Valvular heart disease and bioprosthetic heart valves: Defining mechanisms of degeneration and therapeutic discovery from bedside to bench

Aortic stenosis (AS) is a narrowing of the valve that controls blood flow from the heart to the body. AS results in significant decline in quality of life and can be fatal if untreated. Unlike most types of heart disease, there is no medication to treat AS and the primary therapy option is replacing the diseased valve with an artificial one by open-heart surgery or transcatheter implantation (insertion of an artificial valve through the blood vessels leading to the heart). Unfortunately, artificial valves can be dysfunctional and have limited durability, which can lead to heart failure, the need for repeat valve replacement, or death. With a focus on clot that can form on artificial valves, this research aims to determine the causes of valve dysfunction and degeneration, define methods to detect and predict which patients will experience valve dysfunction, and identify methods to increase valve durability. Overall, this work will provide critical new information to guide clinical care and the future evolution of artificial heart valve use that will improve the outcomes and quality of life of patients with AS.

Risk and protective factors for cognitive health across the adult age span: Impacts of physical and mental health and illness on cognitive outcomes

The virus causing COVID-19 can invade the brain, raising concern over long-term impacts on thinking abilities. We aim to identify long-term impacts on these cognitive abilities in those who have had COVID-19, and pinpoint factors that predict long-term outcomes. Adults positive for COVID-19 and those with no evidence of infection, are completing a series of cognitive and psychological tests in a current study. The proposed project will follow these individuals over time, with one and three-year follow-ups to examine changes in cognition across time. We will examine group differences in cognition, mental health, and other factors at each time point, determine if one or more cognitive profiles (clusters) characterize COVID-19 positive individuals, examine changes in these profiles across time, and test a screening measure to detect these cognitive difficulties. Findings will inform clinicians (e.g. neurologists, rehabilitation specialists) on trajectory of recovery of function and inform healthcare service provision in BC. Results will help ensure long-term impacts of infection are appropriately addressed, so those affected can efficiently resume complex activities requiring cognitive effort (e.g. employment, academic pursuits).

Optimal pregnancy and postpartum health for everyone

After childbirth, mothers are at risk of death and disease. Patient engagement can improve the relevance and impact of research in this area; however, patient partners often do not reflect the diversity of the community. This limits the research and its results. This is especially important in BC, which is the most ethnically diverse province in Canada. The proposed research project aims to answer the following three questions: 1) How can we improve the diversity of patient partners in pregnancy and postpartum-related research? 2) Is a mobile application appropriate and acceptable for self-screening of postpartum complications? 3) What is the frequency, timing, and factors associated with postpartum complications and hospital readmissions in BC? The proposed research will promote equitable representation of pregnant and postpartum individuals in research, improving our understanding of their health and health concerns. It will be a core component of my portfolio of patient-oriented maternal health research in BC and globally.

Development of an ex-vivo-in-silico framework to inform medication use decisions for breastfeeding women

Children can inadvertently be exposed to the medications their mothers receive through breastmilk. As such, breastfeeding mothers need to weigh both the risks and benefits of medication use for themselves as well as their children. Unfortunately, the majority of drugs prescribed to breastfeeding women lack sufficient information to understand these risks. Due to this lack of information, women may opt to delay needed drug therapy or discontinue breastfeeding altogether — choices that can negatively impact the health of both mother and child. The proposed research program looks to address this information gap by combining lab-based studies with advanced computer modelling to predict how drug intake by the mother translates to drug exposure in the breastfed child. Lab-based studies will answer the question, “How much drug is present in breastmilk?” Whereas, advanced computing will be used to create virtual children and mothers to answer the question, “How much of the drug administered to the mother will be transferred to the breastfeeding child?” This will work ultimately serve to provide breastfeeding women and their caregivers with vital information to make the decisions regarding safe and effective drug therapy.