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Exploring the FDA Adverse Event Reporting System to Generate Hypotheses for Monitoring of Disease Characteristics H Fang1, Z Su2, Y Wang2, A Miller3, Z Liu2, PC Howard1, W Tong2 and SM Lin3 The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is a database for postmarketing drug safety monitoring and influences changes in FDA safety guidance documents such as drug labels. The number of cases in the FAERS has rapidly increased with the improvement of submission methods and data standards and thus has become an important resource for regulatory science. Although the FAERS has been predominantly used for safety signal detection, this study explored its utility for disease characteristics. Publicly available health-care information has grown dramatically with recent advances in computing, Internet, and database technologies. With the large amounts of newly available medical data from diverse sources, we can now approach public health issues in ways not previously possible. Electronic medical records (EMRs), clinical studies, and epidemiological studies remain the fundamental sources of information for monitoring of disease characteristics. Intelligently integrating the wealth of health-related data to address current biomedical challenges has gained momentum to improve service delivery and public health. In addition, mining public data from PubMed, the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), and FDA drug labels represents a new venue for health surveillance. Applying data-mining approaches to these public health databases provides unique information that will (i) improve health service delivery by identifying emerging characteristics of the diseases and adverse events (AEs), (ii) guide the development of expensive epidemiological studies, and (iii) identify new opportunities in translational medicine and regulatory science. The FAERS is a database that supports the FDA’s postmarketing drug safety monitoring efforts.1 The database contains valuable information about AEs, medication errors, patient demographics, and more. Since its inception, millions of cases have been reported to the FAERS by manufacturers, health-care professionals, and consumers. Most data-mining efforts to date have used

information from the FAERS for pharmacovigilance, such as drug safety signal detection and the identification of drug–drug interactions and idiosyncratic adverse drug reactions. However, the potential for the database to be used as a disease surveillance tool had not yet been explored. We hypothesize that the disease information embedded in the FAERS can be translated into signals to characterize a disease in a population. This was demonstrated by analyzing >4 million cases in the FAERS between 1997 and 2011 to assess diseases showing sex differences (Figure 1). We identified 115 diseases that showed a significantly sex-biased reporting rate in the FAERS. Almost half (53) of these sex-biased diseases can be confirmed with data from the literature. By examining eight diseases using the patient data from Marshfield Clinic’s EMRs, we found that the sex-biased prevalence for each disease was consistent across all three sources (i.e., the FAERS, literature report, and EMRs; Figures 1 and 2), implying that the FAERS could be a potential resource for disease characterization. STUDY DESIGN AND RESULTS

As depicted in Figure 1, the study can be divided into two parts: AE-centric (top row) and disease-centric (bottom row) analysis. Four ontology-based standards and tools were applied for data manipulation and conversion: the Medical Dictionary for Regulatory Activities (MedDRA) (for AEs), the Systematized Nomenclature of Medicine (for clinical terms), the International Classification of Diseases, Ninth Revision (for diseases), and the Unified Medical Language System (for ontology mapping). Specifically, a total of 19,512 AE terms coded by MedDRA preferred terms in the FAERS were identified. We excluded those terms (1,826 preferred terms) specified by the MedDRA as sexrelated preferred terms. Of the 17,686 AEs that remained, 556 exhibited statistically significant differences between sexes, with P value < 10−10 and at least a twofold difference between sexes. To interpret the context of terms with respect to clinical application, the 556 AE terms were mapped to the Systematized

1Office of Scientific Coordination, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA; 2Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA; 3Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA. Correspondence: W Tong ([email protected]) or SM Lin ([email protected])

Received 30 September 2013; accepted 14 January 2014; advance online publication 12 March 2014. doi:10.1038/clpt.2014.17 496

VOLUME 95 NUMBER 5 | may 2014 | www.nature.com/cpt

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AEs coded by the MedDRA PTs were extracted

1,826 sex-specific PTs by MedDRA were removed

19,512

FAERS

17,686

50

EMRs

8 Diseases were selected for validation

AEs showing sex difference were statistically determined

556

115

Confirmed by literature data

AEs were converted to SNOMED disease terms with UMLS MetaMap

304

Inclusion criteria: >500 cases in FAERS with >100 for each sex and with ICD-9 code identified

Figure 1  Flowchart of study on US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) for monitoring of disease characteristics. Blue boxes show the number of adverse events (AEs; top row) and red boxes (bottom row) show the number of diseases. EMRs, electronic medical records; ICD-9, International Classification of Diseases, Ninth Revision; MedDRA, Medical Dictionary for Regulatory Activities; MedDRA PT, MedDRA preferred term; PT, preferred term; SNOMED, Systematized Nomenclature of Medicine; UMLS, Unified Medical Language System.



12,800

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6,400 Male

3,200

Female

1,600 800 400 200 100 Acne Alopecia

Rheumatoid arthritis Systemic lupus erythematosus

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Autoimmune hepatitis

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Figure 2  Comparison of eight sex-based diseases found in the US Food and Drug Administration Adverse Event Reporting System (FAERS), Marshfield Clinic, and population studies from publications.

