classes in NDF-RT and SNOMED CT
|pdfUrl=https://ceur-ws.org/Vol-714/ShortPaper04_Mortensen.pdf
|volume=Vol-714
|dblpUrl=https://dblp.org/rec/conf/smbm/MortensenB10
}}
==Comparing pharmacologic classes in NDF-RT and SNOMED CT==
https://ceur-ws.org/Vol-714/ShortPaper04_Mortensen.pdf
Comparing Pharmacologic Classes in NDF-RT and
SNOMED CT
Jonathan Mortensen1 , Olivier Bodenreider∗2
1 Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
2 National Library of Medicine, Bethesda, Maryland, USA
Email: Jonathan Mortensen - Jonathan.Mortensen@case.edu; Olivier Bodenreider∗ - olivier@nlm.nih.gov;
∗ Corresponding author
Abstract
Background: Clinical decision support systems and semantic mining require interoperable representations of
pharmacologic classes across reference terminological systems. We explore two such systems: NDF-RT and
SNOMED CT.
Methods: We evaluate the overlap of pharmacologic classes in NDF-RT (VA Classes) and SNOMED CT. We
compare classes based on the set of their members (drugs) across systems, using the Jaccard coefficient as a
measure of overlap between two classes.
Results: There is a limited overlap among the two systems. The average Jaccard value is 0.293. Only 11.5% of
the VA classes have a Jaccard value of 0.75 or above.
Conclusions: The analysis of discrepancies between pharmacologic classes across systems offers a strategy for
identifying classes in need of critical review. Due to the heterogeneity of the representation of pharmacologic
classes in various terminologies, we recommend that drugs, not classes, be annotated in text for semantic mining
purposes.
Introduction therapeutic properties of some drugs on angina
pectoris. Some classes are also defined in reference to
Pharmacologic classes are typically established in several properties, e.g., nitrate vasodilator, referring
reference to some of the properties of the active to both the chemical structure of nitrates and their
moiety, with respect to chemistry, physiology, relaxing action on the musculature of blood vessels
metabolism and therapeutic intent [1]. For example, (physiologic effect). In other words, pharmacologic
the classes platelet aggregation inhibitors and classes provide an abstract representation of drug
anticoagulants refer to the physiologic effect of drugs properties, useful in the context of clinical decision
decreasing platelet aggregation and coagulation, support and for the annotation of biomedical
respectively. In contrast, the class cardiac glycoside resources, including clinical text and the biomedical
refers to the chemical structure of drugs such as literature.
digoxin, while the class antianginal refers to the
116
While interoperability among terminologies is a a basic hierarchy. A drug generally belongs to
requirement for clinical decision support, in which only one class. Examples of VA classes include
decision support rules are defined in reference to ANTIMALARIALS, of which the clinical drug
concepts in various terminologies (e.g., concepts for QUININE SO4 162.5MG TAB is a member. Its
drug classes), it is also important that annotations to parent class is ANTIPROTOZOALS. In addition,
biomedical entities such as drug classes be consistent there are 425 EPCs (not used in this work). Differing
within and across datasets when such datasets are from the VA Classes, the EPCs have a nearly flat
exchanged and integrated, as these annotations form hierarchy and are defined in reference to various
the basis for knowledge discovery through semantic properties, such as physiologic effect, therapeutic
mining. intent, ingredient and mechanism of action.
The National Drug File - Reference Terminology The July 11th 2010 Version of NDF-RT was used
(NDF-RT) is a drug terminology produced by in the evaluation.
the Department of Veterans Affairs in the United SNOMED CT is currently the largest clinical
States and is recommended as the standard in e- terminology. It is developed and maintained
prescribing systems [2]. Other clinical terminologies by the International Health Terminology Standard
such as SNOMED CT also include pharmacologic Development Organization (IHTSDO) [4]. In
information. SNOMED CT, the drugs are simply related to
The objective of this work is to evaluate the pharmacologic classes through the isa relationship.
degree to which annotations to drug classes in For example, there is an isa relationship between the
various terminological systems are interoperable, drug Quinine and the class Cinchona antimalarial.
with a focus on pharmacologic classes from NDF- The January 31, 2010 Version of SNOMED CT was
RT. More specifically, we evaluate the overlap of used in the study.
