ArticleItalian journal of pediatrics2018
A comparison of efficacy of six prediction models for intravenous immunoglobulin resistance in Kawasaki disease.
Article in Italian journal of pediatrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 44 citations in OpenAlex.
- Development and validation of a novel interpretable machine learning model integrating immune-inflammatory indicators for intravenous immunoglobulin resistance in Kawasaki disease.Translational pediatrics · 2026Article
- Nutrition-Associated Biomarkers in Predicting Intravenous Immunoglobulin Resistance and Coronary Artery Lesions in Kawasaki Disease: A Systematic Review and Meta-Analysis.Food science & nutrition · 2025Review
- Combination of S100A12/TLR2 signaling molecules and clinical indicators in a new predictive model for IVIG-resistant Kawasaki disease.Scientific reports · 2024Article
- Intravenous immunoglobulin resistance in Kawasaki disease patients: prediction using clinical data.Pediatric research · 2024Article
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- Predictive value of albumin for intravenous immunoglobulin resistance in a large cohort of Kawasaki disease patients.Italian journal of pediatrics · 2023Article
- Evaluation of Formosa score and diagnostic sensitivity and specificity of four Asian risk scores for predicting intravenous immunoglobulin resistance in Kawasaki disease: a bivariate meta-analysis.Frontiers in cardiovascular medicine · 2023Review
- Predicting immunoglobulin resistance in Kawasaki disease: an assessment of neutrophil to lymphocyte platelet ratio.Italian journal of pediatrics · 2022Article
- Verification of "Japanese Scoring Systems" to Predict IVIG Resistance and Identification of Predictors for IVIG Resistance in Thai Children with Kawasaki Disease.Pediatric cardiology · 2021Article
- Predictive value of Albumin-Bilirubin grade for intravenous immunoglobulin resistance in a large cohort of patients with Kawasaki disease: a prospective study.Pediatric rheumatology online journal · 2021Article
- Prediction of intravenous immunoglobulin resistance in Kawasaki disease in children.World journal of pediatrics : WJP · 2020Article
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- Comparison of Machine Learning Models for Prediction of Initial Intravenous Immunoglobulin Resistance in Children With Kawasaki Disease.Frontiers in pediatrics · 2020Article
- The role of age-specific N-terminal pro-brain natriuretic peptide cutoff values in predicting intravenous immunoglobulin resistance in Kawasaki disease: a prospective cohort study.Pediatric rheumatology online journal · 2019Article
- A new model for predicting intravenous immunoglobin-resistant Kawasaki disease in Chongqing: a retrospective study on 5277 patients.Scientific reports · 2019Article
- A New Scoring System for Prediction of Intravenous Immunoglobulin Resistance of Kawasaki Disease in Infants Under 1-Year Old.Frontiers in pediatrics · 2019Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundKawasaki disease (KD) is the most common pediatric vasculitis. Several models have been established to predict intravenous immunoglobulin (IVIG) resistance. The present study was aimed to evaluate the efficacy of prediction models using the medical data of KD patients.
methodsWe collected the medical records of patients hospitalized in the Department of Cardiology in Children's Hospital of Soochow University with a diagnosis of KD from Jan 2015 to Dec 2016. IVIG resistance was defined as recrudescent or persistent fever ≥36 h after the end of their IVIG infusion.
resultsPatients with IVIG resistance tended to be younger, have higher occurrence of rash and changes of extremities. They had higher levels of c-reactive protein, aspartate aminotransferase, neutrophils proportion (N%), total bilirubin and lower level of albumin. Our prediction model had a sensitivity of 0.72 and a specificity of 0.75. Sensitivity of Kobayashi, Egami, Kawamura, Sano and Formosa were 0.72, 0.44, 0.48, 0.20, and 0.68, respectively. Specificity of these models were 0.62, 0.82, 0.66, 0.91, and 0.48, respectively.
conclusionsOur prediction model had a powerful predictive value in this area, followed by Kobayashi model while all the other prediction models had less excellent performances than ours.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.