Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study
Objective Although the evaluation of left ventricular ejection fraction (LVEF) in patients with atrial fibrillation (AF) or atrial flutter (AFL) is crucial for appropriate medical management, the prediction of reduced LVEF (<50%) with AF/AFL electrocardiograms (ECGs) lacks evidence. This study ai...
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SAGE Publishing
2025-01-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241311460 |
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| author | Soonil Kwon SooMin Chung So-Ryoung Lee Kwangsoo Kim Junmo Kim Dahyeon Baek Hyun-Lim Yang Eue-Keun Choi Seil Oh |
| author_facet | Soonil Kwon SooMin Chung So-Ryoung Lee Kwangsoo Kim Junmo Kim Dahyeon Baek Hyun-Lim Yang Eue-Keun Choi Seil Oh |
| author_sort | Soonil Kwon |
| collection | DOAJ |
| description | Objective Although the evaluation of left ventricular ejection fraction (LVEF) in patients with atrial fibrillation (AF) or atrial flutter (AFL) is crucial for appropriate medical management, the prediction of reduced LVEF (<50%) with AF/AFL electrocardiograms (ECGs) lacks evidence. This study aimed to investigate deep-learning approaches to predict reduced LVEF (<50%) in patients with AF/AFL ECGs and easily obtainable clinical information. Methods Patients with 12-lead ECGs of AF/AFL and echocardiography were divided into those with LVEF <50% and ≥50%. A convolutional neural networks-based model customized to the study (AFibEFNet) and other deep-learning models were investigated. Electrocardiogram signals, ECG features, and clinical features (demographic information, comorbidities, blood cell counts, and blood test results) were collected for training. A hold-out test dataset was constructed using a different recruitment period. Five-fold cross-validation and calibration plots were used to evaluate performance. Results A total of 15,683 patients were analyzed (mean age, 70.0 ± 11.7 years; 61.2% men), with 82.2% having LVEF ≥50% and 17.8% having LVEF < 50%. Among the learning models, the AFibEFNet outperformed other models regarding area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. Using ECG signals alone, the AFibEFNet model predicted reduced LVEF with AUROC of 0.798 (95% confidence interval [CI], 0.767–0.829) and AUPRC of 0.508 (95% CI, 0.434–0.564). For the AFibEFNet model, additional training with ECG and clinical features significantly improved AUROC (0.816 vs. 0.798, p = 0.04) and AUPRC (0.547 vs. 0.508, p < 0.001). The AFibEFNet model primarily focused on the R-wave, QRS onset and offset, and T-wave in ECG signals. Conclusions Among the patients with AF/AFL, machine learning may predict reduced LVEF with 12-lead ECGs of AF/AFL. |
| format | Article |
| id | doaj-art-1acde4b214db47f390b09ea8882408db |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-1acde4b214db47f390b09ea8882408db2025-01-17T17:03:25ZengSAGE PublishingDigital Health2055-20762025-01-011110.1177/20552076241311460Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning studySoonil Kwon0SooMin Chung1So-Ryoung Lee2Kwangsoo Kim3Junmo Kim4Dahyeon Baek5Hyun-Lim Yang6Eue-Keun Choi7Seil Oh8 Division of Cardiology, Department of Internal Medicine, , Seoul, Republic of Korea Interdisciplinary Program in Bioengineering, , Seoul, Republic of Korea Department of Medicine, , Seoul, Republic of Korea Department of Transdisciplinary Medicine, Institute of Convergence Medicine with Innovative Technology, Seoul National University Hospital, Seoul, Republic of Korea Interdisciplinary Program in Bioengineering, , Seoul, Republic of Korea Industrial and Management Engineering, , Pohang, Republic of Korea Office of Hospital Information, Seoul National University Hospital, Seoul, Republic of Korea Department of Medicine, , Seoul, Republic of Korea Department of Medicine, , Seoul, Republic of KoreaObjective Although the evaluation of left ventricular ejection fraction (LVEF) in patients with atrial fibrillation (AF) or atrial flutter (AFL) is crucial for appropriate medical management, the prediction of reduced LVEF (<50%) with AF/AFL electrocardiograms (ECGs) lacks evidence. This study aimed to investigate deep-learning approaches to predict reduced LVEF (<50%) in patients with AF/AFL ECGs and easily obtainable clinical information. Methods Patients with 12-lead ECGs of AF/AFL and echocardiography were divided into those with LVEF <50% and ≥50%. A convolutional neural networks-based model customized to the study (AFibEFNet) and other deep-learning models were investigated. Electrocardiogram signals, ECG features, and clinical features (demographic information, comorbidities, blood cell counts, and blood test results) were collected for training. A hold-out test dataset was constructed using a different recruitment period. Five-fold cross-validation and calibration plots were used to evaluate performance. Results A total of 15,683 patients were analyzed (mean age, 70.0 ± 11.7 years; 61.2% men), with 82.2% having LVEF ≥50% and 17.8% having LVEF < 50%. Among the learning models, the AFibEFNet outperformed other models regarding area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and F1-score. Using ECG signals alone, the AFibEFNet model predicted reduced LVEF with AUROC of 0.798 (95% confidence interval [CI], 0.767–0.829) and AUPRC of 0.508 (95% CI, 0.434–0.564). For the AFibEFNet model, additional training with ECG and clinical features significantly improved AUROC (0.816 vs. 0.798, p = 0.04) and AUPRC (0.547 vs. 0.508, p < 0.001). The AFibEFNet model primarily focused on the R-wave, QRS onset and offset, and T-wave in ECG signals. Conclusions Among the patients with AF/AFL, machine learning may predict reduced LVEF with 12-lead ECGs of AF/AFL.https://doi.org/10.1177/20552076241311460 |
| spellingShingle | Soonil Kwon SooMin Chung So-Ryoung Lee Kwangsoo Kim Junmo Kim Dahyeon Baek Hyun-Lim Yang Eue-Keun Choi Seil Oh Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study Digital Health |
| title | Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study |
| title_full | Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study |
| title_fullStr | Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study |
| title_full_unstemmed | Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study |
| title_short | Prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms: A machine-learning study |
| title_sort | prediction of reduced left ventricular ejection fraction using atrial fibrillation or flutter electrocardiograms a machine learning study |
| url | https://doi.org/10.1177/20552076241311460 |
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