VA classes to those in SNOMED CT. The analysis Related Work. Others have examined many
of the classes reveals discrepancies between the two aspects of NDF-RT. [5] investigated the coverage
systems and offers a strategy for identifying classes of the Physiologic Effects hierarchy in NDF-
in need of critical review. RT. It was found that the physiologic effects
category was sufficient for classifying medications.
[6] investigated the addition of pharmacogenomics
into the hierarchy. [7] applied NDF-RT to mapping
Background text from medication lists at the Mayo Clinic
In this section, we give a brief presentation of NDF- using the SmartAccess Vocabulary Server. NDF-
RT and SNOMED CT and present some related RT covered 97.8% of the concepts found in the
work on NDF-RT. medication lists, indicating NDF-RT can be used
The National Drug File - Reference Ter- in a clinical setting for medication purposes. [8]
minology (NDF-RT) is based upon the National compared NDF-RT to the National Drug File,
Drug File, a listing of medications produced by Medicare Part D and a proprietary knowledge base.
the Department of Veteran Affairs [3]. It serves It was determined that 76% of the classes from the
as a reference standard for a variety of medical three original terminologies were contained in NDF-
situations related to drugs and medications. NDF- RT. In recent work, [9] evaluated the correspondence
RT is a description logic-based model available in of NDF-RT drugs and classes to RxNorm drugs and
OWL and XML formats. It includes 9 “Kinds” classes. As of October 2009, approximately 50% of
of information: Cellular or Molecular Interac- the drugs did not correspond between terminologies.
tions, Clinical Kinetics, Diseases Manifestations or [10] mapped medications to diseases, showing a
Physiologic States, Pharmaceutical Preparations, clear example of how NDF-RT can be applied in
Physiological Effects, RxNorm Dose Forms, Ther- clinical decision support situations. As another
apeutic Categories, and VA Drug Interactions. The example of clinical applications [11] integrated NDF-
Pharmaceutical Preparations hierarchy organizes RT into the process of generating structured product
drugs into three categories: Products by Generic labeling. Finally, [12] used the NDF-RT drug classes
Ingredient Combination, Products by VA Class and to determine the anti-coagulation status of patients
External Pharmacologic Classes (EPC). based on their medication list, demonstrating a first
There are 485 VA Drug Classes organized into step in clinical decision support.
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Our study focuses not on content coverage, but between NDF-RT and SNOMED CT is used to
rather on interoperability among systems of drug summarize the external comparison and determine
classes in various terminologies, including NDF-RT the overall similarity between the two class systems.
and SNOMED CT. These terminologies were chosen To obtain an extension, the clinical drug
as a reference because they contain drug hierarchies, members of a drug class were obtained. As opposed
are mature, and are widely used. More specifically, to VA (where drug classes are linked directly to
we want to assess whether similar sets of drugs are clinical drugs), in SNOMED CT, the ingredients of
linked to the same classes in different systems. a class were first obtained, then the clinical drugs
As part of the evaluation, we use a concept for those ingredients were obtained using relations
alignment technique described by [13]. NDF-RT was in NDF-RT, thus keeping the domain of clinical
loaded into a Virtuoso endpoint [14] for SPARQL drugs limited to only NDF-RT. In addition, the drug
querying, which allowed for evaluation of the drug members of a class included its drugs and all drugs
classes. which were members of any subclasses. For example,
the clinical drug QUININE SO4 260MG TAB is
linked directly to the VA class ANTIMALARIALS,
Methods but is also considered a member of the its
To evaluate the drug classes in NDF-RT, we parent class ANTIPROTOZOALS. Using the drug
developed an extensional method of evaluation, members, the drug member intersection was found,
comparing between VA classes and SNOMED CT comparing the extension of the VA classes to the
drug classes. Instead of comparing pharmacologic SNOMED CT classes.
classes based on lexical resemblance of their names,
we compare the extensions of these classes.
