CT-derived fractional flow reserve for prediction of major adverse cardiovascular events in diabetic patients

医学 狼牙棒 部分流量储备 队列 内科学 危险系数 心脏病学 比例危险模型 冠状动脉疾病 血管病学 糖尿病 前瞻性队列研究 置信区间 心肌梗塞 经皮冠状动脉介入治疗 冠状动脉造影 内分泌学
作者
Ziting Lan,Xiaoying Ding,Yarong Yu,Li Yu,Wen-Li Yang,Da-Zhong Xu,Runjianya Ling,Yufan Wang,Wenyi Yang,Jiayin Zhang
出处
期刊:Cardiovascular Diabetology [BioMed Central]
卷期号:22 (1) 被引量:1
标识
DOI:10.1186/s12933-023-01801-y
摘要

Abstract Objectives To investigate the prognostic value of computed tomography fractional flow reserve (CT-FFR) in patients with diabetes and to establish a risk stratification model for major adverse cardiac event (MACE). Methods Diabetic patients with intermediate pre-test probability of coronary artery disease were prospectively enrolled. All patients were referred for coronary computed tomography angiography and followed up for at least 2 years. In the training cohort comprising of 957 patients, two models were developed: model1 with the inclusion of clinical and conventional imaging parameters, model2 incorporating the above parameters + CT-FFR. An internal validation cohort comprising 411 patients and an independent external test cohort of 429 patients were used to validate the proposed models. Results 1797 patients (mean age: 61.0 ± 7.0 years, 1031 males) were finally included in the present study. MACE occurred in 7.18% (129/1797) of the current cohort during follow- up. Multivariate Cox regression analysis revealed that CT-FFR ≤ 0.80 (hazard ratio [HR] = 4.534, p < 0.001), HbA1c (HR = 1.142, p = 0.015) and low attenuation plaque (LAP) (HR = 3.973, p = 0.041) were the independent predictors for MACE. In the training cohort, the Log-likelihood test showed statistical significance between model1 and model2 ( p < 0.001). The C-index of model2 was significantly larger than that of model1 (C-index = 0.82 [0.77–0.87] vs. 0.80 [0.75–0.85], p = 0.021). Similar findings were found in internal validation and external test cohorts. Conclusion CT-FFR was a strong independent predictor for MACE in diabetic cohort. The model incorporating CT-FFR, LAP and HbA1c yielded excellent performance in predicting MACE.

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