Predicting Long-Term Mortality in TAVI Patients Using Machine Learning Techniques.
Marco PensoEugenio PicanoLaura FusiniManuela MuratoriClaudia CefalùValentina MantegazzaPaola GripariSarah Ghulam AliFranco FabbiocchiAntonio L BartorelliEnrico Gianluca CaianiLuciano SpinelliPublished in: Journal of cardiovascular development and disease (2021)
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.
Keyphrases
- aortic valve
- end stage renal disease
- transcatheter aortic valve implantation
- transcatheter aortic valve replacement
- cardiovascular events
- ejection fraction
- chronic kidney disease
- newly diagnosed
- risk factors
- prognostic factors
- peritoneal dialysis
- heart failure
- cardiovascular disease
- type diabetes
- left ventricular
- coronary artery disease
- atrial fibrillation