The extension of a pharmacologic class is the set Results
of drugs a class has as members. The degree to which
There are 485 VA and 722 SNOMED CT classes.
any two drug classes are similar was determined by
To reduce comparisons (and noise), classes which
the overlap of their extensions. This is measured by
did not have any drug members were removed.
the Jaccard Coefficient,
There were 95 VA (20%) and 195 SNOMED CT
|A ∩ B| (27%) classes without members. Examples of classes
J(A, B) = , with no members include INVESTIGATIONAL
|A ∪ B|
ANTI-TUBERCULAR DRUGS (VA), ANTIFUN-
where the intersection is the number of drugs which GALS,TOPICAL OTIC (VA), Antineoplastic alka-
are the same between any two classes and the union loid (SNOMED CT) and Corticosteroids used in the
is the total number of drugs between any two drug treatment of asthma (SNOMED CT).
classes [15]. An example of extension is presented in Among the 15,027 clinical drugs in NDF-RT,
Figure 1a. Here, the VA class THROMBOLYTICS 8414 correspond to ingredients also present in
and the SNOMED CT class Thrombolytic share SNOMED CT. Examples of clinical drugs specific
6 drugs, including streptokinase, while the drug to NDF-RT include medicinal products from classes
drotrecogin is specific to the SNOMED CT class. such as HERBS/ALTERNATIVE THERAPIES
The Jaccard value is computed as the cardinality (e.g.,WILD CHERRY BARK PWDR).
of the intersection (6) over that of the union of the The extensional comparison was obtained by
classes (7), i.e., 0.86. (In actuality, the classes are calculating the overlap between the sets of drug
compared, not based on the ingredients, but based members of class pairs and can be summarized by
on the clinical drugs they have as members. The the average Jaccard coefficient for all class pairs
corresponding ingredients are shown in Figure 1a for between NDF-RT and SNOMED CT. Through their
brevity.) average Jaccard value, pairs of pharmacologic class
The extension of each VA class is compared to systems can be compared for their overall similarity.
that of every class in SNOMED CT. For a given VA The average Jaccard value is 0.293, indicating
class, the SNOMED CT class for which the highest limited overlap overall between drug extensions
Jaccard value is found is selected as the best match. across the two class systems.
The average Jaccard of the pairwise comparisons The distribution of the average highest Jaccard
118
(a) (b)
Figure 1: (a) Comparison of the extensions of the VA class THROMBOLYTICS and the SNOMED CT class
Thrombolytic, (b) Distribution of Jaccard (highest per class)
value for the VA classes is shown in Figure hydroxide. Conversely, solutions of glycerol are
1b. Very few classes exhibit complete overlap classified as osmotic laxatives in SNOMED CT, but
(Jaccard = 1.0). Examples include the VA class as LAXATIVES, RECTAL in NDF-RT.
DIRECT RENIN INHIBITOR and the SNOMED
CT class Renin Inhibitor. This particular class
contains the clinical drugs corresponding to only Discussion
one ingredient, aliskiren. The proportion of VA Overlap among Class Systems
classes with a Jaccard value of 0.75 or above is
The similarity among the two pharmacologic class
11.5%. For example, the Jaccard value for the
systems under investigation (VA and SNOMED
overlap between the VA class THROMBOLYTICS
CT) is relatively limited. The average Jaccard
and the SNOMED CT class thrombolytic is 0.86. As
values among classes based on shared drugs is
shown in Figure 1a, the clinical drugs corresponding
0.293. The following reasons can be proposed as
to six ingredients are common to both the VA class
an explanation, in addition to sheer differences
and the SNOMED CT class. These ingredients
in classification and discrepancies illustrated in
are alteplase, anistreplase, reteplase, streptokinase,
the section above. In some cases, there is no
tenecteplase and urokinase. Additionally, SNOMED
equivalent class in SNOMED CT for a given
CT also lists drotrecogin as a member of the class
VA class, especially for high-level aggregation
thrombolytic, although the indications for this drug
classes (e.g., BLOODPRODUCTS / MODIFIERS
seem to be limited to severe sepsis.
