A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study
BackgroundAdverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct pos...
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JMIR Publications
2024-11-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2024/1/e54597 |
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| author | Matthew Deady Raymond Duncan Matthew Sonesen Renier Estiandan Kelly Stimpert Sylvia Cho Jeffrey Beers Brian Goodness Lance Daniel Jones Richard Forshee Steven A Anderson Hussein Ezzeldin |
| author_facet | Matthew Deady Raymond Duncan Matthew Sonesen Renier Estiandan Kelly Stimpert Sylvia Cho Jeffrey Beers Brian Goodness Lance Daniel Jones Richard Forshee Steven A Anderson Hussein Ezzeldin |
| author_sort | Matthew Deady |
| collection | DOAJ |
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BackgroundAdverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19.
ObjectiveThis study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the US Food and Drug Administration’s postmarket surveillance capabilities while minimizing the burden of collecting clinical data on suspected postvaccination AEs. The objective of this study was to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a health care data exchange.
MethodsWe detected cases by sharing and applying computable phenotype algorithms to real-world data in health care providers’ electronic health records databases. Using the fast healthcare interoperability resources standard for secure data transmission, we implemented a computable phenotype algorithm on a new health care system. The study focused on the algorithm's positive predictive value, validated through clinical records, assessing both the time required for implementation and the accuracy of AE detection.
ResultsThe algorithm required 200-250 hours to implement and optimize. Of the 6,574,420 clinical encounters across 694,151 patients, 30 cases were identified as potential myocarditis/pericarditis. Of these, 26 cases were retrievable, and 24 underwent clinical validation. In total, 14 cases were confirmed as definite or probable myocarditis/pericarditis, yielding a positive predictive value of 58.3% (95% CI 37.3%-76.9%). These findings underscore the algorithm's capability for real-time detection of AEs, though they also highlight variability in performance across different health care systems.
ConclusionsThe 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. |
| format | Article |
| id | doaj-art-85f2313e0df04449ab31be79a92bc5c8 |
| institution | OA Journals |
| issn | 1438-8871 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-85f2313e0df04449ab31be79a92bc5c82025-08-20T02:33:39ZengJMIR PublicationsJournal of Medical Internet Research1438-88712024-11-0126e5459710.2196/54597A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation StudyMatthew Deadyhttps://orcid.org/0009-0004-1761-8058Raymond Duncanhttps://orcid.org/0000-0001-6764-655XMatthew Sonesenhttps://orcid.org/0009-0003-4701-0687Renier Estiandanhttps://orcid.org/0009-0000-7186-3207Kelly Stimperthttps://orcid.org/0000-0003-0440-1816Sylvia Chohttps://orcid.org/0000-0002-0263-0343Jeffrey Beershttps://orcid.org/0000-0001-5363-0434Brian Goodnesshttps://orcid.org/0009-0002-0626-9142Lance Daniel Joneshttps://orcid.org/0000-0002-4924-0123Richard Forsheehttps://orcid.org/0000-0002-5805-2837Steven A Andersonhttps://orcid.org/0000-0002-2517-0271Hussein Ezzeldinhttps://orcid.org/0000-0001-7375-6456 BackgroundAdverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19. ObjectiveThis study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the US Food and Drug Administration’s postmarket surveillance capabilities while minimizing the burden of collecting clinical data on suspected postvaccination AEs. The objective of this study was to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a health care data exchange. MethodsWe detected cases by sharing and applying computable phenotype algorithms to real-world data in health care providers’ electronic health records databases. Using the fast healthcare interoperability resources standard for secure data transmission, we implemented a computable phenotype algorithm on a new health care system. The study focused on the algorithm's positive predictive value, validated through clinical records, assessing both the time required for implementation and the accuracy of AE detection. ResultsThe algorithm required 200-250 hours to implement and optimize. Of the 6,574,420 clinical encounters across 694,151 patients, 30 cases were identified as potential myocarditis/pericarditis. Of these, 26 cases were retrievable, and 24 underwent clinical validation. In total, 14 cases were confirmed as definite or probable myocarditis/pericarditis, yielding a positive predictive value of 58.3% (95% CI 37.3%-76.9%). These findings underscore the algorithm's capability for real-time detection of AEs, though they also highlight variability in performance across different health care systems. ConclusionsThe 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.https://www.jmir.org/2024/1/e54597 |
| spellingShingle | Matthew Deady Raymond Duncan Matthew Sonesen Renier Estiandan Kelly Stimpert Sylvia Cho Jeffrey Beers Brian Goodness Lance Daniel Jones Richard Forshee Steven A Anderson Hussein Ezzeldin A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study Journal of Medical Internet Research |
| title | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| title_full | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| title_fullStr | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| title_full_unstemmed | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| title_short | A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study |
| title_sort | computable phenotype algorithm for postvaccination myocarditis pericarditis detection using real world data validation study |
| url | https://www.jmir.org/2024/1/e54597 |
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