慢性心力衰竭患者睡眠障碍风险预测模型的建立与验证  

Development and validation of a risk prediction model for sleep disorders in patients with chronic heart failure

作　　者：甘衍梅 李高叶[1] 廖婷婷 路华 陈丽霞[3] 潘琪妮 杜瑶 GAN Yanmei;LI Gaoye;LIAO Tingting;LU Hua;CHEN Lixia;PAN Qini;DU Yao(Department of Cardiovascular Medicine,The First Affiliated Hospital of Guangxi Medical University,Nanning,Guangxi 530021,China;Department of Cardiovascular Medicine,Guilin People’s Hospital,Guilin,Guangxi 541002,China;Department of Cardiovascular Medicine,The First People’s Hospital of Yulin,Yulin,Guangxi 537099,China;Department of Cardiovascular Medicine,The Second Affiliated Hospital of Guangxi Medical University,Nanning,Guangxi 530007,China)

机构地区：[1]广西医科大学第一附属医院心血管内科,南宁530021 [2]桂林市人民医院心血管内科,广西桂林541002 [3]玉林市第一人民医院心血管内科,广西玉林537099 [4]广西医科大学第二附属医院心血管内科,南宁530007

出　　处：《重庆医学》2025年第3期597-605,611,共10页Chongqing Medical Journal

基　　金：广西医疗卫生适宜技术开发与推广应用项目(S2021110);广西医科大学第一附属医院护理临床研究攀登天使启明星计划项目(YYZS2020032)。

摘　　要：目的分析慢性心力衰竭(CHF)患者睡眠障碍的风险因素并构建列线图模型。方法采用简单随机抽样方法,于2023年3月至2024年3月选取来自广西壮族自治区南宁市2所、玉林市1所及桂林市1所三级甲等医院住院并符合纳入标准的306例CHF患者作为研究对象。采用最小绝对收缩和选择算子(LASSO)回归分析初步筛选变量,再基于logistic回归确定预测变量,构建列线图模型,应用受试者工作特征(ROC)曲线、校准曲线和临床决策曲线进行预测模型的验证和效果评价,并采用Boostrap重抽样法进行内部验证。结果306例患者的睡眠障碍发生率为57.5%(176/306)。logistic回归分析显示,年龄、文化程度、家庭人均月收入、NYHA心功能分级、合并症数量、甘油三酯、厌食、焦虑共8个变量是CHF患者发生睡眠障碍的独立风险因素(P<0.05)。该模型的AUC为0.91(95%CI:0.77~0.88),具有较好的区分度及一致性。结论该研究构建的模型预测效果较好,可为临床医护人员早期识别慢性心力衰竭患者发生睡眠障碍并实施预防性护理提供参考。Objective To analyze risk factors for sleep disorders in patients with chronic heart failure(CHF)and construct a nomogram prediction model.Methods Using simple random sampling,306 hospitalized CHF patients meeting inclusion criteria were enrolled from four Grade A tertiary hospitals in Guangxi Zhuang Autonomous Region(two in Nanning,one each in Yulin and Guilin)between March 2023 and March 2024.LASSO regression analysis was initially employed for variable screening,followed by logistic regression to identify predictive variables for constructing the nomogram mod el.Model validation and performance evaluation were conducted using receiver operating characteristic(ROC)curves,calibration curves,and clinical decision curves,with internal validation performed through Bootstrap resampling(1000 iterations).Results The incidence of sleep disorders among the 306 patients was 57.5%(176/306).Logistic regression analysis identified eight independent risk factors for sleep disorders in CHF patients(P<0.05):age,education level,monthly household income per capita,NYHA cardiac function classification,number of comorbidities,triglyceride levels,anorexia,and anxiety.The model demonstrated good discrimination for the AUC of 0.91(95%CI:0.77-0.88)and calibration consistency.Conclusion The prediction model established in this study shows good predictive performance,serving as a valuable reference for healthcare providers to early identify sleep disorders and implement preventive care strategies in patients with CHF.

关 键 词：慢性心力衰竭 睡眠障碍 预测模型 列线图 

分 类 号：R592[医药卫生—老年医学]