N Hirota, S Suzuki, T Arita, N Yagi, T Otsuka, H Semba, H Kano, S Matsuno, Y Kato, T Uejima, Y Oikawa, J Yajima, T Yamashita
European Heart Journal, Volume 41, Issue Supplement_2, November 2020, ehaa946.0536, https://doi.org/10.1093/ehjci/ehaa946.0536 Published: 25 November 2020
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).
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.