Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL.
Am Heart J. 2014 Sep;168(3):391-7. doi: 10.1016/j.ahj.2014.06.009. Epub 2014 Jun 9.
Electrocardiographic (ECG) abnormalities and coronary artery calcium (CAC) identify different aspects of subclinical coronary heart disease (CHD). We sought to determine whether ECG abnormalities improve risk prediction for all CHD and fatal CHD events jointly with CAC measures.
We included 6,406 men and women from the MESA aged 45 to 84 years who were free of cardiovascular disease at the time of enrollment (2000-2002). We stratified participants by presence of ST-T and Q wave abnormalities: any major, any minor/no major, and no major/minor using the Minnesota Code classifications. CAC score was defined into one of the following strata: 0, 1 to 100, 101 to 300, greater than 300. We created risk prediction models using MESA-specific coefficients for traditional risk factors (RFs) and calculated categorical net reclassification improvement (NRI) for all and fatal CHD.
Over a median follow-up of 10 years, we observed that the addition of ECG abnormalities to a risk prediction model for all CHD resulted in a categorical NRI of 0.05 (P = .04). For fatal CHD alone, the addition of ECG abnormalities resulted in categorical NRI of 0.09 (P = .02). Addition of ECG abnormalities to a model containing RFs and CAC resulted in categorical NRI of 0.02 (P = .11) for all CHD events. We also observed differences in the association between ECG abnormalities and CHD when stratifying by CAC presence.
Electrocardiographic abnormalities improved risk prediction for CHD when added to RFs but not when added to CAC. Electrocardiographic abnormalities particularly improved risk prediction for fatal CHD.
心电图(ECG)异常和冠状动脉钙(CAC)可识别亚临床冠心病(CHD)的不同方面。我们旨在确定心电图异常是否可以与 CAC 测量结果一起改善对所有 CHD 和致命性 CHD 事件的风险预测。
我们纳入了年龄在 45 至 84 岁、在入组时(2000-2002 年)无心血管疾病的 MESA 研究中的 6406 名男性和女性。我们根据 ST-T 和 Q 波异常的存在对参与者进行分层:任何主要、任何次要/无主要和无主要/次要,使用明尼苏达州代码分类。CAC 评分定义为以下任何一个区间:0、1 至 100、101 至 300、大于 300。我们使用 MESA 特定的传统危险因素(RFs)系数创建风险预测模型,并计算所有 CHD 和致命性 CHD 的分类净重新分类改善(NRI)。
在中位数为 10 年的随访期间,我们观察到将心电图异常添加到所有 CHD 的风险预测模型中可使分类 NRI 增加 0.05(P=0.04)。对于致命性 CHD 单独而言,添加心电图异常可使分类 NRI 增加 0.09(P=0.02)。将心电图异常添加到包含 RFs 和 CAC 的模型中,可使所有 CHD 事件的分类 NRI 增加 0.02(P=0.11)。我们还观察到在根据 CAC 存在情况对心电图异常与 CHD 之间的关系进行分层时存在差异。
当添加到 RFs 时,心电图异常可以改善 CHD 的风险预测,但当添加到 CAC 时则不然。心电图异常尤其可以改善致命性 CHD 的风险预测。