Predicting cardiovascular biomechanical stiffening due to the interplay of tissue layers with focus on calcific aortic valve disease

The primary function of the cardiovascular system is to supply blood to all parts of our body and stiffness of the tissue structures transporting the blood play a critical role in the optimal functioning of the system. The heart valves are a perfect example, which open and close over three billion times in a human life span. In spite of being robust, in our ageing society many valve-related diseases are becoming a major health problem; the stiffening of the valves leads to unwanted resistance to blood flow making it harder for the heart to pump blood at the same rate, which sometimes leads to heart failure and death. Calcific aortic valve disease (CAVD) is one such disease that progresses through accumulation of calcium within the valve tissue and affects 5% of population older than 75 years. 4 million people in the 75-84 age group are projected by 2018 and the population beyond the age of 85 is set to double by 2028. Thus, with our aging demographics, valvular diseases have been compared to an epidemic.

In this study, we propose to develop a computational tool that will help identify patients at a higher risk of CAVD at an early stage of development. Based upon clinical images of a patient's heart valves and experimental results already collected by our collaborators, we will formulate a pipeline that uses the valve's movement as an input and predicts the speed and severity of its calcification. This will allow close follow-up of high-risk patients and timely intervention before the complications arise. Usually, the patients at an advanced stage of disease are recommended for valve replacement surgery; however, the patients who are seen unfit for surgery have a survival rate of merely 32% after 5 years from the disease onset. The tool developed in this project will tremendously help improve the survival rate of those patients. Furthermore, the new insight obtained from this work will help us improve the design of medical devices such as artificial heart valves and blood pumps, since currently used devices have limited durability because of valve calcification or related issues.

In the long term, this project aims to develop a computational tool for early stage identification of patients who are at a higher risk of calcific aortic valve disease (CAVD). Heart valves are critical components of the cardiovascular system and once diseased they need to be either repaired or replaced. However, the advanced stage of valve diseases involve complications and combined with old age, the resulting conditions are life threatening. Most of the heart valve diseases do not show symptoms at early stages. Therefore, the tool developed through this research will allow a close follow-up of high-risk patients before the disease becomes severe, thus preventing complications related to old age and improving the clinical success rate. Furthermore, the novel knowledge gained from this project will also be helpful in improving the design of artificial valves. Aortic valve replacement is the second-most common cardiac surgery after coronary revascularization. Currently used artificial valves are marred by short life or dependence on blood-thinning medicine. Since we study native valves in this project, the results will reveal the underlying phenomena that make our native valves last much longer than the currently used artificial ones. This information will then will be used to direct the research on artificial valve design.

Clearly, the immediate "Lead Users" of this project will be clinicians and the medical industry. Clinicians will use the insight obtained into the function of various tissue layers of aortic valve in the development of CAVD while making decisions about valve replacement and repair. The medical industry will be able to improve various diagnostic and surgical tools, e.g., the valve image processing, bioprosthetic valve design and ventricular assist device (LVAD) design. Firstly, the heart valve imaging has advanced remarkably in the last decade. Addition of inverse models such as the one developed in this project to imaging capability will add the capacity of providing functional insight in addition to the geometry of the valve. Such advancements will exceedingly accelerate heart valve research. Secondly, bioprosthetic heart valves are limited in their durability to 5-10 years, whereas the native valves last for 70-80 years. The insight obtained from this project will help us improve the design of bioprosthetic valves so that they closely mimic the native valves and last longer. Finally, LVADs have been observed to have an undesired effect of the loss of AV efficiency. Currently, in order to avoid this, LVAD device is intermittently switched off so that AV remains fully functional. The tool developed in this project will help us understand the reasons behind this and optimize the design of LVAD to minimize the side-effect.

In the long term, the main beneficiaries from this project are the public whose healthcare will improve tremendously using this advanced engineering- and technology-based medical science. A predictive tool for determining the risk of patients to develop CAVD will allow early diagnosis, close follow-up, timed intervention and improved clinical outcome. It will also help lower the healthcare costs related to follow-up of low-risk patients who are otherwise considered high-risk (e.g. bicuspid aortic valve patients). The advanced bioprosthetic devices will lower the need for re-intervention and other undesired side effects from those implants.