Machine-learning-based prediction of survival and mitral regurgitation recurrence in patients undergoing mitral valve repair.
Interdiscip Cardiovasc Thorac Surg
; 37(5)2023 Nov 02.
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Interdiscip Cardiovasc Thorac Surg
Año:
2023
Tipo del documento:
Article