基于Lp(a)及LDL-C构建预测模型在经皮冠状动脉介入治疗患者预后评估中的应用价值

摘要/Abstract

摘要： 目的探讨血清脂蛋白（a）[lipoprotein（a）,Lp（a）]和低密度脂蛋白胆固醇（Low Density Lipoprotein cholesterol,LDL-C）在急性冠状动脉综合征（Acute coronary syndrome, ACS）接受经皮冠状动脉介入（Percutaneous coronary intervention,PCI）治疗患者预后评估中的应用价值。方法本研究以2015年1月至2020年12月期间于扬州市江都人民医院住院的ACS且接受过PCI治疗的患者为研究对象,并在PCI治疗后的第一年内进行了随访。结合单因素和多因素Logistic回归模型,分析ACS患者接受PCI治疗后影响其预后的相关风险因素。联合Lp（a）和LDL-C以及相关影响因素构建不良预后的风险预测模型。此外,基于Spearman相关系数评估LP（a）与LDL-C和Gensini评分之间的相关性。采用ROC曲线和Hosmer-Lemeshow拟合优度检验评价预测模型。结果本研究989例患者中有385例（38.9%）在随访期间发生心血管不良事件。单变量Logistic模型显示年龄、颈动脉斑块、PCI支架总长度、冠脉Gensini评分、LP（a）、甘油三脂和是否使用阿司匹林与ACS患者接受PCI后心血管不良事件相关。多变量Logistic模型显示年龄、颈动脉斑块、冠脉Gensini评分、LP（a）和是否服用阿司匹林与ACS患者接受PCI后心血管不良事件相关。Spearman相关分析显示LP（a）和LDL-C、 LDL-C与冠脉Gensini评分存在一定相关性。心血管不良事件Logistic预测模型为Logit（P）= 4.559+0.017（年龄）+0.311（颈动脉斑块）+0.010（冠脉Gensini评分）+0.004（LP(a)）+0.017（LDL）+0.674（是否使用阿司匹林）。预测模型的ROC曲线下面积为0.74,Hosmer-Lemeshow拟合优度检验>0.05。结论基于LP（a）和LDL-C构建的ACS患者PCI术后心血管不良事件预测模型效能较好。

关键词: 急性冠脉综合征, 经皮冠状动脉介入, 预测模型, 血清脂蛋白（a）, 低密度脂蛋白胆固醇

Abstract: Objective To investigate the application value of serum lipoprotein (a) [LP(a)] and low-density lipoprotein cholesterol (LDL-C) in the prognostic evaluation of patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI). Methods The ACS patients who were hospitalized and underwent PCI in Yangzhou Jiangdu People's Hospital from January 2015 to December 2020 were enrolled as subjects, and all patients were followed up within the first year after PCI. Univariate and multivariate logistic regression analyses were used to investigate the influencing factors for the prognosis of ACS patients after PCI, and LP (a) and LDL-C were combined with related influencing factors to establish a risk predictive model for poor prognosis. In addition, Spearman's correlation coefficient was used to investigate the correlation of LP(a) and LDL-C with Gensini score. The ROC curve analysis and the Hosmer-Lemeshow goodness-of-fit test were used to evaluate the predictive model. Results Among the 989 patients in this study, 385 (38.9%) experienced adverse cardiovascular events during follow-up. The univariate logistic model showed that age, carotid plaque, total length of PCI stent, coronary Gensini score, LP(a), triglyceride, and whether aspirin was used or not were associated with adverse cardiovascular events after PCI in patients with ACS, and the multivariable logistic model showed that age, carotid plaque, coronary Gensini score, LP(a), and whether aspirin was used or not were associated with adverse cardiovascular events after PCI in ACS patients. The Spearman correlation analysis showed a certain degree of correlation between LP(a) and LDL-C and between LDL-C and coronary Gensini score. The logistic predictive model for adverse cardiovascular events was logit (P) = 4.559 + 0.017 (age) + 0.311 (carotid plaque) + 0.010 (coronary Gensini score) + 0.004 (LP(a)) + 0.017 (LDL) + 0.674 (whether aspirin was used or not), which had an area under the ROC curve of 0.74 and a value of >0.05 based on the Hosmer-Lemeshow goodness-of-fit test. Conclusion The predictive model based on LP(a) and LDL-C has a good performance in predicting adverse cardiovascular events after PCI in ACS patients.

Key words: Acute coronary syndrome, Percutaneous coronary intervention, Predictive model, Serum lipoprotein (a), Low-density lipoprotein cholesterol

马开阳, 徐日新. 基于Lp(a)及LDL-C构建预测模型在经皮冠状动脉介入治疗患者预后评估中的应用价值[J]. 心血管病防治知识, 2023, 13(3): 9-14.

MA Kai-yang, XU Ri-xin. Application value of the predictive model based on lipoprotein (a) and low-density lipoprotein cholesterol in prognostic evaluation of patients undergoing percutaneous coronary intervention[J]. Prevention and Treatment of Cardiovascular Disease, 2023, 13(3): 9-14.

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