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A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study

Background: predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization. Methods: Routinely acquired data (134 variables) were used from 629 patients, who underwent transf...

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Autores principales: Pollari, Francesco, Hitzl, Wolfgang, Rottmann, Magnus, Vogt, Ferdinand, Ledwon, Miroslaw, Langhammer, Christian, Eckner, Dennis, Jessl, Jürgen, Bertsch, Thomas, Pauschinger, Matthias, Fischlein, Theodor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488486/
https://www.ncbi.nlm.nih.gov/pubmed/37685547
http://dx.doi.org/10.3390/jcm12175481
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author Pollari, Francesco
Hitzl, Wolfgang
Rottmann, Magnus
Vogt, Ferdinand
Ledwon, Miroslaw
Langhammer, Christian
Eckner, Dennis
Jessl, Jürgen
Bertsch, Thomas
Pauschinger, Matthias
Fischlein, Theodor
author_facet Pollari, Francesco
Hitzl, Wolfgang
Rottmann, Magnus
Vogt, Ferdinand
Ledwon, Miroslaw
Langhammer, Christian
Eckner, Dennis
Jessl, Jürgen
Bertsch, Thomas
Pauschinger, Matthias
Fischlein, Theodor
author_sort Pollari, Francesco
collection PubMed
description Background: predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization. Methods: 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. Results: Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence 1-year survival. Before applying the reject option, the model performances in terms of negative predictive value (NPV) and positive predictive value (PPV) were similar between all models. After applying the reject option, the random forest model identified a subcohort showing a negative predictive value of 96% (positive predictive value = 92%, accuracy = 96%). Conclusions: Our model can predict the 1-year survival with very high negative and sufficiently high positive predictive value, with very high accuracy. The “reject option” allows a high performance and harmonic integration of machine learning in the clinical decision process.
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spelling pubmed-104884862023-09-09 A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study Pollari, Francesco Hitzl, Wolfgang Rottmann, Magnus Vogt, Ferdinand Ledwon, Miroslaw Langhammer, Christian Eckner, Dennis Jessl, Jürgen Bertsch, Thomas Pauschinger, Matthias Fischlein, Theodor J Clin Med Article Background: predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization. Methods: 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. Results: Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence 1-year survival. Before applying the reject option, the model performances in terms of negative predictive value (NPV) and positive predictive value (PPV) were similar between all models. After applying the reject option, the random forest model identified a subcohort showing a negative predictive value of 96% (positive predictive value = 92%, accuracy = 96%). Conclusions: Our model can predict the 1-year survival with very high negative and sufficiently high positive predictive value, with very high accuracy. The “reject option” allows a high performance and harmonic integration of machine learning in the clinical decision process. MDPI 2023-08-24 /pmc/articles/PMC10488486/ /pubmed/37685547 http://dx.doi.org/10.3390/jcm12175481 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pollari, Francesco
Hitzl, Wolfgang
Rottmann, Magnus
Vogt, Ferdinand
Ledwon, Miroslaw
Langhammer, Christian
Eckner, Dennis
Jessl, Jürgen
Bertsch, Thomas
Pauschinger, Matthias
Fischlein, Theodor
A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study
title A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study
title_full A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study
title_fullStr A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study
title_full_unstemmed A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study
title_short A Machine Learning Model for the Accurate Prediction of 1-Year Survival in TAVI Patients: A Retrospective Observational Cohort Study
title_sort machine learning model for the accurate prediction of 1-year survival in tavi patients: a retrospective observational cohort study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10488486/
https://www.ncbi.nlm.nih.gov/pubmed/37685547
http://dx.doi.org/10.3390/jcm12175481
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