江苏大学学报（医学版）2025，Vol.35Issue(6)：504-511,8.DOI:10.13312/j.issn.1671-7783.y250015
冠状动脉慢血流的危险因素分析及预测模型构建
Analysis of risk factors for coronary slow flow and construction of a predictive model
摘要
Abstract
Objective:To analyze the risk factors associated with coronary slow flow(CSF)and construct a clinical predictive model.Methods:From January 2018 to December 2023,patients who underwent coronary angiography(CAG)at the Affiliated People's Hospital of Jiangsu University and the First People's Hospital of Lianyungang were retrospectively selected.Among them,201 patients with coronary artery stenosis≥40％and evidence of CSF on CAG were included in the observation group.Meanwhile,153 patients with coronary artery stenosis ≤40％but without CSF during the same period were enrolled as the control group.General clinical characteristics,echocardiographic data,complete blood count,biochemical parameters,and inflammatory markers were collected and compared between the two groups.Least Absolute Shrinkage and Selection Operator(LASSO)regression was employed to preliminarily select candidate variables.Multivariate logistic regression was then performed to identify independent risk factors associated with CSF.A nomogram prediction model was constructed using R software,and a corresponding nomogram was generated.The model's discriminative ability,calibration,and clinical utility were assessed using the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DC A),respectively.Results:LASSO regression identified hypertension,smoking,hemoglobin,high-density lipoprotein cholesterol(HDL-C),triglyceride-glucose(TyG)index,platelet-to-lymphocyte ratio(PLR),and left ventricular end-diastolic diameter(LVEDD)as relevant features for predicting CSF.Multivariate logistic regression analysis demonstrated that hypertension,smoking,elevated TyG index,elevated PLR,and increased LVEDD were independent risk factors for CSF,while increased HDL-C levels were found to be a protective factor.Based on these variables,a nomogram prediction model was constructed.The area under the ROC curve(AUC)was 0.793(95％CI:0.747-0.838),indicating good discrimination.The calibration curve and Hosmer-Lemeshow goodness-of-fit test(P=0.151)showed good agreement between predicted and observed outcomes.Additionally,the DCA curve demonstrated favorable clinical applicability of the model,indicating its potential to provide clinical benefit.Conclusion:Hypertension,smoking,elevated TyG index,elevated PLR,and increased LVEDD are independent risk factors for CSF,while increased HDL-C serves as a protective factor.The predictive model based on these factors exerts good performance in predicting the occurrence of CSF.关键词
冠状动脉慢血流/危险因素/预测模型Key words
coronary slow flow/risk factors/predictive model分类
医药卫生引用本文复制引用
高渊博,赵思玉,李群星,冯松涛,陶艾彬..冠状动脉慢血流的危险因素分析及预测模型构建[J].江苏大学学报（医学版）,2025,35(6):504-511,8.基金项目
国家自然科学基金资助项目(81971817) （81971817）
