Dataset Information

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

ABSTRACT: 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. 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. 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. Machine learning algorithms may improve prognosis after MV repair procedure, thus improving indications for correct candidate selection for MV surgical repair.

SUBMITTER: Penso M

PROVIDER: S-EPMC8469985 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Penso Marco M Pepi Mauro M Mantegazza Valentina V Cefalù Claudia C Muratori Manuela M Fusini Laura L Gripari Paola P Ghulam Ali Sarah S Caiani Enrico G EG Tamborini Gloria G

Bioengineering (Basel, Switzerland) 20210825 9

<h4>Background</h4>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.<h4>Methods</h4>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 echocardiograp ...[more]

PMID: 34562939

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Project description:We aimed to identify predictors of mitral regurgitation recurrence (MR) after percutaneous mitral valve repair (PMVR) in patients with functional mitral regurgitation (FMR). Patients with FMR were enrolled who underwent PMVR using the MitraClip® device. Procedural success was defined as reduction of MR of at least one grade to MR grade ≤ 2 + assessed at discharge. Recurrence of MR was defined as MR grade 3 + or worse at one year after initially successful PMVR. A total of 306 patients with FMR underwent PMVR procedure. In 279 out of 306 patients (91.2%), PMVR was successfully performed with MR grade ≤ 2 + at discharge. In 11.4% of these patients, MR recurrence of initial successful PMVR after 1 year was observed. Recurrence of MR was associated with a higher rate of heart failure rehospitalization during the 12 months follow-up (52.0% vs. 30.3%; p = 0.029), and less improvement in New York Heart Association (NYHA) functional class [68% vs. 19% of the patients presenting with NYHA functional class III or IV one year after PMVR when compared to patients without recurrence (p = 0.001)]. Patients with MR recurrence were characterized by a higher left ventricular sphericity index {0.69 [Interquartile range (IQR) 0.64, 0.74] vs. 0.65 (IQR 0.58, 0.70), p = 0.003}, a larger left atrium volume [118 (IQR 96, 143) ml vs. 102 (IQR 84, 123) ml, p = 0.019], a larger tenting height 10 (IQR 9, 13) mm vs. 8 (IQR 7, 11) mm (p = 0.047), and a larger mitral valve annulus [41 (IQR 38, 43) mm vs. 39 (IQR 36, 40) mm, p = 0.015] when compared to patients with durable optimal long-term results. In a multivariate regression model, the left ventricular sphericity index [Odds Ratio (OR) 1.120, 95% Confidence Interval (CI) 1.039-1.413, p = 0.003)], tenting height (OR 1.207, 95% CI 1.031-1.413, p = 0.019), and left atrium enlargement (OR 1.018, 95% CI 1.000-1.038, p = 0.047) were predictors for MR recurrence after 1 year. In patients with FMR, baseline parameters of advanced heart failure such as spherical ventricle, tenting height and a large left atrium might indicate risk of recurrent MR one year after PMVR.

Dataset Information

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

Publications

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

Similar Datasets

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