Detection of QT interval prolongation using Apple Watch electrocardiogram in children and adolescents with congenital long QT syndrome.

Background: Apple watch-derived electrocardiogram (awECG) may help identify prolongation of corrected QT (QTc) interval. This study aimed to determine its usefulness for assessment of prolongation of QTc interval in children and adolescents with long QT syndrome (LQTS). Methods: Children and adolescents with and without LQTS were recruited for measurement of QTc intervals based on standard 12-lead (sECG) and awECG lead I, II and V5 tracings. Bland-Altman analysis of reproducibility, concordance assessment of T wave morphologies, and receiver operating characteristic (ROC) analysis of sensitivity and specificity of awECG-derived QTc interval for detecting QTc prolongation were performed. Results: Forty-nine patients, 19 with and 30 without LQTS, aged 3-22 years were studied. The intraclass correlation coefficient was 1.00 for both intra- and inter-observer variability in the measurement of QTc interval. The awECG- and sECG-derived QTc intervals correlated strongly in all three leads (r = 0.90-0.93, all p < 0.001). Concordance between awECG and sECG in assessing T wave morphologies was 84% (16/19). For detection of QTc prolongation, awECG lead V5 had the best specificity (94.4% and 87.5%, respectively) and positive predictive value (87.5% and 80.0%, respectively), and for identification of patients with LQTS, awECG leads II and V5 had the greatest specificity (92.3%-94.1%) and positive predictive value (85.7% to 91.7%) in both males and females. Conclusions: Apple Watch leads II and V5 tracings can be used for reproducible and accurate measurement of QTc interval, ascertainment of abnormal T wave morphologies, and detection of prolonged QTc interval in children and adolescents with LQTS.

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 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.