UC San Diego

Coronary heart disease (CHD) causes approximately 1 out of 7 deaths in the United States, and nearly half of all CHD deaths occur within one hour of symptom onset. There is a critical need for risk prediction tools that can distinguish asymptomatic individuals at increased risk for a major adverse CHD event. Traditional tools such as the Framingham Risk Score use clinical biomarkers to stratify individuals and identify those who would benefit from preventive LDL-C lowering therapy. However, these tools identify very few younger individuals at increased risk, despite the fact that longer cumulative exposure to lower LDL-C shows increased protection from CHD events. Coronary artery calcium (CAC) measured via computed tomography (CT) is the best novel predictor of CHD events and is predictive of CHD events among young individuals. Recent guideline updates recommend LDL-C lowering therapy in individuals with any detectable CAC. Coronary calcification progresses with age, so scanning all younger individuals will result in a very low positive rate of detection. Age independent risk factors, such as genetic risk, are needed to determine which younger individuals will benefit from early calcium screening. Additionally, there is a critical need to understand scan sensitivity to non-zero CAC detection. The focus of this work is to apply innovative risk prediction tools to develop a successful CAC screening strategy among young individuals and to quantify the lower limit of calcium detectability using CT. We (1) identify the appropriate age for an individual to begin CT CAC screening using the age independent risk factors of sex and a genetic risk score (GRS). We then (2) evaluate GRS utility for CAC screening among younger individuals and in combination with traditional risk factors. We (3) quantify detectability of small calcium granules using simulations and a physical imaging phantom, and we assess the effect of changes in imaging parameters on improvements to calcium detectability. Overall, these results establish novel risk prediction tools to improve CAC scanning as a screening strategy for CHD and may enable earlier CAC detection in young individuals who have previously been misclassified as disease free.