Coverage of Rare Disease Names in Clinical Coding Systems and Ontologies and Implications for Electronic Health Records-Based Research

Abstract

-This poster will present the completeness of coverage of rare disease names in standard coding systems, including the International Classification of Diseases (ICD) and SNOMED CT, and ontologies such as the Orphanet Rare Diseases Ontology (RDO). Using use cases and a set of 45 rare diseases for the national Patient Centered Outcomes Research Network (PCORnet), the poster will describe the current capacity and implications for electronic health records-based research on these diseases. Authors will provide suggestions on how clinical coding systems and ontologies can be used in a coordinated approach to support the use of electronic health record data for various types of research related to rare diseases.

Keywords

• rare diseases
• clinical classifications
• ontologies
• biomedical research
• electronic health records

I. INTRODUCTION

Rare diseases are defined in the US as conditions that

affect less than 200,000 Americans and in the European Union as those with a prevalence of 5 per 10,000 or less.[ 1,2 ] The

NIH Office of Rare Diseases Research recognizes 6,485 rare

diseases.[ 3 ] Although each rare disease is uncommon, collectively they constitute a significant burden to the health care system. One estimate suggests that 1 in 10 Americans are affected by a rare disease.[ 2 ] Consequently ‘rare diseases’ have emerged as priority topics in public health and research.

Rare disease names are included, at different levels of

completeness and granularity, in a number of clinical coding systems that are embedded in electronic health record (EHR) systems, and in a number of ontologies designed to support the diagnosis rare diseases and investigation of their causes and treatments.[ 4 ] In this poster we present an inventory of various clinical coding systems and ontologies that are relevant to rare diseases, and summarize their coverage of rare disease names from previous studies. We match rare diseases names and synonyms from the Office of Rare Disease Research (ORD) and Orphanet (RDO) to the Unified Medical Language System (UMLS) Metathesaurus and identify maps to SNOMED CT and other terminologies. To characterize the coverage of rare diseases studied in PCORnet, we estimate the number of precise and equivalent matches in the three clinical classifications (ICD-9-CM, ICD-10-CM, and SNOMED CT) for a set of 45 rare diseases studied in PCORnet. Finally, we present the likely use of existing classifications, ontologies, mappings, and tools to support the research process, from the collection of data in clinical settings to their use in various types of EHR-based research.

III. RESULTS

With increased adoption and “meaningful use” of EHRs, there is renewed effort in leveraging EHRs for research. In the U.S., the national Patient Centered Outcomes Research Network (PCORnet) was funded this year from the Affordable Care Act

SNOMED CT has the highest coverage of rare disease names

among clinical terminologies in UMLS, and covers 44% of the

6,485 diseases (19,504 terms) recognized by the Office of

Rare Diseases (ORD), and 48% of the 6,750 diseases (15,585

terms) diseases listed in the Orphanet Rare Disease Ontology. 25% (1,611) of ORD and 14% (1,592) RDO disease names have bi-directional one-to-one maps to SNOMED CT. The rest are one-to-many or many-to-one maps. Two terminologies have higher coverage than SNOMED CT. Medical Subject

Headings (MeSH) covers 75% and 70%, while Online

Mendelian Inheritance in Man (OMIM) covers 49% and 57%, of ORD and RDO respectively. Overall, the UMLS covers 82% of ORD-recognized and 84% of RDO-recognized rare diseases.

All of the rare diseases studied in PCORnet were included in

the UMLS and its source terminologies. 8 diseases did not have any match to SNOMED CT, ICD-9-CM or ICD-10-CM.

The 45 rare diseases studied in PCORnet yielded multiple

matches to terms in clinical coding systems; i.e., many

PCORnet rare disease names matched to more than one (term)

code in a coding system, and many codes from clinical coding systems matched more than one rare disease name. Of 55 ICD-9-CM codes that matched to a PCORnet rare disease, 7 were matched to multiple rare diseases. Of 47 matched ICD10-CM codes, 4 matched to multiple rare diseases, and of 59 matched SNOMED CT codes, one SNOMED CT code matched to multiple PCORnet rare diseases. The proportions of matched codes that were considered equivalent matches (rather than broader matches or related terms) were 25%, 45% and 94% for ICD-9-CM, ICD-10-CM and SNOMED CT respectively.

