Why the study?
To develop an algorithm for predicting the onset of atrial fibrillation in patients with metabolic syndrome and premature atrial contractions.
Can a forecasting algorithm predict the development of atrial fibrillation in patients with metabolic syndrome and premature atrial contractions?
Can a forecasting algorithm predict the development of atrial fibrillation in patients with metabolic syndrome and premature atrial contractions?
A forecasting algorithm incorporating left atrial volume, CHARGE-AF score, and potential index of AF can accurately predict the development of atrial fibrillation in patients with metabolic syndrome and frequent premature atrial contractions.
Answers are grounded in this paper and cite their sources.
May aid AF risk stratification in metabolic syndrome with PACs; hypothesis-generating and requires prospective validation before guiding care.
Aim . To develop an algorithm for the prediction of the atrial fbrillation onset in patients with metabolic syndrome (MS) and premature atrial contractions (PACs) in a prospective study. Materials and Methods . We enrolled 1,726 MS patients (age range 45-75 years) with ≥ 100 PACs per day. We measured cardiac chamber volumes, left ventricle contractility, parameters of the atrial electrocardiogram, P-wave dispersion, and the origin of PACs, further calculating the potential index of atrial fbrillation (PI) according to the CHARGEAF (RCHARGE-AF) model. The follow-up was 1-5 years. Maintenance of sinus rhythm or development of atrial fbrillation (AF) were the study endpoints. Results . Paroxysmal or persistent AF was registered in 218 (12.41%) patients. Elderly patients with MS and RCHARGE-AF score ≥ 0.72 units or predictors of AF development of AF (e.g., PACs or atrial enlargement) belonged to AF risk group. Development of AF in patients with MS correlated (OR > 3) with left atrial end-diastolic volume index > 34 mL/m2 with its average increase of +5% per year (OR = 3.3), RCHARGE-AF ≥ 0.72 (OR = 4.2), and PI < 8 units followed by an average regression of -20%/ year (OR = 14.8). The accuracy of predicting atrial fbrillation within 3-5 years before its onset was ≈ 60%, reaching ≥ 86% for the 3-year time frame. Conclusion . Our forecasting algorithm can identify the risk groups and predict the development of AF in patients with MS and PACs.
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Olesin et al. (2022) studied this question.
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