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Related Concept Videos

Myocarditis II: Clinical features and Diagnostic Tests01:27

Myocarditis II: Clinical features and Diagnostic Tests

2
Myocarditis is an inflammation of the heart muscle. The symptoms vary widely, encompassing asymptomatic presentations to severe, acute manifestations.Clinical PresentationAsymptomatic cases: In some instances, myocarditis may be asymptomatic, with the infection resolving without intervention. These cases often go undetected unless discovered incidentally through diagnostic imaging or tests conducted for other reasons.General Early Symptoms: Early symptoms of myocarditis are non-specific and can...
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Myocarditis III: Medical Management01:14

Myocarditis III: Medical Management

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Myocarditis: Comprehensive Medical ManagementMyocarditis, the heart muscle inflammation, requires a comprehensive medical management strategy that addresses the underlying cause, provides supportive care, manages symptoms, and reduces cardiac workload.Infections and Autoimmune CausesAdminister appropriate antimicrobial therapy when an infectious agent causes myocarditis. For instance, penicillin treats infections caused by Group A Streptococcus. In cases where autoimmune processes are...
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Related Experiment Video

Updated: Jun 6, 2025

Noninvasive Assessment of Cardiac Abnormalities in Experimental Autoimmune Myocarditis by Magnetic Resonance Microscopy Imaging in the Mouse
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A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data:

Matthew Deady1, Raymond Duncan2, Matthew Sonesen3

  • 1IBM Consulting, Bethesda, MD, United States.

Journal of Medical Internet Research
|November 25, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a computable phenotype algorithm for real-time vaccine adverse event (AE) detection. The pilot platform showed promise for improving vaccine safety surveillance by identifying potential myocarditis/pericarditis cases.

Keywords:
COVID-19 vaccineFHIRFood and Drug Administrationadverse eventcomputable phenotypeelectronic health recordsfast healthcare interoperability resourcesinteroperabilitypostmarket surveillance systemreal-world datavaccine safetyvalidation study

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Area of Science:

  • Pharmacovigilance and Drug Safety
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Traditional epidemiological studies are used for vaccine adverse event (AE) evaluation.
  • Postmarket surveillance of AEs is critical, especially for COVID-19 vaccines.
  • The US Food and Drug Administration (FDA) monitors AEs to ensure vaccine safety.

Purpose of the Study:

  • To enhance active surveillance of postvaccination AEs using a pilot platform.
  • To minimize the burden of collecting clinical data on suspected AEs.
  • To enable automatic reporting of AE cases through health care data exchange.

Main Methods:

  • Utilized computable phenotype algorithms applied to real-world data from electronic health records.
  • Implemented algorithms using the Fast Healthcare Interoperability Resources (FHIR) standard for secure data transmission.
  • Focused on validating the algorithm's positive predictive value and assessing implementation time and accuracy.

Main Results:

  • Algorithm implementation took 200-250 hours.
  • Identified 14 confirmed cases of myocarditis/pericarditis out of 6,574,420 encounters, yielding a positive predictive value of 58.3%.
  • Demonstrated real-time AE detection capability with performance variability across health care systems.

Conclusions:

  • Recommends refining and applying distributed computable phenotype algorithms for enhanced AE detection.
  • Highlights the importance of these tools for comprehensive postmarket surveillance and vaccine safety.
  • Suggests further optimization is needed for consistent results across diverse health care settings.