Evidence map›Paper›PMID 41567390›Full record

ArticleFrontiers in cardiovascular medicine2025

A diagnostic prediction model was established based on the clinical characteristics of multicenter children with Kawasaki disease in Xinjiang.

Muqing Niu, Zhaotang Lin, Fengling Zhang, Yonglin Chen, Li Zhang, Jinyong Pan, Cailing Chen, Hongbin Yang, Lian Mao, Lijuan Chen and 4 more

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 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

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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

14 authors.

Muqing Niu *The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Zhaotang Lin *The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Fengling ZhangThe First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Yonglin ChenDepartment of Pediatrics, The First Affiliated Hosptial of Shihezi University, Shihezi, Xinjiang, China.
Li ZhangDepartment of Pediatrics, The First Affiliated Hosptial of Shihezi University, Shihezi, Xinjiang, China.
Jinyong PanDepartment of Pediatrics, The First Affiliated Hosptial of Shihezi University, Shihezi, Xinjiang, China.
Cailing ChenDepartment of Pediatrics, First Division Hospital of Xinjiang Construction Corps, Alar, Xinjiang, China.
Hongbin YangDepartment of Pediatrics, Second Division Hospital of Xinjiang Construction Corps, Tiemenguan, Xinjiang, China.
Lian MaoDepartment of Pediatrics, Third Division Hospital of Xinjiang Construction Corps, Tumushuke, Xinjiang, China.
Lijuan ChenDepartment of Pediatrics, Tumushuke People's Hospital, Tumushuke, Xinjiang, China.
Hua GuanDepartment of Pediatrics, Fourth Division Hospital of Xinjiang Construction Corps, Kekedala, Xinjiang, China.
Yong SunDepartment of Pediatrics, Seventh Division Hospital of Xinjiang Construction Corps, Huyanghe, Xinjiang, China.
Zhou ZhangDepartment of Pediatrics, 10th Division Hospital of Xinjiang Construction Corps, Beitun, Xinjiang, China.
Jing LvDepartment of Pediatrics, Red Star Hospital, 13th Division, Hami, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Kawasaki disease (KD) is a serious pediatric systemic vasculitis that may cause cardiovascular complications, including coronary artery lesions (CAL). This study aimed to evaluate the predictive value of peripheral blood lymphocyte subsets for intravenous immunoglobulin (IVIG) treatment sensitivity in children with KD in Xinjiang. Methods: This multicenter retrospective study collected clinical and laboratory data from 142 children with KD and 120 controls with infectious fever across several hospitals in Xinjiang from June 2022 to December 2024. Peripheral blood lymphocyte subsets and immunoglobulins were analyzed. Multivariate logistic regression was used to identify risk factors for IVIG non-responsiveness, and ROC curve analyses were performed to evaluate diagnostic efficacy. Results: Significant differences in lymphocyte subsets were observed between KD and control groups, including CD3+, CD4+, CD8+, CD19+, and the CD4+/CD8+ ratio. Multivariate logistic regression identified CD4+ cell count as an independent risk factor for IVIG non-responsiveness. ROC analyses suggested that lymphocyte subsets and immunoglobulins (including CD3+, CD+, CD16+CD56+, IgA, and IgM) have diagnostic potential, with CD8+ and CD+ showing high sensitivity and specificity. Discussion: Peripheral blood lymphocyte subsets, particularly CD4+, may serve as useful biomarkers for predicting IVIG treatment response and distinguishing KD from other febrile illnesses. Further studies with larger sample sizes are warranted to refine predictive models and improve KD management.

Indexed as

CD4+ cellsCD8+ cellscoronary artery lesionsdiagnostic biomarkersimmunoglobulinsintravenous immunoglobulin (IVIG)Kawasaki diseaselymphocyte subsets

Identifiers

PMID41567390
PMCPMC12816189

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