Department of Cardiology, Amsterdam UMC, Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.
Department of Vascular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.
Eur Heart J. 2024 Sep 29;45(36):3735-3747. doi: 10.1093/eurheartj/ehae471.
The aim of this study was to determine the prognostic value of coronary computed tomography angiography (CCTA)-derived atherosclerotic plaque analysis in ISCHEMIA.
Atherosclerosis imaging quantitative computed tomography (AI-QCT) was performed on all available baseline CCTAs to quantify plaque volume, composition, and distribution. Multivariable Cox regression was used to examine the association between baseline risk factors (age, sex, smoking, diabetes, hypertension, ejection fraction, prior coronary disease, estimated glomerular filtration rate, and statin use), number of diseased vessels, atherosclerotic plaque characteristics determined by AI-QCT, and a composite primary outcome of cardiovascular death or myocardial infarction over a median follow-up of 3.3 (interquartile range 2.2-4.4) years. The predictive value of plaque quantification over risk factors was compared in an area under the curve (AUC) analysis.
Analysable CCTA data were available from 3711 participants (mean age 64 years, 21% female, 79% multivessel coronary artery disease). Amongst the AI-QCT variables, total plaque volume was most strongly associated with the primary outcome (adjusted hazard ratio 1.56, 95% confidence interval 1.25-1.97 per interquartile range increase [559 mm3]; P = .001). The addition of AI-QCT plaque quantification and characterization to baseline risk factors improved the model's predictive value for the primary outcome at 6 months (AUC 0.688 vs. 0.637; P = .006), at 2 years (AUC 0.660 vs. 0.617; P = .003), and at 4 years of follow-up (AUC 0.654 vs. 0.608; P = .002). The findings were similar for the other reported outcomes.
In ISCHEMIA, total plaque volume was associated with cardiovascular death or myocardial infarction. In this highly diseased, high-risk population, enhanced assessment of atherosclerotic burden using AI-QCT-derived measures of plaque volume and composition modestly improved event prediction.
本研究旨在确定冠状动脉计算机断层扫描血管造影术（CCTA）衍生的动脉粥样硬化斑块分析在 ISCHEMIA 中的预后价值。
对所有基线 CCTA 进行动脉粥样硬化成像定量计算机断层扫描（AI-QCT），以定量斑块体积、组成和分布。使用多变量 Cox 回归来检查基线风险因素（年龄、性别、吸烟、糖尿病、高血压、射血分数、先前的冠心病、估计肾小球滤过率和他汀类药物使用）、病变血管数量、通过 AI-QCT 确定的动脉粥样硬化斑块特征与心血管死亡或心肌梗死的复合主要结局之间的关联，中位随访时间为 3.3 年（四分位间距 2.2-4.4）。通过曲线下面积（AUC）分析比较斑块定量与风险因素的预测价值。
3711 名参与者的可分析 CCTA 数据可用（平均年龄 64 岁，21%为女性，79%为多血管冠状动脉疾病）。在 AI-QCT 变量中，总斑块体积与主要结局最密切相关（调整后的危险比为 1.56，95%置信区间为每增加四分位间距[559 mm3]的 1.25-1.97；P =.001）。将 AI-QCT 斑块定量和特征添加到基线风险因素中，可提高主要结局的模型预测值，分别在 6 个月（AUC 0.688 比 0.637；P =.006）、2 年（AUC 0.660 比 0.617；P =.003）和 4 年随访（AUC 0.654 比 0.608；P =.002）时。其他报告结果的结果也相似。
在 ISCHEMIA 中，总斑块体积与心血管死亡或心肌梗死相关。在这个高度患病、高风险的人群中，使用 AI-QCT 衍生的斑块体积和成分测量值增强对动脉粥样硬化负担的评估，适度提高了事件预测能力。
