Research Papers:

A new model to predict intravenous immunoglobin-resistant Kawasaki disease

Wang Hua, Yameng Sun, Ying Wang, Songling Fu, Wei Wang, Chunhong Xie, Yiying Zhang and Fangqi Gong _

PDF  |  HTML  |  How to cite

Oncotarget. 2017; 8:80722-80729. https://doi.org/10.18632/oncotarget.21083

Metrics: PDF 1273 views  |   HTML 1947 views  |   ?  


Wang Hua1,*, Yameng Sun1,*, Ying Wang1, Songling Fu1, Wei Wang1, Chunhong Xie1, Yiying Zhang1 and Fangqi Gong1

1Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR China

*These authors contributed equally to this work

Correspondence to:

Fangqi Gong, email: [email protected]

Keywords: Kawasaki disease, IVIG resistant, independent risk factors, predictive model

Abbreviations: KD = kawasaki disease, IVIGR = intravenous immunoglobin resistance, CALs = coronary artery lesions, AUC = area under the curve

Received: July 11, 2017     Accepted: September 04, 2017     Published: September 19, 2017


Objectives: To clarify the independent risk factors and construct predictive model for intravenous immunoglobin (IVIG)-resistant KD (IVIGRKD).

Results: The ratio of male to female in the overall samples was 1.62:1 and the incidence of IVIGR was 17.9%. Multivariate regression analysis showed that the OR (95% CI) values of fever duration ≥ 7 days, delayed diagnosis, gamma-glutamyl transferase ≥ 25 U/L, serum sodium ≤ 135 mmol/L, neutrophil-to-lymphocyte ratio ≥ 2.8 and platelets ≤ 350 × 109/L were 2.94 (2.17–4.00), 1.64 (1.07–2.53), 1.38 (1.07–1.79), 1.68 (1.30–2.19), 1.58 (1.22–2.06) and 1.39 (1.08–1.80), respectively. Based on these OR values, a new predictive model was established with an AUC of 0.685, a sensitivity of 60.7% and a specificity of 66.5%, and showed superiority to formerly reported models. Further analysis of patients ≤ 6 months old gave rise to improved predictions for IVIGRKD with an AUC of 0.746 relative the new model for the total samples.

Materials and Methods: A total of 2,126 KD cases were enrolled in this study. Clinical indicators showing significant differences were screened using univariate analysis, and the independent risk factors were further elucidated using multivariate regression analysis. A new model was constructed, and the predictive ability was evaluated with the area under the curve (AUC) value and the sensitivity and specificity by using the receiver operating characteristic (ROC) curve.

Conclusions: The new model for predicting IVIGRKD in this study is superior to those reported previously, and further analysis of patients with IVIGRKD younger than 6 months old allowed optimization of the predictive model.

Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 21083