Department of Imaging, Cedars-Sinai Medical Center and Cedars-Sinai Heart Institute, Los Angeles, California, USA.
JACC Cardiovasc Imaging. 2010 Nov;3(11):1104-12. doi: 10.1016/j.jcmg.2010.07.014.
We evaluated the association between pericardial fat and myocardial ischemia for risk stratification.
Pericardial fat volume (PFV) and thoracic fat volume (TFV) measured from noncontrast computed tomography (CT) performed for calculating coronary calcium score (CCS) are associated with increased CCS and risk for major adverse cardiovascular events.
From a cohort of 1,777 consecutive patients without previously known coronary artery disease (CAD) with noncontrast CT performed within 6 months of single photon emission computed tomography (SPECT), we compared 73 patients with ischemia by SPECT (cases) with 146 patients with normal SPECT (controls) matched by age, gender, CCS category, and symptoms and risk factors for CAD. TFV was automatically measured. Pericardial contours were manually defined within which fat voxels were automatically identified to compute PFV. Computer-assisted visual interpretation of SPECT was performed using standard 17-segment and 5-point score model; perfusion defect was quantified as summed stress score (SSS) and summed rest score (SRS). Ischemia was defined by: SSS - SRS ≥4. Independent relationships of PFV and TFV to ischemia were examined.
Cases had higher mean PFV (99.1 ± 42.9 cm(3) vs. 80.1 ± 31.8 cm(3), p = 0.0003) and TFV (196.1 ± 82.7 cm(3) vs. 160.8 ± 72.1 cm(3), p = 0.001) and higher frequencies of PFV >125 cm(3) (22% vs. 8%, p = 0.004) and TFV >200 cm(3) (40% vs. 19%, p = 0.001) than controls. After adjustment for CCS, PFV and TFV remained the strongest predictors of ischemia (odds ratio [OR]: 2.91, 95% confidence interval [CI]: 1.53 to 5.52, p = 0.001 for each doubling of PFV; OR: 2.64, 95% CI: 1.48 to 4.72, p = 0.001 for TFV). Receiver operating characteristic analysis showed that prediction of ischemia, as indicated by receiver-operator characteristic area under the curve, improved significantly when PFV or TFV was added to CCS (0.75 vs. 0.68, p = 0.04 for both).
Pericardial fat was significantly associated with myocardial ischemia in patients without known CAD and may help improve risk assessment.
我们评估了心包脂肪与心肌缺血之间的关系，以进行危险分层。
从接受非对比 CT 检查以计算冠状动脉钙评分（CCS）的无已知冠状动脉疾病（CAD）的连续 1777 例患者队列中，我们比较了单光子发射 CT（SPECT）显示有缺血的 73 例患者（病例）与 146 例 SPECT 正常的患者（对照），病例与对照按年龄、性别、CCS 类别和 CAD 的症状及危险因素相匹配。自动测量胸内脂肪体积（TFV）。手动定义心包轮廓，其中自动识别脂肪体素以计算心包脂肪体积（PFV）。使用标准的 17 节段和 5 分评分模型对 SPECT 进行计算机辅助视觉解释；灌注缺损定量为总和应激评分（SSS）和总和静息评分（SRS）。缺血定义为：SSS-SRS≥4。检查了 PFV 和 TFV 与缺血之间的独立关系。
病例的平均 PFV（99.1±42.9cm3 比 80.1±31.8cm3，p=0.0003）和 TFV（196.1±82.7cm3 比 160.8±72.1cm3，p=0.001）更高，PFV>125cm3（22%比 8%，p=0.004）和 TFV>200cm3（40%比 19%，p=0.001）的频率更高。在校正 CCS 后，PFV 和 TFV 仍然是缺血的最强预测因子（优势比[OR]：2.91，95%置信区间[CI]：1.53 至 5.52，p=0.001，PFV 加倍；OR：2.64，95%CI：1.48 至 4.72，p=0.001，TFV）。受试者工作特征分析显示，当 PFV 或 TFV 与 CCS 联合使用时，缺血的预测（表示为受试者操作特征曲线下面积）显著改善（0.75 比 0.68，p=0.04）。
在心包脂肪与无已知 CAD 的患者心肌缺血之间存在显著相关性，这可能有助于改善风险评估。
