文章摘要
心脏大血管术后肺部感染危险因素分析及列线图预测模型的构建
Analysis of risk factors for pulmonary infection after cardiac macrovascular surgery and construction of nomogram prediction model
投稿时间:2024-11-01  修订日期:2025-06-13
DOI:
中文关键词: 心脏大血管手术  肺部感染  危险因素  列线图  预测模型
英文关键词: cardiac and macrovascular surgery  pulmonary infection  risk factors  nomogram  prediction model
基金项目:安徽省高等学校省级质量工程项目(2022jyxm745);安徽医科大学护理学院研究生青苗培育项目(hlqm12024062)
作者单位邮编
蔡汶倩 安徽医科大学护理学院安徽医科大学第二附属医院 230032
吴德全* 安徽医科大学护理学院安徽医科大学第二附属医院 
吕文静 安徽医科大学护理学院安徽医科大学第二附属医院 
汪彬琳 安徽医科大学护理学院安徽医科大学第二附属医院 
程瑶瑶 安徽医科大学护理学院安徽医科大学第二附属医院 
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中文摘要:
      目的 分析心脏大血管术后患者肺部感染的危险因素并构建列线图预测模型,为早期识别高危人群和实施防治措施提供参考。方法 收集2019年1月至2023年12月安徽省某三级甲等综合性医院接受心脏大血管手术的1188例患者的病例资料,按7:3的比例随机划分为建模组和验证组。基于Logistic回归分析心脏大血管术后肺部感染的预测因素,建立风险预测模型并验证。模型评价采用受试者工作特征曲线下面积( AUC)、校准曲线和决策曲线分析。 结果 1188例患者中发生肺部感染148例(12.5%)。心脏大血管术后肺部感染的独立危险因素为急诊入院、吸烟史、慢性阻塞性肺疾病、术后肾损伤、再插管和气管插管时间。建模组AUC值为0.923,特异度和敏感度分别为0.804和0.885;验证组AUC值为0.946,特异度和敏感度分别为0.788和0.977,模型区分度较好,校准度和净获益也表现较高。结论 建立的预测模型具有一定预测价值,可用于心脏大血管术后患者肺部感染的风险评估和个体化治疗。
英文摘要:
      Objective Analyze the risk factors of pulmonary infection after cardiac surgery and construct a nomogram prediction model, so as to provide reference for early screening of high-risk groups and targeted prevention and control measures. Methods Case data of 1188 patients undergoing cardiac surgery from January 2019 to December 2023 were collected, and the training group and validation group were randomly divided in a ratio of 7:3. Based on Logistic regression to analyze the predictors of pulmonary infection after cardiac surgery, the risk prediction model was established and validated. The model was evaluated by area under the receiver operating characteristic curve (AUC), calibration curve and decision curve analysis. Results Of the 1,188 patients, 148 (12.5%) had pulmonary infections. Independent risk factors for pulmonary infection after cardiac surgery were emergency department admission, history of smoking, chronic obstructive pulmonary disease, postoperative renal insufficiency, reintubation, intraoperative blood transfusion volume and tracheal intubation time. The AUC value was 0.923, specificity and sensitivity were 0.804 and 0.885, respectively; in the validation group, the AUC value was 0.946, specificity and sensitivity were 0.788 and 0.977, respectively, and high model calibration and net benefit. Conclusions The constructed predictive model has certain predictive value and can be used for risk assessment and individualized treatment of pulmonary infection in patients after cardiac surgery.
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