Nomenclature of Medicine clinical term using the Unified Medical Language System MetaMap.2 This resulted in the identification of 304 sex-biased clinical terms (see Supplementary Table S1 and Supplementary Materials and Methods online). Using International Classification of Diseases, Ninth Revision codes, 115 sex-biased diseases (see Supplementary Table S2 and

Supplementary Materials and Methods online) met the inclusion criteria (at least 500 total FAERS case reports and >100 cases for each sex). Of the 115 diseases, 53 had literature reports (see Supplementary Table S3 and Supplementary Materials and Methods online), and 50 of these showed the sex-biased effect, consistent in fold changes

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Development and direction, with a 94.34% concordance with the FAERS data. To further confirm the literature reports, we selected eight diseases to be investigated using Marshfield Clinic EMRs (Figure 2, Supplementary Table S4 online; for the selection criteria, see Supplementary Materials and Methods online). The results confirmed the findings for seven of the eight diseases (alopecia, rheumatoid arthritis, lupus, autoimmune hepatitis, optic ischemic neuropathy, trigeminal neuralgia, and meningioma). The eighth disease, acne, has a controversial report regarding sex differences.3 Two observations were made from this analysis: (i) three known sex-biased autoimmune diseases (rheumatoid arthritis, lupus, and autoimmune hepatitis) were successfully identified by our FAERSbased approach and confirmed by both publications and the EMRs, demonstrating the potential utility of the FAERS for identifying sex differences in disease occurrence,4 and (ii) it appears that sex-biased diseases are not organ specific because these diseases are associated with various organs (i.e., alopecia with hair, trigeminal neuralgia with nerves, acne with skin, optic ischemic neuropathy with eyes, and meningioma with the brain). CLOSING THOUGHTS AND PERSPECTIVE

Our study demonstrated that the FAERS has the utility to identify previously unstudied or unreported sex-biased diseases, and the methodology could be extended to investigate other risk factors, such as age, geographical location, and ethnicity. This optimistic view is encouraged by the following observations/facts: First, the FAERS-based results were consistent with the literature reports and EMR data, although the cases studied were limited. Second, using the FAERS data from before 2011, we were able to identify optic ischemic neuropathy as a potential sex-biased disease that was not reported until 2012.5 Third, we noticed that there was a much larger patient count in the FAERS reports as compared with published reports for the diseases investigated, indicating that the FAERS could be a better and richer resource for disease characterization. Finally, unlike most EMRs, the FAERS reports are publicly available, which could create a better environment than EMRs for developing innovative methodologies, thus improving disease-monitoring strategies. The number of cases in the FAERS has rapidly increased in the past 15 years and will grow even more rapidly with the improvement of submission methods and data standards, thus becoming a “Big Data” challenge. Some of these challenges involve data manipulation, association, and conversion, which could be minimized with standardization and ontology. Here, we applied several ontology tools to investigate disease terms in the FAERS coded in MedDRA terminologies. Our results demonstrate the power of using existing standard vocabularies to achieve information exchange and integration. Several aspects of our study require further consideration. The current FAERS data may not reflect the “true” prevalence of disease because the population observed for drug exposure and disease prevalence is restricted to patients who report AEs. Underreporting and incomplete information in the FAERS pose another challenge. These shortcomings might diminish as standards for data submission improve, the number of case reports increases, statistical approaches advance, and other relevant 498

databases for cross-referencing and validation are integrated into the analysis. Alternatively, it is worthwhile to point out that the indication field in the FAERS database could supplement the AE source for disease prevalence information. Although potentially valuable, ~36% of data that should be in the indication field are missing—primarily data from older reports. Furthermore, the AE field might confound the analysis by including druginduced effects rather than the specified disease. Nevertheless, as a proof-of-concept study, we explored the FDA FAERS to generate hypotheses for monitoring of disease characteristics. The data integration example presented herein may serve as an approach for using health information and accelerating global monitoring of disease characteristics. A variety of publicly available databases exist for disease surveillance. these data sources include (i) the Centers for Disease Control and Prevention’s National Institute for Occupational Safety and Health, (ii) the Centers for Disease Control and Prevention’s National Notifiable Diseases Surveillance Systems, (iii) the World Health Organization’s Global Atlas of the Health Workforce, (iv) the World Health Organization’s Global Health Observatory, (v) VigiBase, (vi) the National Organization for Rare Disorders, and (vii) eHealthMe. In addition, private medical records at clinics, hospitals, health-care providers, and insurance companies are potential resources for data mining as well. Integrating these data sources is the future of disease monitoring and may have the power to uncover hidden relationships and knowledge at minimal cost. In summary, we aim to integrate the rich information in the FAERS, EMRs, publications, and other public health databases and translate that information to advance medical research and regulatory science. SUPPLEMENTARY MATERIAL is linked to the online version of the paper at http://www.nature.com/cpt ACKNOWLEDGMENTS The project described was supported, in part, by the Clinical and Translational Science Award program, through the National Institutes of Health (NIH) National Center for Advancing Translational Sciences grant UL1TR000427 (to A.M. and S.M.L.). The authors thank Crystal Jacobson for her work on data query using the Marshfield Clinic Research Data Warehouse. The content of this article is solely the responsibility of the authors. The views presented do not necessarily reflect current or future opinion or policy of the US Food and Drug Administration. Any mention of commercial products is for clarification and not intended as an endorsement. CONFLICT OF INTEREST The authors declared no conflict of interest. © 2014 American Society for Clinical Pharmacology and Therapeutics

1. Pratt, L.A. & Danese, P.N. More eyeballs on AERS. Nat. Biotechnol. 27, 601–602 (2009). 2. Bodenreider, O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32, D267–D270 (2004). 3. Schäfer, T., Nienhaus, A., Vieluf, D., Berger, J. & Ring, J. Epidemiology of acne in the general population: the risk of smoking. Br. J. Dermatol. 145, 100–104 (2001). 4. Whitacre, C.C. Sex differences in autoimmune disease. Nat. Immunol. 2, 777–780 (2001). 5. Postoperative Visual Loss Study Group. Risk factors associated with ischemic optic neuropathy after spinal fusion surgery. Anesthesiology 116, 15–24 (2012). VOLUME 95 NUMBER 5 | may 2014 | www.nature.com/cpt

Exploring the FDA adverse event reporting system to generate hypotheses for monitoring of disease characteristics.

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is a database for postmarketing drug safety monitoring and influences...
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