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Objectives: Many children diagnosed with COVID-19 infections did not require hospitalisation. Our objective was to analyse electrocardiographic changes in children with asymptomatic, mild or moderate COVID-19 who did not require hospitalisation Methods: All children are seen in a paediatric cardiology clinic who had asymptomatic, mild or moderate COVID-19 that did not require hospitalisation and had at least one electrocardiogram after their diagnosis were included in this retrospective analysis. Records were reviewed to determine COVID-19 disease severity and presence of Long COVID. Rhythm assessment, atrial enlargement, ventricular hypertrophy, PR/QRS/QT interval duration and ST-T wave abnormalities were analysed by a paediatric electrophysiologist. Clinically ordered echocardiograms were reviewed for signs of myopericarditis (left ventricular ejection fraction and pericardial effusion) on any subject with an electrocardiographic abnormality. Results: Of the 82 children meeting inclusion criteria (14.4 years, range 1–18 years, 57% male), 17 patients (21%) demonstrated electrocardiographic changes. Ten patients (12%) had electrocardiogram of borderline significance, which included isolated mild PR prolongation or mild repolarisation abnormalities. The other seven patients (9%) had concerning electrocardiographic findings consisting of more significant repolarisation abnormalities. None of the patients with an abnormal electrocardiogram revealed any echocardiographic abnormality. All abnormal electrocardiograms normalised over time except in two cases. Across the entire cohort, greater COVID-19 disease severity and long COVID were not associated with electrocardiographic abnormalities. Conclusions: Electrocardiographic abnormalities are present in a minority of children with an asymptomatic, mild or moderate COVID-19 infection. Many of these changes resolved over time and no evidence of myopericarditis was present on echocardiography.

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PURPOSE: To provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids (GA4K) program. METHODS: Extensive analyses of 960 families with suspected genetic disorders including short-read exome (ES) and genome sequencing (srGS); PacBio HiFi long-read GS (HiFi-GS); variant calling for small-nucleotide (SNV), structural (SV) and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants and pedigrees are stored in PhenoTips database, with data sharing through controlled access (dbGAP). RESULTS: Diagnostic rates ranged from 11% for cases with prior negative genetic tests to 34.5% in naive patients. Incorporating SVs from GS added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs than srGS. Variants and genes of unknown significance (VUS/GUS) remain the most common finding (58% of non-diagnostic cases). CONCLUSION: Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated by HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation, and by providing HiFi variant (SNV/SV) resources from >1,000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.

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