BACKGROUND: Diagnosis of atrial fibrillation (AF) based on electrocardiogram (ECG) with sinus rhythm remains a major challenge. Obtaining a panoramic view with hundreds of automatically measured ECG parameters at sinus rhythm on the predictive capability for AF would be informative. METHODS: We used a single-center database of a specialist cardiovascular hospital (Shinken Database 2010-2017; n = 19,170). We analyzed 12,863 index ECGs with sinus rhythm after excluding those showing AF rhythm, other atrial tachyarrhythmia, pacing beat, or indeterminate axis, and those of patients with structural heart diseases. We used 438 automatically measured ECG parameters in the MUSE data management system. The predictive models were developed using random forest algorithm with the 10-fold cross-validation method. RESULTS: In 12,863 index ECGs with sinus rhythm, a predictive capability for current paroxysmal AF (n = 1131) by c-statistics was 0.99981 ± 0.00037 for training dataset and 0.91337 ± 0.00087 for testing dataset, respectively. Excluding AF at baseline (n = 11,732), a predictive capability for newly developed AF (n = 98) by c-statistics was 0.99973 ± 0.00086 for training dataset and 0.99160 ± 0.00038 for testing dataset, respectively. The distribution of parameter importance was mostly similar among P, QRS, and ST-T segment for both current and newly developed AF. CONCLUSIONS: This study intended to provide panoramic information in relation between ECG parameters and AF. The parameter importance of ECG parameters for predicting AF was mostly similar in P, QRS, and ST-T segment in models for both current and future AF.
BACKGROUND: Diagnosis of atrial fibrillation (AF) based on electrocardiogram (ECG) with sinus rhythm remains a major challenge. Obtaining a panoramic view with hundreds of automatically measured ECG parameters at sinus rhythm on the predictive capability for AF would be informative. METHODS: We used a single-center database of a specialist cardiovascular hospital (Shinken Database 2010-2017; n = 19,170). We analyzed 12,863 index ECGs with sinus rhythm after excluding those showing AF rhythm, other atrial tachyarrhythmia, pacing beat, or indeterminate axis, and those of patients with structural heart diseases. We used 438 automatically measured ECG parameters in the MUSE data management system. The predictive models were developed using random forest algorithm with the 10-fold cross-validation method. RESULTS: In 12,863 index ECGs with sinus rhythm, a predictive capability for current paroxysmal AF (n = 1131) by c-statistics was 0.99981 ± 0.00037 for training dataset and 0.91337 ± 0.00087 for testing dataset, respectively. Excluding AF at baseline (n = 11,732), a predictive capability for newly developed AF (n = 98) by c-statistics was 0.99973 ± 0.00086 for training dataset and 0.99160 ± 0.00038 for testing dataset, respectively. The distribution of parameter importance was mostly similar among P, QRS, and ST-T segment for both current and newly developed AF. CONCLUSIONS: This study intended to provide panoramic information in relation between ECG parameters and AF. The parameter importance of ECG parameters for predicting AF was mostly similar in P, QRS, and ST-T segment in models for both current and future AF.