JMIR Publications

ABSTRACT

Background:

Adverse events (AEs) associated with vaccination have been evaluated by epidemiological studies, and more recently gained additional attention with the Emergency Use Authorization (EUA) of several COVID-19 vaccines. As part of its responsibility to conduct post-market surveillance, the U.S. Food and Drug Administration (FDA) continues to monitor several adverse events of special interest (AESIs) to ensure vaccine safety, including for COVID-19.

Objective:

This study is part of the Biologics Effectiveness and Safety (BEST) Initiative, which aims to improve FDA’s post-market surveillance capabilities while minimizing public burden. This study looks to enhance active surveillance efforts through a rules-based, computable phenotype algorithm to identify five AESIs: anaphylaxis, Guillain-Barré syndrome (GBS), myocarditis/pericarditis, thrombosis with thrombocytopenia syndrome (TTS), and febrile seizure. AESI phenotype algorithms can be developed to apply to electronic health record (EHR) data at health provider organizations across the country by querying for standard and interoperable codes. The codes queried in the rules represent symptoms, diagnoses, or treatments of the AESI sourced from published case definitions and input from clinicians.

Methods:

To validate the performance of the algorithms, we applied them to EHR data from a United States academic health system and clinicians evaluated a sample of cases. Performance was assessed using positive predictive value (PPV).

Results:

Our anaphylaxis algorithm was the best performing, having a PPV of 93.3%. The PPVs for our febrile seizure, myocarditis/pericarditis, TTS, and GBS algorithms were 89.0%, 83.5%, 70.2%, and 47.2%, respectively.

Conclusions:

Given our algorithm design and performance, our results support continued research into using interoperable algorithms for widespread AESI post-market detection.