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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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