目的:本研究的目的是确定冠状动脉CT血管造影术(CCTA)衍生的动脉粥样硬化斑块分析在缺血中的预后价值。
方法:对所有可用的基线CCTA进行动脉粥样硬化成像定量计算机断层扫描(AI-QCT),以量化斑块体积,composition,和分配。多变量Cox回归用于检查基线危险因素(年龄,性别,吸烟,糖尿病,高血压,射血分数,既往冠心病,估计肾小球滤过率,和他汀类药物的使用),患病血管的数量,通过AI-QCT确定的动脉粥样硬化斑块特征,中位随访时间为3.3年(四分位距2.2-4.4年),复合主要结局为心血管死亡或心肌梗死.在曲线下面积(AUC)分析中比较了斑块定量对风险因素的预测值。
结果:可分析的CCTA数据来自3711名参与者(平均年龄64岁,21%女性,79%的多支冠状动脉疾病)。在AI-QCT变量中,总斑块体积与主要结局密切相关(校正后风险比1.56,95%置信区间1.25-1.97/四分位距增加[559mm3];P=.001).在基线危险因素中加入AI-QCT斑块定量和表征可改善模型对6个月主要结局的预测价值(AUC0.688vs.0.637;P=.006),在2年(AUC0.660vs.0.617;P=.003),和4年的随访(AUC0.654vs.0.608;P=.002)。其他报告结果的结果相似。
结论:在缺血中,总斑块体积与心血管死亡或心肌梗死相关.在这个高度患病的地方,高危人群,使用AI-QCT衍生的斑块体积和组成指标对动脉粥样硬化负荷的评估增强了事件预测效果.
OBJECTIVE: The aim of this study was to determine the prognostic value of coronary computed tomography angiography (CCTA)-derived atherosclerotic plaque analysis in ISCHEMIA.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.