冠状动脉钙化病变介入治疗术后主要不良心血管事件列线图预测模型的建立与评价

Establishment and evaluation of a nomogram prediction model for major adverse cardiovascular events in patients with coronary artery calcification after PCI

[目的]探讨冠状动脉钙化(CAC)患者经皮冠状动脉介入治疗(PCI)术后主要不良心血管事件(MACE)发生的危险因素,据此构建CAC患者PCI术后MACE发生的列线图预测模型。[方法]回顾性分析2018年1月—2019年12月就诊于新疆医科大学第四附属医院心内科行冠状动脉造影(CAG)或血管内超声(IVUS)明确诊断为CAC并接受PCI术的406名患者的临床资料。根据随访期间是否出现MACE将研究对象分为事件组(60例)和非事件组(346例)。采用LASSO和多因素Logistic回归分析确定CAC患者PCI术后MACE发生的独立危险因素,在此基础上构建列线图预测模型并对模型进行评价。[结果]LASSO和多因素Logistic回归分析结果显示,高龄、糖尿病、肾功能不全、Gensini评分升高和旋磨术是MACE发生的危险因素,而最小管腔直径(MLD)增大是MACE发生的保护因素(P<0.05)。利用上述6个预测指标构建列线图预测模型,内部验证后,列线图预测CAC患者PCI术后MACE发生的ROC曲线下面积(AUC)为0.824(95%CI:0.767~0.875),灵敏度为0.771,特异度为0.720,提示模型具有较好的区分度。校准曲线提示列线图预测模型的偏差校正曲线与理想曲线具有较好的一致性。临床决策曲线分析(DCA)结果显示模型的预测阈值在0~0.6之间时患者的临床净收益水平最高,提示列线图模型具有较好的临床适用性。[结论]本研究建立的列线图预测模型可以较好地定量评估CAC患者PCI术后MACE发生的危险程度,有助于临床医师筛选高危患者,制定个体化针对性治疗措施,改善患者预后。

Aim To explore the risk factors of major adverse cardiovascular events(MACE)after percutaneous coronary intervention(PCI)in patients with coronary artery calcification(CAC),and to construct a nomogram prediction model for MACE in CAC patientsafter PCI.Methods Retrospective analysis of clinical data of 406 patients admitted to the Department of Cardiology of the Fourth Affiliated Hospital of Xinjiang Medical University from January 2018 to December 2019,they were diagnosed with CAC by coronary angiography(CAG)or intravascular ultrasound(IVUS)and underwent PCI.The subjects were divided into event group(60 cases)and non-event group(346 cases)according to the incidence of MACE during the follow-up period.The LASSO regression and multivariate Logistic regression analysis were used to determine the independent risk factors of MACE in CAC patients after PCI,and then a nomogram prediction model was constructed and evaluated.Results LASSO regression and multivariate Logistic regression analysis results showed that advanced age,diabetes,renal dysfunction,elevated Gensini score and rotational atherectomy were risk factors for the incidence of MACE,and enlarged minimum lumen diameter(MLD)was a protective factor for the incidence of MACE(P<0.05).The nomogram prediction model was constructed using the above six predictive indicators.After internal validation,the AUC values of nomogram for predicting MACE in CAC patients after PCI was 0.824(95%CI:0.767~0.875),the sensitivity was 0.771,and the specificity was 0.720,suggesting that the model had a good discrimination.The calibration curve indicated that the deviation correction curve of the nomogram prediction model had good consistency with the ideal curve.The clinical decision curve analysis(DCA)suggested that when the prediction threshold of the model was in range of 0~0.6,the patient s clinical net benefit level was the highest,and the nomogram model had good clinical applicability.Conclusion The nomogram prediction model established in this study can better quantitatively assess the risk degree of MACE in CAC patients after PCI,which is helpful for clinicians to screen high-risk patients,formulate individualized targeted interventions,and improvepatients,prognosis.