据4月28日刊JAMA上的一则研究披露，除了传统的风险因子之外再使用一种基于冠状动脉中钙含量的分数可改善预测冠心病事件的风险分级。
冠状动脉的钙分数（又称CACS；它是用计算机断层扫描来估测冠状动脉管壁上的斑块中钙的集结所决定的）在大型的前瞻性研究中显示，它与未来发生心血管事件的风险有关联。然而，人们对将CACS加入到传统的冠心病（CHD）风险因子之中究竟能够在多大的程度上提高冠心病风险的分级依然不是很清楚。
Northwestern University Feinberg School of Medicine, Chicago的Tamar S. Polonsky, M.D.及其同僚开展了一项研究，旨在确定在一个冠心病预测模型中，在基于传统的风险因子之上再增加CACS是否能够提高冠心病风险分级的精确性。研究人员用电脑断层扫描（这是一种成像的方法）的方式对Multi-Ethnic Study of Atherosclerosis (MESA) 中的6814名参与者的CACS进行了估测。该人群组没有已知的心血管疾病。
模型-1中所用的风险因子有年龄、族裔、性别、是否抽烟、是否使用降压药、收缩压以及总胆固醇和高密度脂蛋白胆固醇的测定值。模型-2在这些风险因子之上再增加上了CACS。研究人员通过对模型-2与模型1进行比较来计算风险重新评估的改善净值。
在一个最终有5878人的群组中，在一个中位数（中点）为5.8年的随访期中发生了209起CHD事件，其中122起为严重事件（其中包括心肌梗塞、因CHD而死亡，或因心跳停止而实行复苏术）。研究人员发现，与模型-1相比，模型-2显着改善了风险预测的结果。在模型-1中，团组中有69%的人被划分到风险最高或最低的部类之中，而在模型-2中，这一数字为77%。在模型中加入了CACS之后，这些经历了心血管事件的人中有另外23% 的人被重新归划到高风险部类，而那些没有经历心血管事件的人中有另外13% 的人被重新归划为低风险部类。在中级风险的人中，有16%的人被重新划归到高风险组中，而39%的人则被划分到低风险组中。
文章的作者写道：“本项研究的结果显示，在一个取自4个美国族裔人群的无症状样本中，当传统的风险因子之中加入了CACS之后，它可显着改善预测患者发生CHD事件的风险分类。加入个人的CACS可获得比仅使用传统的风险因子对CHD未来风险的更为精确的估计。”
“这些结果可鼓励人们进入下一个阶段的评估：用CACS来评估临床后果。”
推荐原文出处：
JAMA. 2010;303(16):1610-1616.
Coronary Artery Calcium Score and Risk Classification for Coronary Heart Disease Prediction
Tamar S. Polonsky, MD; Robyn L. McClelland, PhD; Neal W. Jorgensen, BS; Diane E. Bild, MD, MPH; Gregory L. Burke, MD, MSc; Alan D. Guerci, MD; Philip Greenland, MD
Context The coronary artery calcium score (CACS) has been shown to predict future coronary heart disease (CHD) events. However, the extent to which adding CACS to traditional CHD risk factors improves classification of risk is unclear.
Objective To determine whether adding CACS to a prediction model based on traditional risk factors improves classification of risk.
Design, Setting, and Participants CACS was measured by computed tomography in 6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based cohort without known cardiovascular disease. Recruitment spanned July 2000 to September 2002; follow-up extended through May 2008. Participants with diabetes were excluded from the primary analysis. Five-year risk estimates for incident CHD were categorized as 0% to less than 3%, 3% to less than 10%, and 10% or more using Cox proportional hazards models. Model 1 used age, sex, tobacco use, systolic blood pressure, antihypertensive medication use, total and high-density lipoprotein cholesterol, and race/ethnicity. Model 2 used these risk factors plus CACS. We calculated the net reclassification improvement and compared the distribution of risk using model 2 vs model 1.
Main Outcome Measures Incident CHD events.
Results During a median of 5.8 years of follow-up among a final cohort of 5878, 209 CHD events occurred, of which 122 were myocardial infarction, death from CHD, or resuscitated cardiac arrest. Model 2 resulted in significant improvements in risk prediction compared with model 1 (net reclassification improvement = 0.25; 95% confidence interval, 0.16-0.34; P & .001). In model 1, 69% of the cohort was classified in the highest or lowest risk categories compared with 77% in model 2. An additional 23% of those who experienced events were reclassified as high risk, and an additional 13% without events were reclassified as low risk using model 2.
Conclusion In this multi-ethnic cohort, addition of CACS to a prediction model based on traditional risk factors significantly improved the classification of risk and placed more individuals in the most extreme risk categories.
Author Affiliations: Department of Preventive Medicine, Northwestern University, Chicago, Illinois (Drs Polonsky and Greenland); Department of Biostatistics, University of Washington, Seattle (Dr McClelland and Mr Jorgensen); Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, Maryland (Dr Bild); Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina (Dr Burke); and St Francis Hospital, The Heart Center, Roslyn, New York (Dr Guerci).
