在1型糖尿病患者中人工智能引导的心脏计算机断层扫描 冠状动脉分数低的患者
Peter Wohlfahrt1,2, Michal Pazderník3, Natália Marhefková2,4
1Department of Preventive Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic.
Diabetes technology & therapeutics
|August 6, 2025
概括
人工智能引导的冠状动脉计算机断层扫描 (AI-QCT) 在1型糖尿病 (T1D) 患者中检测出显著的动脉样硬化,即使冠状动脉 (CAC) 评分较低. 这种先进的成像挑战了传统的风险评估,揭示了T1D中隐藏的心血管风险.
科学领域:
- 心脏病学 心脏病学
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 传统的心血管风险因素对于个人风险分层缺乏准确性.
- 冠状动脉 (CAC) 评分检测出化斑块,但可能低估1型糖尿病 (T1D) 患者非化斑块的风险.
- 了解T1D非化动脉样硬化患病率对于改善查至关重要.
研究的目的:
- 为了评估T1D患者的亚临床动脉样硬化负担,CAC<100使用人工智能引导的定量冠状动脉计算机断层血管学 (AI-QCT).
- 评估AI-QCT在确定T1D临床显著动脉样硬化和高风险斑块方面的实用性.
- 将AI-QCT的发现与传统的风险因素和CAC评分进行比较.
主要方法:
- 招募了167名T1D患者 (年龄≥30,持续时间≥10年,没有明显的ASCVD).
- 在所有参与者中评估了CAC和动脉超声波.
- 在一个子集 (n=52) 上进行了AI-QCT,其中CAC为0和心血管斑块或CAC为1至99.
主要成果:
- 只有21%的AI-QCT患者没有冠状动脉疾病.
- 在17%的患者中发现了显著的冠状动脉狭窄; 73%的患者有高风险的斑块.
- 与CAC相比,AI-QCT重新分类了58%的患者的心血管风险,与STENO1风险类别相比,AI-QCT重新分类了21%的患者心血管风险.
- 在AI-QCT和CAC之间达成公平协议 (κ = 0.25);与STENO1略有协议 (κ = 0.02).
结论:
- AI-QCT揭示了亚临床动脉样硬化和高风险斑块特征,这些特征被CAC和T1D的传统模型遗漏了.
- 较低的CAC得分并不可靠地等同于T1D患者心血管风险较低.
- AI-QCT提供了一种更敏感的方法来评估T1D中的心血管风险.
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