12 research outputs found

    Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.

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    To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification

    2022 HRS expert consensus statement on evaluation and management of arrhythmic risk in neuromuscular disorders

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    This international multidisciplinary document is intended to guide electrophysiologists, cardiologists, other clinicians, and health care professionals in caring for patients with arrhythmic complications of neuromuscular disorders (NMDs). The document presents an overview of arrhythmias in NMDs followed by detailed sections on specific disorders: Duchenne muscular dystrophy, Becker muscular dystrophy, and limb-girdle muscular dystrophy type 2; myotonic dystrophy type 1 and type 2; Emery-Dreifuss muscular dystrophy and limb-girdle muscular dystrophy type 1B; facioscapulohumeral muscular dystrophy; and mitochondrial myopathies, including Friedreich ataxia and Kearns-Sayre syndrome, with an emphasis on managing arrhythmic cardiac manifestations. Endof-life management of arrhythmias in patients with NMDs is also covered. The document sections were drafted by the writing committee members according to their area of expertise. The recommendations represent the consensus opinion of the expert writing group, graded by class of recommendation and level of evidence utilizing defined criteria. The recommendations were made available for public comment; the document underwent review by the Heart Rhythm Society Scientific and Clinical Documents Committee and external review and endorsement by the partner and collaborating societies. Changes were incorporated based on these reviews. By using a breadth of accumulated available evidence, the document is designed to provide practical and actionable clinical information and recommendations for the diagnosis and management of arrhythmias and thus improve the care of patients with NMDs

    Genetic susceptibility for atrial fibrillation in patients undergoing atrial fibrillation ablation.

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    Background:Ablation is a widely used therapy for atrial fibrillation (AF); however, arrhythmia recurrence and repeat procedures are common. Studies examining surrogate markers of genetic susceptibility to AF, such as family history and individual AF susceptibility alleles, suggest these may be associated with recurrence outcomes. Accordingly, the aim of this study was to test the association between AF genetic susceptibility and recurrence after ablation using a comprehensive polygenic risk score for AF.Methods:Ten centers from the AF Genetics Consortium identified patients who had undergone de novo AF ablation. AF genetic susceptibility was measured using a previously described polygenic risk score (N=929 single-nucleotide polymorphisms) and tested for an association with clinical characteristics and time-to-recurrence with a 3 month blanking period. Recurrence was defined as >30 seconds of AF, atrial flutter, or atrial tachycardia. Multivariable analysis adjusted for age, sex, height, body mass index, persistent AF, hypertension, coronary disease, left atrial size, left ventricular ejection fraction, and year of ablation.Results:Four thousand two hundred seventy-six patients were eligible for analysis of baseline characteristics and 3259 for recurrence outcomes. The overall arrhythmia recurrence rate between 3 and 12 months was 44% (1443/3259). Patients with higher AF genetic susceptibility were younger (P<0.001) and had fewer clinical risk factors for AF (P=0.001). Persistent AF (hazard ratio [HR], 1.39 [95% CI, 1.22-1.58]; P<0.001), left atrial size (per cm: HR, 1.32 [95% CI, 1.19-1.46]; P<0.001), and left ventricular ejection fraction (per 10%: HR, 0.88 [95% CI, 0.80-0.97]; P=0.008) were associated with increased risk of recurrence. In univariate analysis, higher AF genetic susceptibility trended towards a higher risk of recurrence (HR, 1.08 [95% CI, 0.99-1.18]; P=0.07), which became less significant in multivariable analysis (HR, 1.06 [95% CI, 0.98-1.15]; P=0.13).Conclusions:Higher AF genetic susceptibility was associated with younger age and fewer clinical risk factors but not recurrence. Arrhythmia recurrence after AF ablation may represent a genetically different phenotype compared to AF susceptibility
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