지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 629 - 639 (11page)
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초록· 키워드
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Background and Objectives: Screening and early diagnosis for heart failure (HF) are critical. However, conventional screening diagnostic methods have limitations, and electrocardiography (ECG)-based HF identification may be helpful. This study aimed to develop and validate a deep-learning algorithm for ECG-based HF identification (DEHF). Methods: The study involved 2 hospitals and 55,163 ECGs of 22,765 patients who performed echocardiography within 4 weeks were study subjects. ECGs were divided into derivation and validation data. Demographic and ECG features were used as predictive variables. The primary endpoint was detection of HF with reduced ejection fraction (HFrEF; ejection fraction [EF]≤40%), and the secondary endpoint was HF with mid-range to reduced EF (≤50%). We developed the DEHF using derivation data and the algorithm representing the risk of HF between 0 and 1. We confirmed accuracy and compared logistic regression (LR) and random forest (RF) analyses using validation data. Results: The area under the receiver operating characteristic curves (AUROCs) of DEHF for identification of HFrEF were 0.843 (95% confidence interval, 0.840–0.845) and 0.889 (0.887–0.891) for internal and external validation, respectively, and these results significantly outperformed those of LR (0.800 [0.797–0.803], 0.847 [0.844–0.850]) and RF (0.807 [0.804–0.810], 0.853 [0.850–0.855]) analyses. The AUROCs of deep learning for identification of the secondary endpoint was 0.821 (0.819–0.823) and 0.850 (0.848–0.852) for internal and external validation, respectively, and these results significantly outperformed those of LR and RF. Conclusions: The deep-learning algorithm accurately identified HF using ECG features and outperformed other machine-learning methods.
목차
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참고문헌 (30)
참고문헌 신청
Ziaeian B / 2016 / Epidemiology and aetiology of heart failure / Nat Rev Cardiol 13:368~378
Ponikowski P / 2014 / Heart failure : preventing disease and death worldwide / ESC Heart Fail 1:4~25
Ambrosy AP / 2014 / The global health and economic burden of hospitalizations for heart failure : lessons learned from hospitalized heart failure registries / J Am Coll Cardiol 63:1123~1133
Yancy CW / 2017 / 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure : a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines and the Heart Failure Society of America / J Am Coll Cardiol 70:776~803
Bagley SC / 2001 / Logistic regression in the medical literature : standards for use and reporting, with particular attention to one medical domain / J Clin Epidemiol 54:979~985
Ponikowski P / 2014 / Heart failure : preventing disease and death worldwide / ESC Heart Fail 1:4~25
Ambrosy AP / 2014 / The global health and economic burden of hospitalizations for heart failure : lessons learned from hospitalized heart failure registries / J Am Coll Cardiol 63:1123~1133
Yancy CW / 2017 / 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure : a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines and the Heart Failure Society of America / J Am Coll Cardiol 70:776~803
Bagley SC / 2001 / Logistic regression in the medical literature : standards for use and reporting, with particular attention to one medical domain / J Clin Epidemiol 54:979~985
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