biostudies-literatureUnknown12(17)Pollari F<h4>Background</h4>predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization.<h4>Methods</h4>Routinely acquired data (134 variables) were used from 629 patients, who underwent transfemoral TAVI from 2012 up to 2018. Support vector machines, neuronal networks, random forests, nearest neighbour and Bayes models were used with new, previously unseen patients to predict 1-year mortality in TAVI patients. A genetic variable selection algorithm identified a set of predictor variables with high predictive power.<h4>Results</h4>Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence Journal of clinical medicine5481https://www.ebi.ac.uk/biostudies/studies/S-EPMC10488486biostudies-literatureA Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study.PMC10488486Vogt FBertsch TPollari FHitzl WLanghammer CRottmann MFischlein TJessl JLedwon MEckner DPauschinger MfalseA Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study.<h4>Background</h4>predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization.<h4>Methods</h4>Routinely acquired data (134 variables) were used from 629 patients, who underwent transfemoral TAVI from 2012 up to 2018. Support vector machines, neuronal networks, random forests, nearest neighbour and Bayes models were used with new, previously unseen patients to predict 1-year mortality in TAVI patients. A genetic variable selection algorithm identified a set of predictor variables with high predictive power.<h4>Results</h4>Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence 2023-01-01T00:00:00Z2023 Aug2025-04-22T04:11:42.066Z2024-11-13T07:46:54.081ZS-EPMC104884863768554710.3390/jcm12175481