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Prediction of post‐vaccination Guillain‐Barré syndrome using data from a passive surveillance system.

Authors :
Luo, Chongliang
Jiang, Ying
Du, Jingcheng
Tong, Jiayi
Huang, Jing
Lo Re, Vincent
Ellenberg, Susan S.
Poland, Gregory A.
Tao, Cui
Chen, Yong
Source :
Pharmacoepidemiology & Drug Safety; May2021, Vol. 30 Issue 5, p602-609, 8p
Publication Year :
2021

Abstract

Purpose Severe adverse events (AEs), such as Guillain‐Barré syndrome (GBS) occur rarely after influenza vaccination. We identify highly associated AEs with GBS and develop prediction models for GBS using the US Vaccine Adverse Event Reporting System (VAERS) reports following trivalent influenza vaccination (FLU3). Methods: This study analyzed 80 059 reports from the US VAERS between 1990 and 2017. Several AEs were identified as highly associated with GBS and were used to develop the prediction model. Some common and mild AEs that were suspected to be underreported when GBS occurred simultaneously were removed from the final model. The analyses were validated using European influenza vaccine AEs data from EudraVigilance. Results: Of the 80 059 reports, 1185 (1.5%) were annotated as GBS related. Twenty‐four AEs were identified as having strong association with GBS. The full prediction model, using age, sex, and all 24 AEs achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 85.4% (90% CI: [83.8%, 86.9%]). After excluding the nine (e.g., pruritus, rash, injection site pain) likely underreported AEs, the final AUC became 77.5% (90% CI: [75.5%, 79.6%]). Two hundred and one (0.25%) reports were predicted as of high risk of GBS (predicted probability >25%) and 84 actually developed GBS. Conclusion: The prediction performance demonstrated the potential of developing risk‐prediction models utilizing the VAERS cohort. Excluding the likely underreported AEs sacrificed some prediction power but made the model more interpretable and feasible. The high absolute risk of even a small number of AE combinations suggests the promise of GBS prediction within the VAERS dataset. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10538569
Volume :
30
Issue :
5
Database :
Complementary Index
Journal :
Pharmacoepidemiology & Drug Safety
Publication Type :
Academic Journal
Accession number :
149599093
Full Text :
https://doi.org/10.1002/pds.5196