Online Archive of University of Virginia Scholarship
Spectral Doppler Processing and Quantification in the Context of Progressive Aortic Stenosis (AS); The Downfall of the “Fen-Phen” Weight Loss Drug Combination134 views
Author
Thomson, Lily, School of Engineering and Applied Science, University of Virginia
Advisors
Lindner, Jonathan, Division of Cardiovascular Medicine, University of Virginia
Hossack, John, Biomedical Engineering, University of Virginia
Laugelli, Benjamin, Engineering and Society , University of Virginia
Abstract
My technical work and my STS research are connected through a shared focus on how we provide care to patients - whether through developing new tools or evaluating the ethical standards that guide healthcare and pharmaceutical practices. While my technical project aims to develop a tool that will hopefully help better understand aortic stenosis (AS), my STS research examines a historical failure to provide ethical care to patients. Both projects explore how tools and therapies are designed and implemented for patient care, with the goal of maintaining transparency and trust in the healthcare system.
My technical work focuses on quantifying signals on spectral Doppler images of patients who have AS, without having access to raw Doppler data. My capstone partner and I created a MATLAB program that can quantify low-amplitude, high-velocity (LAHV) Doppler signals, which are conventionally ignored by sonographers. In collaboration with Dr. Lindner’s lab, we applied the program on Doppler images from patients in a clinical trial with mild to moderate AS. AS currently lacks nonsurgical treatments to stop or slow its progression. The goal of the trial is to further understand the mechanisms behind AS progression and to determine potential therapeutic targets. These LAHV regions are thought to correspond to regional microdomains of high-velocity flow that may accelerate AS progression through shear signaling. Our program reliably processes the Doppler images and distinguishes and quantifies the LAHV signal. These results correlate with blood markers of increased valve shear and with 4D flow MRI results of 3D printed valves showing this increased shear stress. We hope that this program contributes meaningful insights throughout the trial and supports progress towards therapeutic interventions.
My STS research explores the ethical responsibilities of pharmaceutical companies regarding drug safety and transparency. In the mid-1990s, the weight-loss drug combination of fenfluramine and phentermine, known as fen-phen, was prescribed for weight-loss. Following reports of the fen-phen being linked to valvular heart disease (VHD) and primary pulmonary hypertension (PPH), the FDA requested fenfluramine to be removed from the market. Wyeth Pharmaceuticals was the manufacturer of this drug. Using Joan Tronto’s theory of care ethics, I conduct an ethical analysis of Wyeth Pharmaceuticals’ role in the fen-phen drug case. My findings show that this company neglected to meet the criteria that Tronto outlines, and thus acted unethically, deepening my understanding of how corporate interests can conflict with patient care.
Working on these projects concurrently helped inform the other. Though on a much smaller scale than a pharmaceutical company, I was able to examine care from two perspectives: as a developer of a tool that may support care, and as a critic of systems that historically failed to deliver it. My research has shown me the importance of patient-centered design and the need for a healthcare system that builds trust in order to make real progress.
School of Engineering and Applied Science
Bachelor of Science in Biomedical Engineering
Technical Advisors: Jonathan Lindner, John A. Hossack
STS Advisor: Benjamin Laugelli
Technical Team Member: Sophie Phillips
Language
English
Rights
All rights reserved (no additional license for public reuse)
Thomson, Lily. Spectral Doppler Processing and Quantification in the Context of Progressive Aortic Stenosis (AS); The Downfall of the “Fen-Phen” Weight Loss Drug Combination. University of Virginia, School of Engineering and Applied Science, BS (Bachelor of Science), 2025-05-08, https://doi.org/10.18130/r7x7-w867.