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Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response
- Jeong, Joo Hee;
- Kang, Sora;
- Lee, Hak Seung;
- Lee, Min Sung;
- Son, Jeong Min;
- ... Choi, Jong-Il;
- 외 10명
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12초록
Aims Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting LV systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR).Methods and results This study is an external validation of a pre-existing deep learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF <= 40%, assessed using 12-lead electrocardiography (ECG). Secondary outcome involved predicting LVSD using 1-lead ECG (Lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep learning algorithm demonstrated fair performance in predicting LVSD [area under the curve (AUC) 0.78]. Negative predictive value for excluding LVSD was 0.88. Deep learning algorithm resulted competent performance in predicting LVSD compared with N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, P = 0.12). Predictive performance of the deep learning algorithm was lower in Lead I (AUC 0.68); however, negative predictive value remained consistent (0.88).Conclusion Deep learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR. Graphical Abstract ECG, electrocardiography; LV, left ventricular; AF, atrial fibrillation; RVR, rapid ventricular response; LVSD; left ventricular systolic dysfunction; AUROC, area under the receiver operating characteristic curve.
키워드
- 제목
- Deep learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response
- 저자
- Jeong, Joo Hee; Kang, Sora; Lee, Hak Seung; Lee, Min Sung; Son, Jeong Min; Kwon, Joon-myung; Lee, Hyoung Seok; Choi, Yun Young; Kim, So Ree; Cho, Dong-Hyuk; Kim, Yun Gi; Kim, Mi-Na; Shim, Jaemin; Park, Seong-Mi; Kim, Young-Hoon; Choi, Jong-Il
- 발행일
- 2024-08-29
- 유형
- Article; Early Access
- 권
- 5
- 호
- 6
- 페이지
- 683 ~ 691
- 언어
- ENG
- 출판사
- OXFORD UNIV PRESS
- 발행국가
- 영국
- 분량
- 9 페이지
- ISSN
- E 2634-3916
P 2634-3916