Zhu K, Xu H, Zheng S, Liu S, Zhong Z, Sun H, Duan F, Liu S. A
complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning.
Hellenic J Cardiol 2024:S1109-9666(24)00078-2. [PMID:
38636776 DOI:
10.1016/j.hjc.2024.04.003]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/19/2024] [Accepted: 04/10/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND
To develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms.
METHODS
From March 2021 to March 2023, 231 consecutive patients underwent mitral valve repair. Clinical and echocardiographic data were included in the analysis. End points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or greater mitral regurgitation before discharge). Various machine learning algorithms were used to establish the complexity evaluation system.
RESULTS
A total of 231 patients were included in this study, the median ejection fraction was 66 (63,70) %, and 159 (68.8%) patients were men. Mitral repair was successful in 90.9% (210 of 231) of patients. Linear Support Vector Classification (LSVC) model has the best prediction results in both training and test cohorts and the variables of age, A2 lesions, leaflet height, mitral regurgitation grades et al. were risk factors for failure of mitral valve repair.
CONCLUSION
LSVC prediction model may allow evaluation of the complexity of mitral valve repair. Age, A2 lesions, leaflet height, and mitral regurgitation grades et al. may be associated with mitral repair failure.
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