【摘要】 目的 构建系统凝血-炎症指数(systemic coagulation-inflammation index, SCI)、甘油三酯葡萄糖指数(triglyceride glucose, TyG)、血浆动脉粥样硬化指数(atherogenic index of plasma, AIP)对冠状动脉粥样硬化性心脏病[以下简称为冠心病(coronary heart disease, CHD)]患病风险的预测模型，并对模型的预测效果进行验证。方法 回顾性收集2014-01/2021-01月在作者医院就诊的患者，根据冠状动脉造影结果分为对照组(n=616)和CHD组(n=1056);同时根据7∶3的比例，将研究对象随机分为训练集(n=1170)和验证集(n=502)。收集患者的血常规及血生化等临床资料。在训练集中采用逐步向后回归法筛选出发生CHD的独立危险因素、构建列线图模型，在验证集中对模型的预测效果和适用性进行内部验证。结果 训练集人群的多因素Logistic回归分析结果显示，SCI、AIP、D-二聚体(D-dimer, D-D)、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol, HDL-C)是CHD发生的独立保护因素，而年龄、TyG、PLR、Fib、ApoA是CHD发生的独立危险因素。对该模型进行内部验证，训练集中曲线下面积(area under the curve, AUC)为0.739(95%CI:0.624～0.775),验证集中AUC为0.846(95%CI:0.697～0.886)。校准曲线结果提示该模型有良好的校准度，通过决策分析曲线(decision curve analysis, DCA)检测该模型临床有效性，当训练集与验证集阈概率分别在10%～50%及10%～75%范围内，该预测模型具有良好的临床有效性。通过临床影响曲线(clinical impact curve, CIC)检验发现该模型的具有有效的预测能力。结论 该预测模型具有良好的区分度、校准度，净收益率较好，可用于CHD风险的预测。更多还原

【Abstract】 Objective To construct a predictive model for the risk of coronary atherosclerotic heart disease [coronary heart disease(CHD) for short] using systemic coagulation-inflammation index(SCI), triglyceride glucose(TyG) and atherogenic index of plasma(AIP), and to validate the predictive efficiency of the model. Methods A retrospective collection of patients was conducted from January 2014 to January 2021 in author′s hospital. Patients were divided into control group(n=616) and CHD group(n=1056) based on the results of coronary angiography; the study population was then randomly assigned to training set(n=1170) and validation set(n=502) according to the ratio of 7:3. The clinical data such as blood routine and blood biochemistry of the patients were collected. In the training set, a stepwise backward regression analysis was performed to identify independent risk factors for developing CHD, the nomogram model was constructed, and the predictive efficiency and applicability of the model were internally validated using the validation set. Results The results of the multifactor Logistic regression analysis on the training set population indicated that SCI, AIP, D-dimer(D-D) and high-density lipoprotein cholesterol(HDL-C) were independent protective factors for the occurrence of CHD, while age, TyG, PLR, Fib and ApoA were independent risk factors for the occurrence of CHD. Internal validation of the model showed that the area under the curve(AUC) was 0.739(95%CI: 0.624-0.775) in the training set and 0.846(95%CI: 0.697-0.886) in the validation set. The calibration curve results suggested good calibration of the model, and the clinical effectiveness of the model was tested by decision curve analysis(DCA), showing that the model has good clinical effectiveness when the threshold probabilities for the training set and validation set were within the range of 10%-50% and 10%-75% respectively, clinical impact curve(CIC) confirmed the model′s effective predictive ability. Conclusion The predictive model demonstrates good discriminative ability and calibration, as well as a favorable net benefit, which indicating its potential for predicting the risk of CHD.更多还原