/ VOLUME EXPANDERS ), residual classes (e.g.,
Finally, 75.6% of the VA classes have a CARDIOVASCULAR AGENTS, OTHERS ) and
Jaccard value lower than 0.5. For example, classes specific to topical forms (e.g., BETA-
the Jaccard value for the overlap between the BLOCKERS, TOPICAL OPHTALMIC ). Another
VA class HYPEROSMOTIC LAXATIVES and the reason is that partially overlapping classes are
SNOMED CT class osmotic laxatives is only 0.16. defined using different classificatory criteria. For
While clinical drugs corresponding to the ingredients example, ophtalmic forms of beta-blockers such
lactulose and magnesium sulfate are common to as TIMOLOL MALEATE 0.5% GEL, OPH are
both classes, many clinical drugs found in the VA classified as BETA-BLOCKERS, TOPICAL OPH-
class are not in the SNOMED CT class (e.g., other TALMIC in NDF-RT and as anti glaucoma agent
magnesium salts such as magnesium biphosphate in SNOMED CT. While the former class only
and magnesium hydroxide). Interestingly, clinical contains beta-blockers, the latter includes a wider
drugs corresponding to magnesium hydroxide are range of products (e.g., apraclonidine). Another
part of a different SNOMED CT class, saline difference between the two class systems is that
119
the pharmacologic class is a property of the clinical corresponding classes can be added automatically
drug for the VA classes, whereas it is inherited in reference to the most useful pharmacologic
through the ingredient for SNOMED CT classes. class system in a particular context. Annotations
For example, injectable forms of acetylcysteine to another pharmacologic class system can be
are classified as ANTIDOTES/DETERRENTS, recomputed from the ingredients in case of reuse of
OTHERS in NDF-RT, while topical solutions (e.g., these resources for a different purpose.
for inhalation) are classified as MUCOLYTICS. In
contrast, all forms of this drug are classified as
both drugs used in the treatment of paracetamol
poisoning and mucolytic agent in SNOMED CT.
Finally, we also found a limited number of errors,
such as the classification of the antibacterial drug Limitations and Future Work
NORFLOXACIN 0.3% SOLN, OPH as BETA-
BLOCKERS, TOPICAL OPHTALMIC in NDF- There are a few limitations to this work. The
RT. evaluation was only a quantitative evaluation,
comparing the two terminologies. It was not an
evaluation of the clinical quality or the use of NDF-
Classes without Drug Members RT in a clinical situation. In addition, the domain
of drugs used in the comparison was only clinical
One particular difference between the two pharma-
drugs in NDF-RT, as we assumed the clinical drugs
cologic class systems is the number of classes for
to be complete. No comparisons were done at
which there is no drug in NDF-RT. (These classes
the ingredient level. Because of this, we obtained
were omitted from our statistics). These differences
all the NDF-RT clinical drugs of an ingredient
have different causes in different systems. For VA
from the terminology. Some classes that have no
classes, most classes with no drugs correspond to
drugs members may have had ingredients; however,
investigational drugs. In contrast, in SNOMED
these ingredients were either not present in NDF-
CT, such classes correspond essentially to classes for
RT or they did not have clinical drugs associated
which the corresponding medicinal products are out
with them, resulting in the class not having drug
of the scope of NDF-RT, including blood products
members.
(e.g., Red cells - irradiated ), dietary products (e.g.,
Gluten free food product) and various prescribable For a complete evaluation of NDF-RT, the
entities (e.g., Sterile maggots). external pharmacologic classes (EPCs) will be
included in future work. To leverage these classes,
they first must be enriched with drugs. A technique
Consequences for Semantic Mining to do such an operation has been piloted by [12],
As the sets of drugs available in terminological which utilizes a description logics based classifier to
systems vary considerably across systems, with classify drugs into EPCs.
