Evidence map›Paper›PMID 41089686›Full record

ArticleFrontiers in immunology2025

Value of dynamic changes in inflammatory biomarkers for predicting intravenous immunoglobulin resistance in children with Kawasaki disease.

Zhiyuan Liu, Yidan Zhang, PanPan Liu, Weiguo Qian, Qiuqin Xu, Miao Hou, Ying Liu, Guanghui Qian, Jiajia Tan, Qianzi Ge and 5 more

Abstract readMulticenter Study
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Zhiyuan Liu *Department of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Yidan Zhang *Department of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
PanPan LiuDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Weiguo QianDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Qiuqin XuDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Miao HouDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Ying LiuDepartment of Pediatrics, Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Guanghui QianDepartment of Pediatrics, Institute of Pediatric Research, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Jiajia TanDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Qianzi GeDepartment of Emergency, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Mingyang ZhangDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Jing LiDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Sheng ZhaoDepartment of Cardiology, Anhui Provincial Children's Hospital, Hefei, Anhui, China.
Haitao LvDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.
Shuhui WangDepartment of Cardiology, Children's Hospital of Soochow University, Suzhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study assessed the predictive value of dynamic laboratory parameter changes before and after intravenous immunoglobulin (IVIG) treatment for IVIG resistance in children with Kawasaki disease (KD). Methods: Children with KD were stratified based on the occurrence of IVIG resistance. Logistic regression analyses were conducted to identify independent risk factors. The predictive performance of variables and their fractional changes (FC) was evaluated through receiver operating characteristic (ROC) curve analysis. Nonlinear associations between predictors and outcomes were examined via restricted cubic spline (RCS) analysis. Results: The Soochow cohort analyzed 1,796 children, with IVIG resistance observed in 140 cases (7.8%). 636 children from the Anhui cohort were included in external validation. Multivariate regression analysis identified pre-treatment CLR and Hb, post-treatment CLR, LMR, NLR, Hb, and FCs in WBC, Hb, NE%, and NE count as significant independent predictors of IVIG resistance (P < 0.05). ROC analysis demonstrated that WBC(FC) and NE count(FC) were the strongest predictors of IVIG resistance, with AUCs of 0.7677 and 0.7818, respectively, outperforming other parameters. The combined AUC of FC was 0.8307 in the Soochow cohort and 0.8564 in the validation cohort. RCS analysis revealed significant nonlinear relationships between predictors and IVIG resistance. Conclusion: Fractional changes in WBC and NE count were established as robust predictors of IVIG resistance in KD. Future efforts should focus on developing predictive models with thresholds and dynamic risk assessments at various time points to enhance the accuracy of IVIG resistance prediction. Clinicians should closely monitor children with IVIG resistance risk factors and reassess the risk after first treatment.

Indexed as

Drug ResistanceImmunoglobulins, IntravenousMucocutaneous Lymph Node SyndromeBiomarkersChild, PreschoolFemaleHumansInfantMaleNomogramsNonlinear DynamicsRetrospective StudiesROC CurveBiomarkersImmunoglobulins, Intravenousfractional changesinflammatory biomarkersintravenous immunoglobulinKawasaki diseasepredictors

Identifiers

PMID41089686
PMCPMC12515928

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