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  • Title: Predictive factors of medium-giant coronary artery aneurysms in Kawasaki disease.
    Author: Jiang S, Li M, Xu K, Xie Y, Liang P, Liu C, Su Q, Li B.
    Journal: Pediatr Res; 2024 Jan; 95(1):267-274. PubMed ID: 37670043.
    Abstract:
    BACKGROUND: We aimed to examine predictive measures for medium and giant coronary artery aneurysms (CAA) in Kawasaki disease (KD) patients. METHODS: Patients who were diagnosed with KD from 2015 to 2021 were retrospectively reviewed. The clinical and laboratory data were compared between medium-giant group and non-medium-giant group. RESULTS: A total of 1331 KD patients were investigated, of whom 63 patients (4.7%) developed medium-giant CAA including 27 patients (2%) with giant CAA. Sex, age, fever duration, intravenous immunoglobulin (IVIG) resistance, platelet count, and albumin level independently predicted medium or giant CAA by multivariate logistic regression analysis. Male, age, duration of fever, IVIG resistance, platelet count, hemoglobin, and erythrocyte sedimentation rate were independent predictors for giant CAA. The two new scoring systems using these factors in identifying patients with medium-giant CAA and giant CAA had respectively sensitivities of 86.89% and 92.59%, and specificities of 81.65% and 87.93%. Validation in 2021 dataset (193 KD patients) showed comparable sensitivity and specificity to development dataset. CONCLUSIONS: Male, age, fever duration, IVIG resistance, platelet count, albumin, hemoglobin, and erythrocyte sedimentation rate might be significant predictors of medium and giant CAA. The sensitivity and specificity in our risk prediction model were higher than in previous research. IMPACT: This is the first study to search for risk factors and establish a prediction model for the development of medium-giant CAA in the Chinese population using z-scores and absolute inner diameter values based on large sample sizes. The sensitivity and specificity in our model were higher than in previous studies. Our research could help clinicians better predict medium-giant CAA and choose more appropriate treatment.
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