Budoff Matthew J, Shaw Leslee J, Liu Sandy T, Weinstein Steven R, Mosler Tristen P, Tseng Philip H, Flores Ferdinand R, Callister Tracy Q, Raggi Paolo, Berman Daniel S
Harbor-UCLA Los Angeles Biomedical Research Institute, Torrance, California 90502, USA.
J Am Coll Cardiol. 2007 May 8;49(18):1860-70. doi: 10.1016/j.jacc.2006.10.079. Epub 2007 Apr 20.
The purpose of this study was to develop risk-adjusted multivariable models that include risk factors and coronary artery calcium (CAC) scores measured with electron-beam tomography in asymptomatic patients for the prediction of all-cause mortality.
Several smaller studies have documented the efficacy of CAC testing for assessment of cardiovascular risk. Larger studies with longer follow-up will lend strength to the hypothesis that CAC testing will improve outcomes, cost-effectiveness, and safety of primary prevention efforts.
We used an observational outcome study of a cohort of 25,253 consecutive, asymptomatic individuals referred by their primary physician for CAC scanning to assess cardiovascular risk. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and CAC scores.
The frequency of CAC scores was 44%, 14%, 20%, 13%, 6%, and 4% for scores of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively. During a mean follow-up of 6.8 +/- 3 years, the death rate was 2% (510 deaths). The CAC was an independent predictor of mortality in a multivariable model controlling for age, gender, ethnicity, and cardiac risk factors (model chi-square = 2,017, p < 0.0001). The addition of CAC to traditional risk factors increased the concordance index significantly (0.61 for risk factors vs. 0.81 for the CAC score, p < 0.0001). Risk-adjusted relative risk ratios for CAC were 2.2-, 4.5-, 6.4-, 9.2-, 10.4-, and 12.5-fold for scores of 11 to 100, 101 to 299, 300 to 399, 400 to 699, 700 to 999, and >1,000, respectively (p < 0.0001), when compared with a score of 0. Ten-year survival (after adjustment for risk factors, including age) was 99.4% for a CAC score of 0 and worsened to 87.8% for a score of >1,000 (p < 0.0001).
This large observational data series shows that CAC provides independent incremental information in addition to traditional risk factors in the prediction of all-cause mortality.
本研究旨在建立风险调整多变量模型，纳入无症状患者的风险因素及通过电子束断层扫描测量的冠状动脉钙化（CAC）评分，以预测全因死亡率。
多项较小规模研究已证明CAC检测在评估心血管风险方面的有效性。随访时间更长的大规模研究将为CAC检测可改善一级预防工作的结局、成本效益及安全性这一假说提供有力支持。
我们对一组由初级医师转诊来进行CAC扫描以评估心血管风险的25253例连续无症状个体进行了观察性结局研究。建立多变量Cox比例风险模型以预测全因死亡率。风险调整模型纳入了冠心病的传统风险因素及CAC评分。
CAC评分为0、1至10、11至100、101至400、401至1000及>1000时的频率分别为44%、14%、20%、13%、6%和4%。在平均6.8±3年的随访期间，死亡率为2%（510例死亡）。在控制年龄、性别、种族和心脏风险因素的多变量模型中，CAC是死亡率的独立预测因子（模型卡方=2017，p<0.0001）。将CAC加入传统风险因素后，一致性指数显著增加（风险因素为0.61，而CAC评分为0.81，p<0.0001）。与评分为0相比，CAC评分在11至100、101至299、300至399、400至699、700至999及>1000时的风险调整相对风险比分别为2.2倍、4.5倍、6.4倍、9.2倍、10.4倍和12.5倍（p<0.0001）。CAC评分为0时，经风险因素（包括年龄）调整后的10年生存率为99.4%，评分为>1000时降至87.8%（p<0.0001）。
这个大型观察性数据系列表明，在预测全因死亡率方面，除传统风险因素外，CAC还提供了独立的增量信息。