IV. CONCLUSIONS

The coverage and quality (i.e., precision and equivalence) of terms for rare diseases in clinical coding systems is less than ideal, but is markedly improved with SNOMED CT in comparison to ICD 9 and 10 classifications. The lack of precise and complete coverage of rare disease names in clinical coding systems will inhibit the automated identification patients with rare diseases from EHR data for clinical trial recruitment or observational research. The coverage of rare disease names in specialized ontologies (e.g.,

OMIM) is higher, but these are not designed for use in clinical

EHR systems.

Given the intended purpose for each classification and ontology and the completeness and coverage of rare disease names, we propose how these various clinical coding systems, ontologies, and UMLS mappings can be leveraged to support an efficient national research infrastructure and learning healthcare system. The UMLS is a vital tool to support the linkage across clinical coding systems and specialized ontologies that will be essential for a national EHR-based rare diseases research infrastructure.

Ontologies can support advances in understanding disease

etiology and potential treatments. Specialized ontologies, such as OMIM, RDO, and others (such as the Human Phenotype Ontology) can provide the vocabulary for detailed clinical documentation , or “deep phenotyping”, of genetic diseases (e.g., in the NIH Undiagnosed Diseases Network), and complement clinical terminologies and administrative classifications widely used in EHRs. This poster will include an illustrative representation of the collection of rare diseasespecific data in dedicated ontologies to support diagnosis, and the use of mappings to standardized clinical terminologies or classifications as needed for clinical documentation, data exchange, billing and public health reporting.

ACKNOWLEDGMENT

This work was partly supported by the Intramural Research

Program of the National Institutes of Health and the National

Library of Medicine. This work was also supported in part by

PCORnet, funded by the Patient Centered Outcomes Research

Institute (PCORI).

REFERENCES

This poster highlights clinical coding systems and ontologies relevant to rare diseases, including the International Classification of Diseases (ICD) and SNOMED CT, and ontologies such as the Human Phenotype Ontology (HPO) and the Orphanet Rare Diseases Ontology (ORDO). Using use cases and a set of rare diseases for the national Patient Centered Outcomes Research Network (PCORnet), the poster will describe the current capacity and implications for EHR-based research on these diseases. Authors will provide suggestions on where mappings across classifications and ontologies are needed to support the use of EHR data for various types of research related to rare diseases.

BACKGROUND

Rare diseases are defined in the US as conditions that affect less than 200,000 Americans and in the European Union as those with a prevalence of 5 per 10,000 or less. There is no globally authoritative list of rare diseases, but there are several online disease catalogues developed by reliable sources.[ 1-3 ] Although each rare disease is uncommon, collectively they are more common,a ndc onsequently‘ rared iseases’h avee merged asp riority topicsi np ublich ealtha nd  research.

Table 1. Sources of Rare Disease Names

Source

NCATS, Office of Rare Diseases Research (United States) [ 1 ] # of rare diseases 6,485 6,750 Table 2. Terminologies, Coding Systems, and Ontologies with Coverage of Rare Diseases

Intended Purpose

Estimated Coverage World Health Organization

Disease Surveillance; Mortality World Health Organization; national government/public health sponsors by country International Standards Development Organization http://www.ihtsdo.org/ International Federation of Pharmaceutical Manufacturers and Associations (IFPMA ); maintained and supported by MSSO Various translational and genetics research collaborators http://www.human-phenotypeontology.org/

Medical Billing Coding the clinical content of electronic health records to support patient care and other secondary data uses.

Adverse event reporting; regulatory submissions for new drugs and devices.

To index article topics for the published medical literature.

A catalog of human genes and genetic disorders and traits, with phenotypic expression; considered a “phenotypicc ompanion”t ot he  Human Genome Project. “Deep  phenotyping”f orE HRs in  genetics and specialty clinics; support interoperability between current major genetics databases.

A research resource for computational analysis and data mining/knowledge discovery for rare diseases. Supports editorial procedures of Orphanet knowledge bases and services. 67% [ 4 ] 45% [ 4 ] Contains 8,435 rare disease names

Rare disease names are included, at different levels of completeness and granularity, in a number of clinical coding systems that are embedded in electronic health record (EHR) systems, and in a number of ontologies designed to support the diagnosis of rare diseases and investigation of genetic causes and treatments.

