A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendation...

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Autores principales:	Ena Hasimbegovic, Laszlo Papp, Marko Grahovac, Denis Krajnc, Thomas Poschner, Waseem Hasan, Martin Andreas, Christoph Gross, Andreas Strouhal, Georg Delle-Karth, Martin Grabenwöger, Christopher Adlbrecht, Markus Mach
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	TAVR TAVI iSAVR aortic stenosis machine learning heart team Medicine R
Acceso en línea:	https://doaj.org/article/d6695260055c474b95d1d171dae217a8
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spelling	oai:doaj.org-article:d6695260055c474b95d1d171dae217a82021-11-25T18:06:49ZA Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis10.3390/jpm111110622075-4426https://doaj.org/article/d6695260055c474b95d1d171dae217a82021-10-01T00:00:00Zhttps://www.mdpi.com/2075-4426/11/11/1062https://doaj.org/toc/2075-4426Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.Ena HasimbegovicLaszlo PappMarko GrahovacDenis KrajncThomas PoschnerWaseem HasanMartin AndreasChristoph GrossAndreas StrouhalGeorg Delle-KarthMartin GrabenwögerChristopher AdlbrechtMarkus MachMDPI AGarticleTAVRTAVIiSAVRaortic stenosismachine learningheart teamMedicineRENJournal of Personalized Medicine, Vol 11, Iss 1062, p 1062 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	TAVR TAVI iSAVR aortic stenosis machine learning heart team Medicine R
spellingShingle	TAVR TAVI iSAVR aortic stenosis machine learning heart team Medicine R Ena Hasimbegovic Laszlo Papp Marko Grahovac Denis Krajnc Thomas Poschner Waseem Hasan Martin Andreas Christoph Gross Andreas Strouhal Georg Delle-Karth Martin Grabenwöger Christopher Adlbrecht Markus Mach A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
description	Transcatheter aortic valve replacement (TAVR) has rapidly become a viable alternative to the conventional isolated surgical aortic valve replacement (iSAVR) for treating severe symptomatic aortic stenosis. However, data on younger patients is scarce and a gap exists between data-based recommendations and the clinical use of TAVR. In our study, we utilized a machine learning (ML) driven approach to model the complex decision-making process of Heart Teams when treating young patients with severe symptomatic aortic stenosis with either TAVR or iSAVR and to identify the relevant considerations. Out of the considered factors, the variables most prominently featured in our ML model were congestive heart failure, established risk assessment scores, previous cardiac surgeries, a reduced left ventricular ejection fraction and peripheral vascular disease. Our study demonstrates a viable application of ML-based approaches for studying and understanding complex clinical decision-making processes.
format	article
author	Ena Hasimbegovic Laszlo Papp Marko Grahovac Denis Krajnc Thomas Poschner Waseem Hasan Martin Andreas Christoph Gross Andreas Strouhal Georg Delle-Karth Martin Grabenwöger Christopher Adlbrecht Markus Mach
author_facet	Ena Hasimbegovic Laszlo Papp Marko Grahovac Denis Krajnc Thomas Poschner Waseem Hasan Martin Andreas Christoph Gross Andreas Strouhal Georg Delle-Karth Martin Grabenwöger Christopher Adlbrecht Markus Mach
author_sort	Ena Hasimbegovic
title	A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
title_short	A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
title_full	A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
title_fullStr	A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
title_full_unstemmed	A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis
title_sort	sneak-peek into the physician’s brain: a retrospective machine learning-driven investigation of decision-making in tavr versus savr for young high-risk patients with severe symptomatic aortic stenosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d6695260055c474b95d1d171dae217a8
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A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis

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