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背景冠状动脉钙化评分(CACS)已经用于预测冠心病(CHD)事件。然而,将CACS加入传统的CHD风险因子,是否可改善风险分级程度尚未确定。目的探讨将CACS加入基于传统风险因素预示模式中是否可改善风险分级。方法对来自多种族动脉粥样硬化研究的6 814例参试者,通过CT检查获得CACS。从2000年7月—2002年9月招募受试者,跟踪至2008年5月。排除糖尿病者。CHD 5年风险评估(采用Cox比例风险模型)分为0~<3%、3%~<10%、≥10%3个等级。模型1使用了年龄、性别、吸烟、收缩压、抗高血压药使用、总胆固醇、高密度脂蛋白胆固醇及种族风险因素。模型2使用了这些风险因素加CACS。我们计算了重新分级净改善,比较了模型2和模型1风险分布。观察冠心病事件发生情况。结果跟踪中位年5.8年,最终5 878例中有209个冠心病事件发生,122例为心肌梗死、死于冠心病或做了心脏骤停复苏。与模型1相比,模型2导出的风险预示明显改善〔重新分级净改善=0.25;95%C I(0.16,0.34);P<0.001〕。模型1中,人群中69%被分在最高或最低风险范围,而模型2有77%。使用模型2,23%的经历过冠心病事件者被重新分入高风险范围,13%的未经历过冠心病事件者被重新分到低风险范围。结论在多种族人群研究中,将CACS加入基于传统风险预示模式,可使风险分级显著改善,将更多的个体放入最极端风险范围内。
Background Coronary artery calcification score (CACS) has been used to predict coronary heart disease (CHD) events. However, the degree to which the inclusion of CACS in traditional CHD risk factors improves risk stratification has not been established. Objective To explore whether CBS can be improved based on the traditional risk factor prediction model. Methods CACS were obtained by CT examination of 6 814 participants from the Multi-Ethnic Atherosclerosis Study. Subjects were recruited from July 2000 to September 2002 and tracked until May 2008. Exclude people with diabetes. CHD 5-year risk assessment (Cox proportional hazards model) is divided into 3 levels of 0 ~ <3%, 3% ~ <10%, ≥ 10%. Model 1 used age, gender, smoking, systolic blood pressure, antihypertensive use, total cholesterol, high density lipoprotein cholesterol, and ethnic risk factors. Model 2 used these risk factors plus CACS. We calculated the net reclassification improvement, comparing the risk distributions of Model 2 and Model 1. Observe the occurrence of coronary heart disease events. Results The median follow-up was 5.8 years. Of the final 5,878 cases, 209 were CHD events, 122 were myocardial infarctions, died of CHD or had a cardiac arrest. The risk derived from Model 2 predicts a significant improvement over model 1 (net improvement in re-grade = 0.25; 95% CI (0.16, 0.34); P <0.001). In Model 1, 69% of the population was classified as highest or lowest risk, while Model 2 had 77%. Using the model, 2,23% of those who had experienced coronary heart disease were reintroduced into the high-risk range and 13% of those who had not experienced coronary disease were redirected to a lower risk range. Conclusion In a multi-ethnic population study, adding CACS to a model based on traditional risk prediction can significantly improve the risk rating and place more individuals within the most extreme risk ranges.