The SPL/MeSH method detects twice the number of patients with drug-intolerance issues and four times the number of issues than the current Gopher method, which uses manually maintained drug sets. This effect is substantial, outweighing the incomplete mappings from RI/Gopher terms to MeSH (70%), UNII to MeSH (70%), and surprisingly even the NDC to SPL mapping (26%). Although these mappings were all limited, recall that most of them (except NDC) were completed by descending instance count. The improved issue detection was of course attributable mostly to these frequent drugs and intolerances. Although the mapping tables developed for this study are not complete enough to drive actual patient care yet, the study shows that where SPL coverage does exist it appears to provide value for decision support.
Although a 2- and 4-fold increase in number of detected issues seem impressive and certainly demonstrates the value of the approach, using Gopher prescription data and patients with allergies noted in the Gopher system may have introduced bias. Medication orders written at the time an allergy was present on a patient's allergy list would have created a warning and the prescriber would have had the chance to select a different drug. Hence in the comparison of decision support performance we may see fewer cases than if we had selected a sample that had been entered in a CPOE system without decision support. However, inclusion of drug dispensing data, and our 90-windows allowing drugs to match even retrospectively, added drug-intolerance records, and the fact that the better performance is consistent even on a class-by-class level all confirm that the SPL method indeed outperforms the Gopher method.
There are two main reasons why the SPL-method outperforms the Gopher method. Firstly the SPL method is more systematic and complete, capable of resolving a substance class to all drugs that include any ingredient of that class. The two main improvements of the SPL design over the Gopher methods are the use of a systematic chemical class terminology (MeSH) but also the use of a medication knowledge model (SPL) that describes the medicines by ingredients rather than merely classifying them in hierarchies. Therefore the SPL method outperformed the Gopher method specifically in multi-ingredient products that were not always mentioned in all of the Gopher drug sets where they belonged.
The second important reason why the SPL method outperforms the Gopher method is the deeper structure in which the MeSH hierarchy organizes different chemical classes, such as hydrocodone
, and azithromycin
. However, one should ask whether pointing out more remote members of the classes implicated in the intolerance is always beneficial. At the RI it is believed that the intolerance warnings do not cause as much consternation with our users as some overly zealous drug-interaction alerts; however, there is evidence that false alarms cause users to habitually ignore even potentially serious issues. 23,24
For example, in we can see for penicillin allergy how the MeSH hierarchy works very well. Although the structure of MeSH headings are not strictly a taxonomy, we can—and NDF-RT does—interpret it as a taxonomy for the case of chemical structures, if we carefully disambiguate the concepts. It is not correct to say that “amoxicillin is-a penicillin-G” because it is not true that amoxicillin inherits all properties from penicillin-G. However, it is correct to say that amoxicillin is-a penicillin-G-derivative.
Figure 6 MeSH headings under the β-lactam family are an example how MeSH concepts are a taxonomy only if we understand the MeSH chemical structures as derivatives. It is not true that the molecule amoxicillin is-a penicillin-G molecule, but we can say (more ...)
We propose that more precise terms for the chemical structures should be formed by distinguishing derivatives
[D] from the specific complete and unaltered molecule
[M]. That is, a terminology design pattern analogous to the structure-entirety-part (SEP) triplets should be used. The SEP triplets were first described by Schulz et al. 25
and are now used in SNOMED-CT for modeling anatomic terminology. For example, an anatomic name, such as “kidney”, can be understood as kidney-structure
(what looks like kidney tissue under a microscope), entire kidney
(complete organ), or kidney-part
(e.g., a calyx). For chemical structures, one should at least distinguish derivative
(structurally similar) relationships from the entire molecule
, and one might also consider parts
, moieties, as well to complete the triple. Thus, although it is not true that amoxicillin molecule
[M] is-a penicillin-G molecule
[M], it is true that amoxicillin molecule
[M] is-a penicillin-G derivative
The default names for the MeSH headings for drugs [? Or do you mean this more generally??] are often unfortunate. For example, instead of adding [D] to the MeSH headings “Penicillin G”, “Ampicillin”, and “Amoxicillin”, the synonyms “benzylpenicillin”, “amino-benzylpenicillin”, and “hydroxyamino-benzylpenicillin” would be more clearly recognizable as chemical derivative class names rather than entire molecule names. However, in the context of SPL, we may interpret all of the MeSH concepts as derivative structure classes because in SPL it is the UNII codes that are used for specific ingredient molecules, and MeSH is only used for chemical structure generalizations.
In general, anyone who uses the MeSH structure classes for drug-intolerance issue detection must rely on the assumption that every molecule that is a derivative of the molecule that is not being tolerated will induce a similar adverse effect (immunologic or otherwise). This assumption might not always be true, and when it is not true, may lead to false-positive issue detection.
