1. Predictive model based on gene and laboratory data for intravenous immunoglobulin resistance in Kawasaki disease in a Chinese population
- Author
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Li Meng, Zhen Zhen, Qian Jiang, Xiao-hui Li, Yue Yuan, Wei Yao, Ming-ming Zhang, Ai-jie Li, and Lin Shi
- Subjects
Kawasaki disease ,Intravenous immunoglobulin resistance ,Single nucleotide polymorphism ,Pediatrics ,RJ1-570 ,Diseases of the musculoskeletal system ,RC925-935 - Abstract
Abstract Background Here, we investigated the predictive efficiency of a newly developed model based on single nucleotide polymorphisms (SNPs) and laboratory data for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD) in a Chinese population. Methods Data relating to children with KD were acquired from a single center between December 2015 and August 2019 and used to screen target SNPs. We then developed a predictive model of IVIG resistance using previous laboratory parameters. We then validated our model using data acquired from children with KD attending a second center between January and December 2019. Results Analysis showed that rs10056474 GG, rs746994GG, rs76863441GT, rs16944 (CT/TT), and rs1143627 (CT/CC), increased the risk of IVIG-resistance in KD patients (odds ratio, OR > 1). The new predictive model, which combined SNP data with a previous model derived from laboratory data, significantly increased the area under the receiver-operator-characteristic curves (AUC) (0.832, 95% CI: 0.776-0.878 vs 0.793, 95%CI:0.734-0.844, P
- Published
- 2021
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