[Source: PLoS ONE, full page: (LINK). Abstract, edited.]
Ontology-Based Combinatorial Comparative Analysis of Adverse Events Associated with Killed and Live Influenza Vaccines
Sirarat Sarntivijai1,2, Zuoshuang Xiang3,4, Kerby A. Shedden5, Howard Markel6, Gilbert S. Omenn1,2,8, Brian D. Athey1,2,7*, Yongqun He1,2,3,4*
1 National Center for Integrative Biomedical Informatics, University of Michigan, Ann Arbor, Michigan, United States of America, 2 Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America, 3 Unit of Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, Michigan, United States of America, 4 Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America, 5 Biostatistics Department, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America, 6 Center for the History of Medicine and Department of Pediatrics, University of Michigan, Ann Arbor, Michigan, United States of America, 7 Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, United States of America, 8 Departments of Internal Medicine and Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
Vaccine adverse events (VAEs) are adverse bodily changes occurring after vaccination. Understanding the adverse event (AE) profiles is a crucial step to identify serious AEs. Two different types of seasonal influenza vaccines have been used on the market: trivalent (killed) inactivated influenza vaccine (TIV) and trivalent live attenuated influenza vaccine (LAIV). Different adverse event profiles induced by these two groups of seasonal influenza vaccines were studied based on the data drawn from the CDC Vaccine Adverse Event Report System (VAERS). Extracted from VAERS were 37,621 AE reports for four TIVs (Afluria, Fluarix, Fluvirin, and Fluzone) and 3,707 AE reports for the only LAIV (FluMist). The AE report data were analyzed by a novel combinatorial, ontology-based detection of AE method (CODAE). CODAE detects AEs using Proportional Reporting Ratio (PRR), Chi-square significance test, and base level filtration, and groups identified AEs by ontology-based hierarchical classification. In total, 48 TIV-enriched and 68 LAIV-enriched AEs were identified (PRR>2, Chi-square score >4, and the number of cases >0.2% of total reports). These AE terms were classified using the Ontology of Adverse Events (OAE), MedDRA, and SNOMED-CT. The OAE method provided better classification results than the two other methods. Thirteen out of 48 TIV-enriched AEs were related to neurological and muscular processing such as paralysis, movement disorders, and muscular weakness. In contrast, 15 out of 68 LAIV-enriched AEs were associated with inflammatory response and respiratory system disorders. There were evidences of two severe adverse events (Guillain-Barre Syndrome and paralysis) present in TIV. Although these severe adverse events were at low incidence rate, they were found to be more significantly enriched in TIV-vaccinated patients than LAIV-vaccinated patients. Therefore, our novel combinatorial bioinformatics analysis discovered that LAIV had lower chance of inducing these two severe adverse events than TIV. In addition, our meta-analysis found that all previously reported positive correlation between GBS and influenza vaccine immunization were based on trivalent influenza vaccines instead of monovalent influenza vaccines.
Citation: Sarntivijai S, Xiang Z, Shedden KA, Markel H, Omenn GS, et al. (2012) Ontology-Based Combinatorial Comparative Analysis of Adverse Events Associated with Killed and Live Influenza Vaccines. PLoS ONE 7(11): e49941. doi:10.1371/journal.pone.0049941
Editor: Gary P. Kobinger, Public Health Agency of Canada, Canada
Received: June 28, 2012; Accepted: October 17, 2012; Published: November 28, 2012
Copyright: © 2012 Sarntivijai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the National Institutes of Health (NIH) grant U54 DA021519 for the National Center for Integrative Biomedical Informatics and NIH National Institute of Allergy and Infectious Diseases (NIAID) grant R01AI081062. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.