Intermittent Erythema in Guillain-Barré Syndrome

Tomari, Kouki; Suzuki, Hiromi; Miyama, Sahoko

doi:10.1016/j.pediatrneurol.2017.06.007

Cited by 3 publications

(4 citation statements)

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“…Furthermore, the symptom of erythema, which is an observable and not easily overlooked body sign, provides more explicit information than sensory signs for vaccinees or clinicians to evaluate GBS risk. Moreover, additional symptoms of chills, dizziness, myalgia, nausea, and pain in extremity should also raise doubt about an increased risk of GBS; these symptoms have been presented in many case reports [ 33 - 35 ] but have not been used for GBS identification. In addition, our BN structure presented complex interactions among variables visually, which helped in understanding trigger mechanisms of occurrence of different postvaccination symptoms, although their causality still warrant further verification.…”

Section: Discussionmentioning

confidence: 99%

“…In line with previous studies [ 29 - 32 ], our study also found that men had a higher risk of GBS than women. As for the negative correlation we found between erythema and GBS, we searched medical archives extensively and a study pointed out that intermittent erythema in GBS was quite rare and should be recognized as a rare manifestation of GBS [ 33 ]. To sum up, our findings 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.…”

Section: Discussionmentioning

confidence: 99%

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A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study

Huang¹,

Luo²,

Jiang³

et al. 2022

JMIR Public Health Surveill

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Background Identifying 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. 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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study

Huang¹,

Luo²,

Jiang³

et al. 2022

JMIR Public Health Surveill

View full text Add to dashboard Cite

show abstract

“…Furthermore, the symptom of erythema, which is an observable and not easily overlooked body sign, provides more explicit information than sensory signs for vaccinees or clinicians to evaluate GBS risk. Moreover, additional symptoms of chills, dizziness, myalgia, nausea, and pain in extremity should also raise doubt about an increased risk of GBS; these symptoms have been presented in many case reports [33][34][35] but have not been used for GBS identification. In addition, our BN structure presented complex interactions among variables visually, which helped in understanding trigger mechanisms of occurrence of different postvaccination symptoms, although their causality still warrant further verification.…”

Section: Discussionmentioning

confidence: 99%

“…In line with previous studies [29][30][31][32], our study also found that men had a higher risk of GBS than women. As for the negative correlation we found between erythema and GBS, we searched medical archives extensively and a study pointed out that intermittent erythema in GBS was quite rare and should be recognized as a rare manifestation of GBS [33]. To sum up, our findings 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.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study (Preprint)

Huang¹,

Luo²,

Jiang³

et al. 2020

Preprint

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BACKGROUND Identifying 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. 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 <i>bnlearn</i>. 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.

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Guillain-Barrè Syndrome—Retrospective Analysis of Data from a Cohort of Patients Referred to a Tertiary Care Pediatric Neuromuscular Center from 2000 to 2017: Electrophysiological Findings, Outcomes, and a Brief Literature Review

Cavirani,

Baga,

Cesaroni

et al. 2024

Medicina

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Background and Objectives: Guillain-Barré syndrome (GBS) is the most frequent cause of acute flaccid paresis in children. The aim of this study was to describe the clinical and electrophysiological findings and outcomes of children with GBS diagnosed in our unit. Moreover, the literature on pediatric GBS cases from the past 5 years was reviewed. In this retrospective study, we reported data on 12 patients (9 male and 3 female patients; mean age: 5 y, 4 mo; range: 9 mo–11 y) clinically diagnosed at the Child Neurology Unit of the AUSL-IRCCS of Reggio Emilia, Italy, between 2000 and 2017 and a brief analysis/comparison with data from the literature. Materials and Methods: Data were collected from medical charts. Results: In our cohort, male patients were more frequent than female ones (9 vs. 3), and upper respiratory tract infection (n = 8, 66.7%) was the most frequent triggering factor. The main clinical symptoms on admission were distal lower limbs’ weakness with gait difficulties (83.3%), pain (50%), upper limbs’ weakness (50%), and dysphagia for liquids (25%). Peripheral neurophysiological studies revealed acute inflammatory demyelinating polyradiculoneuropathy (AIDP) in 66.6% of the children, acute motor and sensory axonal neuropathy (AMSAN) in 25%, and acute motor axonal neuropathy (AMAN) in 8.3%. Ten individuals (83.3%) received timely treatment with intravenous immunoglobulins (IVIG), and, out of these ten patients, 58% received concomitant treatment with IV methylprednisolone because of a progressive disease course. Complete remission was observed in the majority of individuals (91.6%) within 6 months of symptom onset. Conclusions: Different subtypes of GBS can affect children; however, the outcome is usually positive. Early treatment appears to be important for a favorable outcome.

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Intermittent Erythema in Guillain-Barré Syndrome

Cited by 3 publications

References 1 publication

A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study

A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study

A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barré Syndrome: Development and Validation Study (Preprint)

Guillain-Barrè Syndrome—Retrospective Analysis of Data from a Cohort of Patients Referred to a Tertiary Care Pediatric Neuromuscular Center from 2000 to 2017: Electrophysiological Findings, Outcomes, and a Brief Literature Review

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