Machine Learning Prediction Models for Mitral Valve Repairability and Mitral Regurgitation Recurrence in Patients Undergoing Surgical Mitral Valve Repair

Abstract

Background: Mitral valve regurgitation (MR) is the most common valvular heart disease and current variables associated with MR recurrence are still controversial. We aim to develop a machine learning-based prognostic model to predict causes of mitral valve (MV) repair failure and MR recurrence. Methods: 1000 patients who underwent MV repair at our institution between 2008 and 2018 were enrolled. Patients were followed longitudinally for up to three years. Clinical and echocardiographic data were included in the analysis. Endpoints were MV repair surgical failure with consequent MV replacement or moderate/severe MR (>2+) recurrence at one-month and moderate/severe MR recurrence after three years. Results: 817 patients (DS1) had an echocardiographic examination at one-month while 295 (DS2) also had one at three years. Data were randomly divided into training (DS1: n = 654; DS2: n = 206) and validation (DS1: n = 164; DS2 n = 89) cohorts. For intra-operative or early MV repair failure assessment, the best area under the curve (AUC) was 0.75 and the complexity of mitral valve prolapse was the main predictor. In predicting moderate/severe recurrent MR at three years, the best AUC was 0.92 and residual MR at six months was the most important predictor. Conclusions: Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.

Shannon entropy as a reliable score to diagnose human fibroelastic degenerative mitral chords: A micro-ct ex-vivo study

“…Our work shows that distribution differences between normal and pathological conditions of MCT, from an optical method as micro-CT, can be captured through a scalar metric as the Shannon entropy under different levels of quantization and scales. However, it must be noted that we do not provide a robust binary classifier based on this metric, but we present statistical differences between conditions that suggest that advanced machine learning methods could harness the power of bundle changes as captured by optical methods, in contrast with current acoustic approaches that are very data-intensive [44].…”

3D echocardiography in mitral valve prolapse

“…Patients with a complex prolapse, specifically with A2 prolapse, more frequently underwent MV replacement or showed early failure of MV repair. These findings suggest that, in the future, machine learning could have an important clinical role in evaluating prognosis in patients undergoing MV repair for MVP ( 99 ).…”

“…Favorable cardiac remodeling, observed in all cases at 6-months follow-up, was maintained at 3 years only when MR was <2 ( 91 ). A machine learning-based prognostic model was developed and tested to predict the risk of MV repair failure and MR recurrence based on pre-operative clinical, 2D and 3D TTE data ( 99 ). Patients with a complex prolapse, specifically with A2 prolapse, more frequently underwent MV replacement or showed early failure of MV repair.…”

Application of AI in cardiovascular multimodality imaging

“…Recently, the use of unsupervised models for automated detection of bioprosthetic aortic valve degeneration has been shown to have a high sensitivity for the detection of valve degeneration which can be particularly useful in the follow-up of patients with aortic valve replacement [ 114 ]. Additionally, the use of AI has been shown to be promising in the appropriate selection of patients eligible for mitral valve repair [ 115 ].…”