两种模型对老年冠状动脉钙化病变患者术后主要不良心血管事件的预测效能对比
Comparison on predictive efficacy of two models for MACE in elderly patients with coronary artery calcification
李传波 1李喜定 1吉苗苗 1王玥焜1
作者信息
- 1. 473000 南阳市第一人民医院心血管重症监护科
- 折叠
摘要
目的 比较老年冠状动脉钙化(coronary artery calcification,CAC)病变患者经皮冠状动脉介入治疗(percuta-neous coronary intervention,PCI)后主要不良心血管事件(major adverse cardiovascular events,MACE)预测中多因素logistic回归和XGBoost模型的效能.方法 回顾性选取2020年6月至2023年6月南阳市第一人民医院收治的老年CAC病变患者120例,均行PCI,术后随访1年统计MACE发生率,随访期间失访9例,发生MACE的患者纳入 MACE组(28例),其余纳入无 MACE组(83例);采用多因素logistic回归分析和XGBoost模型筛选老年CAC病变患者PCI后MACE的影响因素;绘制ROC曲线、Calibration校准曲线比较两种模型的预测效能.结果 MACE组年龄、吸烟、糖尿病、低密度脂蛋白胆固醇、Gensini积分、病变血管数量≥3支、重度钙化、联合旋磨术、置入支架数量比例显著高于无MACE组(P＜0.05,P＜0.01).多因素logistic回归模型显示,吸烟、合并糖尿病、低密度脂蛋白胆固醇、Gensini积分、置入支架数量是CAC患者PCI后 MACE的独立危险因素(P＜0.05,P＜0.01).XGBoost模型显示,重要特征评分排前5位的为Gensini积分35分,置入支架数量25分,合并糖尿病22分,吸烟18分,低密度脂蛋白胆固醇15分;ROC曲线分析显示,多因素logistic回归模型预测老年CAC患者PCI后MACE的曲线下面积为0.925(95％CI:0.859～0.966),敏感性为82.14％,特异性为97.59％;XGBoost模型预测老年CAC患者PCI后MACE的曲线下面积为0.918(95％CI:0.850～0.961),敏感性为89.29％,特异性为78.31％.2种模型预测比较,差异无统计学意义(Z=0.148,P=0.8823).结论 多因素logistic回归和XGBoost模型预测老年CAC患者PCI后MACE的效能相当,吸烟、糖尿病、低密度脂蛋白胆固醇、Gensini积分、置入支架数量是老年CAC患者PCI后MACE的独立危险因素.
Abstract
Objective To compare the efficacy of multivariate logistic regression and XGBoost models in predicting major adverse cardiovascular events(MACE)after percutaneous coronary in-tervention(PCI)in elderly patients with coronary artery calcification(CAC).Methods A total of 120 elderly patients with CAC lesions undergoing PCI in our hospital from June 2020 to June 2023 were retrospectively enrolled in this study.The incidence of MACE was observed during 1 year of follow-up.Nine patients were lost during the period,and the left patients were divided into MACE group(28 patients)and non-MACE group(83 patients).Multivariate logistic regression analysis and XGBoost model were used to screen the influencing factors of MACE in elderly CAC patients after PCI.ROC curve and calibration curve were drawn to compare the predictive efficiency of the two models.Results The MACE group had significantly advanced age,larger proportions of smoking and diabetes,higher LDL-C and Gensini score,and increased ratios of diseased vessels ≥3,severe calcification,combined rotary grinding and number of stent implantation when compared with the non-MACE group(P＜0.05,P＜0.01).Multivariate logistic regression model showed that smoking,diabetes,LDL-C,Gensini score,and number of stents implanted were independent risk factors for MACE in CAC patients after PCI(P＜0.05,P＜0.01).XGBoost model indicated that the top five important feature scores were Gensini score of 35,number of stent implantation score of 25,combined diabetes score of 22,smoking score of 18,and LDL-C score of 15.ROC curve analysis revealed that the AUC value of multivariate logistic regression model in predicting MACE in elderly CAC patients after PCI was 0.925(95％CI:0.859-0.966),with a sensitivity of 82.14％and a specificity of 97.59％,and the value of the XGBoost model was 0.918(95％CI:0.850-0.961),with a sensitivity of 89.29％and a specificity of 78.31％.There was no significant difference in predictive efficacy between the two models(Z=0.148,P=0.8823).Conclusion Multiple logistic regression model and XGBoost model show equally efficacy in predicting MACE in elderly CAC patients after PCI.Smoking,diabetes,LDL-C,Gensini score and number of stents implanted are independent risk factors for MACE in the patients.
关键词
危险因素/主要不良心血管事件/冠状动脉钙化Key words
risk factors/major adverse cardiovascular events/coronary artery calcification引用本文复制引用
出版年
2025
NETL
NSTL
万方数据