Phenomapping approach to interpreting coronary dimensions in febrile children.

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 scores were performed.

Results: Using a cutoff 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 scores, and lower hematocrit and albumin levels. Abnormal blood parameters in febrile children with scores of coronary artery segments <0.5 and 0.5-1.5 was associated with increased risks of having KD to 8.7 ( = 0.003) and 4.4 ( = 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.