JMIR Publications

ABSTRACT

Background:

Adverse events (AEs) associated with vaccination have been evaluated by epidemiological studies, and, more recently, have 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 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 (BEST) initiative, which aims to improve FDA’s post-market surveillance capabilities while minimizing the burden of collecting clinical data on suspected post-vaccination AEs. This study was designed to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a healthcare data exchange.

Methods:

These cases were detected by distributing and applying computable phenotype algorithms to real-world data (RWD) in healthcare organizations’ electronic health records (EHR) databases. Next, data were transmitted to the pilot platform in the Fast Healthcare Interoperability Resources (FHIR) standard for analysis and validation. To assess this platform’s usefulness for detection of AEs, we distributed an algorithm for identifying myocarditis/pericarditis following COVID-19 vaccination to be applied to a new EHR system connected to the healthcare exchange and collected metrics on 1) the length of time necessary to implement the algorithm, 2) the performance of detecting post-vaccination AE using Positive Predicted Value (PPV), and 3) the % of cases with sufficient evidence for clinician validation.

Results:

The algorithm took longer than expected (~200-250 hours) to design, implement, and optimize the query on the partner EHR database. Performance was assessed among cases with sufficient information to meet case validation criteria for myocarditis or pericarditis. Of the 30 potential myocarditis/pericarditis cases selected from a population of ~6.5M clinical encounters in the study period, 26 could be transferred through the exchange, and 24 had sufficient information to meet the case criteria. Of these cases, 14 were validated as definite or probable myocarditis/pericarditis for a PPV of 58.3% (Confidence Interval (CI): 37.3%, 76.9%).

Conclusions:

Our results support continued research using distributed phenotype algorithms and health data exchange platforms for widespread AE post-market detection and electronic case reporting.