Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT.
Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT.
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DOI：
10.1016/j.jcmg.2010.07.014
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发表时间：
2010-11
影响因子：
14
通讯作者：
Berman, Daniel S.
中科院分区：
文献类型：
--
作者：
Tamarappoo, Balaji;Dey, Damini;Shmilovich, Haim;Nakazato, Ryo;Gransar, Heidi;Cheng, Victor Y.;Friedman, John D.;Hayes, Sean W.;Thomson, Louise E. J.;Slomka, Piotr J.;Rozanski, Alan;Berman, Daniel S.
我们评估了心包脂肪和心肌缺血之间的关系，以进行危险分层。通过计算冠状动脉钙化评分（CCS）的非造影计算机断层扫描（CT）测量的心包脂肪体积（PFV）和胸部脂肪体积（TFV）与CCS增加和主要不良心血管事件风险相关。从1,777例在6个月内进行单光子发射计算机断层扫描（SPECT）的无已知冠状动脉疾病（CAD）的连续患者队列中，我们比较了73例SPECT缺血患者（病例）和146例正常SPECT患者（对照组），这些患者的年龄、性别、CCS类别、症状和CAD风险因素相匹配。自动测量TFV。手动定义心包轮廓，其中自动识别脂肪体素以计算PFV。使用标准的17段和5点评分模型进行SPECT的计算机辅助视觉解释;灌注缺损量化为总应力评分（SSS）和总静息评分（SRS）。缺血定义为：SSS - SRS ≥4。研究了PFV和TFV与缺血的独立关系。平均PFV较高的病例（99.1 ± 42.9 cm 3 vs. 80.1 ± 31.8 cm 3，p = 0.0003）和TFV（196.1 ± 82.7 cm 3 vs. 160.8 ± 72.1 cm 3，p = 0.001）和更高频率的PFV >125 cm 3（22% vs. 8%，p = 0.004）和TFV >200 cm3（40% vs. 19%，p = 0.001）。校正CCS后，PFV和TFV仍然是缺血的最强预测因子（PFV每增加一倍的比值比[OR]：2.91，95%置信区间[CI]：1.53 - 5.52，p = 0.001; TFV的比值比[OR]：2.64，95% CI：1.48 - 4.72，p = 0.001）。受试者操作特征分析显示，当在CCS中加入PFV或TFV时，缺血预测（如受试者-操作者特征曲线下面积所示）显著改善（均为0.75 vs. 0.68，p = 0.04）。心包脂肪与无已知CAD患者的心肌缺血显著相关，可能有助于改善风险评估。
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 cm3 vs. 80.1 ± 31.8 cm3, p = 0.0003) and TFV (196.1 ± 82.7 cm3 vs. 160.8 ± 72.1 cm3, p = 0.001) and higher frequencies of PFV >125 cm3 (22% vs. 8%, p = 0.004) and TFV >200 cm3 (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.
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影响因子：
37.8
作者：
D'Agostino, Ralph B., Jr.
通讯作者：
D'Agostino, Ralph B., Jr.
影响因子：
2.4
作者：
Berman, Daniel S.;Kang, Xingping;Gransar, Heidi;Gerlach, James;Friedman, John D.;Hayes, Sean W.;Thomson, Louise E. J.;Hachamovitch, Rory;Shaw, Leslee J.;Slomka, Piotr J.;De Yang, Ling;Germano, Guido
通讯作者：
Germano, Guido
影响因子：
2.4
作者：
Berman, DS;Abidov, A;Hachamovitch, R
通讯作者：
Hachamovitch, R
影响因子：
7.1
作者：
KVIST, H;CHOWDHURY, B;SJOSTROM, L
通讯作者：
SJOSTROM, L
影响因子：
37.8
作者：
Mazurek, T;Zhang, LF;Shi, Y
通讯作者：
Shi, Y