摘要
目的探讨冠状动脉粥样硬化性心脏病(冠心病)患者多支病变的独立危险因素,并构建预测多支病变风险的列线图预测模型。方法回顾性选取2023年9月至2024年12月于首都医科大学宣武医院急诊科住院完成冠状动脉造影检查并诊断为冠心病的患者311例,根据冠状动脉病变支数分为单支病变组(126例)和多支病变组(185例)。比较两组基线资料,采用单因素及多因素Logistic回归分析冠心病患者冠状动脉多支病变的影响因素,将所有患者根据3∶2分为训练集(186例)和验证集(125例),在训练集中构建列线图预测模型,验证集被用于内部验证,通过ROC曲线、校准曲线以及决策曲线综合评估模型的区分度、校准度及临床应用价值。结果多支病变组饮酒、糖尿病、白细胞计数、中性粒细胞计数、三酰甘油葡萄糖乘积指数、系统性免疫炎症指数、血小板与淋巴细胞比值高于单支病变组,高血压、年龄、淋巴细胞-白蛋白-中性粒细胞比值低于单支病变组(P<0.05)。多因素Logistic回归分析显示,饮酒、糖尿病、三酰甘油葡萄糖乘积指数、血小板与淋巴细胞比值是冠心病患者冠状动脉多支病变的独立危险因素(OR=2.231,95%CI:1.992~5.031,P=0.042;OR=13.607,95%CI:6.961~26.596,P<0.001;OR=1.113,95%CI:1.053~1.177,P<0.001;OR=1.013,95%CI:1.006~1.021,P<0.001)。ROC曲线分析显示,训练集曲线下面积为0.860(95%CI:0.807~0.914),验证集曲线下面积为0.888(95%CI:0.832~0.945);Hosmer-Lemeshow拟合优度检验显示(训练集P=0.798,验证集P=0.966);决策曲线分析显示,当患者阈值概率介于0~0.9时,使用列线图预测多支病变风险能够获得更高的净收益。结论饮酒、糖尿病、三酰甘油葡萄糖乘积指数、血小板与淋巴细胞比值是冠心病多支病变的独立危险因素,由此构建的列线图预测模型区分度与校准度良好,可辅助临床早期识别高危人群。
Objective To identify independent risk factors for multivessel coronary artery disease(MVD)and to develop a nomogram for predicting MVD risk.Methods A total of 311 patients diagnosed with coronary artery disease(CAD)who underwent coronary angiography during hospitalization in the Emergency Department of Xuanwu Hospital,Capital Medical University,from September 2023 to December 2024 were retrospectively enrolled.They were divided into the singlevessel disease group(126 cases)and the multi-vessel disease group(185 cases)according to the number of diseased coronary artery branches.Baseline data were compared between the two groups.Univariate and multivariate logistic regression analyses were performed to identify the influencing factors for coronary multi-vessel disease in CAD patients.All patients were assigned to the training set(186 cases)and the validation set(125 cases)at a ratio of 3:2.A nomogram prediction model was established based on the training set,and the validation set was used for internal validation.The discriminative ability,calibration performance,and clinical application value of the model were comprehensively evaluated by means of receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis.Results The multi-vessel disease group had higher levels of alcohol consumption,diabetes mellitus,white blood cell count,neutrophil count,triglyceride-glucose index,systemic immune-inflammation index,and platelet-to-lymphocyte ratio,but lower prevalence of hypertension,lower age,and lower lymphocyte-albumin-neutrophil ratio compared with the single-vessel disease group(P<0.05).Multivariate logistic regression analysis showed that alcohol consumption,diabetes mellitus,triglyceride-glucose index,and platelet-to-lymphocyte ratio were independent risk factors for coronary multi-vessel disease in patients with coronary artery disease(alcohol consumption:OR=2.231,95%CI:1.992–5.031,P=0.042;diabetes mellitus:OR=13.607,95%CI:6.961–26.596,P<0.001;triglyceride-glucose index:OR=1.113,95%CI:1.053–1.177,P<0.001;platelet-to-lymphocyte ratio:OR=1.013,95%CI:1.006–1.021,P<0.001).ROC curve analysis indicated that the area under the curve was 0.860(95%CI:0.807–0.914)in the training set and 0.888(95%CI:0.832–0.945)in the validation set.The Hosmer-Lemeshow goodness-of-fit test showed good calibration(P=0.798 for the training set,P=0.966 for the validation set).Decision curve analysis demonstrated that the nomogram yielded a higher net benefit for predicting the risk of multi-vessel disease when the threshold probability ranged from 0 to 0.9.Conclusion Alcohol consumption,diabetes mellitus,triglyceride-glucose index,and platelet-to-lymphocyte ratio were identified as independent risk factors for multi-vessel coronary artery disease in patients with coronary heart disease.The nomogram prediction model constructed based on these factors exhibited good discrimination and calibration,which can assist in the early clinical identification of high-risk populations.
作者
王雪莲
刘宇琦
高文慧
张秀梅
刘志
WANG Xuelian;LIU Yuqi;GAO Wenhui;ZHANG Xiumei;LIU Zhi(Department of Emergency,Xuanwu Hospital Capital Medical University,Beijing 100053,China;National Clinical Research Center for Geriatric Diseases,Beijing 100053,China)
出处
《转化医学杂志》
2026年第3期367-373,共7页
Translational Medicine Journal
基金
国家自然科学基金面上项目(62172288)。
关键词
冠状动脉多支病变
列线图
预测模型
coronary artery multivessel disease
nomogram
predictive model
