Risk factor–based models fail to accurately estimate risk in select populations, in particular younger individuals. A sizable number of people are also classified as being at intermediate risk, for whom the optimal preventive strategy could be more precise. Several personalized risk prediction tools, including coronary artery calcium scoring, polygenic risk scores, and metabolic risk scores may be able to improve risk assessment, pending supportive outcome data from clinical trials. Other tools may well emerge in the near future. A multidimensional approach to risk prediction holds the promise of precise risk prediction. This could allow for targeted prevention minimizing unnecessary costs and risks while maximizing benefits. High-risk individuals could also be identified early in life, creating opportunities to arrest the development of nascent coronary atherosclerosis and prevent future clinical events.

基于风险因素的模型无法准确估计特定人群的风险，尤其是年轻人。相当多的人也被归类为处于中等风险中，对他们来说，最佳预防策略可能更精确。几种个性化风险预测工具，包括冠状动脉钙化评分，多基因风险评分和代谢风险评分可能能够改善风险评估，等待临床试验的支持性结果数据。其他工具很可能在不久的将来出现。风险预测的多维方法有望实现精确的风险预测。这可以实现有针对性的预防，最大限度地减少不必要的成本和风险，同时最大限度地提高收益。还可以在生命早期识别出高危个体，从而为阻止新生冠状动脉粥样硬化的发展和预防未来的临床事件创造机会。