微创医学

目的探讨急性心肌梗死（AMI）患者经皮冠状动脉介入治疗（PCI）后发生慢血流/无复流的影响因素，并构建列线图预测模型。方法选取226例行PCI的AMI患者作为研究对象，将患者按7∶3的比例，分为训练集（158例）和验证集（68例）。采用多因素Logistic回归模型分析AMI患者PCI后发生慢血流/无复流的危险因素，采用R 4.4.1软件构建列线图预测模型并评价模型的预测效能。结果根据AMI患者PCI后是否发生慢血流/无复流，将训练集158例患者分为慢血流/无复流组（27例）和正常血流组（131例），慢血流/无复流组的收缩压低于正常血流组，γ-谷氨酰胺转移酶、D-二聚体、胱抑素C水平高于正常血流组（P<0.05）。多因素Logistic回归分析结果显示，收缩压、γ-谷氨酰胺转移酶、D-二聚体及胱抑素C水平是AMI患者行PCI后发生慢血流/无复流的影响因素（P<0.05）。基于上述影响因素构建的列线图预测模型在训练集中的受试者工作特征曲线下的面积（AUC）为0.910（95%CI：0.838，0.981），特异度为0.977，敏感度为0.778；验证集中的AUC为0.917（95%CI：0.806，1.000），特异度为1.000，敏感度为0.812。校准曲线结果显示，训练集和验证集的预测曲线均接近于理想曲线。结论收缩压、γ-谷氨酰胺转移酶、D-二聚体及胱抑素C水平是AMI患者PCI后发生慢血流/无复流的影响因素，基于上述指标构建的列线图模型具有良好的预测价值。

Objective To investigate the influencing factors for the occurrence of slow flow/no-reflow in patients with acute myocardial infarction (AMI) after percutaneous coronary intervention (PCI), and to construct a nomogram predictive model. Methods A total of 226 AMI patients who underwent PCI were selected as the research subjects. These patients were divided into a training set (n=158) and a validation set (n=68) at a ratio of 7∶3. A multivariate Logistic regression model was used to analyze the risk factors for the occurrence of slow flow/no-reflow in AMI patients after PCI, while R 4.4.1 software was employed to construct a nomogram predictive model and evaluate the model's predictive performance. Results According to whether slow flow/no-reflow occurred in AMI patients after PCI, the 158 patients in the training set were divided into the slow flow/no-reflow group (27 cases) and the normal flow group (131 cases). The systolic blood pressure in the slow flow/no-reflow group was lower than that in the normal flow group, while the levels of γ glutamyl transferase, D-dimer, and cystatin C in the slow flow/no-reflow group were higher than those in the normal flow group (P<0.05). Results of multivariate Logistic regression analysis showed that systolic blood pressure, γ glutamyl transferase, D-dimer, and cystatin C levels were influencing factors for the occurrence of slow flow/no-reflow in AMI patients after PCI (P<0.05). The nomogram predictive model constructed based on the aforementioned influencing factors showed the following performance: in the training set, the area under the receiver operating characteristic curve (AUC) was 0.910 (95%CI: 0.838, 0.981), with a specificity of 0.977 and a sensitivity of 0.778; while in the validation set, the AUC was 0.917 (95%CI: 0.806, 1.000), with a specificity of 1.000 and a sensitivity of 0.812. Results of the calibration curve indicated that the predictive curves of both the training set and the validation set were close to the ideal curve. Conclusion Systolic blood pressure, γ glutamyl transferase, D-dimer, and cystatin C levels are influencing factors for the occurrence of slow flow/no-reflow in AMI patients after PCI, and the nomogram model constructed based on the aforementioned indicators has good predictive value.