Prediction of atrial fibrillation by 12-lead electrocardiogram parameters in patients without structural heart disease

Abstract

Abstract Background Recently, the analysis of electrocardiogram (ECG) waveform by artificial intelligence has been reported to pick out those who have atrial fibrillation (AF) or have a high potential of developing AF, which, however, cannot explain the mechanisms or algorisms for the prediction from its nature. Purpose The purpose of this study is to conduct a comprehensive analysis to investigate the difference of weighting in predicting capability for AF among hundreds of automatically-measured ECG parameters using a single ECG at sinus rhythm. Methods and results Out of Shinken Database 2010–2017 (n=19170), 12825 patients were extracted, where those with ECG showing AF rhythm at the initial visit (including all persistent/permanent AF and a part of paroxysmal AF) and those with structural heart diseases were excluded. Out of 639 automatically-measured ECG parameters in MUSE data management system (GE Healthcare, USA), 438 were used. [Analysis 1] A predicting model for paroxysmal AF were determined by logistic regression analysis (Total, n=12825; paroxysmal AF, n=1138), showing a high predictive capability (AUC = 0.780, p<0.001). In this model, the relative contribution of ECG parameters (by coefficient of determination) according to the time phase were P:72.4%, QRS:32.7%, and ST-T:13.7%, respectively (Figure A). [Analysis 2] Excluding AF at baseline, a predicting model for new-developed AF were determined by Cox regression analysis (Total, n=11687; new-developed AF, n=87), showing a high predictive capability (AUC = 0.887, p<0.001). In this model, the relative contribution of parameters (by log likelihood) according to the time phase were P:40.8%, QRS:42.5%, and ST-T:24.9%, respectively (Figure B). Conclusions We determined ECG parameters that potentially contribute to picking up existing AF or predicting future development of AF, where the measurement of P wave strongly contributed in the former whereas all time phases were similarly important in the latter. Weighting of parameters to predict AF Funding Acknowledgement Type of funding source: Private hospital(s). Main funding source(s): Self funding of the institute