基于CT血流储备分数的Duke危险评分对冠状动脉狭窄中危患者预后价值的研究

Prognostic Value of Coronary CT-Derived Fractional Flow Reversebased Functional Duke Jeopardy Score in Patients with Intermediate Coronary Stenosis

目的评估基于CT血流储备分数(CT-FFR)的Duke危险评分(fDJS _(CTA))对中等冠状动脉狭窄患者的预后价值。方法回顾性纳入中等冠状动脉狭窄患者(CAD-RADS分级:2~3),随访时间2018年11月至2023年9月,共5年。主要终点为主要不良心血管事件(MACE)。使用单因素、多因素分析进行预后分析,并绘制Kaplan-Meier生存曲线。临床因素结合fDJS _(CTA)模型预测MACE的能力通过受试者工作特征曲线(ROC)曲线下面积(AUC)进行评估。结果共2336例患者被纳入,平均年龄为(63±10)岁,其中有158例发生了MACE。与CT-FFR及其他临床因素比较,fDJS _(CTA)是最强的独立危险预测因素(HR=1.952;95%CI:1.370~2.781;P<0.001)。Kaplan-Meier生存曲线表明fDJS _(CTA)<4组的预后显著优于fDJS _(CTA)≥4组(P<0.05)。临床因素结合fDJS _(CTA)模型的AUC为0.682(P<0.001)。结论在中等冠状动脉狭窄患者中,fDJS _(CTA)对MACE的预测价值显著。临床因素结合fDJS _(CTA)可以提升MACE的预测效能。

Objective The study aims to assess the prognostic value of fDJS _(CTA) in patients with intermediate coronary stenosis.Methods This study represented a 5-year follow-up of patients with intermediate coronary stenosis(CAD RADS:2-3),between September 2023 and November 2018.The primary end point was(major adverse cardiovascular events,MACE).Prognostic analysis was performed using univariable and multivariable Cox proportional hazards regressions.Kaplan-Meier survival curves were plotted.The ability of the fusion model of clinical factors and fDJS _(CTA) to predict MACE outcomes was assessed by using area under the receiver-operating characteristic curve(AUC).Results This study included 2336 patients(mean age,63 years±10),158 of whom experienced MACE.Compared to CT-FFR and other clinical factors,the fDJS _(CTA) was the strongest independent predictor of MACE(HR:1.952;95%CI:1.370-2.781;P<0.001).Kaplan-Meier survival curves indicated that the prognosis in the fDJS _(CTA)<4 group was significantly better than in the fDJS _(CTA)≥4 group(P<0.05).The AUC of the fusion model of clinical factors and fDJS _(CTA) was 0.682(P<0.001).Conclusion In patients with intermediate coronary stenosis severity,the value of fDJS _(CTA) in predicting MACE is considerable.And the combination of clinical factors and fDJS _(CTA) can enhance the predictive efficacy of MACE.