PET心肌灌注-代谢不匹配能准确诊断阻塞性冠心病

Value of PET myocardial perfusion-metabolism mismatch in diagnosis of obstructive coronary artery disease

目的探讨定量PET心肌灌注-代谢不匹配(MIS)能否进一步提升阻塞性冠心病(CAD)诊断的准确性。方法回顾性收集阻塞性CAD疑似患者97例,将其按照5∶3的比例随机划分为训练集(n=61)和测试集(n=36),并将阻塞性CAD定义为冠脉血管狭窄≥75%。单变量和多变量的Logistic回归分析用于分类模型的训练和性能评估。ROC曲线用于确定诊断阻塞性CAD的最佳截断值以及比较模型间的差异性。结果单变量Logistic回归模型的AUC值分别为0.735、0.758和0.823,MIS的最佳截断值为9%;多变量Logistic回归模型的AUC值分别为0.766、0.823和0.839,以包含MIS指标的Model_3最高,且高于对照模型(P=0.0034);基于Model_3构建的决策树,在测试集中的准确度最高可达96%。结论PET MIS是阻塞性CAD的有效预测因子,能有效提升诊断准确性,对疑似病例快速、准确的鉴别诊断具有重要的临床价值。

Objective To investigate whether quantitative PET myocardial perfusion-metabolism mismatch can further improve the diagnosis accuracy in predicting obstructive coronary artery disease(CAD).Methods A total of 97 patients with suspected obstructive CAD were retrospectively collected,and randomly divided into training set(61 patients)and validation set(36 patients)according to 5∶3.Obstructive CAD was defined as coronary artery≥75%stenosis.Univariate and multivariate logistic regression analysis were used for model training and performance evaluation.The ROC curve analysis was used to determine the best trade-off for diagnosing obstructive CAD and compare the differences between models.Results The areas under the ROC curve(AUC)of three univariate logistic regression models were 0.735,0.758 and 0.823,respectively,and the best trade-off of MIS was 9%.The AUC values of three multivariate logistic regression models were 0.766,0.823 and 0.839,respectively.The model including TPD/MBF/MIS achieved the highest AUC and it was significantly higher than the control model including TPD/MBF(P=0.0034).The decision tree based on this model had an accuracy of up to 96%in the validation set.Conclusion PET myocardial perfusion-metabolism mismatch is an effective predictor of obstructive CAD,which can effectively improve the diagnostic accuracy and has important clinical value for the rapid and accurate differential diagnosis of suspected cases.