Incidental coronary calcification on thoracic computed tomography

The identification of coronary artery disease is critical to guide targeted preventative treatment to reduce the risk of subsequent myocardial infarction.

Coronary artery calcification is pathognomonic of coronary artery disease and provides additive prognostic information to established cardiovascular risk scores.

Although routinely assessed on dedicated cardiac computed tomography imaging, coronary artery calcification can be identified on thoracic computed tomography performed for non-cardiac indications.

In multiple previous cohort studies, I have shown that incidental coronary artery calcification can identify patients at risk of subsequent cardiovascular events.

This fellowship aims to investigate the prevalence of coronary artery calcification at scale in national computed tomography datasets using automated machine learning techniques to quantify coronary artery calcification.

This will enable me to establish associations with subsequent outcomes and co-existing disease, and ultimately to determine the potential implications for changes in management based on the identification of incidental coronary artery calcification.