Dissertation defence (Cardiology and Cardiovascular Medicine): MD Sarah Bär
Time
8.6.2024 at 12.00 - 16.00
MD Sarah Bär defends the dissertation in Cardiology and Cardiovascular Medicine titled “Novel Imaging Approaches for the Detection of Hemodynamically Significant Coronary Artery Disease: Quantitative Flow Ratio and Artificial Intelligence-Based Ischemia Algorithm” at the University of Turku on 08 June 2024 at 12.00 (TYKS T-hospital, Risto Lahesmaa Auditorium, Hämeentie 11, Turku).
Opponent: Professor Göran Bergström (University of Gotheburg, Sweden)
Custos: Professor Juhani Knuuti (University of Turku)
Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-951-29-9710-7
The audience can participate in the defence by remote access: https://utu.zoom.us/j/66263868923 (Meeting-ID: 662 6386 8923, code: 468652).
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Summary of the Doctoral Dissertation:
Coronary artery disease (CAD) affects 5.8 million new patients per year and remains a leading cause of death worldwide. CAD is characterized by a process of atherosclerotic plaque accumulation, lumen narrowing, and eventually impairment of blood flow to the heart muscle. Lesions that cause impaired blood flow may require interventional treatment and their identification is key for patient management.
We investigated two novel imaging approaches to diagnose blood flow-limiting CAD and their ability to identify patients with worse outcome. Quantitative flow ratio (QFR) is a novel technique based on computational fluid dynamics to model coronary flow from invasive coronary angiography. QFR measured in arteries of patients with myocardial infarction, successfully identified patients with worse prognosis throughout 5 years. However, QFR was not able to identify patients that could benefit from earlier intervention of significant bystander-lesions of patients with myocardial infarction. AI-QCTischemia is an artificial intelligence-based method to determine flow-limiting CAD from coronary computed tomography angiography (CCTA). Among patients who underwent CCTA for suspected CAD, AI-QCTischemia identified patients with worse prognosis throughout 7 years. This pertained especially to patients with no or non-lumen-narrowing CAD or those without reduced blood flow on conventional imaging with positron emission tomography.
This thesis has filled an important knowledge gap on the potential of QFR and AI-QCTischemia to identify patients with non-favorable long term prognosis. It revealed that, among patients diagnosed and managed according to the current treatment recommendations, QFR and AI-QCTischemia identified patients with worse outcome. Further research is warranted to reveal, whether treatment strategies informed by these novel techniques could potentially improve patient prognosis.
Opponent: Professor Göran Bergström (University of Gotheburg, Sweden)
Custos: Professor Juhani Knuuti (University of Turku)
Doctoral Dissertation at UTUPub: https://urn.fi/URN:ISBN:978-951-29-9710-7
The audience can participate in the defence by remote access: https://utu.zoom.us/j/66263868923 (Meeting-ID: 662 6386 8923, code: 468652).
***
Summary of the Doctoral Dissertation:
Coronary artery disease (CAD) affects 5.8 million new patients per year and remains a leading cause of death worldwide. CAD is characterized by a process of atherosclerotic plaque accumulation, lumen narrowing, and eventually impairment of blood flow to the heart muscle. Lesions that cause impaired blood flow may require interventional treatment and their identification is key for patient management.
We investigated two novel imaging approaches to diagnose blood flow-limiting CAD and their ability to identify patients with worse outcome. Quantitative flow ratio (QFR) is a novel technique based on computational fluid dynamics to model coronary flow from invasive coronary angiography. QFR measured in arteries of patients with myocardial infarction, successfully identified patients with worse prognosis throughout 5 years. However, QFR was not able to identify patients that could benefit from earlier intervention of significant bystander-lesions of patients with myocardial infarction. AI-QCTischemia is an artificial intelligence-based method to determine flow-limiting CAD from coronary computed tomography angiography (CCTA). Among patients who underwent CCTA for suspected CAD, AI-QCTischemia identified patients with worse prognosis throughout 7 years. This pertained especially to patients with no or non-lumen-narrowing CAD or those without reduced blood flow on conventional imaging with positron emission tomography.
This thesis has filled an important knowledge gap on the potential of QFR and AI-QCTischemia to identify patients with non-favorable long term prognosis. It revealed that, among patients diagnosed and managed according to the current treatment recommendations, QFR and AI-QCTischemia identified patients with worse outcome. Further research is warranted to reveal, whether treatment strategies informed by these novel techniques could potentially improve patient prognosis.
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