列线图评估PCI术后服用氯吡格雷发生血小板高反应性风险模型的建立

Establishment of A Nomogram to Assess the Risk of High On-treatment Platelet Reaction After Taking Clopidogrel After PCI

关键词:  经皮冠状动脉介入  氯吡格雷  血小板高反应性  列线图

Key words:  percutaneous coronary intervention  clopidogrel  high on-treatment platelet reaction  nomogram

摘要:

目的 构建可评估经皮冠状动脉介入(percutaneous coronary intervention,PCI)术后服用氯吡格雷发生血小板高反应性(high on-treatment platelet reaction,HTPR)风险的列线图模型。方法 选取2020年1月-10月于安徽医科大学附属六安医院心血管内科住院并行PCI术治疗的冠状动脉粥样硬化性心脏病患者作为研究对象,采用整群抽样法将患者分为建模集(n=223)和验证集(n=61),分析建模集患者的临床资料,分别使用单因素和Logistic回归多因素分析发生氯吡格雷HTPR的危险因素,并建立相关列线图预测模型。结果 糖尿病、同型半胱氨酸≥ 15 μmol·L^-1、超敏C反应蛋白≥ 3.0 mg·L^-1、多支血管病变和携带CYP2C19^*2是PCI术后服用氯吡格雷发生HTPR的独立危险因素(P<0.05)。基于以上危险因素建立预测PCI术后服用氯吡格雷发生HTPR的列线图模型,并对该模型进行验证。结果显示建模集和验证集的C-index分别为0.814(95% CI:0.732～0.846)和0.778(95% CI:0.741～0.802),校正曲线均与标准曲线拟合较好,ROC曲线AUC分别为0.845和0.774,表明该列线图模型具有良好的预测能力。结论 导致PCI术后服用氯吡格雷发生HTPR的危险因素较多,本研究基于危险因素建立的列线图模型具有良好的预测能力,可为临床筛查高风险患者和优化抗栓药物治疗方案提供参考依据。

Abstract:

OBJECTIVE To construct a nomogram model that can assess the risk of high on-treatment platelet reaction (HTPR) after taking clopidogrel after percutaneous coronary intervention(PCI). METHODS The patients with coronary atherosclerotic heart disease who were hospitalized in the department of cardiovascular medicine and PCI in Lu’an Hospital Affiliated to Anhui Medical University between January and October 2020 were selected as the research objects. The cluster sampling method was used to divide the patients into modeling set(n=223) and validation set(n=61), analyze the clinical data of patients in the modeling set, use single factor and logistic regression to analyze the risk factors of clopidogrel HTPR, and establish related nomograms forecast model. RESULTS Diabetes, homocysteine≥ 15 μmol·L^-1, hypersensitivity C-reactive protein≥ 3.0 mg·L^-1, multivessel disease and carrying CYP2C19^*2 were independent risk factors for HTPR after taking clopidogrel after PCI(^P<0.05). Based on the above risk factors, a nomogram model was established to predict the occurrence of HTPR after taking clopidogrel after PCI, and the model was verified. The results showed that the C-index of the modeling set and the validation set were 0.814(95%CI: 0.732-0.846) and 0.778(95%CI: 0.741-0.802), respectively. The calibration curve fitted well with the standard curve, and the AUC of ROC curve was 0.845 and 0.774, respectively, indicating that the nomogram model had good predictive ability. CONCLUSION There are many risk factors that cause HTPR after taking clopidogrel after PCI. The nomogram model established in this study based on risk factors has good predictive ability and can provide reference for clinical screening for high-risk patients and optimization of antithrombotic drug treatment plans.