人工智能预测心血管事件使用机会主义的心上皮质脂肪组织评估从计算机断层扫描分数
Tao Hu1, Joshua Freeze1, Prerna Singh1
1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.
JACC. Advances
|October 7, 2024
概括
与传统评估相比,来自心表脂肪组织 (EAT) 的新型"脂肪基因"特征显著改善了主要不良心血管事件 (MACE) 的预测.
科学领域:
- 心血管成像和诊断系统
- 放射学和计算机病理学
- 为动脉样硬化发现生物标志物
背景情况:
- 基本的心表脂肪组织 (EAT) 评估,如体积和霍恩斯菲尔德单位 (HU),用于预测主要不良心血管事件 (MACE).
- 目前的EAT评估对于准确预测动脉样硬化相关的MACE存在局限性.
研究的目的:
- 开发新的,手工制作的EAT特征,称为"脂肪基因",以更好地捕捉EAT病理生理学.
- 为了提高MACEs的预测准确度,使用这些新的脂肪-omics功能.
主要方法:
- 研究了400名患有低剂量心脏CT分数检查的患者队列,其MACE丰富设计 (56%的事件率).
- 使用深度学习对心底脂肪组织 (EAT) 进行了细分,并提取了148个初始的脂肪-omics特征.
- 考克斯弹性网回归用于特征减少和MACE预测,并进行引导验证.
主要成果:
- 传统的EAT特征显示了边际预测性能 (C指数0.530.57).
- 一个15个特征的脂肪-奥米克模型显著改善了MACE预测 (C指数=0.69).
- 高风险特征包括高的HU值和EAT厚度的异质性,与炎症和接近冠状动脉相关.
结论:
- 精心调整的,可解释的EAT评估,如fat-omics,可以更好地预测心血管风险.
- 这种方法有望更准确地识别患有MACEs高风险的患者.
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