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Deep Learning-Based Quantification of Epicardial Adipose Tissue Volume and Attenuation Predicts Major Adverse Cardiovascular Events in Asymptomatic Subjects.
Circulation: Cardiovascular Imaging ( IF 7.5 ) Pub Date : 2020-02-17 , DOI: 10.1161/circimaging.119.009829
Evann Eisenberg 1 , Priscilla A McElhinney 2 , Frederic Commandeur 2 , Xi Chen 2 , Sebastien Cadet 1 , Markus Goeller 2, 3 , Aryabod Razipour 2 , Heidi Gransar 1 , Stephanie Cantu 1 , Robert J H Miller 1 , Piotr J Slomka 1 , Nathan D Wong 4 , Alan Rozanski 5 , Stephan Achenbach 3 , Balaji K Tamarappoo 1 , Daniel S Berman 1 , Damini Dey 2
Affiliation  

BACKGROUND Epicardial adipose tissue (EAT) volume (cm3) 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], P<0.01 for all) and EAT attenuation was inversely associated with MACE (hazard ratio, 0.83 [95% CI, 0.72-0.96]; P=0.01), with corresponding Harrell C statistic of 0.76. MACE risk progressively increased with EAT volume ≥113 cm3 and coronary artery calcium ≥100 AU and was highest in subjects with both (P<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.

中文翻译:

基于深度学习的心外膜脂肪组织体积和衰减量化预测无症状受试者的主要不良心血管事件。

背景心外膜脂肪组织 (EAT) 体积 (cm3) 和衰减(Hounsfield 单位)可预测主要不良心血管事件 (MACE)。我们旨在评估从非对比心脏计算机断层扫描量化的全自动基于深度学习的 EAT 体积和衰减测量的预后价值。方法 我们的研究包括来自 EISNER 试验(通过无创成像研究早期识别亚临床动脉粥样硬化)的 2068 名无症状受试者(56±9 岁,59% 男性),在冠状动脉钙测量后进行长期随访。使用来自非对比剂心脏计算机断层扫描的自动深度学习软件量化 EAT 体积和平均衰减。MACE 被定义为心肌梗死、晚期(>180 天)血运重建和心源性死亡。将 EAT 测量值与冠状动脉钙化评分和动脉粥样硬化心血管疾病风险评分进行比较,以预测 MACE。结果 在 14±3 年时,223 名受试者患有 MACE。EAT 体积增加和 EAT 衰减减少均与 MACE 独立相关。动脉粥样硬化心血管疾病风险评分、冠状动脉钙化和 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] P<0.01)和 EAT 衰减与 MACE 呈负相关(风险比,0.83 [95% CI,0.72-0.96];P=0.01),相应的 Harrell C 统计量为 0.76。MACE 风险随着 EAT 体积 ≥ 113 cm3 和冠状动脉钙 ≥ 100 AU 逐渐增加,并且在两者的受试者中最高(所有 P<0.02)。在 1317 个科目中,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 通过非对比剂心脏计算机断层扫描的深度学习,全自动 EAT 体积和衰减量化可以为无症状患者提供预后价值,无需额外的成像或医生互动。
更新日期:2020-02-18
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