Predictive model based on gene and laboratory data for intravenous immunoglobulin resistance in Kawasaki disease in a Chinese population

Association of Genetic Polymorphisms in Kawasaki Disease with the Response to Intravenous Immunoglobulin Therapy

Kawasaki disease (KD) is an acute febrile and systemic vasculitis disease mainly affecting children < 5 years old. Although the first case of KD was reported in 1967 and despite extensive research on KD since then, the cause of the disease remains largely unknown. The most common complications of KD are coronary artery lesions (CAL), which significantly increase the risk of coronary heart disease. The standard treatment for KD is high-dose intravenous immunoglobulin (IVIG) plus aspirin within 10 days from symptoms' appearance, which has been shown to decrease the incidence of CAL to 5-7%. Despite the benefits of IVIG, about 25% of the patients treated with IVIG develop resistance or are unresponsive to the therapy, which represents an important risk factor for CAL development. The cause of IVIG unresponsiveness has not been fully elucidated. However, the role of gene polymorphisms in IVIG response has been suggested. Herein, we comprehensively review genetic polymorphisms in KD that have been associated with IVIG resistance/unresponsiveness and further discuss available models to predict IVIG unresponsiveness.Kindly check and confirm inserted city in affiliation [1] is correctly identified.confirm.

Diagnostic model based on bioinformatics and machine learning to distinguish Kawasaki disease using multiple datasets

Background

Kawasaki disease (KD), characterized by systemic vasculitis, is the leading cause of acquired heart disease in children. Herein, we developed a diagnostic model, with some prognosis ability, to help distinguish children with KD.

Methods

Gene expression datasets were downloaded from Gene Expression Omnibus (GEO), and gene sets with a potential pathogenic mechanism in KD were identified using differential expressed gene (DEG) screening, pathway enrichment analysis, random forest (RF) screening, and artificial neural network (ANN) construction.

Results

We extracted 2,017 DEGs (1,130 with upregulated and 887 with downregulated expression) from GEO. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in innate/adaptive immune response-related processes. Subsequently, the results of weighted gene co-expression network analysis and DEG screening were combined and, using RF and ANN, a model with eight genes (VPS9D1, CACNA1E, SH3GLB1, RAB32, ADM, GYG1, PGS1, and HIST2H2AC) was constructed. Classification results of the new model for KD diagnosis showed excellent performance for different datasets, including those of patients with KD, convalescents, and healthy individuals, with area under the curve values of 1, 0.945, and 0.95, respectively.

Conclusions

We used machine learning methods to construct and validate a diagnostic model using multiple bioinformatic datasets, and identified molecules expected to serve as new biomarkers for or therapeutic targets in KD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03557-y.

Efficacy of Gamma Globulins in Children with Kawasaki Disease and Factors Influencing Children’s Short-Term Prognosis

Purpose

To explore and analyze the therapeutic effect of gamma globulins (GG) on Kawasaki disease (KD) in children and the influencing factors of short-term prognosis.

Methods

First, 90 pediatric KD patients admitted between January 2019 and January 2021 were selected and divided into a control group (n = 40) and a research group (n = 50) according to the difference in treatment. In addition to routine treatment and nursing given to both groups, control group was given aspirin (ASA), based on which research group was supplemented with GG therapy. The treatment outcome and adverse events (AEs) of the two cohorts of patients were analyzed and compared, and the influencing factors of children's short-term prognosis were analyzed by logistics multivariate analysis.

Results

Research group had a statistical higher overall response rate than control group, with significantly fewer cases suffering from AEs such as nausea and vomiting, diarrhea, rash, dizziness and headache, and coronary artery injury. On the other hand, logistics multivariate analysis identified that gender, body mass index (BMI), onset time, platelet (PLT), and treatment mode all independently influence the short-term prognosis of children with KD.

Conclusions

GG therapy is effective in treating pediatric KD patients and can effectively prevent AEs. In addition, gender, BMI, onset-to-treatment time, PLT, C-reactive protein (CRP), and treatment methods are independent risk factors for short-term prognosis of children with KD.