PMID- 35333192 OWN - NLM STAT- MEDLINE DCOM- 20220412 LR - 20220613 IS - 2369-2960 (Electronic) IS - 2369-2960 (Linking) VI - 8 IP - 3 DP - 2022 Mar 25 TI - A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barre Syndrome: Development and Validation Study. PG - e25658 LID - 10.2196/25658 [doi] LID - e25658 AB - BACKGROUND: Identifying the key factors of Guillain-Barre 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. OBJECTIVE: The 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. METHODS: Large-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. RESULTS: Age, 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. 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 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. CI - (c)Yun Huang, Chongliang Luo, Ying Jiang, Jingcheng Du, Cui Tao, Yong Chen, Yuantao Hao. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 25.03.2022. FAU - Huang, Yun AU - Huang Y AUID- ORCID: 0000-0001-7200-5992 AD - Department of Medical Statistics, Sun Yat-Sen University, Guangzhou, China. AD - Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China. FAU - Luo, Chongliang AU - Luo C AUID- ORCID: 0000-0003-3682-9454 AD - Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States. AD - Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO, United States. FAU - Jiang, Ying AU - Jiang Y AUID- ORCID: 0000-0002-0040-4211 AD - Department of Neurology and Multiple Sclerosis Research Center, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. FAU - Du, Jingcheng AU - Du J AUID- ORCID: 0000-0002-0322-4566 AD - School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States. FAU - Tao, Cui AU - Tao C AUID- ORCID: 0000-0002-4267-1924 AD - School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States. FAU - Chen, Yong AU - Chen Y AUID- ORCID: 0000-0003-0835-0788 AD - Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, United States. FAU - Hao, Yuantao AU - Hao Y AUID- ORCID: 0000-0001-8024-5312 AD - Department of Medical Statistics, Sun Yat-Sen University, Guangzhou, China. AD - Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20220325 PL - Canada TA - JMIR Public Health Surveill JT - JMIR public health and surveillance JID - 101669345 RN - 0 (Influenza Vaccines) SB - IM MH - Bayes Theorem MH - *Guillain-Barre Syndrome/diagnosis/epidemiology/etiology MH - Humans MH - *Influenza Vaccines/adverse effects MH - *Influenza, Human/prevention & control MH - Male MH - Vaccination PMC - PMC8994148 OTO - NOTNLM OT - Bayesian network OT - Guillain-Barre syndrome OT - adverse events OT - risk prediction OT - trivalent influenza vaccine COIS- Conflicts of Interest: None declared. EDAT- 2022/03/26 06:00 MHDA- 2022/04/13 06:00 PMCR- 2022/03/25 CRDT- 2022/03/25 12:10 PHST- 2020/11/10 00:00 [received] PHST- 2022/02/02 00:00 [accepted] PHST- 2020/12/27 00:00 [revised] PHST- 2022/03/25 12:10 [entrez] PHST- 2022/03/26 06:00 [pubmed] PHST- 2022/04/13 06:00 [medline] PHST- 2022/03/25 00:00 [pmc-release] AID - v8i3e25658 [pii] AID - 10.2196/25658 [doi] PST - epublish SO - JMIR Public Health Surveill. 2022 Mar 25;8(3):e25658. doi: 10.2196/25658.