As was shown in Table 2, a range of coverage for rare disease names across coding systems has been reported using a variety of methods. In 2010, the NLM mapped 8,435 rare disease names (collected from ORDR, Orphanet, and the National Organization for Rare Disorders, a patient advocacy and voluntary health organization in the US) to the UMLS, and found different levels of coverage for Medical Subject Headings (MeSH) (5,663 ; 67%), Online Mendelian Inheritance in Man (OMIM) (3,802 ; 45%), SNOMED CT (4,192 ; 50%), and ICD-10 (1,029 ;12%).[ 4 ] More recently, we used the UMLS and the published maps from SNOMED CT to ICD-9-CM (developed by IHTSDO) and ICD-10-CM (developed by NLM).[ 5 ] Withi ncreaseda doptiona nd“ meaningful use”o fE HRs, therei s renewede fforti nl everagingE HRs  for research. The national Patient Centered Outcomes Research Network (PCORnet) was funded from the Affordable Care act to examine real-world treatment decisions, and is specifically tasked to conduct observational and interventional research on the comparative effectiveness of various treatments, using distributed and heterogeneous EHR systems. The PCORnet research portfolio currently includes 48 rare diseases and conditions.

Figure 1. The Patient Centered Outcomes Research Network (PCORnet) of Networks 3 13

1 13 2 14 2 15 1 13 12

13 2 12 2 12 1 12

1 14 1 12 12 12

District of Columbia

2 12 1 13

14 1 13

METHODS

• Overall, the UMLS covers 82% of ORD and 62% of ORDO-recognized rare diseases.

SNOMED CT

Table 4. Precision of Coverage of PCORnet Rare Diseases in Different Clinical Coding Systems

RESULTS

Coding System

UMLS

OMIM

• SNOMED CT has the highest coverage among clinical coding systems, and covers 44% of the 6,485 diseases recognized by the Office of Rare Diseases, and 28% of the 6,750 diseases that are listed in the Orphanet Rare Disease Ontology (ORDO). Table 3. Coverage of Rare Diseases from 2 Sources by Coding Systems % coverage of 6,485 diseases from US NCATS/ORDR % coverage of 6,750 diseases from Orphanet ORDO

References

[1] Orphanet . Orphanet Rare Disease Ontology (ORDO) . 2014 [cited 2014 March 14 ]; Available from: http://www.orphadata.org/cgibin/inc/ordo_orphanet.inc.php.

[2] NORD. Rare Disease Information. 2014 [cited 2014 March 14 ]; Available from: http://www.rarediseases.org/rare-disease-information.

[3] NIH. Office of Rare Diseases Research (ORDR) Brochure . 2009 [cited 2010 20/08/2010]; Available from: http://rarediseases.info.nih.gov/asp/resources/ord_brochure.html.

[4] Fung , K.W. , R.L. Richesson , and O. Bodenreider , Coverage of Rare Disease Names in Standard Terminologies and Implications for Patients, Providers , and Research, in Paper accepted for presentation: American Medical Informatics Association Annual Symposium2014: Washington, D.C.

[5] PCORI. PCORnet: The National Patient-Centered Clinical Research Network. 2014 [cited 2014 March 13 ]; Available from: http://www.pcori.org/funding-opportunities/ pcornet-national-patientcentered-clinical-research-network/.

Orphanet Rare Diseases Ontology [3] Terminology or Coding System International Classification of Diseases version 10 (ICD-10) International Classification of Diseases Clinical Modifications (ICD-CM, versions 9 and 10) Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) Medical Dictionary for Regulatory Activities (MedDRA) Medical Subject Headings (MeSH) Online Mendelian Inheritance in Man (OMIM) Human Phenotype Ontology (HPO) Orphanet Rare Disease Ontology (ORDO) U.S. National Library of Medicine Distributed by the U.S. National Center for Biotechnology Information; Authored and edited at fboectuwseoenn tgheenmetoiclevcaurilaatriorenlaatniodnship the McKusick-Nathans Institute of Genetic Medicine , Johns Hopkins University School of Medicine