A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study (Preprint) Article Swipe
Matthew Deady
,
Ray Duncan
,
Matthew Sonesen
,
Renier Estiandan
,
Kelly K. Stimpert
,
Sylvia Cho
,
Jeffrey Beers
,
Brian Goodness
,
Lance Daniel Jones
,
Richard A. Forshee
,
Steven A. Anderson
,
Hussein Ezzeldin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.54597
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.54597
BACKGROUND Adverse 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. OBJECTIVE This 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. METHODS We 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. RESULTS The 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. CONCLUSIONS 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.54597
- OA Status
- gold
- References
- 20
- Related Works
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- OpenAlex ID
- https://openalex.org/W4388856531
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388856531Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/preprints.54597Digital Object Identifier
- Title
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A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study (Preprint)Work title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-15Full publication date if available
- Authors
-
Matthew Deady, Ray Duncan, Matthew Sonesen, Renier Estiandan, Kelly K. Stimpert, Sylvia Cho, Jeffrey Beers, Brian Goodness, Lance Daniel Jones, Richard A. Forshee, Steven A. Anderson, Hussein EzzeldinList of authors in order
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https://doi.org/10.2196/preprints.54597Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2196/preprints.54597Direct OA link when available
- Concepts
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Health care, Vaccination, Medicine, Interoperability, Data exchange, Medical emergency, Computer science, World Wide Web, Virology, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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20Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy | 199 |
| abstract_inverted_index.applying | 139 |
| abstract_inverted_index.clinical | 96, 188, 218, 239 |
| abstract_inverted_index.definite | 248 |
| abstract_inverted_index.detected | 134 |
| abstract_inverted_index.findings | 263 |
| abstract_inverted_index.improved | 316 |
| abstract_inverted_index.interest | 52 |
| abstract_inverted_index.outcomes | 321 |
| abstract_inverted_index.platform | 116 |
| abstract_inverted_index.positive | 183, 254 |
| abstract_inverted_index.probable | 250 |
| abstract_inverted_index.records, | 189 |
| abstract_inverted_index.reported | 121 |
| abstract_inverted_index.required | 194, 208 |
| abstract_inverted_index.standard | 160 |
| abstract_inverted_index.studies. | 14 |
| abstract_inverted_index.systems. | 284 |
| abstract_inverted_index.yielding | 252 |
| abstract_inverted_index.6,574,420 | 217 |
| abstract_inverted_index.Biologics | 72 |
| abstract_inverted_index.COVID-19. | 62 |
| abstract_inverted_index.advocates | 290 |
| abstract_inverted_index.algorithm | 170, 207 |
| abstract_inverted_index.assessing | 190 |
| abstract_inverted_index.attention | 20 |
| abstract_inverted_index.confirmed | 246 |
| abstract_inverted_index.continues | 46 |
| abstract_inverted_index.detection | 270, 305 |
| abstract_inverted_index.different | 281 |
| abstract_inverted_index.emergency | 24 |
| abstract_inverted_index.evaluated | 11 |
| abstract_inverted_index.exchange. | 129 |
| abstract_inverted_index.highlight | 276 |
| abstract_inverted_index.implement | 212 |
| abstract_inverted_index.including | 59 |
| abstract_inverted_index.objective | 103 |
| abstract_inverted_index.optimize. | 214 |
| abstract_inverted_index.patients, | 222 |
| abstract_inverted_index.phenotype | 141, 169, 300 |
| abstract_inverted_index.potential | 228 |
| abstract_inverted_index.real-time | 269 |
| abstract_inverted_index.recently, | 16 |
| abstract_inverted_index.resources | 159 |
| abstract_inverted_index.settings. | 337 |
| abstract_inverted_index.suspected | 99 |
| abstract_inverted_index.underwent | 238 |
| abstract_inverted_index.vaccines, | 58 |
| abstract_inverted_index.vaccines. | 30 |
| abstract_inverted_index.validated | 186 |
| abstract_inverted_index.algorithms | 142, 301 |
| abstract_inverted_index.associated | 5 |
| abstract_inverted_index.capability | 267 |
| abstract_inverted_index.collecting | 95 |
| abstract_inverted_index.computable | 140, 168, 299 |
| abstract_inverted_index.consistent | 331 |
| abstract_inverted_index.databases. | 153 |
| abstract_inverted_index.detection. | 202 |
| abstract_inverted_index.electronic | 150 |
| abstract_inverted_index.encounters | 219 |
| abstract_inverted_index.healthcare | 157 |
| abstract_inverted_index.identified | 226 |
| abstract_inverted_index.minimizing | 91 |
| abstract_inverted_index.postmarket | 38, 87, 313 |
| abstract_inverted_index.predictive | 184, 255 |
| abstract_inverted_index.real-world | 144 |
| abstract_inverted_index.refinement | 294 |
| abstract_inverted_index.underscore | 264 |
| abstract_inverted_index.Initiative, | 76 |
| abstract_inverted_index.algorithm's | 182, 266 |
| abstract_inverted_index.application | 296 |
| abstract_inverted_index.distributed | 298 |
| abstract_inverted_index.implemented | 166 |
| abstract_inverted_index.monitoring. | 319 |
| abstract_inverted_index.performance | 279 |
| abstract_inverted_index.vaccination | 7 |
| abstract_inverted_index.validation. | 240 |
| abstract_inverted_index.variability | 277 |
| abstract_inverted_index.capabilities | 89 |
| abstract_inverted_index.optimization | 327 |
| abstract_inverted_index.providers’ | 149 |
| abstract_inverted_index.retrievable, | 235 |
| abstract_inverted_index.surveillance | 88, 111, 314 |
| abstract_inverted_index.37.3%-76.9%). | 261 |
| abstract_inverted_index.Effectiveness | 73 |
| abstract_inverted_index.authorization | 26 |
| abstract_inverted_index.automatically | 120 |
| abstract_inverted_index.capabilities. | 306 |
| abstract_inverted_index.comprehensive | 312 |
| abstract_inverted_index.surveillance, | 39 |
| abstract_inverted_index.traditionally | 9 |
| abstract_inverted_index.transmission, | 164 |
| abstract_inverted_index.Administration | 45 |
| abstract_inverted_index.implementation | 196 |
| abstract_inverted_index.responsibility | 35 |
| abstract_inverted_index.epidemiological | 13 |
| abstract_inverted_index.postvaccination | 100 |
| abstract_inverted_index.interoperability | 158 |
| abstract_inverted_index.Administration’s | 86 |
| abstract_inverted_index.<title>METHODS</title> | 132 |
| abstract_inverted_index.<title>RESULTS</title> | 205 |
| abstract_inverted_index.<title>OBJECTIVE</title> | 65 |
| abstract_inverted_index.<title>BACKGROUND</title> | 1 |
| abstract_inverted_index.myocarditis/pericarditis, | 251 |
| abstract_inverted_index.myocarditis/pericarditis. | 229 |
| abstract_inverted_index.<title>CONCLUSIONS</title> | 287 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.22810995 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |