AIMC Topic: Transcatheter Aortic Valve Replacement

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Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling.

Science robotics Feb 22, 2023
Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symp...
Robotics Ventricular Remodeling Hydrodynamics Transcatheter Aortic Valve Replacement Humans United States Treatment Outcome Aortic Valve Stenosis
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CT Image Feature Diagnosis on the Basis of Deep Learning Algorithm for Preoperative Patients and Complications of Transcatheter Aortic Valve Implantation.

Journal of healthcare engineering Nov 29, 2021
This work was aimed to explore the role of CT angiography information provided by deep learning algorithm in the diagnosis and complications of the disease focusing on congenital aortic valve disease and severe aortic valve stenosis. 120 patients who...
Humans Algorithms Treatment Outcome Deep Learning Aortic Valve Computed Tomography Angiography Transcatheter Aortic Valve Replacement
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Deep learning-based prediction of early cerebrovascular events after transcatheter aortic valve replacement.

Scientific reports Sep 21, 2021
Cerebrovascular events (CVE) are among the most feared complications of transcatheter aortic valve replacement (TAVR). CVE appear difficult to predict due to their multifactorial origin incompletely explained by clinical predictors. We aimed to build...
Deep Learning Incidence Stroke Transcatheter Aortic Valve Replacement Risk Factors Aged Aged, 80 and over Male Humans Prospective Studies Tomography, X-Ray Computed Female
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Predicting Infective Endocarditis After Transcatheter Aortic Valve Implantation Via a Risk Model.

The American journal of cardiology May 9, 2021
Age Factors Predictive Value of Tests Endocarditis Transcatheter Aortic Valve Replacement Risk Factors Humans Male Machine Learning Postoperative Complications Female United States
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Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement.

Circulation. Arrhythmia and electrophysiology Mar 9, 2021
Prospective Studies Retrospective Studies Risk Factors Risk Assessment Humans Machine Learning Decision Support Techniques Cardiac Pacing, Artificial Heart Rate Transcatheter Aortic Valve Replacement Pacemaker, Artificial Treatment Outcome Action Potentials Aortic Valve Predictive Value of Tests Atrioventricular Block
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Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

Pacing and clinical electrophysiology : PACE Jan 28, 2021
BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcatheter aortic valve replacement (TAVR) is important for clinical decision making. The aims of this study were to investigate the significance and utili...
Pacemaker, Artificial Male Risk Assessment Postoperative Complications Logistic Models Aortic Valve Stenosis Arrhythmias, Cardiac Transcatheter Aortic Valve Replacement Prosthesis Implantation Retrospective Studies Female Aged Humans Aged, 80 and over Machine Learning Forecasting
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Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation.

The American journal of cardiology Sep 15, 2020
Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic va...
Humans Machine Learning Phenotype Transcatheter Aortic Valve Replacement Female Severity of Illness Index Cohort Studies Aged Treatment Outcome Aged, 80 and over Aortic Valve Stenosis Male Risk Assessment
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Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement.

The Annals of thoracic surgery Jul 17, 2020
BACKGROUND: This study evaluated the performance of a machine learning (ML) algorithm in predicting outcomes of surgical aortic valve replacement (SAVR).
Aortic Valve Stenosis Aortic Valve Education, Medical, Graduate Transcatheter Aortic Valve Replacement Female Aged Humans Male Machine Learning ROC Curve Algorithms Prognosis
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Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI.

Clinical research in cardiology : official journal of the German Cardiac Society Jun 24, 2020
BACKGROUND: Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indicat...
Morbidity Male Humans Female Hospital Mortality Risk Assessment Heart Valve Diseases Risk Factors Aortic Valve Postoperative Complications Machine Learning Germany Aged, 80 and over Inpatients Time Factors Retrospective Studies Transcatheter Aortic Valve Replacement Cause of Death Survival Rate
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Robotic transcatheter aortic valve replacement with magnetic resonance imaging is feasible in animal models, but is it feasible in patients?

The Journal of thoracic and cardiovascular surgery Jan 7, 2016
Heart Valve Prosthesis Implantation Magnetic Resonance Imaging Humans Models, Animal Aortic Valve Stenosis Transcatheter Aortic Valve Replacement Heart Valve Prosthesis Aortic Valve Animals Cardiac Catheterization Robotics Treatment Outcome
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