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Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, JungMin | - |
dc.contributor.author | Lee, Sungjae | - |
dc.contributor.author | Chang, Mineok | - |
dc.contributor.author | Lee, Yeha | - |
dc.contributor.author | Oh, Gyu Chul | - |
dc.contributor.author | Lee, Hae-Young | - |
dc.date.accessioned | 2022-10-12T00:54:23Z | - |
dc.date.available | 2022-10-12T00:54:23Z | - |
dc.date.created | 2022-09-02 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.citation | Scientific Reports, Vol.12 No.1, p. 14235 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://hdl.handle.net/10371/185899 | - |
dc.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. | - |
dc.language | 영어 | - |
dc.publisher | Nature Publishing Group | - |
dc.title | Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41598-022-18640-8 | - |
dc.citation.journaltitle | Scientific Reports | - |
dc.identifier.wosid | 000843446300006 | - |
dc.citation.number | 1 | - |
dc.citation.startpage | 14235 | - |
dc.citation.volume | 12 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Lee, Hae-Young | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
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