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Building A Knowledge Base of Severe Adverse Drug Events Based On AERS Reporting Data Using Semantic Web Technologies. Guoqian Jiang, MD, PhD Mayo Clinic College of Medicine, Rochester, MN, USA. MEDINFO 2013 Copenhagen, Denmark August 21, 2013. Acknowledgements. Co-authors
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Building A Knowledge Base of Severe Adverse Drug Events Based On AERS Reporting Data Using Semantic Web Technologies Guoqian Jiang, MD, PhD Mayo Clinic College of Medicine, Rochester, MN, USA MEDINFO 2013 Copenhagen, Denmark August 21, 2013
Acknowledgements • Co-authors • Liwei Wang – Jilin University, China • Hongfang Liu – Mayo Clinic, USA • Harold R. Solbrig – Mayo Clinic, USA • Christopher G. Chute – Mayo Clinic, USA • This work was supported in part by the SHARP Area 4: Secondary Use of EHR Data (90TR000201).
Introduction • Adverse Drug Events (ADEs) have been a well-recognized cause of patient morbidity and increased health care costs. • A semantically coded knowledge base of ADEs with severity information is critical for clinical decision support systems and translational research applications.
In the Field of Translational Research • Pharmacogenomics study of ADEs • the genetic component of ADEs is being considered as one of significant contribution factors for drug response variability and drug toxicity. • PharmGKB – initiated by NIH • To collect and disseminate human-curated information about the impact of human genetic variation on drug responses • Canadian Pharmacogenomics Network for Drug Safety • to identify novel predictive genomic markers of severe ADEs in children and adults
ADEpedia Project • A standardized knowledge base of ADEs that intends to integrate existing known ADE knowledge for drug safety surveillance from disparate resources such as • the FDA Structured Product Labeling (SPL), • the FDA Adverse Event Reporting System (AERS) and • the Unified Medical Language System (UMLS). • A framework of knowledge integration and discovery that aims to support pharmacogenomic-target prediction of ADEs.
Severe ADEs • Since the clinical applications of pharmacogenomics on ADEs are usually focused on the clinically severe ADEs, we designed a module in the ADEpedia framework for extracting severe ADE knowledge. • However, few open-source ADE knowledge resources with severity information are available and it remains a challenging task for measuring and identifying the severity information of ADEs.
Objective of the study • To develop and evaluate a semantic web based approach for building a knowledge base of severe ADEs based on the FDA AERS reporting data.
Semantic Web Technologies • The W3C standards • The Resource Description Framework (RDF) • A model of directed, labeled graphs • Using a set of triples (subject, predicate, object) • The SPARQL • A query language for RDF graphs • The Web Ontology Language (OWL) • A standard ontology language used for ontology modeling
Materials (I) • Normalized AERS Dataset – AERS-DM • Reporting data from 2004 to 2011 • Drug names – RxNorm Codes by MedEx • Mapped to the NDF-RT drug classes • ADE names – MedDRA codes • Aggregated to the System Organ Class (SOC) codes • Contains 4,639,613 putative Drug-ADE pairs • Unique report ID number (ISR) • Used to identify the outcome codes
Materials (II) • Common Terminology Criteria for Adverse Event (CTCAE) and Its Grading System • We used the CTCAE version 4.0 rendered in the Web Ontology Language (OWL) format that is publicly available. • This version contains 764 AE terms and 26 “Other, specify” options for reporting text terms not listed in CTCAE. • Each adverse event (AE) term is associated with a 5-point severity scale. The AE terms are grouped by MedDRA Primary SOC classes. • In the CTCAE, “Grade” refers to the severity of the adverse event.
Materials (III) • ADE Datasets • SIDER 2 • Released on October 17, 2012 • Contains 996 drugs, 4,192 side effects (SE), and 99,423 drug-SE pairs • UMLS ADE dataset from ADEpedia • Contains 266,832 drug-disorder concept pairs, covering 14,256 (1.69%) distinct drug concepts and 19,006 (3.53%) distinct disorder concepts. • There are a total of 102 relationships between the drug-disorder concept pairs. - 1. Indications; 2. Contraindications; 3. Adverse drug effects; and 4. Other associations.
Methods • Linking outcome codes with putative drug-ADE pairs • Validating the drug-ADE associations • Data integration in a semantic web framework • Classifying the AERS ADEs into the CTCAE in OWL • We asserted the mappings between AERS outcome codes and CTCAE grades
Results • Produced a cardiac-AERS-DM dataset • contains 164,895 entries with 21,757 unique putative Drug-ADE pairs, • covering 3,073 unique drug codes in RxNorm and 251 unique ADE codes in MedDRA.
For validated drug-ADE pairs • We had 2,444 unique pairs, of which 760 pairs are in Grade 5; 775 pairs in Grade 4 and 2,196 pairs in Grade 3. • The drug-ADE pairs cover 821 unique drug codes in RxNorm and 69 unique ADE codes in MedDRA, whereas 20 of 36 (55.6%) of AE terms under the Cardiac Disorders category in CTCAE were covered.
Discussion • We utilized a normalized AERS dataset, in which the drug names are normalized using standard drug ontologies RxNorm and NDF-RT and the ADEs are normalized using MedDRA. • Which facilitated the interoperability between ADE datasets (e.g., mappings to SIDER)
Validation Pipeline • The SIDER dataset should be considered as a “silver” standard rather than a “gold” standard for the validation. • Although the UMLS drug-disorder pairs only covered a small portion of putative drug-ADE pairs (1.4%), the validation illustrated the usefulness of known ADE knowledge asserted in the UMLS in discerning the indications from the ADEs. • For those new ADEs that have not been recognized, a robust ADE detection algorithm will be required in the future.
Rationale for the use of CTCAE grading system • The CTCAE as a standard has been widely used in clinical cancer study for recording the AE severity; • It has clear severity definitions using a 5-scale grading system; • It includes the most common AE terms that have been well classified and mapped with a standard AE vocabulary MedDRA; • It contains well-defined conditions for grading the severity of AE terms based on the domain knowledge.
Leveraging Semantic Web Technologies • We leveraged semantic web technologies that provide a scalable framework for data integration of heterogeneous ADE resources. • In particular, we represented validated drug-ADE pairs in the OWL format, which not only provides seamless integration with the CTCAE, but also enables a standard infrastructure for automatic classification of ADEs based on the severity conditions specified in the CTCAE.
Summary • We developed a semantic web based approach for building a standard severe ADE knowledge base using a normalized FDA AERS reporting data. • The datasets produced in this study is publicly available from our ADEpediawebsite • http://adepedia.org • Although we were focused on the Cardiac Disorders domain, we believe the approach can be easily generalized to analyze the data in all other domains available in the AERS reporting data.
