基于心脏彩色多普勒超声参数分析肥厚型心肌病继发心房颤动的危险因素并构建风险列线图模型
Risk factors of secondary atrial fibrillation in hypertrophic cardiomyopathy were analyzed and the risk nomogram model was constructed based on cardiac color ultrasound parameters
王艳 1乔宝剑2
作者信息
- 1. 459000 河南省济源市第二人民医院超声科
- 2. 河南省济源市五龙口卫生院心血管内科
- 折叠
摘要
目的 基于心脏彩色多普勒超声参数分析构建肥厚型心肌病(HCM)继发心房颤动的风险列线图模型.方法 回顾分析 2020 年 1月至 2022 年 12 月收治的 150例HCM继发心房颤动患者临床资料,根据是否继发心房颤动分为继发组(85 例)和未继发组(65 例),比较 2 组一般资料、心脏彩色多普勒超声参数,采用Logistic多因素回归分析法筛选影响肥厚型心肌病继发心房颤动的危险因素.利用R软件基于筛选出的危险因素构建肥厚型心肌病继发心房颤动的风险列线图预测模型,并验证该模型预测肥厚型心肌病继发心房颤动风险的准确性.结果 继发组与未继发组的性别、体质指数(BMI)、烟酒史、既往病史(高血压病、糖尿病)、左心室收缩末期直径(LVESD)、左心室舒张末期直径(LVEDD)、左心室壁厚度相比差异无统计学意义(P＞0.05),年龄、有HCM家族史、纽约心脏病协会心功能分级(NYHA)Ⅲ～Ⅳ级、左心室射血分数(LVEF)、左心房内径(LAD)、室间隔厚度差异有统计学意义(P<0.05);Logistic多因素回归结果显示,年龄[OR(95%CI)=4.341(3.218,6.223),P<0.001]、有HCM家族史[OR(95%CI)=4.468(3.176,6.137),P<0.001]、HYHA分级Ⅲ～Ⅳ级[OR(95%CI)=4.204(2.897,5.861),P<0.001]、LAD[OR(95%CI)=4.726(2.512,5.861),P<0.001]、室间隔厚度[OR(95%CI)=4.527(3.075,6.114),P<0.001]是影响HCM继发心房颤动的危险因素,LVEF是HCM继发心房颤动的保护因素[OR(95%CI)=0.560(0.231,0.787),P=0.003];基于多因素Logistic回归分析结果构建HCM继发心房颤动风险列线图模型,结果显示:C-index指数为 0.914(95%CI:0.753,0.965),提示该模型区分度、准确度均较好.结论 年龄、有HCM家族史、NYHA分级Ⅲ～Ⅳ级、LVEF、LAD、室间隔厚度为HCM继发心房颤动的独立危险因素,基于心脏多普勒超声参数构建的HCM继发心房颤动的风险列线图模型预测效能良好,临床应用价值较高.
Abstract
Objective To analyze the risk factors of secondary atrial fibrillation in hypertrophic cardiomyopathy(HCM)and construct a risk nomogram model based on cardiac color ultrasound parameters.Methods A retrospective analysis was conducted on the clinical data of 150 patients with HCM secondary atrial fibrillation admitted to the hospital from January 2020 to December 2022.Based on whether or not secondary atrial fibrillation occurred,the secondary group(85 cases)and the non-secondary group(65 cases)were compared in terms of general data and cardiac ultrasound parameters.Logistic multiple factor regression analysis was used to screen for risk factors affecting secondary atrial fibrillation in hypertrophic cardiomyopathy.Use R software to construct a risk column chart prediction model for secondary atrial fibrillation in hypertrophic cardiomyopathy based on the screened risk factors,and verify the accuracy of the model in predicting the risk of secondary atrial fibrillation in HCM.Results There was no significant difference between the secondary group and the non-secondary group in gender,body mass index(BMI),smoking and drinking history,past medical history(hypertension,diabetes),left ventricular end-systolic diameter(LVESD),left ventricular end-diastolic diameter(LVEDD),left ventricular wall thickness(P＞0.05).The difference in age,family history of HCM,heart function classification(NYHA)Ⅲ～Ⅳ,left ventricular ejection fraction(LVEF),left atrial diameter(LAD),and ventricular septal thickness was statistically significant(P<0.05).Logistic multivariate regression results showed that age[OR(95%CI)=4.341(3.218,6.223),P<0.001],family history of HCM[OR(95%CI)=4.468(3.176,6.137),P<0.001],HYHA gradeⅢ～Ⅳ[OR(95%CI)=4.204(2.897,5.861),P<0.001],LAD[OR(95%CI)=4.726(2.512,5.861),P<0.001],and ventricular septal thickness[OR(95%CI)=4.527(3.075,6.114),P<0.001]were risk factors for secondary atrial fibrillation in HCM,LVEF is a protective factor for atrial fibrillation secondary to HCM[OR(95%CI)=0.560(0.231,0.787),P=0.003];Based on the results of multivariate logistic regression analysis,a risk column chart model for secondary atrial fibrillation in HCM was constructed.The results showed that the C-index index was 0.914(95%CI:0.753～0.965),indicating that the model had good discrimination and accuracy.Conclusion Age,family history of HCM,NYHA grade Ⅲ～Ⅳ,LVEF,LAD,and interventricular septal thickness are independent risk factors for secondary atrial fibrillation in HCM.The risk column chart model for secondary atrial fibrillation in HCM based on cardiac ultrasound parameters has good predictive performance and high clinical application value.
关键词
心脏成像技术/心肌病,肥厚性/心房颤动/危险因素/列线图Key words
Cardiac imaging techniques/Cardiomyopathy,hypertrophic/Atrial fibrillation/Risk factors/Nomograms引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据