Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features

Frontiers in Medicine

article

Wei-Ya Shi¹  Nian Xiong²  Fei Shan¹  Shao-Ping Hu²  Hao-Ling Zhang³  Tie-Fu Liu⁴  Su Zhou⁵  Yu-Hong Tang⁶  Xin-Lei Zhang¹  Yu-Xin Shi¹  Zhi-Yong Zhang¹ 

[1] Department of Radiology, Shanghai Public Health Clinical Center, Fudan University;Department of Radiology, Wuhan Union Red Cross Hospital;Department of Radiology, Zhongshan Hospital, Fudan University;Department of Scientific Research, Shanghai Public Health Clinical Center, Fudan University;Department of Interventional Radiology, Shanghai Public Health Clinical Center, Fudan University;Department of Research and Development, Winning Health Technology Group Co., Ltd.

关键词: coronavirus disease 2019;    influenza A (H1N1);    computed tomography;    multivariate analysis;    differential diagnosis;   

DOI  :  10.3389/fmed.2021.651556

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Objectives: Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. Methods: In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals. Patients were randomly grouped into a primary cohort and a validation cohort using a seven-to-three ratio, and their clinicoradiologic data on admission were compared. The clinicoradiologic features were optimized by the least absolute shrinkage and selection operator (LASSO) logistic regression analysis to generate a model for differential diagnosis. Receiver operating characteristic (ROC) curves were plotted for assessing the performance of the model in the primary and validation cohorts. Results: The COVID-19 pneumonia mainly presented a peripheral distribution pattern (262/291, 90.0%); in contrast, H1N1 pneumonia most commonly presented a peribronchovascular distribution pattern (52/97, 53.6%). In LASSO logistic regression, peripheral distribution patterns, older age, low-grade fever, and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia, whereas, a peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia, and a higher level of AST were associated with H1N1 pneumonia. For the primary and validation cohorts, the LASSO model containing above eight clinicoradiologic features yielded an area under curve (AUC) of 0.963 and 0.943, with sensitivity of 89.7 and 86.2%, specificity of 89.7 and 89.7%, and accuracy of 89.7 and 87.1%, respectively. Conclusions: Combination of distribution pattern and category of pulmonary opacity on chest CT with clinical features facilitates the differentiation of COVID-19 pneumonia from H1N1 pneumonia.

【 授权许可】

CC BY