Phenomapping approach to interpreting coronary dimensions in febrile children

Abstract

ABSTRACT Importance Coronary artery dilation may occur in febrile children with and without Kawasaki disease (KD). Objective We explored the application of unsupervised learning algorithms in the detection of novel patterns of coronary artery phenotypes in febrile children with and without KD. Methods A total of 239 febrile children (59 non‐KD and 180 KD patients), were recruited. Unsupervised hierarchical clustering analysis of phenotypic data including age, hemoglobin, white cell count, platelet count, C‐reactive protein, erythrocyte sedimentation rate, albumin, alanine aminotransferase, aspartate aminotransferase, and coronary artery z scores were performed. Results Using a cutoff z score of 2.5, the specificity was 98.3% and the sensitivity was 22.1% for differentiating non‐KD from KD patients. Clustering analysis identified three phenogroups that differed in a clinical, laboratory, and echocardiographic parameters. Compared with phenogroup I, phenogroup III had the highest prevalence of KD (91%), worse inflammatory markers, more deranged liver function, higher coronary artery z scores, and lower hematocrit and albumin levels. Abnormal blood parameters in febrile children with z scores of coronary artery segments <0.5 and 0.5–1.5 was associated with increased risks of having KD to 8.7 (P = 0.003) and 4.4 (P = 0.002), respectively. Interpretation Phenomapping of febrile children with and without KD identified useful laboratory parameters that aid the diagnosis of KD in febrile children with relatively normal‐sized coronary arteries.

Keywords

• Coronary artery
• Febrile children
• Kawasaki disease
• Phenomapping