Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction
Abstract The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-18640-8 |
| _version_ | 1818000199953416192 |
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| author | JungMin Choi Sungjae Lee Mineok Chang Yeha Lee Gyu Chul Oh Hae-Young Lee |
| author_facet | JungMin Choi Sungjae Lee Mineok Chang Yeha Lee Gyu Chul Oh Hae-Young Lee |
| author_sort | JungMin Choi |
| collection | DOAJ |
| description | Abstract The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic dysfunction (LVSD), defined by an ejection fraction (EF) < 40%. Symptomatic HF patients admitted at Seoul National University Hospital between 2011 and 2014 were included. The performance of DeepECG-HFrEF was determined using the area under the receiver operating characteristic curve (AUC) values. The 5-year mortality according to DeepECG-HFrEF results was analyzed using the Kaplan–Meier method. A total of 690 patients contributing 18,449 ECGs were included with final 1291 ECGs eligible for the study (mean age 67.8 ± 14.4 years; men, 56%). HFrEF (+) identified an EF < 40% and HFrEF (−) identified EF ≥ 40%. The AUC value was 0.844 for identifying HFrEF among patients with acute symptomatic HF. Those classified as HFrEF (+) showed lower survival rates than HFrEF (−) (log-rank p < 0.001). The DeepECG-HFrEF algorithm can discriminate HFrEF in a real-world HF cohort with acceptable performance. HFrEF (+) was associated with higher mortality rates. The DeepECG-HFrEF algorithm may help in identification of LVSD and of patients at risk of worse survival in resource-limited settings. |
| first_indexed | 2024-04-14T03:20:05Z |
| format | Article |
| id | doaj.art-6c545314571349d8b249d63f4abe8a09 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-14T03:20:05Z |
| publishDate | 2022-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6c545314571349d8b249d63f4abe8a092022-12-22T02:15:21ZengNature PortfolioScientific Reports2045-23222022-08-0112111010.1038/s41598-022-18640-8Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fractionJungMin Choi0Sungjae Lee1Mineok Chang2Yeha Lee3Gyu Chul Oh4Hae-Young Lee5Department of Internal Medicine, Seoul National University HospitalVUNO IncVUNO IncVUNO IncDivision of Cardiology, Department of Internal Medicine, Seoul St. Mary’s HospitalDepartment of Internal Medicine, Seoul National University HospitalAbstract The performance and clinical implications of the deep learning aided algorithm using electrocardiogram of heart failure (HF) with reduced ejection fraction (DeepECG-HFrEF) were evaluated in patients with acute HF. The DeepECG-HFrEF algorithm was trained to identify left ventricular systolic dysfunction (LVSD), defined by an ejection fraction (EF) < 40%. Symptomatic HF patients admitted at Seoul National University Hospital between 2011 and 2014 were included. The performance of DeepECG-HFrEF was determined using the area under the receiver operating characteristic curve (AUC) values. The 5-year mortality according to DeepECG-HFrEF results was analyzed using the Kaplan–Meier method. A total of 690 patients contributing 18,449 ECGs were included with final 1291 ECGs eligible for the study (mean age 67.8 ± 14.4 years; men, 56%). HFrEF (+) identified an EF < 40% and HFrEF (−) identified EF ≥ 40%. The AUC value was 0.844 for identifying HFrEF among patients with acute symptomatic HF. Those classified as HFrEF (+) showed lower survival rates than HFrEF (−) (log-rank p < 0.001). The DeepECG-HFrEF algorithm can discriminate HFrEF in a real-world HF cohort with acceptable performance. HFrEF (+) was associated with higher mortality rates. The DeepECG-HFrEF algorithm may help in identification of LVSD and of patients at risk of worse survival in resource-limited settings.https://doi.org/10.1038/s41598-022-18640-8 |
| spellingShingle | JungMin Choi Sungjae Lee Mineok Chang Yeha Lee Gyu Chul Oh Hae-Young Lee Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction Scientific Reports |
| title | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction |
| title_full | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction |
| title_fullStr | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction |
| title_full_unstemmed | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction |
| title_short | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction |
| title_sort | deep learning of ecg waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction |
| url | https://doi.org/10.1038/s41598-022-18640-8 |
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