Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Nov 10, 2020
Date Accepted: Feb 2, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Bayesian Network to Predict the Risk of Post-vaccination Guillain-Barré Syndrome: A Development and Validation Study
ABSTRACT
Background:
There is a need for an accurate, interpretable, and interactions-sensitive model that enables early warning of Guillain-Barré syndrome (GBS) occurrence.
Objective:
The aim of this study was to determine the most significant factors of GBS, and further develop and validate a Bayesian network (BN) model for predicting GBS risk.
Methods:
Large-scale vaccine post-marketing surveillance data, including 79,165 U.S. (obtained from Vaccine Adverse Event Reporting System between 1990 to 2017) and 12,495 European (obtained from EudraVigilance system between 2003 to 2016) adverse events (AEs) reports, were extracted for model development and validation. GBS, age, gender, and the top 50 prevalent AEs were included for initial BN construction using R package ‘bnlearn’.
Results:
Age, gender, and ten AEs were identified as the most significant factors of GBS. The posttest probability table suggested that vaccinees aged 50-64 years old, male, and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also suffer symptoms of asthenia, hypoaesthesia, muscular weakness, or paraesthesia. The established BN model achieved an area under the receiver operating characteristic curve (AUC) of 0.866 (95% confidence interval [CI]: 0.865, 0.867), a sensitivity of 0.752 (95% CI: 0.749, 0.756), a specificity of 0.882 (95% CI: 0.879, 0.885), and an accuracy of 0.882 (95% CI: 0.879, 0.884) for predicting GBS risk during the internal validation, and obtained 0.829, 0.673, 0.854, and 0.843 for AUC, sensitivity, specificity, and accuracy respectively during the external validation.
Conclusions:
The findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions between different post-vaccination symptoms by its graphical representation, and accurately predict the risk of GBS. The established BN model could further assist clinical decision making by providing an estimated risk of GBS for a specific vaccinee, or be developed into an open-access platform for vaccinees’ self-monitoring.
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