Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement-Reference-Cited by-同舟云学术

Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement

Published:2021-01-28 Issue:2 Volume:44 Page:334-340
ISSN:0147-8389
Container-title:Pacing and Clinical Electrophysiology
language:en
Short-container-title:Pacing Clin Electrophysiol

Author:

Truong Vien T.¹²,Beyerbach Daniel¹,Mazur Wojciech¹,Wigle Matthew¹,Bateman Emma³,Pallerla Akhil⁴,Ngo Tam N.M.¹,Shreenivas Satya¹,Tretter Justin T.⁵,Palmer Cassady¹,Kereiakes Dean J.¹,Chung Eugene S.¹^ORCID

Affiliation:

1. The Christ Hospital Health Network and The Lindner Research Center Cincinnati Ohio USA

2. The Sue and Bill Butler Research Fellow The Linder Research Center Cincinnati Ohio USA

3. University of Kentucky Lexington Kentucky USA

4. University of Pittsburgh Pittsburgh Pennsylvania USA

5. Heart Institute Cincinnati Children's Hospital Medical Center Cincinnati Ohio USA

Publisher

Wiley

Subject

Cardiology and Cardiovascular Medicine,General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/pace.14163

Reference27 articles.

1. The evolving epidemiology of valvular aortic stenosis. The Tromsø Study

2. 2018 AATS/ACC/SCAI/STS Expert Consensus Systems of Care Document: operator and Institutional Recommendations and Requirements for Transcatheter Aortic Valve Replacement;Bavaria JE;Catheterization and Cardiovascular Interventions,2019

3. Conduction Disturbances After Transcatheter Aortic Valve Replacement

4. Permanent Pacemaker Implantation After Transcatheter Aortic Valve Implantation

5. Management of Conduction Disturbances Associated With Transcatheter Aortic Valve Replacement

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