Search

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.

BackgroundKawasaki disease (KD) is a systemic vasculitis that mainly affects children under 5 years of age. Up to 30% of patients develop coronary artery abnormalities, which are reduced with early treatment. Timely diagnosis of KD is challenging but may become more straightforward with the recent discovery of a whole-blood host response classifier that discriminates KD patients from patients with other febrile conditions. Here, we bridged this microarray-based classifier to a clinically applicable quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay: the Kawasaki Disease Gene Expression Profiling (KiDs-GEP) classifier.MethodsWe designed and optimized a qRT-PCR assay and applied it to a subset of samples previously used for the classifier discovery to reweight the original classifier.ResultsThe performance of the KiDs-GEP classifier was comparable to the original classifier with a cross-validated area under the ROC curve of 0.964 [95% CI: 0.924-1.00] vs 0.992 [95% CI: 0.978-1.00], respectively. Both classifiers demonstrated similar trends over various disease conditions, with the clearest distinction between individuals diagnosed with KD vs viral infections.ConclusionWe successfully bridged the microarray-based classifier into the KiDs-GEP classifier, a more rapid and more cost-efficient qRT-PCR assay, bringing a diagnostic test for KD closer to the hospital clinical laboratory.ImpactA diagnostic test is needed for Kawasaki disease and is currently not available. We describe the development of a One-Step multiplex qRT-PCR assay and the subsequent modification (i.e., bridging) of the microarray-based host response classifier previously described by Wright et al. The bridged KiDs-GEP classifier performs well in discriminating Kawasaki disease patients from febrile controls. This host response clinical test for Kawasaki disease can be adapted to the hospital clinical laboratory.

BackgroundGenetic variation in calcium signaling pathways is associated with Kawasaki disease (KD) susceptibility and coronary artery aneurysms (CAA). Expression quantitative trait locus analysis for KD-associated variants in calcium/sodium channel gene solute carrier family 8 member 1 (SLC8A1) revealed an effect on expression of urotensin 2 (UTS2). We speculated that UTS2 is influenced by genetic variation in SLC8A1 and contributes to disease pathogenesis.MethodsWe measured levels of UTS2 and its receptor in blood and tissues using quantitative reverse transcription-polymerase chain reaction, enzyme-linked immunosorbent assay, and immunohistochemical staining.ResultsUTS2 transcript levels were higher in the whole blood of subjects with KD homozygous for three risk alleles in SLC8A1 (P=0.002-0.006). Increased levels of plasma UTS2 varied as a function of SLC8A1 genotype (P=0.008-0.04). UTS2 and UTS2 receptor were expressed in mononuclear inflammatory cells and spindle-shaped cells in the coronary arterial wall of a patient suffering from KD with CAA and in a femoral endarterectomy specimen from an adult patient with peripheral aneurysms following KD in childhood.ConclusionHost genetics influences UTS2 levels, which may contribute to inflammation and cardiovascular damage in KD.

BackgroundAs Kawasaki disease (KD) shares many clinical features with other more common febrile illnesses and misdiagnosis, leading to a delay in treatment, increases the risk of coronary artery damage, a diagnostic test for KD is urgently needed. We sought to develop a panel of biomarkers that could distinguish between acute KD patients and febrile controls (FC) with sufficient accuracy to be clinically useful.MethodsPlasma samples were collected from three independent cohorts of FC and acute KD patients who met the American Heart Association definition for KD and presented within the first 10 d of fever. The levels of 88 biomarkers associated with inflammation were assessed by Luminex bead technology. Unsupervised clustering followed by supervised clustering using a Random Forest model was used to find a panel of candidate biomarkers.ResultsA panel of biomarkers commonly available in the hospital laboratory (absolute neutrophil count, erythrocyte sedimentation rate, alanine aminotransferase, γ-glutamyl transferase, concentrations of α-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81-96% of KD patients in a series of three independent cohorts.ConclusionAfter prospective validation, this eight-biomarker panel may improve the recognition of KD.