Towards an adverse event reporting ontology


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Presentation made during the PCIRN AGM in Ottawa, April 2011

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Towards an adverse event reporting ontology

  1. 1. TOWARDS AN ADVERSE EVENTREPORTING ONTOLOGYPCIRN ANNUAL GENERAL MEETINGAPRIL 20-21 2011OTTAWA, ON Mélanie Courtot and Ryan Brinkman on behalf of the PCIRN IT support group
  2. 2. Current problem  No standard reporting system for adverse events (AE)  PCIRN: Medical Dictionary of Regulatory Activities (MedDRA)   No definitions   No unambiguous meaning of terms  Loss of information, low data quality
  3. 3. How to solve it  Use of standard definitions for AE reporting   Ensuring that adverse events following immunization (AEFIs) are reported in accordance to the selected guideline   Specification of signs and symptoms defining each AEFI  Computer tractable  Implemented in reporting systems
  4. 4. Help confirm reported diagnostic   Suggests missing information to reach unambiguous case determination   For example, check skin color to distinguish seizure from Hypotonic- Hyporesponsive Episode (HHE)
  5. 5. Enables complex data querying  “which of my patients have an AEFI involving part of the nervous system?”
  6. 6. Use of standards definitions  Brighton collaboration   Adopted by PHAC   300 participants from patient care, public health, scientific, pharmaceutical, regulatory and professional organizations Bonhoeffer et al. Vaccine, 2002.   Good applicability, sensitivity, and specificity Kohl et al. Vaccine, 2007.   Performs well against other standards Erlewyn-Lajeunesse et al. Drug safety, 2010.   Used within PCIRN Gagnon et al., Journal of allergy and clinical immunology, 2010.
  7. 7. Brighton publication - seizure
  8. 8. Brighton case definition - seizure
  9. 9. Computer tractable  Domain is modeled using an ontology.  Each entity is defined textually   Human readable definition, label  Each entity is defined logically   Relations to other entities  Web Ontology Language (OWL) allows processing by computer
  10. 10. Enables complex data querying  Computer can infer link such as “Encephalitis unfolds in the Nervous system”
  11. 11. Implementation in reporting systems  Assist in confirming a reported clinical entity (e.g., seizure) at up to three levels of diagnostic certainty   Checklist at data entry time  Report on missing elements to confirm the event with some degree of certainty  Allow interoperability and querying on reports
  12. 12. Perspectives  Early stage work   Ontological issues to solve   Add more AEFIs  Collaboration with Brighton  Discussion with Dacima to implement into their Electronic Data Capture system (Daciforms)
  13. 13. Acknowledgements  PHAC/CIHR Influenza Research Network (PCIRN)  IT support group: Jean-Paul Collet, Victor Espinosa, Kim Marty, Lesley Sturrock, Nataliya Yuskiv, Evelyn Chan  PCIRN scholarship
  14. 14. Adverse event workshop  July 26th 2011, Buffalo, USA  Co-located with International Conference on Biomedical Ontologies
  15. 15. Sources  The development of standardized case definitions and guidelines for adverse events following immunization. Kohl et al. Vaccine, Volume 25, Issue 31, 1 August 2007, Pages 5671-5674  The Brighton Collaboration: addressing the need for standardized case definitions of adverse events following immunization (AEFI) Bonhoeffer et al., Vaccine Volume 21, Issues 3-4, 13 December 2002, Pages 298-302  Diagnostic Utility of Two Case Definitions for Anaphylaxis: A Comparison Using a Retrospective Case Notes Analysis in the UK. Erlewyn-Lajeunesse et al., Drug Safety, 2010 Jan 1; Vol. 33 (1), pp. 57-64.  Safe vaccination of patients with egg allergy with an adjuvanted pandemic H1N1 vaccine. Gagnon et al., Journal of allergy and clinical immunology, 2010; Vol. 126, pp 317.