Predicting Transcatheter Aortic Valve Leaflet Thrombosis Risk Using Pre-Procedural Computed Tomography Angiogram and Computational Modeling

Aortic stenosis (AS) is a disease that affects nearly 3% of adults above the age of 65 and is characterized by narrowing of the aortic heart valve due to calcium buildup. It restricts blood flow from the heart and, if left untreated, can lead to irreversible damage to the left ventricle wall and even death. The stenosed aortic valve is usually replaced with a bioprosthetic valve either via open-heart surgery or transcatheter aortic valve replacement (TAVR), a less invasive procedure that has shown similar long-term outcomes as surgery. However, there have been reported instances of complications occurring post-TAVR, one of the most common ones being leaflet thrombosis (LT). LT is characterized by the formation of blood clots along the bioprosthetic valve leaflets, increasing the risk of stroke and further valve deterioration. Currently, the only method of detecting LT is by identifying hypoattenuating leaflet thickening (HALT) in computed tomography angiogram (CTA) images taken days to months after the procedure. Therefore, this research aims to assess various post-TAVR geometric and hemodynamic parameters in their ability to quantitatively predict risk of LT for multiple patients using pre-procedural CTA. This study presents a novel computational pipeline consisting of 1) pre-TAVR CTA reconstruction and reduced order modeling (ROM) simulations to automatically measure post-TAVR geometrical parameters, 2) a landmark-guided, intensity-based automated left ventricular segmentation method to measure hemodynamic parameters, and 3) statistical and machine learning (ML) analyses to assess the HALT predictive power of each parameter. The proposed computational pipeline can be utilized as part of a TAVR procedural planning service to predict LT risk following different TAV deployments from pre-procedural CTA.

Level thesis:degree_level

Masters

Department dc:contributor.department

Biomedical Engineering (Joint GT/Emory Department)

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Venkatesh, Aniket

Advisor dc:contributor.advisor

• Dasi, Lakshmi

Committee members dc:contributor.committeemember

• Oshinski, John
• Lindsey, Brooks
• Thourani, Vinod
• Sellers, Stephanie

• Aortic stenosis
• Transcatheter aortic valve replacement
• Leaflet thrombosis
• Hypo-attenuated leaflet thickening
• Computed tomography angiogram
• Machine learning
• Reduced order modeling

Language dc:language.iso

en_US

Handle dc:identifier.uri

https://hdl.handle.net/1853/75593

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/75593