The assumption is generally true for the case of penicillin allergy. We wish to clarify a confusion found in some informatics articles published previously on the subject: a certain commercial knowledge base reportedly warns routinely about cephalosporin
in the presence of allergy against penicillin
, which was perceived to produce excess allergy alerting. 22
According to the MeSH hierarchy, this alert should not be generated, because cephalosporin is not a derivative of penicillin. Only if an allergy against β-lactam
[D] was asserted should cephalosporin derivatives be detected as issues. However, a β-lactam allergy is seldom asserted because the β-lactam ring is not the major determinant for the penicillin allergy. 26
Thus, the MeSH taxonomy works correctly for cases like this.
The reasoning based on derivative class becomes problematic, however, when we turn to the sulfonamide allergy, the second most common antibiotic allergy. In common jargon, sulfonamide allergy is abbreviated to “sulfa-allergy”, leading to the misconception that this is an allergy against the element Sulfur or “sulfur containing drugs”. This is incorrect. Sulfonamide is a specific R-(SO2
NH)-R structure shown in . The different residues of the different sulfonamides account both for their many clinical uses as anti-bacterial, anti-malaria, anti-diabetic, diuretic, and β-antagonist, and for the immune responses against them. The question arises whether, for instance, a thiazide or furosemide given to a patient with allergy to sulfamethoxazole is a drug-allergy issue. Although most drug labels remain conservative, it is now known that the sulfonamide structure is not a determinant for the immune response, but that instead the aryl amine
moiety plays an important role and this is absent in thiazide and furosemide. The very concept of sulfonamide allergy has therefore been questioned, and sulfonylarylamine
allergy had been proposed instead. 27
Although this is not reflected in the MeSH hierarchy today, the chemical class taxonomy could be easily modified to accommodate this change in paradigm and to increase precision.
Figure 7 MeSH hierarchy for sulfonamides. Both allergenic sulfonamide antibiotics and nonallergenic compounds are under the same heading Sulfanilamides. The term Sulfonyl-aryl-amine is used in the literature to describe the allergen class more appropriately. Thus (more ...)
Codeine and morphine are frequently implicated in “opioid allergies”, but rarely are these true allergies. In most cases they are gastrointestinal side effects such as vomiting, and sometimes a specific direct histaminergic action that can lead to severe adverse events clinically presenting as anaphylaxis. Therefore recording this intolerance is justified. However, the effect is not the same for all opioids, only for the morphine-codeine group, not, for example, for pethidine. These viable alternatives are in separate MeSH classes, suggesting again that if the correct MeSH class is chosen, then it is correct to increase the number of detected issues for morphine, codeine, hydrocodone. and oxycodone. Unfortunately the vast majority of reported “opioid allergies” are not of this kind; hence the opioid intolerance issue generates the most frequently overridden warning. 24,22
The use of chemical structure classes from NDF-RT/MeSH does seem to lead to appropriate drug-intolerance issue detection. Where that is not the case now, it should be modified to allow more precise coding and detection of the intolerance issues. Indeed the most important remedy for spurious allergy warnings in the presence of a terminologically correct reasoning system seems to be a greater accuracy of the intolerance records themselves. More even than the documentation of the clinical features and severity of the last observed effect, it seems that an evidence-based guidance for the user to a better choice of chemical structure class is the most important intervention to reduce false positives and improve effective prevention of true adverse effects—a decision support intervention of a new kind.
Of course, given the very preliminary mapping of terminology, the SPL/MeSH method has also missed many issues that the Gopher method found. Most of them were due to the intolerance concept not having been mapped to MeSH. The high-frequency terms not mapped were not true chemical classes but a mechanism of action (e.g., “beta-blocker”) or therapeutic class (e.g., “antibiotics”). Although these could have been mapped to an eclectic ad-hoc set of MeSH terms, it would have introduced the same maintenance problem we see so clearly with the ad-hoc nature of the Gopher sets. Also, if false-negative reminders are a concern, it would be better not to offer such broad concepts as “opioid” or “NSAI” for intolerance records. Without further explanation, these intolerance statements are not actionable and in many cases not accurate.
One limitation of this study is, of course, that we only compared the SPL method against one particular in-house maintained CPOE decision support system and its knowledge base. Hence the study shows only an exemplary value of the SPL method for knowledge management. Today only a few leading institutions use locally maintained drug lists for intolerance checking. Most large institutions use one of the commercial drug knowledge base products (First Data Bank, Medispan, etc.). However, many smaller institutions or outpatient practices may not be able to afford them. Even for larger institutions, using locally maintained knowledge bases that combine public and commercially derived content may be advantageous to better handle intolerance classes, because, as Hsieh et al. 22
reported, commercial intolerance checking systems are not necessarily superior in all aspects to public ones. Naturally, any actual content distributed publicly through SPL is open for use by commercial vendors, and we should validate our study using a commercial knowledge base. That said, it is not possible to conduct the same study with all existing CPOE systems, and the findings would be quickly outdated by improvements made to the systems studied.
The important point of this article is to show that the SPL initiative is delivering value to our field in the form of a product model that works well for decision support functions and an increasing amount of public computer actionable knowledge content. Both are available to health care providers, information system vendors, and commercial knowledge vendors. The specific but generalizable practical approaches offered in this article may assist others in using both the product model and the knowledge content to improve decision support in their systems.