| 齐凯欣,赵瑞平,李云盛.定量血流分数离线评估冠状动脉病变严重程度及对经皮冠状动脉介入治疗效果的预测价值[J].安徽医药,2026,30(5):1008-1012. |
| 定量血流分数离线评估冠状动脉病变严重程度及对经皮冠状动脉介入治疗效果的预测价值 |
| Quantitative flow ratio off-line assessment of coronary artery lesions and percutaneous coronary intervention intervention |
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| DOI：10.3969/j.issn.1009-6469.2026.05.032 |
| 中文关键词: 冠状动脉疾病 定量血流分数 冠状动脉造影 经皮冠状动脉介入治疗 虚拟支架植入技术 残余定量血流分数 |
| 英文关键词: Coronary artery disease Quantitative flow ratio Virtual PCI technology Residual quantitative flow ratio |
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| 中文摘要: |
| 目的通过对比定量血流分数( QFR)与冠状动脉造影(CAG)在临床中的应用，探讨 QFR在冠状动脉病变严重程度中的评估价值及运用虚拟支架植入技术对经皮冠状动脉介入治疗( PCI)效果的预测价值。方法选取 2022年 12月至 2023年 10月在包头市中心医院因考虑冠状动脉粥样硬化性心脏病而进行 CAG检查的病人 180例进行入组筛选，其中 147例病人( 309支血管)入组。计算入选病人冠状动脉 QFR值，通过 Spearman相关系数反映 QFR值与 CAG结果的相关性。以 CAG为标准，分析 QFR对冠状动脉病变病人心肌缺血诊断的准确率、灵敏度、特异度、阳性预测值、阴性预测值、阳性似然比和阴性似然比，绘制受试者操作特征曲线( ROC曲线)并计算曲线下面积( AUC)使用双侧 95％置信区间( CI)评价 QFR诊断冠状动脉功能性狭窄的统计学意义。运用第二代 QFR虚拟支架植入技术测出残余定，量血流分数(rQFR)并将其与实际经皮冠状动脉介入( PCI)治疗后的 QFR值进行比较，通过 Spearman相关系数来反映两者之间的相关性。以 PCI术，后 QFR值为标准，分析 rQFR对预测 PCI术后治疗效果的准确率、灵敏度、特异度、阳性预测值、阴性预测值、阳性似然比和阴性似然比，绘制 ROC并计算 AUC。结果 CAG与 QFR经 Spearman相关性分析，呈负相关性( r=.0.79，P<0.001)；以 CAG为标准， QFR诊断准确率为 89.64%，95%CI:(0.86，0.93)，灵敏度为 86.02%，95%CI:(0.79，0.93)，特异度为 91.2%，95%CI:(0.87，0.95)，阳性预测值为 80.81%，95%CI:(0.73，0.89)阴性预测值为 93.81%，95%CI:(0.90，0.97)经过一致性检验分析，发现 QFR与 CAG检测结果 KAPPA=0.76(P<0.001)；QFR诊，断冠状动脉功能性狭窄 ROC曲线面积为0.93，95%CI:(0.90，0.96，P<0.001)。 rQFR值与 PCI术后 QFR值经 Spearman相关性分析，呈正相关( r=0.60，P<0.001)。以 PCI术后 QFR为标准，计算可得 rQFR诊断的准确率为 87.18%，95%CI:(0.76，0.98)灵敏度为 66.67%，95%CI:(0.52，0.81)，特异度为 90.91%，95%CI:(0.86，0.96)，阳性预测值为 57.14%，95%CI:(0.28，0.87)阴性预测值为 93.75%，95%CI:(0.89，0.99)；经过一致性检验分析，发现 QFR与 rQFR检测结果 KAPPA=0.54(P=0.001)； rQFR的ROC曲线面积为 0.87，95%CI:(0.71，1.00，P=0.005)。结论 QFR评估冠状动脉病变与 CAG具有良好相关性， QFR的虚拟支架植入技术对 PCI术后治疗效果具有预测价值，有助于术者优化指导行 PCI治疗的策略。 |
| 英文摘要: |
| Objective To explore the correlation, accuracy, and feasibility of Quantitative Flow Ratio (QFR) in evaluating the severityof coronary artery disease and predicting the effectiveness of percutaneous coronary intervention (PCI) treatment using virtual stent im.plantation technology by comparing the clinical application of QFR and Coronary Angiography (CAG). Methods One hundred and eighty patients who underwent CAG for suspected coronary atherosclerotic heart disease in the Baotou Central Hospital from December2022 to October 2023 were conducted for enrollment screening. Among them, 147 patients (with 309 vessels) were included in thestudy. A blinded analysis was performed on the CAG and QFR images of the enrolled patients. Basic clinical information and vascularstatus of the patients were analyzed. QFR values of the coronary arteries of the enrolled patients were calculated and analyzed, and thecorrelation between QFR values and CAG was reflected through the Spearman correlation coefficient. Using CAG as the standard, theaccuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio of QFR in diagnosing myocardial ischemia in patients with coronary artery disease were analyzed. Receiver operating characteris.tic curves (ROC curves) were plotted, and the area under the curve (AUC) was calculated. Residual quantitative flow ratio (rQFR) wasmeasured using the second-generation QFR virtual PCI technique and compared with the QFR values after actual PCI. The correlationbetween the two was reflected through the Spearman correlation coefficient. Using the QFR value after PCI as the standard, the accura.cy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio ofrQFR in predicting the therapeutic effect post-PCI were analyzed. ROC curves were plotted, and AUC values were calculated according. ly.Results Spearman correlation analysis revealed a negative correlation between CAG and QFR (r=.0.79, P<0.001). Using CAG as the standard, the sensitivity of QFR in diagnosing myocardial ischemia was 86.02%, 95%CI:(0.79, 0.93), specificity was 91.2%, 95%CI: (0.87, 0.95), accuracy was 89.64%, 95%CI:(0.86, 0.93), positive predictive value (PPV) was 80.81%, 95%CI:(0.73, 0.89), and negative predictive value (NPV) was 93.81%, 95%CI:(0.90, 0.97). Consistency test analysis showed that KAPPA value of QFR and CAG results was 0.76 (P<0.001). The AUC for QFR in diagnosing coronary functional stenosis was 0.93, 95%CI:(0.90, 0.96) (P<0.001). Spearman correlation analysis between rQFR values and post-PCI QFR revealed a positive correlation (r=0.60, P<0.001). Using post-PCI QFR as the standard, the sensitivity of rQFR in predicting PCI outcomes was 66.67%, 95%CI: (0.52, 0.81), specificity was 90.91%, 95%CI: (0.86, 0.96), accuracy was 87.18%, 95%CI:(0.76, 0.98), PPV was 57.14%, 95%CI:(0.28, 0.87), and NPV was 93.75%, 95%CI:(0.89, 0.99). Consistency test analysis revealed that KAPPA value of QFR and rQFR results was 0.54 (P=0.001). The AUC for rQFR in pre. dicting PCI outcomes was 0.87, 95%CI:(0.71,1.00) (P=0.005).Conclusions Clinical practice has confirmed that QFR assessment ofcoronary artery disease exhibits a good correlation with CAG. The virtual PCI technology using QFR demonstrates predictive value forthe therapeutic outcomes post-PCI, thereby assisting surgeons in optimizing the strategy for guiding PCI treatment. |
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