Prediction for Intravenous Immunoglobulin Resistance Combining Genetic Risk Loci Identified From Next Generation Sequencing and Laboratory Data in Kawasaki Disease

Background: Kawasaki disease (KD) is the most common cause of acquired heart disease. A substantial proportion of patients were resistant to intravenous immunoglobulin (IVIG), the primary treatment of KD, and the mechanism of IVIG resistance remains unclear. The accuracy of current models predictive of IVIG resistance is insufficient and doesn’t meet the clinical expectations. Objectives: To develop a scoring model predicting IVIG resistance of patients with KD. Methods: We recruited 330 KD patients (50 IVIG non-responders, 280 IVIG responders) and 105 healthy children to explore the susceptibility loci of IVIG resistance in Kawasaki disease. A next generation sequencing technology that focused on 4 immune-related pathways and 472 single nucleotide polymorphisms (SNPs) was performed. An R package SNPassoc was used to identify the risk loci, and student’s t test was used to identify risk factors associated with IVIG resistance. A random forest-based prediction model of IVIG resistance was built based on the identified specific SNP loci with the laboratory data. Results: A total of 544 significant risk loci were found associated with IVIG resistance, including 27 previous published SNPs. Laboratory test variables, including erythrocyte sedimentation rate (ESR), platelet (PLT), and C reactive protein, were found significantly different between IVIG responders and non-responders. A predictive model was built using the top 9 SNPs and clinical features achieving an area under the ROC curve of 0.974. Conclusions: It is the first study that focused on immune system in KD using high-throughput sequencing technology. Our findings improved the accuracy of prediction for IVIG resistance by integrating the genotype and laboratory data. It also provided a new perspective on the pathogenesis of IVIG resistance.