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Transcatheter Aortic Valve Replacement

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Risk factors and predictive models for post-operative moderate-to-severe mitral regurgitation following transcatheter aortic valve replacement: a machine learning approach.

BMC cardiovascular disorders 40348949
BACKGROUND: Post-operative moderate-to-severe mitral regurgitation (MR) following transcatheter aortic valve replacement (TAVR) is associated with poor outcomes, yet the factors contributing to this complication are not well understood. This study ai...
Aged Aged, 80 and over Aortic Valve Stenosis Decision Support Techniques Female Humans Machine Learning Male Mitral Valve Mitral Valve Insufficiency Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Severity of Illness Index Time Factors Transcatheter Aortic Valve Replacement Treatment Outcome
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Technical implications of a novel deep learning system in the segmentation and evaluation of computed tomography angiography before transcatheter aortic valve replacement.

Therapeutic advances in cardiovascular disease 40123453
OBJECTIVE: The goal of this study was to compare the computed tomography angiography scans of the segmentation results from the Cvpilot, 3mensio, and Volume Viewer systems to explore the practicability of the Cvpilot system in the automatic segmentat...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Aortography Computed Tomography Angiography Deep Learning Female Humans Male Multidetector Computed Tomography Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Retrospective Studies Transcatheter Aortic Valve Replacement
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Machine-learning versus traditional methods for prediction of all-cause mortality after transcatheter aortic valve implantation: a systematic review and meta-analysis.

Open heart 39842939
BACKGROUND: Accurate mortality prediction following transcatheter aortic valve implantation (TAVI) is essential for mitigating risk, shared decision-making and periprocedural planning. Surgical risk models have demonstrated modest discriminative valu...
Aortic Valve Stenosis Cause of Death Global Health Humans Machine Learning Risk Assessment Risk Factors Survival Rate Transcatheter Aortic Valve Replacement
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Predicting mortality after transcatheter aortic valve replacement using AI-based fully automated left atrioventricular coupling index.

Journal of cardiovascular computed tomography 39794233
BACKGROUND: This study aimed to determine whether artificial intelligence (AI)-based automated assessment of left atrioventricular coupling index (LACI) can provide incremental value above other traditional risk factors for predicting mortality among...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Artificial Intelligence Atrial Function, Left Automation Computed Tomography Angiography Coronary Angiography Female Humans Male Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Risk Assessment Risk Factors Severity of Illness Index Stroke Volume Time Factors Transcatheter Aortic Valve Replacement Treatment Outcome Ventricular Function, Left
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Using novel machine learning tools to predict optimal discharge following transcatheter aortic valve replacement.

Archives of cardiovascular diseases 39424448
BACKGROUND: Although transcatheter aortic valve replacement has emerged as an alternative to surgical aortic valve replacement, it requires extensive healthcare resources, and optimal length of hospital stay has become increasingly important. This st...
Aged Aged, 80 and over Aortic Valve Aortic Valve Stenosis Clinical Decision-Making Databases, Factual Decision Support Techniques Female Humans Length of Stay Machine Learning Male Neural Networks, Computer Patient Discharge Predictive Value of Tests Retrospective Studies Risk Assessment Risk Factors Time Factors Transcatheter Aortic Valve Replacement Treatment Outcome United States
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An Integrative Machine Learning Model for Predicting Early Safety Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation.

Medicina (Kaunas, Lithuania) 40142184
: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are critical for patient prognosis. Accurate prediction of adverse events can enhance patient management and improve outcomes. : This study ai...
Aged Aged, 80 and over Aortic Valve Stenosis Female Humans Machine Learning Male Postoperative Complications Prognosis Retrospective Studies Transcatheter Aortic Valve Replacement Treatment Outcome
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Machine learning for predicting outcomes of transcatheter aortic valve implantation: A systematic review.

International journal of medical informatics 39965432
BACKGROUND: Transcatheter aortic valve implantation (TAVI) therapy has demonstrated its clear benefits such as low invasiveness, to treat aortic stenosis. Despite associated benefits, still post-procedural complications might occur. The severity of t...
Aortic Valve Stenosis Humans Machine Learning Risk Assessment Transcatheter Aortic Valve Replacement Treatment Outcome
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Artificial Intelligence Improves Prediction of Major Adverse Cardiovascular Events in Patients Undergoing Transcatheter Aortic Valve Replacement Planning CT.

Academic radiology 39389811
RATIONALE AND OBJECTIVES: Coronary CT angiography (CCTA) is mandatory before transcatheter aortic valve replacement (TAVR). Our objective was to evaluate the efficacy of artificial intelligence (AI)-powered software in automatically analyzing cardiac...
Aged Aged, 80 and over Aortic Valve Stenosis Artificial Intelligence Computed Tomography Angiography Coronary Angiography Female Humans Male Retrospective Studies Transcatheter Aortic Valve Replacement
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Clinical value of aortic arch morphology in transfemoral TAVR: artificial intelligence evaluation.

International journal of surgery (London, England) 39869394
BACKGROUND: The impact of aortic arch (AA) morphology on the management of the procedural details and the clinical outcomes of the transfemoral artery (TF)-transcatheter aortic valve replacement (TAVR) has not been evaluated. The goal of this study w...
Aged Aged, 80 and over Aorta, Thoracic Aortic Valve Stenosis Artificial Intelligence Female Femoral Artery Humans Male Retrospective Studies Transcatheter Aortic Valve Replacement Treatment Outcome
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Multimodal Visualization and Explainable Machine Learning-Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study.

Journal of medical Internet research 40310672
BACKGROUND: Currently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter ...
Aged Aged, 80 and over Aortic Valve Stenosis Biomarkers Cohort Studies Female Heart Failure Humans Machine Learning Male Prognosis Risk Assessment Stroke Volume Transcatheter Aortic Valve Replacement
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