冠心病稳定型心绞痛患者再入院中西医风险预测模型构建与应用评估——基于真实世界临床数据的前瞻性研究  

Construction and Application Evaluation of an Integrated Traditional Chinese and Western Medicine Risk Prediction Model for Readmission in Patients with Stable Angina of Coronary Heart Disease:A Prospective Study Based on Real-World Clinical Data

作　　者：韩文杰 朱明军[1] 王新陆[1] 于瑞[1] 彭广操[1] 赵齐飞 王建茹 聂闪闪 王永霞[1,2] 卫靖靖 HAN Wenjie;ZHU Mingjun;WANG Xinlu;YU Rui;PENG Guangcao;ZHAO Qifei;WANG Jianru;NIE Shanshan;WANG Yongxia;WEI Jingjing(The First Affiliated Hospital of Henan University of Chinese Medicine,Zhengzhou,450000;Henan University of Chinese Medicine)

机构地区：[1]河南中医药大学第一附属医院,河南省郑州市450000 [2]河南中医药大学

出　　处：《中医杂志》2025年第6期604-611,共8页Journal of Traditional Chinese Medicine

基　　金：国家重点研发计划重点专项(2019YFC1710003,2019YFC1710000);国家中医药管理局循证能力建设项目(2019XZZX-XXG003);河南省科技攻关课题(232102310469);国家自然科学基金(82004178,82030120);河南中医药大学研究生科研创新项目(2023KYCX032)。

摘　　要：目的基于真实世界临床数据探讨冠心病稳定型心绞痛患者再入院的影响因素,建立中西医风险预测模型,以期为早期识别高风险人群、降低患者再入院率提供依据。方法采用前瞻性临床研究方法,纳入冠心病稳定型心绞痛患者按照7∶3的比例分为训练集和验证集。统一采集其一般信息、中医资料、实验室检查等相关信息,随访1年后根据是否发生再入院事件分为再入院组和非再入院组。采用单因素Logistic回归和多因素Logistic回归分析筛选冠心病稳定型心绞痛再入院的独立影响因素,构建再入院中西医风险预测模型,并通过列线图进行可视化展示。分别从区分度、校准度、临床决策曲线方面对模型进行验证和评价。结果共纳入分析682例患者,训练集477例,验证集205例,共有89例患者发生再入院。多因素Logistic回归分析发现,心力衰竭病史[OR=6.93,95%CI(1.58,30.45)]、弦脉[OR=2.58,95%CI(1.42,4.72)]、虚脉[OR=3.97,95%CI(2.06,7.67)]、齿痕舌[OR=4.38,95%CI(2.32,8.27)]、血瘀质[OR=2.17,95%CI(1.06,4.44)]、痰瘀互结证[OR=3.64,95%CI(1.87,7.09)]、非高密度脂蛋白胆固醇升高[OR=1.30,95%CI(1.01,1.69)]为冠心病稳定型心绞痛患者再入院的影响因素。基于以上筛选出的影响因素作为模型的最终预测因子,构建冠心病稳定型心绞痛再入院风险列线图,该模型显示出中等预测能力。模型验证结果显示,在建模人群中受试者工作特征曲线下面积(AUC)为0.818[95%CI(0.781,0.852)],在验证人群中AUC为0.816[95%CI(0.779,0.850)]。Hosmer-Lemeshow检验结果表明,模型的区分度较好(χ^(2)=4.55,P=0.80),且预测能力较稳定。阈值概率>5%时该模型预测冠心病稳定型心绞痛患者再入院风险的临床净获益显著高于对所有患者实施干预。结论心力衰竭病史、齿痕舌、虚脉、弦脉、痰瘀互结证、血瘀质、非高密度脂蛋白胆固醇是冠心病稳定型心绞痛患者再入院的�Objective By exploring the influencing factors of readmission in patients with stable angina of coronary heart disease(CHD)based on real-world clinical data,to establish a risk prediction model of integrated traditional Chinese and western medicine,in order to provide a basis for early identification of high-risk populations and reducing readmission rates.Methods A prospective clinical study was conducted involving patients with stable angina pectoris of CHD,who were divided into a training set and a validation set at a 7∶3 ratio.General information,traditional Chinese medicine(TCM)-related data,and laboratory test results were uniformly collected.After a one-year followup,patients were classified into a readmission group and a non-readmission group based on whether they were readmitted.Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for readmission.A risk prediction model of integrated traditional Chinese and western medicine was constructed and visualized using a nomogram.The model was validated and evaluated in terms of discrimination,calibration,and clinical decision curve analysis.Results A total of 682 patients were included,with 477 in the training set and 205 in the validation set,among whom 89 patients were readmitted.Multivariate logistic regression analysis identified heart failure history[OR=6.93,95%CI(1.58,30.45)],wiry pulse[OR=2.58,95%CI(1.42,4.72)],weak pulse[OR=3.97,95%CI(2.06,7.67)],teeth-marked tongue[OR=4.38,95%CI(2.32,8.27)],blood stasis constitution[OR=2.17,95%CI(1.06,4.44)],phlegm-stasis mutual syndrome[OR=3.64,95%CI(1.87,7.09)],and elevated non-high-density lipoprotein cholesterol[OR=1.30,95%CI(1.01,1.69)]as influencing factors of readmission.These factors were used as predictors to construct a nomogram-based risk prediction model for readmission in patients with stable angina.The model demonstrated moderate predictive capability,with an area under the receiver operating characteristic curve(AUC)of 0.818[95%CI(0.781,0.852)]in th

关 键 词：冠心病 稳定型心绞痛 再入院 中医证候 体质 风险预测模型 

分 类 号：R54[医药卫生—心血管疾病]