作者：涂光，王能，童随阳，黄小密，钱进

单位：

441300湖北省随州市，湖北医药学院附属随州医院心血管内科

Units：

Department of Cardiovascular Medicine, Suizhou Hospital, Hubei Medicine University, Suizhou 441300, China

关键词：

心肌桥；动脉粥样硬化；预测；列线图

CLC：

　R 541.1

DOI：

10.12114/j.issn.1008-5971.2022.00.281

Funds：

湖北省自然科学基金资助项目（2020CFB179）；湖北省卫生健康委员会指导性项目（WJ2019F133）

摘要：

目的　构建心肌桥患者合并冠状动脉硬化风险预测列线图模型，并验证其有效性。方法　选取2020年1月至2022年1月湖北医药学院附属随州医院收治的心肌桥患者207例为研究对象。收集患者入院时临床资料，包括性别、年龄、合并高血压情况、合并糖尿病情况、合并脑梗死情况、合并高脂血症情况、吸烟情况、饮酒情况、体质指数、实验室检查指标。患者均行冠状动脉造影检查，根据患者是否合并冠状动脉硬化将其分为硬化组102例与非硬化组105例，比较两组临床资料。采用多因素Logistic回归分析探讨心肌桥患者合并冠状动脉硬化的影响因素，采用R 4.0.2软件和rms程序包构建心肌桥患者合并冠状动脉硬化风险预测列线图模型；采用Bootstrap法重复抽样1 000次，计算一致性指数（CI ），绘制ROC曲线以评价该模型的区分度，采用校准曲线和Hosmer-Lemeshow拟合优度检验评估其准确性，绘制决策曲线以评价该列线图模型的临床有效性。结果　硬化组年龄、合并高血压者占比、合并糖尿病者占比、合并高脂血症者占比、吸烟者占比、三酰甘油高于非硬化组，HDL-C低于非硬化组（P ＜0.05）。多因素Logistic回归分析结果显示，年龄、高血压、糖尿病、吸烟、HDL-C是心肌桥患者合并冠状动脉硬化的影响因素（P ＜0.05）。基于上述影响因素构建心肌桥患者合并冠状动脉硬化风险预测的列线图模型，采用Bootstrap法重复抽样1 000次，结果显示，CI 为0.760；ROC曲线分析结果显示，该列线图模型预测心肌桥患者合并冠状动脉硬化的曲线下面积为0.779〔95%CI （0.716，0.842）〕；校准曲线分析结果显示，该列线图模型预测心肌桥患者冠状动脉硬化发生率与实际发生率基本吻合；Hosmer-Lemeshow拟合优度检验结果显示，该列线图模型预测心肌桥患者冠状动脉硬化发生率与实际发生率比较，差异无统计学意义（χ2=1.920，P =0.383）；决策曲线分析结果显示，当该列线图模型预测心肌桥患者合并冠状动脉硬化风险的概率阈值为0.10~0.73时，患者的净获益率大于0。结论　年龄、高血压、糖尿病、吸烟、HDL-C是心肌桥患者合并冠状动脉硬化的影响因素，基于以上因素构建的心肌桥患者合并冠状动脉硬化风险预测列线图模型有助于早期发现伴有冠状动脉硬化高风险的心肌桥患者，具有一定的预测价值及较好的准确性。

Abstract：

Objective　To construct a nomogram model for predicting the risk of coronary atherosclerosis in patientswith myocardial bridge, and to verify its validity. Methods　A total of 207 patients with myocardial bridge admitted to SuizhouHospital, Hubei Medicine University from January 2020 to January 2022 were selected as the research objects. The clinicaldata of the patients at admission were collected, including gender, age, hypertension, diabetes mellitus, cerebral infarction,hyperlipidemia, smoking status, drinking status, body mass index, laboratory examination indexes. All the patients were examinedby coronary angiography and were divided into 102 cases in sclerotic group and 105 cases in non sclerotic group according tothe presence or absence of coronary atherosclerosis. Clinical data were compared between the two groups. Multivariate Logisticregression analysis was used to explore the influencing factors of coronary atherosclerosis in patients with myocardial bridge. Based on the results of multivariate Logistic regression analysis, R 4.0.2 software and rms program package were used to constructa nomogram model for predicting the risk of coronary atherosclerosis in patients with myocardial bridge. The Bootstrap samplingmethod was used to repeat sampling 1 000 times, and the consistency index (CI ) was calculated. The ROC curve was drawn toevaluate the discrimination of this model. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the consistency of themodel, the decision curve was drawn to evaluate the clinical effectiveness of the model. Results　Age, proportion of hypertension,proportion of diabetes, proportion of hyperlipidemia, proportion of smoking and triglyceride in sclerotic group were higher thanthose in non sclerotic group, and HDL-C was lower than that in non sclerotic group (P < 0.05) . Multivariate Logistic regressionanalysis showed that age, hypertension, diabetes, smoking and HDL-C were influencing factors for coronary atherosclerosis inpatients with myocardial bridge (P < 0.05) . Bootstrap sampling method was used to repeat sampling 1 000 times, and the resultshowed that the CI was 0.760. The results of ROC curve analysis showed that the AUC of the model for predicting coronaryatherosclerosis in patients with myocardial bridge was 0.779 [95%CI (0.716, 0.842) ] . The Hosmer-Lemeshow goodness of fitshowed that, there was no significant difference between the incidence of coronary atherosclerosis predicted by the model andthe actual incidence of patients with myocardial bridge (χ2=1.920, P =0.383) . The results of decision curve analysis showed thatwhen the high risk threshold of the model for predicting the the risk of coronary atherosclerosis in patients with myocardial bridgewas 0.10-0.73, the standardized net benefit of patients was greater than 0. Conclusion　Age, hypertension, diabetes, smoking,and HDL-C are influencing factors for coronary atherosclerosis in patients with myocardial bridge. Based on the above factors,a nomogram model was developed to predict coronary atherosclerosis in patients with myocardial bridge, it can help to detectmyocardial bridge patients with a high risk of coronary atherosclerosis early, which has certain predictive value and good accuracy.

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