侵袭性肺真菌感染病临床影像预测模型的初步建立及评价  

Establishment and evaluation of an image-based clinical prediction model for invasive pulmonary fungal infection

作　　者：庞小莉 王显棋 陈伟[2] 熊玮 PANG Xiaoli;WANG Xianqi;CHEN Wei;XIONG Wei(Department of Geriatrics and Secret Service Medicine,Army Medical University(Third Military Medical University),Chongqing,400038,China;Department of Radiology,First Affiliated Hospital,Army Medical University(Third Military Medical University),Chongqing,400038,China)

机构地区：[1]陆军军医大学(第三军医大学)第一附属医院老年医学与特勤医学科,重庆400038 [2]陆军军医大学(第三军医大学)第一附属医院放射科,重庆400038

出　　处：《陆军军医大学学报》2023年第7期677-688,共12页Journal of Army Medical University

基　　金：国家重点研发计划子课题(2018YFC2000300)。

摘　　要：目的建立侵袭性肺部真菌感染的临床影像预测模型并进行评价,为侵袭性肺部真菌感染患者的早期识别提供依据。方法采用病例-对照研究设计方案,筛选2016年1月1日至2021年9月30日我科住院患者中187例侵袭性肺部真菌感染和190例肺部非真菌感染患者的临床、影像资料进行分析。按照7∶3的比例将样本采用随机数字表分为训练集(264例)和验证集(113例)。通过二元Logistic回归进行单因素、多因素分析,采用R软件包构建列线图预测模型,并通过受试者工作曲线(ROC)、曲线下面积(AUC)、校准曲线、DCA曲线等对模型的有效性和应用价值进行评价。结果多因素Logistic回归分析结果显示:贫血(OR:6.41,95%CI:1.62~25.43)、糖尿病(OR:6.77,95%CI:1.20~38.11)、COPD(OR:12.82,95%CI:2.71~60.55)、入院后营养不良(OR:8.78,95%CI:2.17~35.55)、面罩/鼻氧管吸氧(OR:3.53,95%CI:0.911~13.66)、胆碱酯酶(OR:7.47,95%CI:1.66~33.57)、右肺下叶(OR:11.17,95%CI:2.07~60.16)、左肺下叶(OR:16.25,95%CI:3.31~79.71)、磨玻璃影(OR:19.22,95%CI:4.17~88.48)、空气支气管征(OR:6.44,95%CI:1.27~32.68)、支气管扩张(OR:11.58,95%CI:1.50~89.25)为侵袭性肺部真菌感染的相关因素。由上述11个因子构建列线图(R^(2)=0.830,C-index=0.97,95%CI:0.96~0.99)。训练集和验证集的AUC分别为0.972(95%CI:0.955~0.988)和0.945(95%CI:0.905~0.985),显示均具有很高的区分度。训练集和验证集的校准曲线均基本沿45°线分布,DCA曲线均显示在阈概率为10%~90%时存在净获益。结论研究筛选出贫血、COPD、糖尿病、入院后营养不良、面罩/鼻氧管吸氧、胆碱酯酶、右肺下叶、左肺下叶、磨玻璃影、空气支气管征和支气管扩张11个参数建立侵袭性肺部真菌感染列线图预测模型,准确性较高。ObjectiveTo establish and evaluate a prediction model for invasive pulmonary fungal infection based on clinical images in order to provide reference for early identification of the disease.MethodsA case-control trial was designed and conducted on 187 hospitalized patients with invasive pulmonary fungal infection and 190 patients with pulmonary infection of other pathogens in our department from January 1,2016 to September 30,2021.Their clinical and imaging data were collected and analyzed.The participants were randomly assigned into training set(n=264)and verification set(n=113)at a ratio of 7∶3.Univariate and multivariate analyses were conducted through binary logistic regression,and a nomograph prediction model was constructed using R software package.The validity and application value of the model were evaluated through receiver operating characteristic(ROC)curve,area under ROC curve(AUC),calibration curve,and decision curve(DCA).ResultsMultivariate logistic regression analysis showed that anemia(OR=6.41,95%CI:1.62~25.43),diabetes(OR=6.77,95%CI:1.20~38.11),COPD(OR=12.82,95%CI:2.71~60.55),malnutrition after admission(OR=8.78,95%CI:2.17~35.55),mask/nasal oxygen inhalation(OR=3.53,95%CI:0.911~13.66),cholinesterase(OR=7.47,95%CI:1.66~33.57),right lung lower lobe(OR=11.17,95%CI:2.07~60.16),left lung lower lobe(OR=16.25,95%CI:3.31~79.71),ground glass opacity(OR=19.22,95%CI:4.17~88.48),air bronchogram(OR=6.44,95%CI:1.27~32.68),and bronchiectasis(OR=11.58,95%CI:1.50~89.25)were related factors of pulmonary fungal infection.The nomogram was constructed from the above 11 factors(R^(2)=0.830,C-index=0.97,95%CI:0.96~0.99).The AUC of the training and verification sets were 0.972(95%CI:0.955~0.988)and 0.945(95%CI:0.905~0.985),respectively,indicating a high efficiency of differentiation.The calibration curves of the training set and the verification set were distributed along the 45°line basically,and the DCA curves showed that there was a net benefit when the threshold probability was 10%~90%.ConclusionBased on 11 paramet

关 键 词：侵袭性肺部真菌感染 临床特征 影像特征 预测模型 

分 类 号：R181.32[医药卫生—流行病学] R519[医药卫生—公共卫生与预防医学] R563