黄书苑,杨宝珠,于鑫鑫,王锡明.CT血流储备分数与冠状动脉周围脂肪衰减指数联合临床及冠状动脉CT血管造影特征预测主动脉瓣置换术术后不良心血管事件[J].中国医学影像技术,2024,40(6):848~852
CT血流储备分数与冠状动脉周围脂肪衰减指数联合临床及冠状动脉CT血管造影特征预测主动脉瓣置换术术后不良心血管事件
CT-derived fractional flow reserve and pericoronary fat attenuation index combined with clinical and coronary CT angiography characteristics for predicting major adverse cardiovascular events after aortic valve replacement
投稿时间:2023-12-01  修订日期:2024-04-10
DOI:10.13929/j.issn.1003-3289.2024.06.011
中文关键词:  冠状动脉疾病  主动脉瓣狭窄  不良心血管事件  CT血流储备分数  脂肪衰减指数
英文关键词:coronary artery disease  aortic valve stenosis  major adverse cardiovascular events  CT-derived fractional flow reserve  fat attenuation index
基金项目:国家自然科学基金(82271993)。
作者单位E-mail
黄书苑 山东第一医科大学附属省立医院医学影像科, 山东 济南 250021  
杨宝珠 山东第一医科大学附属省立医院医学影像科, 山东 济南 250021  
于鑫鑫 山东第一医科大学附属省立医院医学影像科, 山东 济南 250021  
王锡明 山东第一医科大学附属省立医院医学影像科, 山东 济南 250021 wxming369@163.com 
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中文摘要:
      目的 评估CT血流储备分数(CT-FFR)与冠状动脉周围脂肪衰减指数(FAI)联合临床及冠状动脉CT血管造影(CCTA)特征预测主动脉瓣置换术(AVR)术后发生主要不良心血管事件(MACE)的价值。方法 回顾性分析139例接受AVR的主动脉瓣狭窄患者,根据随访中是否发生MACE将其分为MACE组与非MACE组;以Cox比例风险回归分析临床、CCTA及冠状动脉CT-FFR、FAI,筛选术后发生MACE的独立预测因素,基于临床、CCTA特征,以及CT-FFR及右冠状动脉(RCA)FAI建立嵌套模型。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC)及Harrell C指数,评估各模型诊断效能及其拟合优度。结果 MACE组22例、无MACE组117例。CT-FFR(HR=3.683)及RCA-FAI(HR=3.261)均为AVR术后发生MACE的独立预测因素。临床模型、临床+CCTA、临床+CCTA+CT-FFR模型及临床+CCTA+CT-FFR+RCA-FAI模型预测AVR术后MACE的AUC分别为0.636、0.730、0.758及0.817,C指数分别为0.614、0.707、0.733及0.782;其中,临床+CCTA+CT-FFR+RCA-FAI模型预测结果与实际结果的一致性最高、拟合优度最佳。结论 CT-FFR及RCA-FAI联合临床及CCTA特征能有效预测AVR术后MACE。
英文摘要:
      Objective To explore the value of CT-derived fractional flow reserve (CT-FFR) and pericoronary fat attenuation index (FAI) combined with clinical and coronary CT angiography (CCTA) characteristics for predicting major adverse cardiovascular events (MACE) after aortic valve replacement (AVR). Methods Data of 139 patients with aortic stenosis who underwent AVR were retrospectively analyzed. According to occurrence of MACE or not during follow-up, the patients were divided into MACE group and non-MACE group. Cox proportional hazard regression was used to analyze clinical and CCTA data, as well as CT-FFR and FAI to screen independent predictors of MACE after AVR, and nested models based on clinical data, CCTA characteristics, CT-FFR and right coronary artery (RCA) FAI were constructed. Receiver operating characteristic (ROC) curves were drawn, the area under the curve (AUC) and Harrell C index (C-index) were calculated to assess the diagnostic efficacy of each model, and their goodness of fit were evaluated. Results There were 22 cases in MACE group and 117 in non-MACE group. CT-FFR (HR=3.683) and RCA-FAI (HR=3.261) were both independent predictors of MACE in patients after AVR. The AUC of clinical model, modelclinical+CCTA, modelclinical+CCTA+CT-FFR and modelclinical+CCTA+CT-FFR+RCA-FAI was 0.636, 0.730, 0.758 and 0.817, and the C-index was 0.614, 0.707, 0.733 and 0.782, respectively. The predicted results of modelclinical+CCTA+CT-FFR+RCA-FAI were most consistent with actual results, with the best goodness of fit. Conclusion CT-FFR and RCA-FAI combined with clinical and CCTA characteristics could effectively predict MACE in patients after AVR.
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