A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study
The study advocates for the ongoing refinement and application of distributed computable phenotype algorithms to enhance AE detection capabilities. These tools are crucial for comprehensive postmarket surveillance and improved vaccine safety monitoring. The outcomes suggest the need for further optimization to achieve more consistent results across diverse health care settings.
Authors
- Hussein Ezzeldin (ORCID: https://orcid.org/0000-0001-7375-6456)
- Steven A. Anderson
- Matthew Deady (ORCID: https://orcid.org/0009-0004-1761-8058)
- Brian Goodness (ORCID: https://orcid.org/0009-0002-0626-9142)
- Matthew Sonesen
- Sylvia Cho (ORCID: https://orcid.org/0000-0002-0263-0343)
- Kelly K. Stimpert (ORCID: https://orcid.org/0000-0003-0440-1816)
- Ray Duncan (ORCID: https://orcid.org/0000-0003-0621-1485)
- Lance Daniel Jones (ORCID: https://orcid.org/0000-0002-4924-0123)
- Jeffrey Beers (ORCID: https://orcid.org/0000-0001-5363-0434)
- Richard A. Forshee (ORCID: https://orcid.org/0000-0002-5805-2837)
- Renier Estiandan (ORCID: https://orcid.org/0009-0000-7186-3207)
Institutions
- Center for Biologics Evaluation and Research (US)
- Institute for Global Environmental Strategies (US)
- Scientific Consulting Group (US)
- Sinai Health System (US)
- SciTech Strategies (United States) (US)
- Strategic Analysis (United States) (US)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-09-21
- DOI
- https://doi.org/10.2196/54597
- Citations
- 3
- Primary Topic
- Viral Infections and Immunology Research
- Type
- article
- Field-Weighted Citation Impact
- 1.21