Baker Heart and Diabetes Research Institute, Melbourne, Victoria, Australia; Baker Department of Cardio-Metabolic Health, University of Melbourne, Melbourne, Victoria, Australia; Monash Heart, Melbourne, Victoria, Australia.
Baker Heart and Diabetes Research Institute, Melbourne, Victoria, Australia; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.
J Am Coll Cardiol. 2022 Jul 26;80(4):373-387. doi: 10.1016/j.jacc.2022.05.015.
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评估某些特定人群（尤其是年轻人）的风险。相当一部分人也被归类为处于中危风险，对于他们来说，最佳的预防策略可能更加精确。一些个性化的风险预测工具，包括冠状动脉钙评分、多基因风险评分和代谢风险评分，可能能够提高风险评估，前提是临床试验有支持性的结果数据。其他工具也可能在不久的将来出现。多维风险预测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，将不必要的成本和风险降到最低，同时使获益最大化。高危人群也可以在生命早期被识别，从而有机会阻止初发冠状动脉粥样硬化的发展，预防未来的临床事件。
