摘要
目的 探讨重症社区获得性肺炎(community-acquired pneumonia,CAP)并发呼吸衰竭(respiratory failure,RF)的预测因素,构建临床预测模型并进行内部验证。方法 回顾性选择2022年9月至2024年12月武汉科技大学附属天佑医院重症CAP患者350例,按照7∶3随机分为训练集(n=245)和验证集(n=105),并根据是否并发RF分为RF组和非RF组。采用LASSO回归分析优化变量选择,多因素logistic回归分析构建预测模型并进行内部验证。结果 单因素回归分析显示男性、高血压、糖尿病、冠心病、年龄、CURB-65评分、白细胞计数、中性粒细胞计数、C反应蛋白(C-reactive protein,CRP)、淀粉样蛋白A、降钙素原和住院天数是重症肺炎并发RF的危险因素;白蛋白水平是重症肺炎并发RF的保护因素。经LASSO回归分析,最终将CURB-65评分、白蛋白水平和CRP纳入预测模型,受试者工作特征曲线在训练集和验证集中的曲线下面积分别为0.903和0.919。校准曲线分析中训练集和验证集均显示出非常好的拟合度,Hosmer-Lemeshow拟合优度检验显示训练集和验证集中的预测值与真实值间均无显著性差异,阈值概率均为0.01~0.99。结论 CURB-65评分、白蛋白水平及CRP是重症肺炎并发RF的独立预测因素,基于上述预测因素建立的重症肺炎并发RF的临床预测模型具有良好的区分度、校准度、拟合度及临床实用性。
Objective To explore predictive factors of severe community-acquired pneumonia(CAP)complicated with respiratory failure(RF)and to develop and internally validate a clinical prediction model.Methods A retrospective study was conducted on 350 patients with severe CAP admitted to Tianyou Hospital Affiliated to Wuhan University of Science and Technology from September 2022 to December 2024.Patients were randomly divided into a training set(n=245)and a validation set(n=105)in a 7∶3 ratio,and further categorized into RF and non-RF groups.LASSO regression was applied to optimize variable selection.Multivariate logistic analysis was used to construct the prediction model,followed by internal validation.Results Univariate regression analysis identified male,hypertension,diabetes,coronary heart disease,age,CURB-65 score,white blood cell count,neutrophil count,C-reactive protein(CRP),serum amyloid A,procalcitonin,and hospital stay as risk factors for RF in severe CAP,while albumin level was a protective factor.LASSO regression selected CURB-65 score,albumin level,and CRP for inclusion in the final model.The area under the receiver operating characteristic curve was 0.903 in the training set and 0.919 in the validation set.Calibration curve analysis demonstrated excellent agreement between predicted and observed probabilities in both sets,and Hosmer-Lemeshow goodness-of-fit tests indicated no significant deviations.Threshold probabilities ranged from 0.01 to 0.99 in both training and validation sets.Conclusions CURB-65 score,albumin level,and CRP are independent predictors of RF in severe CAP.The clinical prediction model based on these factors exhibits strong discrimination,calibration,goodness-of-fit,and clinical utility.
作者
高斯宇
张盛
陈曦
张志霞
杨玉梅
GAO Siyu;ZHANG Sheng;CHEN Xi;ZHANG Zhixia;YANG Yumei(Department of Respiratory and Critical Care Medicine,Tianyou Hospital Affiliated to Wuhan University of Science and Technology,Wuhan 430064,Hubei,China;Cancer Center,Union Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430022,Hubei,China;Brooks College(Sunnyvale),California 94089,the United States;School of Public Health,Zhejiang University,Hangzhou 310000,Zhejiang,China)
出处
《中国临床医学》
2025年第3期449-457,共9页
Chinese Journal of Clinical Medicine
基金
国家卫生健康委员会医疗质量(循证)管理研究项目(YLZLXZ24G021)。
关键词
重症肺炎
呼吸衰竭
临床预测模型
内部验证法
LASSO回归
severe pneumonia
respiratory failure
clinical predictive model
internal validation
LASSO regression
