AI-CVD-AF:基于冠状动脉扫描的新型心房预测模型
Seyed Reza Mirjalili1, Kyle Atlas1, Anthony P Reeves2
1HeartLung.AI, Houston, TX, 77021, USA.
European heart journal. Cardiovascular Imaging
|January 27, 2026
概括
来自冠状动脉扫描的左至右心室体积比率有效预测心力衰竭 (HF). 左心室与右心室 (LV/RV) 的比率显示为HF发病率的最强的预测能力.
科学领域:
- 心血管成像 - 心血管成像
- 人工智能在医学中的应用
- 预测分析是一种预测分析.
背景情况:
- 冠状动脉 (CAC) 扫描传统上提供了CAC分数.
- 人工智能-CVD倡议从CAC扫描中提取了更深入的见解.
- 以前的研究表明,人工智能衍生的心室体积可以预测心力衰竭 (HF).
研究的目的:
- 评估左至右心室体积比率是否比单独的心室体积更好地预测HF.
- 评估LV/RV,LA/RA和LA/RV比率对事件HF的预测性能.
主要方法:
- 利用了来自5732名无症状的多民族动脉样硬化研究 (MESA) 参与者的AI-CVD心脏体积测量数据.
- 分析了左至右心室 (LV/RV),心房 (LA/RA) 和左心室至右心室 (LA/RV) 的体积比率.
- 采用多变量Cox模型和特征选择;在Framingham心脏研究后代 (FHS-O) 队列中得到验证.
主要成果:
- 升高的LV/RV,LA/RA和LA/RV比率与MESA中的事件HF有很强的关联.
- LV/RV比率 (≥95百分位) 显示HF的危险比率 (HR) 为4.04.
- 即使在正常 LV 尺寸的参与者中,LV/RV 比率仍然是一个重要的 HF 预测指标,并且是 FHS-O 中最强的预测指标.
结论:
- 从CAC扫描中获得的AI衍生的LV/RV比率是两个潜在队列中事件HF的强有力的预测指标.
- 这些成像生物标志物在HF风险分层中具有临床实用性的潜力.
- 需要进一步的临床试验来确定这些AI衍生的成像生物标志物的实用性.
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