Decreased hemoglobin after initial treatment is associated with treatment resistance in Kawasaki disease in Kobayashi risk stratification

Atsumi, Yukari; Sakakibara, Hiroshi; Morikawa, Yoshihiko; Miyata, Koichi; Yamagishi, Hiroyuki; Misawa, Masahiro; Miura, Masaru

doi:10.1007/s12519-020-00385-x

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…HGB can be combined with other indicators to differentiate KD from other febrile children 30 . A decrease in HGB after IVIG treatment is associated with IVIG resistance 31 . The ability of HGB alone to diagnose KD remains limited, but HGB is still a predictor worth considering.…”

Section: Discussionmentioning

confidence: 99%

Exploring the diagnostic value of eosinophil-to-lymphocyte ratio to differentiate Kawasaki disease from other febrile diseases based on clinical prediction model

Guo

Liao²,

Fan³

et al. 2023

Sci Rep

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Kawasaki disease (KD) is a febrile disease that affects children under 5 years of age and leads to serious cardiovascular complications such as coronary artery disease. The development of markers that can predict early is important to reduce the under- and misdiagnosis of KD. The aim of this research was to develop a diagnostic predictive model to differentiate Kawasaki disease (KD) from other febrile diseases using eosinophil-to-lymphocyte ratio (ELR) and other biomarkers. We recruited a total of 190 children with KD and 1604 children with other febrile diseases. We retrospectively collected clinical information from the children, which included laboratory data on the day of admission, such as white blood cells (WBC), hemoglobin (HGB), calcitoninogen (PCT), hypersensitive c-reactive protein (CRP), snake prognostic nutritional index (PNI), peripheral blood neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and ELR. We performed analyses using univariate analysis, multivariate logistic regression, and column line plots, and evaluated the diagnostic parameters of the predictive models. ELR was significantly increased in patients with KD. After multivariate logistic regression, WBC, HGB, CRP, NLR, ELR and PNI were finally included as indicators for constructing the prediction model. The ROC curve analysis suggested that the C-index of the diagnostic prediction model was 0.921. The calibration curve showed good diagnostic performance of the columnar graph model. The cut-off value of ELR alone for KD was 0.04, the area under the ROC curve was 0.809. Kids with KD show highly expressive level of ELR compared to children with febrile disease, which can be used to diagnose KD, and column line graphs constructed together with other indicators can help pediatricians to identify KD more effectively from febrile children.

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Section: Discussionmentioning

confidence: 99%

Exploring the diagnostic value of eosinophil-to-lymphocyte ratio to differentiate Kawasaki disease from other febrile diseases based on clinical prediction model

Guo

Liao²,

Fan³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In addition to the traditional diagnostic clinical criteria, KD patients may have a lots of nonspecific clinical presentations, like headaches; non-specific abdominal pain; hydrops of the gallbladder; erythematous rash at the bacillus Calmette–Guérin inoculation site; abnormal elevation of γ-glutamyl transferase, alanine aminotransferase, and aspartate aminotransferase; hypoalbuminemia, and decreased hemoglobin [ 7 , 8 , 9 , 10 ]. Notably, decreased hemoglobin in patients with KD is the most common character and is known as acute systemic inflammation or hemolysis [ 11 , 12 , 13 ]. Early and correct diagnosis of KD is necessary to assure promptly intravenous immunoglobulin (IVIG) treatment and prevent adverse coronary arterial aneurysms [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Combination of Hemoglobin-for-Age Z-Score and Plasma Hepcidin Identified as a Novel Predictor for Kawasaki Disease

et al. 2022

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Kawasaki disease (KD) is a febrile coronary vasculitis that affects younger children and includes complications such as coronary artery aneurysm. KD diagnoses are diagnosed based on clinical presentations, a process that still poses a challenge for front-line physicians. In the current study, we developed a novel predictor using the hemoglobin-for-age z-score (HbZ) and plasma hepcidin to differentiate Kawasaki disease (KD) from febrile children (FC). There were 104 FC and 115 KD subjects (89 typical KD; 26 incomplete KD) for this study, and data were collected on the biological parameters of hemoglobin and plasma hepcidin levels. A receiver operating characteristic curve (auROC), multiple logistics regression, and support vector machine analysis were all adopted to develop our prediction condition. We obtained both predictors, HbZ and plasma hepcidin, for distinguishing KD and FC. The auROC of the multivariate logistic regression of both parameters for FC and KD was 0.959 (95% confidence interval = 0.937–0.981), and the sensitivity and specificity were 85.2% and 95.9%, respectively. Furthermore, the auROC for FC and incomplete KD was 0.981, and the sensitivity and specificity were 92.3% and 95.2%, respectively. We further developed a model of support vector machine (SVM) classification with 83.3% sensitivity and 88.0% specificity in the training set, and the blind cohort performed well (78.4% sensitivity and 100% specificity). All data showed that sensitivity and specificity were 81.7% and 91.3%, respectively, by SVM. Overall, our findings demonstrate a novel predictor using a combination of HbZ and plasma hepcidin with a better discriminatory ability for differentiating from WBC and CRP between children with KD and other FC. Using this predictor can assist front-line physicians to recognize and then provide early treatment for KD.

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Uncovering Potential Novel Biomarkers in Immunoglobulin-Resistant Kawasaki Disease Using Bioinformatics Analysis

2023

j biomater tissue eng

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Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) is a complicated disorder, which can induce multiple-system damage. The pathogenic factor inducing KD remains unclear. The present study focused on identifying potential novel biomarkers for IVIG-resistant KD using integrated analyses. Eight IVIG-resistant KD samples and twelve IVIG-sensitive KD samples were included in the GSE18606 dataset. A Linear Model for Microarray Data (LIMMA) identified 504 differentially expressed genes (DEGs), An IVIG-resistant KD sample was compared with an IVIG-sensitive KD sample to identify 17 modules through weighted gene co-expression network analysis (WGCNA). A common gene (CG) is the intersection of DEGs and genes in the most significant module. Analysis of functional enrichment revealed that CGs were mainly enriched in TNF signaling pathways and NF-kappa B signaling pathways. Ten of these genes were selected as hub genes because of their high degree of connectivity (KLF1, AHSP, HBQ1, HBA2, HBA1, EPB42, GYPB, UBB, KRT1 and BPIFB2).

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Decreased hemoglobin after initial treatment is associated with treatment resistance in Kawasaki disease in Kobayashi risk stratification

Cited by 5 publications

References 15 publications

Exploring the diagnostic value of eosinophil-to-lymphocyte ratio to differentiate Kawasaki disease from other febrile diseases based on clinical prediction model

Exploring the diagnostic value of eosinophil-to-lymphocyte ratio to differentiate Kawasaki disease from other febrile diseases based on clinical prediction model

Combination of Hemoglobin-for-Age Z-Score and Plasma Hepcidin Identified as a Novel Predictor for Kawasaki Disease

Uncovering Potential Novel Biomarkers in Immunoglobulin-Resistant Kawasaki Disease Using Bioinformatics Analysis

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