minimal overlap among them, annotation of the
In addition to the extensional approach used in
literature directly with classes from a given system
this study, we would like to explore an intensional
is likely to result in annotated datasets that will
approach to comparing the classes, leveraging
not be interoperable, and whose annotations will be
synonymy relations in the Unified Medical Language
difficult to reconcile. Even if some terminologies
System (UMLS). In practice, classes across systems
such as SNOMED CT and NDF-RT tend to
could be mapped through the UMLS and the
provide good coverage of clinical drugs, their overlap
extensions of equivalent classes could be compared.
with other terminologies in terms of pharmacologic
classes remains limited. Finally, this work may be considered a class
In practice, a better option for semantic mining is centric approach, focused around drugs associated
to annotate drugs rather than pharmacologic classes. with classes. Future work will include a drug centric
Drugs names are relatively standard (at least at approach, which focuses on classes associated with
the ingredient level) and integration resources such drugs. More specifically, we will study the set of
as RxNorm are already available. Once resources pharmacologic classes associated with a given drug
have been annotated at the ingredient level, the in different pharmacologic class systems.
120
Conclusions 3. Lincoln MJ, Brown SH, Nguyen V, Cromwell T, Carter J,
By using an automated method of comparing Erlbaum M, Tuttle M: US Department of Veterans
Affairs enterprise reference terminology strategic
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inconsistencies serve as an indicator for possible Medicine-Clinical Terms)[http://www.ihtsdo.org/
review. The automated method of pairwise class snomed-ct/].
member comparison complements standard lexical 5. Rosenbloom ST, Awad J, Speroff T, Elkin PL, Rothman
matching and can serve as an additional quality R, III AS, Peterson J, Bauer BA, Wahner-Roedler DL,
assurance tool for terminologies. This methodology Lee M, et al.: Adequacy of representation of the
National Drug File Reference Terminology Phys-
sets a framework for pairwise comparison of iologic Effects reference hierarchy for commonly
drug classes between terminological systems using prescribed medications. In AMIA Annu Symp Proc
only their drug members. Finally, due to the 2003:569–78.
heterogeneity of the representation of pharmacologic 6. Chute CG, Carter JS, Tuttle MS, Haber M, Brown
classes in various terminologies, we recommend that SH: Integrating pharmacokinetics knowledge into
a drug ontology as an extension to support
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7. Brown SH, Elkin PL, Rosenbloom ST, Husser C, Bauer
BA, Lincoln MJ, Carter J, Erlbaum M, Tuttle MS: VA
Competing interests National Drug File Reference Terminology: a
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The authors declare that they have no competing 2004, 11(Pt 1):477–81.
interests.
8. Carter JS, Brown SH, Bauer BA, Elkin PL, Erlbaum
MS, Froehling DA, Lincoln MJ, Rosenbloom ST, Wahner-
Roedler DL, Tuttle MS: Categorical information in
Authors’ contributions pharmaceutical terminologies. In AMIA Annu Symp
Proc 2006:116–20.
Jonathan Mortensen and Olivier Bodenreider con-
9. Pathak J, Chute CG: Analyzing categorical informa-
ceived and designed the study. Jonathan Mortensen tion in two publicly available drug terminologies:
acquired the data and performed the analysis and RxNorm and NDF-RT. Journal of the American
interpretation of the data. Both authors contributed Medical Informatics Association 2010, 17(4):432–439.
to the redaction of the manuscript and approved its 10. Burton MM, Simonaitis L, Schadow G: Medication
final version. and Indication Linkage: A Practical Therapy for
the Problem List? In AMIA Annual Symposium
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11. Schadow G: Structured product labeling improves
Acknowledgements detection of drug-intolerance issues. Journal of
This research was supported in part by the Intramural the American Medical Informatics Association 2009,
Research Program of the National Institutes of Health 16(2):211–219.
(NIH), National Library of Medicine (NLM) in addition 12. Bodenreider O, Mougin F, Burgun A: Automatic
to the Choose Ohio First Scholarship Program. determination of anticoagulation status with
NDF-RT. In Proceedings of the 13th ISMB’2010 SIG
meeting ”Bio-ontologies” 2010:140–143.
13. Bodenreider O, Burgun A: Aligning knowledge
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