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Immune response to intravenous immunoglobulin in patients with Kawasaki disease and MIS-C.

ABSTRACT: BACKGROUNDMultisystem inflammatory syndrome in children (MIS-C) is a rare but potentially severe illness that follows exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Kawasaki disease (KD) shares several clinical features with MIS-C, which prompted the use of intravenous immunoglobulin (IVIG), a mainstay therapy for KD. Both diseases share a robust activation of the innate immune system, including the IL-1 signaling pathway, and IL-1 blockade has been used for the treatment of both MIS-C and KD. The mechanism of action of IVIG in these 2 diseases and the cellular source of IL-1β have not been defined.METHODSThe effects of IVIG on peripheral blood leukocyte populations from patients with MIS-C and KD were examined using flow cytometry and mass cytometry (CyTOF) and live-cell imaging.RESULTSCirculating neutrophils were highly activated in patients with KD and MIS-C and were a major source of IL-1β. Following IVIG treatment, activated IL-1β+ neutrophils were reduced in the circulation. In vitro, IVIG was a potent activator of neutrophil cell death via PI3K and NADPH oxidase, but independently of caspase activation.CONCLUSIONSActivated neutrophils expressing IL-1β can be targeted by IVIG, supporting its use in both KD and MIS-C to ameliorate inflammation.FUNDINGPatient Centered Outcomes Research Institute; NIH; American Asthma Foundation; American Heart Association; Novo Nordisk Foundation; NIGMS; American Academy of Allergy, Asthma and Immunology Foundation.

SUBMITTER: Zhu YP

PROVIDER: S-EPMC8516453 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Immune response to intravenous immunoglobulin in patients with Kawasaki disease and MIS-C.

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