医学
心脏病学
内科学
肥厚性心肌病
心外膜脂肪
脂肪组织
接收机工作特性
磁共振成像
心外膜脂肪组织
切断
狼牙棒
心脏磁共振
回顾性队列研究
四分位数
比例危险模型
队列
生物标志物
心脏磁共振成像
试验预测值
危险分层
冲程容积
心肌病
放射科
曲线下面积
预测值
生存分析
心源性猝死
风险评估
核医学
心脏成像
不利影响
作者
Yipei Song,Mengyao Hu,Haibo Ren,Qimin Fang,Tian Zheng,Ziyan Feng,Xiwen Wang,Lin Xu,Lianggeng Gong
标识
DOI:10.1093/ehjci/jeaf305
摘要
Abstract Aims This study aimed to investigate the prognostic and incremental value of epicardial adipose tissue (EAT) parameters, including volume index (EATVI) and regional fat thickness, assessed by cardiac magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM). Methods and Results In this retrospective cohort of 457 HCM patients who underwent CMR between 2018 and 2024, EATVI and regional fat thickness at key cardiac grooves were quantified along with conventional clinical and imaging risk factors. Over a median follow-up of 27.5 months (IQR 15.6-47.6), major adverse cardiovascular events (MACE) occurred in 18.1% of patients. Significantly higher EATVI (72.21±14.21 vs. 56.71±10.36 ml/m², P<.001) and increased regional fat thickness (all P<0.05) were observed in patients with MACE. In multivariable Cox regression, EATVI remained an independent predictor of MACE (HR 1.03, 95% CI 1.01-1.05, P<.001). Incorporating EATVI into the clinical-CMR model improved discrimination (C-statistic 0.82 to 0.84; P=0.002), enhanced calibration, and provided greater net clinical benefit on decision-curve analysis. When appended to the 2014 ESC HCM Risk-SCD model, EATVI further improved discrimination (C-statistic 0.80 vs 0.71; P<.001) and calibration. Kaplan–Meier analyses using quartiles and the 62.5 ml/m² cutoff showed progressively worse event-free survival with higher EATVI; within the ESC-defined low-risk subgroup, curves also separated significantly (log-rank P<0.05). Time-dependent Receiver Operating Characteristic analyses confirmed stable predictive performance of these parameters. Conclusion EATVI and regional fat thickness derived from CMR independently predict adverse outcomes in HCM and improve risk stratification. Comprehensive EAT assessment may serve as a promising imaging biomarker for personalized management.
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