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Last Posted: Apr 17, 2024
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Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
M Gadaleta et al, NPJ Digital Medicine, December 12, 2023

From the abstract: "Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even after a two-week continuous monitoring period. We developed a model to quantify the risk of near-term AF in a two-week period, based on AF-free ECG intervals of up to 24?h from 459,889 patch-based ambulatory single-lead ECG (modified lead II) recordings of up to 14 days "

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.
Xin Wang et al. Circ Genom Precis Med 2023 6 e003808

We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.

Genetic testing in monogenic early-onset atrial fibrillation.
Brandon Chalazan et al. Eur J Hum Genet 2023 5

We aim to determine the prevalence of likely pathogenic and pathogenic variants from AF genes with robust evidence in a well phenotyped early-onset AF population. We performed whole exome sequencing on 200 early-onset AF patients. Variants from exome sequencing in affected individuals were filtered in a multi-step process, prior to undergoing clinical classification using current ACMG/AMP guidelines. There was a 3.0% diagnostic yield for identifying a likely pathogenic or pathogenic variant across AF genes with robust gene-to-disease association evidence.

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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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