基于深度学习的心外膜脂肪组织体积和衰减定量预测无症状患者的主要不良心血管事件。
Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.
机构信息
Department of Imaging and Medicine and the Smidt Heart Institute (E.E., S.C., H.G., S.C., R.J.H.M., P.J.S., B.K.T., D.S.B.), Cedars-Sinai Medical Center, Los Angeles, CA.
Biomedical Imaging Research Institute (P.A.M., F.C., X.C., M.G., A.R., D.D.), Cedars-Sinai Medical Center, Los Angeles, CA.
出版信息
Circ Cardiovasc Imaging. 2020 Feb;13(2):e009829. doi: 10.1161/CIRCIMAGING.119.009829. Epub 2020 Feb 17.
BACKGROUND
Epicardial adipose tissue (EAT) volume (cm) and attenuation (Hounsfield units) may predict major adverse cardiovascular events (MACE). We aimed to evaluate the prognostic value of fully automated deep learning-based EAT volume and attenuation measurements quantified from noncontrast cardiac computed tomography.
METHODS
Our study included 2068 asymptomatic subjects (56±9 years, 59% male) from the EISNER trial (Early Identification of Subclinical Atherosclerosis by Noninvasive Imaging Research) with long-term follow-up after coronary artery calcium measurement. EAT volume and mean attenuation were quantified using automated deep learning software from noncontrast cardiac computed tomography. MACE was defined as myocardial infarction, late (>180 days) revascularization, and cardiac death. EAT measures were compared to coronary artery calcium score and atherosclerotic cardiovascular disease risk score for MACE prediction.
RESULTS
At 14±3 years, 223 subjects suffered MACE. Increased EAT volume and decreased EAT attenuation were both independently associated with MACE. Atherosclerotic cardiovascular disease risk score, coronary artery calcium, and EAT volume were associated with increased risk of MACE (hazard ratio [95%CI]: 1.03 [1.01-1.04]; 1.25 [1.19-1.30]; and 1.35 [1.07-1.68], <0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72-0.96]; =0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm and coronary artery calcium ≥100 AU and was highest in subjects with both (<0.02 for all). In 1317 subjects, EAT volume was correlated with inflammatory biomarkers C-reactive protein, myeloperoxidase, and adiponectin reduction; EAT attenuation was inversely related to these biomarkers.
CONCLUSIONS
Fully automated EAT volume and attenuation quantification by deep learning from noncontrast cardiac computed tomography can provide prognostic value for the asymptomatic patient, without additional imaging or physician interaction.
背景
心外膜脂肪组织（EAT）体积（cm）和衰减（亨氏单位）可预测主要不良心血管事件（MACE）。我们旨在评估基于深度学习的全自动 EAT 体积和衰减测量值从非对比心脏 CT 量化的预后价值。
方法
我们的研究纳入了来自 EISNER 试验（非侵入性影像学研究早期识别亚临床动脉粥样硬化）的 2068 例无症状受试者（56±9 岁，59%为男性），并在冠状动脉钙测量后进行了长期随访。使用自动深度学习软件从非对比心脏 CT 量化 EAT 体积和平均衰减。比较 EAT 测量值与冠状动脉钙评分和动脉粥样硬化性心血管疾病风险评分对 MACE 的预测。
结果
在 14±3 年时，有 223 例受试者发生 MACE。增加的 EAT 体积和降低的 EAT 衰减均与 MACE 独立相关。动脉粥样硬化性心血管疾病风险评分、冠状动脉钙和 EAT 体积与 MACE 风险增加相关（风险比[95%CI]：1.03[1.01-1.04]；1.25[1.19-1.30]；和 1.35[1.07-1.68]，均 <0.01），EAT 衰减与 MACE 呈负相关（风险比，0.83[95%CI，0.72-0.96]；=0.01），哈雷尔 C 统计值为 0.76。随着 EAT 体积≥113cm 和冠状动脉钙≥100AU，MACE 风险逐渐增加，在同时存在这两种情况的患者中风险最高（均 <0.02）。在 1317 例患者中，EAT 体积与炎症生物标志物 C 反应蛋白、髓过氧化物酶和脂联素减少相关；EAT 衰减与这些生物标志物呈负相关。
结论
基于深度学习的非对比心脏 CT 全自动 EAT 体积和衰减量化可为无症状患者提供预后价值，无需额外的影像学检查或医生干预。
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