NDLI: Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques

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Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques

Content Provider	MDPI
Author	Claudia, Cefalù Penso, Marco Pepi, Mauro Fusini, Laura Muratori, Manuela Mantegazza, Valentina Gripari, Paola Ali, Sarah Fabbiocchi, Franco Bartorelli, Antonio Caiani, Enrico Tamborini, Gloria
Copyright Year	2021
Description	Background: Whereas transcatheter aortic valve implantation (TAVI) has become the gold standard for aortic valve stenosis treatment in high-risk patients, it has recently been extended to include intermediate risk patients. However, the mortality rate at 5 years is still elevated. The aim of the present study was to develop a novel machine learning (ML) approach able to identify the best predictors of 5-year mortality after TAVI among several clinical and echocardiographic variables, which may improve the long-term prognosis. Methods: We retrospectively enrolled 471 patients undergoing TAVI. More than 80 pre-TAVI variables were collected and analyzed through different feature selection processes, which allowed for the identification of several variables with the highest predictive value of mortality. Different ML models were compared. Results: Multilayer perceptron resulted in the best performance in predicting mortality at 5 years after TAVI, with an area under the curve, positive predictive value, and sensitivity of 0.79, 0.73, and 0.71, respectively. Conclusions: We presented an ML approach for the assessment of risk factors for long-term mortality after TAVI to improve clinical prognosis. Fourteen potential predictors were identified with the organic mitral regurgitation (myxomatous or calcific degeneration of the leaflets and/or annulus) which showed the highest impact on 5 years mortality.
Starting Page	44
e-ISSN	23083425
DOI	10.3390/jcdd8040044
Journal	Journal of cardiovascular development and disease
Issue Number	4
Volume Number	8
Language	English
Publisher	MDPI
Publisher Date	2021-04-16
Access Restriction	Open
Subject Keyword	Journal of cardiovascular development and disease Journal of Cardiovascular Development and Disease Peripheral Vascular Disease Machine Learning Tavi Mortality Prediction Aortic Valve Disease
Content Type	Text
Resource Type	Article

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