Lumped parameter model based surgical planning for CABG

Impact of TAVR on coronary artery hemodynamics using clinical measurements and image-based patient-specific in silico modeling

In recent years, transcatheter aortic valve replacement (TAVR) has become the leading method for treating aortic stenosis. While the procedure has improved dramatically in the past decade, there are still uncertainties about the impact of TAVR on coronary blood flow. Recent research has indicated that negative coronary events after TAVR may be partially driven by impaired coronary blood flow dynamics. Furthermore, the current technologies to rapidly obtain non-invasive coronary blood flow data are relatively limited. Herein, we present a lumped parameter computational model to simulate coronary blood flow in the main arteries as well as a series of cardiovascular hemodynamic metrics. The model was designed to only use a few inputs parameters from echocardiography, computed tomography and a sphygmomanometer. The novel computational model was then validated and applied to 19 patients undergoing TAVR to examine the impact of the procedure on coronary blood flow in the left anterior descending (LAD) artery, left circumflex (LCX) artery and right coronary artery (RCA) and various global hemodynamics metrics. Based on our findings, the changes in coronary blood flow after TAVR varied and were subject specific (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). Additionally, valvular pressure gradient, left ventricle (LV) workload and maximum LV pressure decreased by 61.5%, 4.5% and 13.0% respectively, while mean arterial pressure and cardiac output increased by 6.9% and 9.9% after TAVR. By applying this proof-of-concept computational model, a series of hemodynamic metrics were generated non-invasively which can help to better understand the individual relationships between TAVR and mean and peak coronary flow rates. In the future, tools such as these may play a vital role by providing clinicians with rapid insight into various cardiac and coronary metrics, rendering the planning for TAVR and other cardiovascular procedures more personalized.

A predictive patient-specific computational model of coronary artery bypass grafts for potential use by cardiac surgeons to guide selection of graft configurations

Cardiac surgeons face a significant degree of uncertainty when deciding upon coronary artery bypass graft configurations for patients with coronary artery disease. This leads to significant variation in preferred configuration between different surgeons for a particular patient. Additionally, for the majority of cases, there is no consensus regarding the optimal grafting strategy. This situation results in the tendency for individual surgeons to opt for a “one size fits all” approach and use the same grafting configuration for the majority of their patients neglecting the patient-specific nature of the diseased coronary circulation. Quantitative metrics to assess the adequacy of coronary bypass graft flows have recently been advocated for routine intraoperative use by cardiac surgeons. In this work, a novel patient-specific 1D-0D computational model called “COMCAB” is developed to provide the predictive haemodynamic parameters of functional graft performance that can aid surgeons to avoid configurations with grafts that have poor flow and thus poor patency. This model has significant potential for future expanded applications.

Effects of Choice of Medical Imaging Modalities on a Non-invasive Diagnostic and Monitoring Computational Framework for Patients With Complex Valvular, Vascular, and Ventricular Diseases Who Undergo Transcatheter Aortic Valve Replacement

Due to the high individual differences in the anatomy and pathophysiology of patients, planning individualized treatment requires patient-specific diagnosis. Indeed, hemodynamic quantification can be immensely valuable for accurate diagnosis, however, we still lack precise diagnostic methods for numerous cardiovascular diseases including complex (and mixed) valvular, vascular, and ventricular interactions (C3VI) which is a complicated situation made even more challenging in the face of other cardiovascular pathologies. Transcatheter aortic valve replacement (TAVR) is a new less invasive intervention and is a growing alternative for patients with aortic stenosis. In a recent paper, we developed a non-invasive and Doppler-based diagnostic and monitoring computational mechanics framework for C3VI, called C3VI-DE that uses input parameters measured reliably using Doppler echocardiography. In the present work, we have developed another computational-mechanics framework for C3VI (called C3VI-CT). C3VI-CT uses the same lumped-parameter model core as C3VI-DE but its input parameters are measured using computed tomography and a sphygmomanometer. Both frameworks can quantify: (1) global hemodynamics (metrics of cardiac function); (2) local hemodynamics (metrics of circulatory function). We compared accuracy of the results obtained using C3VI-DE and C3VI-CT against catheterization data (gold standard) using a C3VI dataset (N = 49) for patients with C3VI who undergo TAVR in both pre and post-TAVR with a high variability. Because of the dataset variability and the broad range of diseases that it covers, it enables determining which framework can yield the most accurate results. In contrast with C3VI-CT, C3VI-DE tracks both the cardiac and vascular status and is in great agreement with cardiac catheter data.