Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features - Shi, Wei-Ya; Hu, Shao-Ping; Zhang, Hao-Ling; Liu, Tie-Fu; Zhou, Su; Tang, Yu-Hong; Zhang, Xin-Lei; Shi, Yu-Xin; Zhang, Zhi-Yong; Xiong, Nian; Shan, Fei (Frontiers in Medicine, 2021)
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: Two hundred ninety-one 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 in two hospitals were enrolled. Patients were randomly grouped in seven-to-three ratio into a primary cohort and a validation cohort 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 pattern, older age, low-grade fever and slightly elevated aspartate aminotransferase (AST) were associated with COVID-19 pneumonia; whereas, peribronchovascular distribution pattern, centrilobular nodule or tree-in-bud sign, consolidation, bronchial wall thickening or bronchiectasis, younger age, hyperpyrexia and 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%, 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.