目的:作为一种无创性冠状动脉功能检查,冠状动脉计算机断层扫描血管造影(CCTA)得出的血流储备分数(CT-FFR)在一些非心脏手术中显示出预测价值.本研究旨在评价CT-FFR在肺癌手术中的预测价值。
方法:我们回顾性收集了2017年1月至2022年6月的227例患者,并使用基于机器学习的CT-FFR评估接受肺癌手术的稳定性冠状动脉疾病(CAD)患者。主要不良心脏事件(MACE)定义为围手术期心肌损伤(PMI),心肌梗塞,心力衰竭,房室性心律失常伴血流动力学紊乱,心源性休克和心源性死亡。采用多因素logistic回归分析确定MACE和PMI的危险因素。辨别能力,拟合优度,并在添加CT-FFR≤0.8前后确定预测模型的再分类改进。
结果:MACE发生率为20.7%,PMI发生率为15.9%。CT-FFR在预测MACE的准确性方面显著优于CCTA(0.737vs.0.524)。在多元回归分析中,CT-FFR≤0.8是两种MACE的独立危险因素[OR=10.77(4.637,25.016),P<0.001和PMI[OR=8.255(3.372,20.207),P<0.001]。此外,我们发现,添加CT-FFR后,MACE和PMI预测模型的性能均有所改善.
结论:CT-FFR可用于评估接受肺癌手术的稳定性CAD患者围手术期MACE和PMI的风险。它在接受肺癌手术的患者的心脏评估中增加了预后信息。
OBJECTIVE: As a non-invasive coronary functional examination, coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) showed predictive value in several non-cardiac surgeries. This study aimed to evaluate the predictive value of CT-FFR in lung cancer surgery.
METHODS: We retrospectively collected 227 patients from January 2017 to June 2022 and used machine learning-based CT-FFR to evaluate the stable coronary artery disease (CAD) patients undergoing lung cancer surgery. The major adverse cardiac event (MACE) was defined as perioperative myocardial injury (PMI), myocardial infarction, heart failure, atrial and ventricular arrhythmia with hemodynamic disorder, cardiogenic shock and cardiac death. The multivariate logistic regression analysis was performed to identify risk factors for MACE and PMI. The discriminative capacity, goodness-of-fit, and reclassification improvement of prediction model were determined before and after the addition of CT-FFR≤0.8.
RESULTS: The incidence of MACE was 20.7 % and PMI was 15.9 %. CT-FFR significantly outperformed CCTA in terms of accuracy for predicting MACE (0.737 vs 0.524). In the multivariate regression analysis, CT-FFR≤0.8 was an independent risk factor for both MACE [OR=10.77 (4.637, 25.016), P<0.001] and PMI [OR=8.255 (3.372, 20.207), P<0.001]. Additionally, we found that the performance of prediction model for both MACE and PMI improved after the addition of CT-FFR.
CONCLUSIONS: CT-FFR can be used to assess the risk of perioperative MACE and PMI in patients with stable CAD undergoing lung cancer surgery. It adds prognostic information in the cardiac evaluation of patients undergoing lung cancer surgery.