| 摘要: |
| ［摘要］ 目的 建立预测合并冠状动脉钙化病变（CAC）冠心病患者经皮冠状动脉介入治疗（PCI）术后支架膨胀不良的列线图模型，并对模型的预测效能进行评估。方法 招募2022年1月至2024年6月榆林市第一医院收治的合并CAC的冠心病患者206例作为训练集，选择2024年7月至2025年2月收治的合并CAC的冠心病患者40例作为验证集，所有患者均接受PCI。PCI术前进行临床生化指标检测及血管内超声（IVUS）检查。PCI术后即刻复查IVUS，评估支架膨胀效果。支架膨胀良好定义为支架植入后的支架直径狭窄率<10%（膨胀良好组），否则为膨胀不良（膨胀不良组）。采用多因素logistic回归分析PCI术后支架膨胀不良的影响因素。基于多因素logistic回归分析筛得指标构建列线图模型，并通过受试者工作特征（ROC）曲线、校准曲线以及决策曲线分析（DCA）评估列线图模型的预测效能及应用价值。基于验证集数据分析列线图模型预测结果与临床实际情况的一致性。结果 训练集膨胀不良组（55例）富含Gla蛋白（GRP）水平低于膨胀良好组（151例），消皮素D（GSDMD）、钙化弧度水平高于膨胀良好组，最小管腔面积（MLA）、钙化断裂占比小于膨胀良好组，差异有统计学意义（P<0.05）。多因素logistic回归分析结果显示，较高的GSDMD［OR（95%CI）=1.303（1.128～1.504），P<0.001］、钙化弧度［OR（95%CI）=1.314（1.165～1.482），P<0.001］水平是促进PCI术后支架膨胀不良发生的危险因素，较高的GRP［OR（95%CI）=0.410（0.274～0.613），P<0.001］、MLA［OR（95%CI）=0.454（0.315～0.654），P<0.001］水平以及发生钙化断裂［OR（95%CI）=0.397（0.300～0.525），P<0.001］是抑制PCI术后支架膨胀不良发生的保护因素。基于GRP、GSDMD、MLA、钙化弧度、钙化断裂指标构建预测合并CAC冠心病患者PCI术后支架膨胀不良的列线图模型。ROC曲线分析结果显示，列线图模型具有较好的预测效能［AUC（95%CI）=0.818（0.754～0.882），P<0.001］，灵敏度为83.64%，特异度为75.50%。校准曲线分析结果显示，预测结果与理想曲线贴合度较好，提示列线图模型校准度良好。DCA结果显示，在阈概率3%～76%范围内，列线图模型临床获益较好。基于验证集的Kappa一致性分析结果显示，列线图模型预测结果与临床实际情况具有良好的一致性（Kappa=0.806，P<0.001），结果符合率为92.50%。结论 基于GRP、GSDMD、MLA、钙化弧度、钙化断裂指标建立的列线图模型可有效预测合并CAC冠心病患者PCI术后支架膨胀不良的风险，为临床医师进行术前风险分层提供参考。 |
| 关键词: 冠心病 冠状动脉钙化病变 血管内超声 列线图 经皮冠状动脉介入治疗 支架膨胀不良 |
| DOI：10.3969/j.issn.1674-3806.2026.08.10 |
| 分类号:R 541.4 |
| 基金项目:陕西省重点研发计划项目（编号：2023SF-124） |
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| Construction of a nomogram model for predicting stent under-expansion in coronary heart disease patients complicated with coronary artery calcification after PCI and assessment of its predictive efficacy |
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Gao Yue1, Gao Gangli2, Gao Yang1
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1.Department of Ultrasound Diagnosis, the First Hospital of Yulin, Yulin 719000, China; 2.The First Department of Cardiology, the First Hospital of Yulin, Yulin 719000, China
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| Abstract: |
| ［Abstract］ Objective To construct a nomogram model for predicting stent under-expansion in coronary heart disease patients complicated with coronary artery calcification(CAC) after percutaneous coronary intervention(PCI) and to assess the predictive efficacy of the model. Methods A total of 206 coronary heart disease patients complicated with CAC who were admitted to the First Hospital of Yulin from January 2022 to June 2024 were recruited as the training set, and 40 coronary heart disease patients complicated with CAC who were admitted to the same hospital from July 2024 to February 2025 were selected as the validation set. All the patients underwent PCI. The patients underwent the detection of clinical biochemical indicators and intravascular ultrasound(IVUS) examination before PCI. The IVUS re-examination was performed on the patients immediately after PCI to assess stent expansion status. Optimal stent expansion was defined as stenosis rate of post-implantation stent diameter <10%(the optimal expansion group), otherwise it was considered stent under-expansion(the under-expansion group). Multivariate logistic regression was used to analyze the factors influencing stent under-expansion after PCI. The nomogram model was constructed based on the indicators screened by using multivariate logistic regression analysis, and the predictive efficacy and application value of the nomogram model were assessed through the receiver operating characteristic(ROC) curve, calibration curve and decision curve analysis(DCA). The consistency between the prediction results of the nomogram model and the actual clinical results was analyzed based on the validation set data. Results In the training set, the levels of Gla-rich protein(GRP) in the under-expansion group(n=55) were lower than those in the optimal expansion group(n=151); the levels of gasdermin D(GSDMD) and calcification arc in the under-expansion group were higher than those in the optimal expansion group; the minimum lumen area(MLA) and the proportion of calcification fractures in the under-expansion group were smaller than those in the optimal expansion group. The above differences were statistically significant(P<0.05). The results of multivariate logistic regression analysis indicated that the elevated levels of GSDMD［OR(95%CI)=1.303(1.128-1.504), P<0.001］ and calcification arc［OR(95%CI)=1.314(1.165-1.482), P<0.001］ were risk factors for facilitating stent under-expansion after PCI, and the elevated levels of GRP［OR(95%CI)=0.410(0.274-0.613), P<0.001］ and MLA［OR(95%CI)=0.454(0.315-0.654), P<0.001］, and the presence of calcification fractures［OR(95%CI)=0.397(0.300-0.525), P<0.001］ were protective factors against stent under-expansion after PCI. A nomogram model for predicting stent under-expansion in coronary heart disease patients complicated with CAC after PCI was constructed based on the indicators of GRP, GSDMD, MLA, calcification arc and calcification fractures. The results of ROC curve analysis showed that the nomogram model had good predictive efficacy［AUC(95%CI)=0.818(0.754-0.882), P<0.001］, with a sensitivity of 83.64% and a specificity of 75.50%. The results of calibration curve analysis showed that the prediction results had high goodness of fit with the ideal curve, suggesting that the calibration of the nomogram model was good. The results of DCA showed satisfactory net clinical benefit of the nomogram model within the threshold probability ranging from 3% to 76%. The results of the Kappa consistency analysis based on the validation set showed that the prediction results of the nomogram model had good consistency with the actual clinical outcomes(Kappa=0.806, P<0.001), and the coincidence rate of the results was 92.50%. Conclusion The nomogram model established based on the indicators of GRP, GSDMD, MLA, calcification arc and calcification fractures can effectively predict the risk of stent under-expansion in coronary heart disease patients complicated with CAC after PCI, providing reference for clinicians to conduct preoperative risk stratification. |
| Key words: coronary heart disease coronary artery calcification(CAC) intravascular ultrasound(IVUS) nomogram percutaneous coronary intervention(PCI) stent under-expansion |
