The Role of Myocardial Disease in Phenotypes of Cardiac Ageing

2019 Health Professional Scholarship

Years funded: 2020-2021

Cardiovascular disease (CVD) is the leading cause of death and disability in the world and has the greatest impact on patients over 65 years old. As the heart muscle (myocardium) weakens with advancing age, it is unable to provide enough blood to the bodys vital organs, leading to an increased risk of developing heart failure. Diagnostic and management strategies for CVD are currently extrapolated from clinical trials with sparse representation of the elderly. An understanding of how to control myocardial ageing is key to developing new strategies to improve the management of medical conditions common in the rapidly expanding ageing population for example, heart failure and aortic stenosis. Echocardiography (echo) is a clinically relevant and useful non-invasive tool for cardiac assessment. The direct relationship between echo parameters and myocardial ageing and how these correlate with clinical outcomes remains unclear.

Echo has the potential to improve early recognition of age-related cardiac-specific physiological changes and monitor responses to interventions. A large number of parameters are relevant to the echocardiographic examination, and these multi-dimensional data exceed the capability of standard analysis. Machine learning and big data analysis in health care is an emerging field that has the capability to identify associations in these datasets. This project aims to use big data and machine learning to integrate echo parameters, clinical and laboratory data to improve prognostication and risk assessment of the contributions of myocardial ageing to aortic stenosis and heart failure. The ultimate goal is to improve and enhance treatment of myocardial diseases by devising age-appropriate management guidelines.