| 老年冠心病PCI术后院内不良心血管事件风险预测列线图模型构建 |
| 投稿时间：2024-07-20 修订日期：2024-08-16 点此下载全文 |
| 引用本文：酉鹏华,王晓晶,陈海潮.老年冠心病PCI术后院内不良心血管事件风险预测列线图模型构建[J].医学研究杂志,2025,54(1):67-72 |
| DOI：
10.11969/j.issn.1673-548X.2025.01.013 |
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| 基金项目:陕西省自然科学基础研究计划项目(2022JM-540) |
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| 中文摘要:目的 分析老年冠心病(coronary heart disease, CHD)患者经皮冠状动脉介入治疗(percutaneous coronary intervention,PCI)术后发生院内不良心血管事件(major adverse cardiovascular events, MACE)的影响因素,构建列线图预测模型并对模型进行评价。方法 收集2021年2月~2024年3月于陕西省人民医院进行PCI治疗的304例老年CHD患者的临床资料。根据是否发生院内MACE,将患者分为MACE组(n=81)和非MACE组(n=223)。通过受试者操作特征(receiver operating characteristic, ROC)曲线分析获取各因素的最佳截断值；Logistic多元回归模型分析影响老年CHD患者PCI术后发生院内MACE的危险因素并构建列线图预测模型,列线图模型的内部验证及预测效能分别用校正曲线、决策曲线评价。结果 MACE组心绞痛比例、Gensini积分、支架置入数量＞2个比例以及PAG、Scr、hs-CRP、Lp-PLA2、Lp(a)水平均高于非MACE组,且差异有统计学意义(P＜0.05)。ROC曲线分析结果显示,Gensini积分、PAG、Scr、hs-CRP、Lp-PLA2、Lp(a)的最佳截断值分别为21分、38.64%、96.92μmol/L、8.56mg/L、247.67μg/L、475.14mg/L。Logistic多元回归模型结果显示,支架置入数量、Gensini积分、PAG、Scr、hs-CRP、Lp-PLA2、Lp(a)是老年CHD患者PCI术后发生MACE的危险因素。验证显示,本研究所构建的列线图模型C-index 为0.991 (0.982 ~0.999)。观测值与预测值较统一。列线图模型的阈值＞0.08,所提供的临床净收益均高于支架置入数量、Gensini积分、PAG、Scr、hs-CRP、Lp-PLA2、Lp(a)。结论 本研究基于支架置入数量、Gensini积分、PAG、Scr、hs-CRP、Lp-PLA2、Lp(a)所构建的列线图预测模型,对老年CHD患者PCI术后发生MACE的预测价值较好,可为临床进行针对性干预提供依据,以减少MACE发生。 |
| 中文关键词:老年 冠心病 经皮冠状动脉介入治疗 不良心血管事件 列线图模型 |
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| Construction of A Column-Line Diagram Model for Predicting the Risk of In-hospital Adverse Cardiovascular Events after PCI for Coronary Heart Disease among Elderly Patients. |
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| Abstract:Objective To analyze the impact factors on the occurrence of in-hospital adverse cardiovascular events (MACE) after percutaneous coronary intervention (PCI) in elderly patients with coronary heart disease (CHD), and to construct a prediction model in the form of a column-line diagram and to evaluate the effectiveness of the model. Methods The clinical data of 304 elderly coronary heart disease patients who underwent PCI from February 2021 to March 2024 in our hospital were collected. Patients were divided into MACE group (n=81) and non-MACE group (n=223) based on the occurrence of in-hospital MACE. The optimal cutoff values of each factor were obtained by receiver operating characteristic(ROC)curve analysis. Logistic multiple regression modeling was used to investigate the risk factors of in-hospital MACE after PCI in elderly patients with CHD and a predictive model with columnar graphs was constructed. The correction curve was used for the internal validation of the column chart model and the decision curve was used for evaluating the prediction efficacy of the column chart model. Results The proportion of angina pectoris, Gensini score, the proportion of the implanted stent number>2, and the levels of PAG, Scr, hs-CRP, Lp-PLA2 and Lp(a) were higher in the MACE group than in the non MACE group, and the difference was statistically significant (P<0.05). The results of ROC curve analysis showed that the optimal cut-off values for Gensini integral, PAG, Scr, hs-CRP, Lp-PLA2 and Lp(a) were 21 points, 38.64%, 96.92μmol/L, 8.56mg/L, 247.67μg/L and 475.14mg/L, respectively. The results of Logistic multiple regression modeling showed that the number of implanted stents, Gensini score, PAG, Scr, hs-CRP, Lp-PLA2 and Lp(a) were risk factors for the occurrence of in-hospital MACE after PCI in elderly patients with CHD. Internal validation shows that the C-index of the column-line graph model constructed in this study was 0.991 (0.982-0.999). The observed values aligned well with the predicted values. The column-line diagram model with a threshold >0.08 provided net clinical benefits above the number of implanted stent, Gensini score, PAG, Scr, hs-CRP, Lp-PLA2 and Lp(a). Conclusion In this study, the column-line graph prediction model constructed based on the number of implantated stent, Gensini score, PAG, Scr, hs-CRP, Lp-PLA2 and Lp(a) had good predictive value for the occurrence of in-hospital MACE after PCI in elderly patients with CHD, which may provide a basis for targeted clinical interventions to reduce the occurrence of in-hospital MACE. |
| keywords:Elderly patient Coronary heart disease Percutaneous coronary intervention Adverse cardiovascular events Columnar graphic modeling |
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