Imaging atherosclerosis for cardiovascular risk prediction- in search of the holy grail!

摘要

Assessment of cardiovascular (CV) risk forms the cornerstone of treatment of individuals requiring primary prevention of atherosclerotic CV disease (ASCVD). An estimate of the future risk of CV events is integral to clinical decision-making in these subjects as it permits tailoring therapy according to the likelihood of developing a vascular event in future. Traditionally, such an assessment is performed using population-based risk algorithms such as the Framingham risk score (FRS), the pooled cohort equation, QRISK2 etc. that return the probability of a person developing ASCVD event over next 10 years. Unfortunately, while such risk algorithms have proven accuracy for risk prediction at population level, their accuracy at individual-level is unsatisfactory, leading to treatment decisions that cannot be individualized. For example, a 20% 10-year ASCVD risk (considered high risk by most definitions) merely implies that 20 of 100 such individuals will develop an event over 10 years but cannot predict which 20.