Predicting Silent Atrial Fibrillation in the Elderly: A Report from the NOMED-AF Cross-Sectional Study

Abstract

Background: Silent atrial fibrillation (SAF) is common and is associated with poor outcomes. Aims: to study the risk factors for AF and SAF in the elderly (≥65 years) general population and to develop a risk stratification model for predicting SAF. Methods: Continuous ECG monitoring was performed for up to 30 days using a vest-based system in a cohort from NOMED-AF, a cross-sectional study based on a nationwide population sample. The independent risk factors for AF and SAF were determined using multiple logistic regression. ROC analysis was applied to validate the developed risk stratification score. Results: From the total cohort of 3014 subjects, AF was diagnosed in 680 individuals (mean age, 77.5 ± 7.9; 50.1% men) with AF, and, of these, 41% had SAF. Independent associations with an increased risk of AF were age, male gender, coronary heart disease, thyroid diseases, prior ischemic stroke or transient ischemic attack (ICS/TIA), diabetes, heart failure, chronic kidney disease (CKD), obesity, and NT-proBNP >125 ng/mL. The risk factors for SAF were age, male gender, ICS/TIA, diabetes, heart failure, CKD, and NT-proBNP >125 ng/mL. We developed a clinical risk scale (MR-DASH score) that achieved a good level of prediction in the derivation cohort (AUC 0.726) and the validation cohort (AUC 0.730). Conclusions: SAF is associated with various clinical risk factors in a population sample of individuals ≥65 years. Stratifying individuals from the general population according to their risk for SAF may be possible using the MR-DASH score, facilitating targeted screening programs of individuals with a high risk of SAF.

Left Atrium Assessment by Speckle Tracking Echocardiography in Cryptogenic Stroke: Seeking Silent Atrial Fibrillation

“…Atrial remodelling represents an alteration in atrial size and function secondary to cellular changes in the atrial tissue. Clinical observations suggest that several risk factors such as advanced age, arterial hypertension, and diabetes mellitus may lead to atrial remodelling with the development of interstitial fibrosis and increased atrial volume [18,19]. In clinical practice, these parameters can be assessed by using imaging techniques [20,21].…”

Using Minimum Redundancy Maximum Relevance Algorithm to Select Minimal Sets of Heart Rate Variability Parameters for Atrial Fibrillation Detection

“…The proposed methods and results presented in the paper might contribute to developing practical AF detection solutions in miniature wearable, bio-patches, implantable devices, and hand-held single- or multi-lead ECG devices [ 3 , 67 , 68 , 69 , 70 ].…”

Left atrial cardiopathy as an early predictor for atrial fibrillation in patients with cryptogenic stroke.

“…Atrial remodeling (atrial cardiopathy) represents an alteration in atrial size and function secondary to cellular changes in the atrial tissue. Clinical observations suggest that several risk factors such as advanced age, hypertension, and diabetes mellitus may lead to atrial remodeling with the development of interstitial fibrosis and increased atrial volume (7).…”