Cardiovascular disease, including coronary artery disease and aortic valve stenosis, impacts tens of millions of people annually and carries a massive global economic burden. Advances in medical imaging, hardware and software are leading to an increased interest in the field of cardiovascular computational modelling to help combat the devastating impact of cardiovascular disease. Lumped parameter modelling (a branch of computational modelling) holds the potential of aiding in the early diagnosis of these diseases, assisting clinicians in determining personalized and optimal treatments and offering a unique in-silico setting to study cardiac and circulatory diseases due to its rapid computation time, ease of automation and relative simplicity.
In this thesis, cardiovascular lumped parameter modelling is presented in detail and a patient-specific framework capable of simulating blood flow waveforms and hemodynamic data in the heart and coronary arteries was developed. The framework used only non-invasive clinical data and images (Computed Tomography images, echocardiography data and cuff blood pressure) as inputs. The novel model was then applied to 19 patients with aortic stenosis who underwent transcatheter aortic valve replacement. The diastolic coronary flow waveforms in the left anterior descending artery, left circumflex artery and right coronary artery were validated against a previously developed patient-specific 3D fluid-structure interaction model for all 19 subjects (pre and post intervention). There were strong qualitative and quantitative agreements between the two models.
After the procedure, aortic valve area and net pressure gradient across the aortic valve improved for almost all the subjects. As for the hemodynamic data, according to the model, there was substantial variability in terms of the increase or decrease post intervention. On average, left ventricle workload and maximum left ventricle pressure decreased by 4.5% and 13.0% while cardiac output, mean arterial pressure and resting heart rate increased by 9.9%, 6.9% and 1.9% respectively. There were also subject specific changes in coronary blood flow (37% had increased flow in all three coronary arteries, 32% had decreased flow in all coronary arteries, and 31% had both increased and decreased flow in different coronary arteries). All in all, a proof-of-concept cardiac and coronary lumped parameter framework was developed, validated, and applied in this thesis. / Thesis / Master of Applied Science (MASc) / The heart is a vital part of the cardiovascular system, which helps deliver and regulate blood flow through the entire human body. The coronary arteries are a crucial part of this system since they deliver blood directly to heart muscles. For numerous reasons, the cardiovascular system can become diseased over time and require clinical treatment. Coronary artery disease and aortic valve stenosis are among the most prevalent cardiovascular diseases globally. While medical imaging on its own is a crucial part of the disease management and treatment process, advanced computational models can further enhance the process and provide clinics with data and predictions they might otherwise miss. In this thesis, a patient specific computational framework capable of simulating blood flow waveforms and cardiovascular data in the heart and coronary arteries using only non-invasive clinical data and images was developed and validated. The novel model was applied to a series of patients with aortic stenosis who underwent heart valve replacement with the aim of studying the impact on coronary blood flow and global cardiovascular metrics.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/28259 |
| Date | January 2023 |
| Creators | Garber, Louis |
| Contributors | Motamed, Zahra, Biomedical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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