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Machine-learning-based prediction of survival and mitral regurgitation recurrence in patients undergoing mitral valve repair.
Kang, Yoonjin; Sohn, Suk Ho; Choi, Jae Woong; Hwang, Ho Young; Kim, Kyung Hwan.
Afiliación
  • Kang Y; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Sohn SH; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Choi JW; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Hwang HY; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
  • Kim KH; Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital, Seoul, Republic of Korea.
Article en En | MEDLINE | ID: mdl-37966944
ABSTRACT

OBJECTIVES:

This study was conducted to assess long-term clinical outcomes after mitral valve repair using machine-learning techniques.

METHODS:

We retrospectively evaluated 436 consecutive patients (mean age 54.7 ± 15.4; 235 males) who underwent mitral valve repair between January 2000 and December 2017. Actuarial survival and freedom from significant (≥ moderate) mitral regurgitation (MR) were clinical end points. To evaluate the independent risk factors, random survival forest (RSF), extreme gradient boost (XGBoost), support vector machine, Cox proportional hazards model and general linear models with elastic net regularization were used. Concordance indices (C-indices) of each model were estimated.

RESULTS:

The operative mortality was 0.9% (N = 4). Reoperation was required in 15 patients (3.5%). In terms of C-index, the overall performance of the XGBoost (C-index 0.806) and RSF models (C-index 0.814) was better than that of the Cox model (C-index 0.733) in overall survival. For the recurrent MR, the C-index for XGBoost was 0.718, which was the highest among the 5 models. Compared to the Cox model (C-index 0.545), the C-indices of the XGBoost (C-index 0.718) and RSF models (C-index 0.692) were higher.

CONCLUSIONS:

Machine-learning techniques can be a useful tool for both prediction and interpretation in the survival and recurrent MR. From the machine-learning techniques examined here, the long-term clinical outcomes of mitral valve repair were excellent. The complexity of MV increased the risk of late mitral valve-related reoperation.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Interdiscip Cardiovasc Thorac Surg Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Interdiscip Cardiovasc Thorac Surg Año: 2023 Tipo del documento: Article