Katarzyna Mitrega1, Gregory Y H Lip2,3, Beata Sredniawa1,4,5, Adam Sokal1, Witold Streb1, Karol Przyludzki1, Tomasz Zdrojewski6, Lukasz Wierucki6, Marcin Rutkowski6, Piotr Bandosz6, Jaroslaw Kazmierczak7, Tomasz Grodzicki8, Grzegorz Opolski9, Zbigniew Kalarus1,4,5. 1. Department of Cardiology, Silesian Centre of Heart Diseases, 41-800 Zabrze, Poland. 2. Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool 14 3PE, UK. 3. Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, DK-9100 Aalborg, Denmark. 4. Department of Cardiology, Medical University of Silesia, DMS in Zabrze, 40-055 Katowice, Poland. 5. Silesian Park of Medical Technology Kardio-Med Silesia in Zabrze, 41-800 Zabrze, Poland. 6. Department of Preventive Medicine and Education, Medical University of Gdansk, 80-210 Gdansk, Poland. 7. Department of Cardiology, Pomeranian Medical University, 70-204 Szczecin, Poland. 8. Department of Internal Medicine and Gerontology, Jagiellonian University Medical College, 31-007 Krakow, Poland. 9. First Chair and Department of Cardiology, Medical University of Warsaw, 02-091 Warsaw, Poland.
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.
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.
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