糖尿病合并稳定型心绞痛患者发生主要不良心脑血管事件的中医预测模型构建及应用评估研究

Development and Applicability Assessment of a TCM-based Risk Prediction Model for Major Adverse Cardiovascular and Cerebrovascular Events in Type 2 Diabetics with Stable Angina Pectoris

背景2型糖尿病合并稳定型心绞痛(T2DM-SAP)致残、病死率高,预后差,早期治疗对延缓T2DM-SAP的发展具有重要作用。中医药在疾病预防方面具有独特的临床优势,构建融合中医元素的风险预测模型可为临床防治T2DM-SAP患者发生主要不良心脑血管事件(MACCE)提供可靠依据。目的探究T2DM-SAP患者发生MACCE的危险因素,构建并评估风险预测模型。方法选取2012—2019年在河南中医药大学第一附属医院诊治的674例T2DM-SAP住院患者,依据医院信息系统收集患者的电子病历和随访数据,包括人口学资料、临床特征、实验室检查指标、中医资料、结局指标(MACCE发生情况)。根据是否发生MACCE,将患者分为MACCE组(n=190)和非MACCE组(n=484)。采用单因素和多因素Logistic回归分析筛选T2DM-SAP患者发生MACCE的独立危险因素,建立MACCE风险预测模型,并构建列线图。采用Bootstrap法进行内部验证。通过受试者工作特征(ROC)曲线、C-index、Calibration plot、Hosmer-Lemeshow检验及临床决策曲线(DCA曲线)验证预测模型的预测效能。结果多因素Logistic回归分析结果显示,年龄〔OR=1.033,95%CI(1.014,1.052)〕、脑血管病史〔OR=3.799,95%CI(2.529,5.750)〕、血肌酐〔OR=1.005,95%CI(1.002,1.008)〕、暗紫舌〔OR=2.756,95%CI(1.285,5.935)〕、少苔〔OR=2.083,95%CI(1.025,4.166)〕、细弱脉〔OR=5.822,95%CI(1.867,20.359)〕、风痰阻络〔OR=2.525,95%CI(1.466,4.387)〕是T2DM-SAP患者发生MACCE的影响因素(P<0.05)。基于筛选出以上独立危险因素构建预测模型,该模型显示出中等预测能力,C-index为0.769〔95%CI(0.729,0.809)〕,灵敏度为69.47%,特异度为75.00%,区分度良好;Calibration plot显示预测不良结局风险与实际不良结局风险平均绝对误差为0.011,校正拟合偏倚后的C-index为0.761,Hosmer-Lemeshow检验结果显示校准度良好(χ^(2)=6.004,P=0.647);DCA结果显示,阈值概率>30%,预测模型在临床上是有益的。结论年龄、脑血管病史、血肌酐、暗紫舌、少苔、细弱脉、风痰阻络是T2DM-SAP患者发生MACCE的影响因素,并以此建立了临床预测模型。该模型具有良好的区分度、校准度以及临床有效性,能为防治T2DM-SAP患者发生MACCE提供科学依据。

Background Early treatment is crucial to the delay of the progression of type 2 diabetes mellitus with stable angina pectoris(T2DM-SAP),which has poor prognosis,such as high rates of disability and mortality.As traditional Chinese medicine(TCM)has unique advantages in preventing diseases,developing a model with TCM and western medicine factors associated with major adverse cardiovascular and cerebrovascular events(MACCEs)incorporated may be a reliable tool that could be used to predict the risk of MACCEs in patients with T2DM-SAP.Objective To develop and assess the applicability of a risk prediction model for MACCEs in T2DM-SAP patients using identified risk factors associated with MACCEs in this group.Methods Participants were 674 inpatients with T2DM-SAP who received diagnostic and treatment services from The First Affiliated Hospital of Henan University of CM from 2012 to 2019.Through the hospital information system,electronic medical records and follow-up data of these patients were collected,including demographics,clinical characteristics,laboratory parameters,TCM symptoms and syndrome differentiation,and outcome(prevalence of MACCEs).Patients were classified into a MACCEs group(n=190)and a non-MACCEs group(n=484)by prevalence of MACCEs.Independent risk factors for MACCEs in T2DM with SAP were identified using univariate and multivariate Logistic regression,and used to develop a nomogram-based predictive model.Then the model was internally validated using the bootstrap approach,and its predictive value was estimated using ROC analysis,C-index,calibration plot,Hosmer-Lemeshow test and decision curve analysis.Results Based on the multivariate Logistic regression analysis,the factors associated with MACCEs in T2DM-SAP patients(P<0.05)included age〔OR=1.033,95%CI(1.014,1.052)〕,cerebrovascular disease history〔OR=3.799,95%CI(2.529,5.750)〕,serum creatinine〔OR=1.005,95%CI(1.002,1.008)〕,dark purple tongue〔OR=2.756,95%CI(1.285,5.935)〕,decreased tongue coating〔OR=2.083,95%CI(1.025,4.166)〕,thready pulse〔OR=5.822,95%CI(1.867,20.359)〕,and obstruction of collateral channels caused by wind-phlegm〔OR=2.525,95%CI(1.466,4.387)〕.The predictive model constructed using the above-mentioned factors showed moderate predictive power{C-index=0.769〔95%CI(0.729,0.809)〕,sensitivity=69.47%,specificity=75.00%},indicating a good degree of distinction.The calibration plot showed the average absolute error between the predictive and actual adverse outcome risks was 0.011,with a C-index of 0.761 after fitting bias correction.The Hosmer-Lemeshow test showed a good calibration(χ^(2)=6.004,P=0.647).The decision curve analysis displayed a threshold probability of>30%,indicating that the model may be clinically beneficial.Conclusion The risk predictive model for MACCEs in T2DM-SAP patients was developed using the associated factors(including age,cerebrovascular disease history,serum creatinine,dark purple tongue,decreased tongue coating,thready pulse,and obstruction of collateral channels caused by wind-phlegm)identified by us,which has been proven to have good discrimination,calibration,and clinical effectiveness,and could be used as a tool for assessing the risk of MACCEs in patients with T2DM-SAP.