Electrocardiographic biomarkers to predict atrial fibrillation in sinus rhythm electrocardiograms. Issue 22 (4th June 2021)

• Record Type: Journal Article
• Authors: Sanz-García, Ancor; Cecconi, Alberto; Vera, Alberto; Camarasaltas, Juan Miguel; Alfonso, Fernando; Ortega, Guillermo Jose; Jimenez-Borreguero, Jesus
• Is Part Of: Heart. Volume 107:Issue 22(2021)
• Page Start: 1813
• Page End: 1819
• Publication Date: 2021-06-04
• Subjects: atrial fibrillation -- biomarkers; Heart -- Diseases -- Treatment -- Periodicals; Cardiology -- Periodicals; 616.12
• DOI: 10.1136/heartjnl-2021-319120
• Languages: English
• ISSNs: 1355-6037

Abstract

Abstract : Objective: Early prediction of atrial fibrillation (AF) development would improve patient outcomes. We propose a simple and cheap ECG based score to predict AF development. Methods: A cohort of 16 316 patients was analysed. ECG measures provided by the computer-assisted ECG software were used to identify patients. A first group included patients in sinus rhythm who showed an ECG with AF at any time later (n=505). A second group included patients with all their ECGs in sinus rhythm (n=15 811). By using a training set (75% of the cohort) the initial sinus rhythm ECGs of both groups were analysed and a predictive risk score based on a multivariate logistic model was constructed. Results: A multivariate regression model was constructed with 32 variables showing a predictive value characterised by an area under the curve (AUC) of 0.776 (95% CI: 0.738 to 0.814). The subsequent risk score included the following variables: age, duration of P-wave in aVF, V4 and V5; duration of T-wave in V3, mean QT interval adjusted for heart rate, transverse P-wave clockwise rotation, transverse P-wave terminal angle and transverse QRS complex terminal vector magnitude. Risk score values ranged from 0 (no risk) to 5 (high risk). The predictive validity of the score reached an AUC of 0.764 (95% CI: 0.722 to 0.806) with a global specificity of 61% and a sensitivity of 55%. Conclusions: The automatic assessment of ECG biomarkers from ECGs in sinus rhythm is able to predict the risk for AF providing a low-cost screening strategy for early detection of this pathology.