Risk Factors and an Early Predictive Model for Kawasaki Disease Shock Syndrome in Chinese Children

Overview

Journal Ital J Pediatr

Publisher Biomed Central

Specialty Pediatrics

Date 2024 Feb 3

PMID 38310292

Authors

Mingming Zhang

Congying Wang

Qirui Li

Hongmao Wang

Xiaohui Li

Affiliations

Soon will be listed here.

Abstract

Background: Kawasaki disease shock syndrome (KDSS), though rare, has increased risk for cardiovascular complications. Early diagnosis is crucial to improve the prognosis of KDSS patients. Our study aimed to identify risk factors and construct a predictive model for KDSS.

Methods: This case-control study was conducted from June, 2015 to July, 2023 in two children's hospitals in China. Children initially diagnosed with KDSS and children with Kawasaki disease (KD) without shock were matched at a ratio of 1:4 by using the propensity score method. Laboratory results obtained prior to shock syndrome and treatment with intravenous immunoglobulin were recorded to predict the onset of KDSS. Univariable logistic regression and forward stepwise logistic regression were used to select significant and independent risk factors associated with KDSS.

Results: After matching by age and gender, 73 KDSS and 292 KD patients without shock formed the development dataset; 40 KDSS and 160 KD patients without shock formed the validation dataset. Interleukin-10 (IL-10) > reference value, platelet counts (PLT) < 260 × 10/L, C-reactive protein (CRP) > 80 mg/ml, procalcitonin (PCT) > 1ng/ml, and albumin (Alb) < 35 g/L were independent risk factors for KDSS. The nomogram model including the above five indicators had area under the curves (AUCs) of 0.91(95% CI: 0.87-0.94) and 0.90 (95% CI: 0.71-0.86) in the development and validation datasets, with a specificity and sensitivity of 80% and 86%, 66% and 77%, respectively. Calibration curves showed good predictive accuracy of the nomogram. Decision curve analyses revealed the predictive model has application value.

Conclusions: This study identified IL-10, PLT, CRP, PCT and Alb as risk factors for KDSS. The nomogram model can effectively predict the occurrence of KDSS in Chinese children. It will facilitate pediatricians in early diagnosis, which is essential to the prevention of cardiovascular complications.

Citing Articles

An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.

Duan M, Geng Z, Gao L, Zhao Y, Li Z, Chen L Sci Rep. 2025; 15(1):7927.

PMID: 40050685 PMC: 11885592. DOI: 10.1038/s41598-025-92277-1.

Unraveling the gut: the pivotal role of intestinal mechanisms in Kawasaki disease pathogenesis.

Tao E, Lang D Front Immunol. 2024; 15():1496293.

PMID: 39664384 PMC: 11633670. DOI: 10.3389/fimmu.2024.1496293.

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