Dataset Information

Simulation study of the effect of influenza and influenza vaccination on risk of acquiring Guillain-Barre syndrome.

ABSTRACT: It is unclear whether seasonal influenza vaccination results in a net increase or decrease in the risk for Guillain-Barré syndrome (GBS). To assess the effect of seasonal influenza vaccination on the absolute risk of acquiring GBS, we used simulation models and published estimates of age- and sex-specific risks for GBS, influenza incidence, and vaccine effectiveness. For a hypothetical 45-year-old woman and 75-year-old man, excess GBS risk for influenza vaccination versus no vaccination was -0.36/1 million vaccinations (95% credible interval -1.22% to 0.28) and -0.42/1 million vaccinations (95% credible interval, -3.68 to 2.44), respectively. These numbers represent a small absolute reduction in GBS risk with vaccination. Under typical conditions (e.g. influenza incidence rates >5% and vaccine effectiveness >60%), vaccination reduced GBS risk. These findings should strengthen confidence in the safety of influenza vaccine and allow health professionals to better put GBS risk in context when discussing influenza vaccination with patients.

SUBMITTER: Hawken S

PROVIDER: S-EPMC4313628 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Simulation study of the effect of influenza and influenza vaccination on risk of acquiring Guillain-Barré syndrome.

Hawken Steven S Kwong Jeffrey C JC Deeks Shelley L SL Crowcroft Natasha S NS McGeer Allison J AJ Ducharme Robin R Campitelli Michael A MA Coyle Doug D Wilson Kumanan K

Emerging infectious diseases 20150201 2

It is unclear whether seasonal influenza vaccination results in a net increase or decrease in the risk for Guillain-Barré syndrome (GBS). To assess the effect of seasonal influenza vaccination on the absolute risk of acquiring GBS, we used simulation models and published estimates of age- and sex-specific risks for GBS, influenza incidence, and vaccine effectiveness. For a hypothetical 45-year-old woman and 75-year-old man, excess GBS risk for influenza vaccination versus no vaccination was -0.3 ...[more]

PMID: 25625590

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Project description:BackgroundIdentifying the key factors of Guillain-Barré syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction.ObjectiveThe aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk.MethodsLarge-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained from the EudraVigilance system between 2003 and 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 the R package bnlearn.ResultsAge, gender, and 10 AEs were identified as the most significant factors of GBS. The posttest probability of GBS suggested that male vaccinees aged 50-64 years and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also experience symptoms of asthenia, hypesthesia, muscular weakness, or paresthesia. The established BN model achieved an area under the receiver operating characteristic curve of 0.866 (95% CI 0.865-0.867), sensitivity of 0.752 (95% CI 0.749-0.756), specificity of 0.882 (95% CI 0.879-0.885), and accuracy of 0.882 (95% CI 0.879-0.884) for predicting GBS risk during the internal validation and obtained values of 0.829, 0.673, 0.854, and 0.843 for area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, respectively, during the external validation.ConclusionsThe findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions among different postvaccination symptoms through 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.

Dataset Information

Simulation study of the effect of influenza and influenza vaccination on risk of acquiring Guillain-Barre syndrome.

Publications

Simulation study of the effect of influenza and influenza vaccination on risk of acquiring Guillain-Barré syndrome.

Similar Datasets

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