Dataset Information

Prediction for Intravenous Immunoglobulin Resistance by Using Weighted Genetic Risk Score Identified From Genome-Wide Association Study in Kawasaki Disease.

ABSTRACT: BACKGROUND:Intravenous immunoglobulin (IVIG) is the treatment of choice in Kawasaki disease (KD). IVIG is used to prevent cardiovascular complications related to KD. However, a proportion of KD patients have persistent fever after IVIG treatment and are defined as IVIG resistant. METHODS AND RESULTS:To develop a risk scoring system based on genetic markers to predict IVIG responsiveness in KD patients, a total of 150 KD patients (126 IVIG responders and 24 IVIG nonresponders) were recruited for this study. A genome-wide association analysis was performed to compare the 2 groups and identified risk alleles for IVIG resistance. A weighted genetic risk score was calculated by the natural log of the odds ratio multiplied by the number of risk alleles. Eleven single-nucleotide polymorphisms were identified by genome-wide association study. The KD patients were categorized into 3 groups based on their calculated weighted genetic risk score. Results indicated a significant association between weighted genetic risk score (groups 3 and 4 versus group 1) and the response to IVIG (Fisher's exact P value 4.518×10-03 and 8.224×10-10, respectively). CONCLUSIONS:This is the first weighted genetic risk score study based on a genome-wide association study in KD. The predictive model integrated the additive effects of all 11 single-nucleotide polymorphisms to provide a prediction of the responsiveness to IVIG.

SUBMITTER: Kuo HC

PROVIDER: S-EPMC5647111 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Prediction for Intravenous Immunoglobulin Resistance by Using Weighted Genetic Risk Score Identified From Genome-Wide Association Study in Kawasaki Disease.

Kuo Ho-Chang HC Wong Henry Sung-Ching HS Chang Wei-Pin WP Chen Ben-Kuen BK Wu Mei-Shin MS Yang Kuender D KD Hsieh Kai-Sheng KS Hsu Yu-Wen YW Liu Shih-Feng SF Liu Xiao X Chang Wei-Chiao WC

Circulation. Cardiovascular genetics 20171001 5

<h4>Background</h4>Intravenous immunoglobulin (IVIG) is the treatment of choice in Kawasaki disease (KD). IVIG is used to prevent cardiovascular complications related to KD. However, a proportion of KD patients have persistent fever after IVIG treatment and are defined as IVIG resistant.<h4>Methods and results</h4>To develop a risk scoring system based on genetic markers to predict IVIG responsiveness in KD patients, a total of 150 KD patients (126 IVIG responders and 24 IVIG nonresponders) were ...[more]

PMID: 29025760

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Project description:BackgroundAbout 10-20% of Kawasaki disease (KD) patients are resistant to the initial infusion of intravenous immunoglobin (IVIG). The aim of this study was to assess whether IVIG resistance in KD patients could be predicted using standard clinical and laboratory features.MethodsData were from two cohorts: a Korean cohort of 7101 KD patients from 2015 to 2017 and a cohort of 649 KD patients from San Diego enrolled from 1998 to 2021. Features included laboratory values, the worst Z-score from the initial echocardiogram or during hospitalization, and the five clinical KD signs at presentation.ResultsFive machine learning models achieved a maximum median AUC of 0.711 [IQR: 0.706-0.72] in the Korean cohort and 0.696 [IQR: 0.609-0.722] in the San Diego cohort during stratified 10-fold cross-validation using significant laboratory features identified from univariate analysis. Adding the Z-score, KD clinical signs, or both did not considerably improve the median AUC in either cohort.ConclusionsUsing commonly measured clinical laboratory data alone or in conjunction with echocardiographic findings and clinical features is not sufficient to predict IVIG resistance. Further attempts to predict IVIG resistance will need to incorporate additional data such as transcriptomics, proteomics, and genetics to achieve meaningful predictive utility.ImpactWe demonstrated that laboratory, echocardiographic, and clinical findings cannot predict intravenous immunoglobin (IVIG) resistance to a clinically meaningful extent using machine learning in a homogenous Asian or ethnically diverse population of patients with Kawasaki disease (KD). Visualizing these features using uniform manifold approximation and projection (UMAP) is an important step to evaluate predictive utility in a qualitative manner. Further attempts to predict IVIG resistance in KD patients will need to incorporate novel biomarkers or other specialized features such as genetic differences or transcriptomics to be clinically useful.

Dataset Information

Prediction for Intravenous Immunoglobulin Resistance by Using Weighted Genetic Risk Score Identified From Genome-Wide Association Study in Kawasaki Disease.

Publications

Prediction for Intravenous Immunoglobulin Resistance by Using Weighted Genetic Risk Score Identified From Genome-Wide Association Study in Kawasaki Disease.

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