Differentiating between 2019 novel coronavirus pneumonia and influenza using a non-specific laboratory marker-based dynamic nomogram

Wang, Linghang; Liu, Yao; Zhang, Ting; Jiang, Yuyong; Yang, Siyuan; Xu, Yanli; Song, Rui; Song, Meihua; Wang, Lin; Zhang, Wei; Han, Bing; Yang, Li; Fan, Ying; Cheng, Cheng; Wang, Jingjing; Xiang, Pan; Pu, Lin; Xiong, Haofeng; Li, Chuansheng; Zhang, Ming; Tan, Jianbo; Chen, Zhihai; Liu, Jingyuan; Wang, Xianbo

BACKGROUND: There is currently a lack of non-specific laboratory indicators as a quantitative standard to distinguish between the 2019 coronavirus disease (COVID-19) and an influenza A or B virus infection. Thus, the aim of this study was to establish a nomogram to detect COVID-19. METHODS: A nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) at China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza. RESULTS: Our nomogram predicted probabilities of COVID-19 with an AUROC of 0.913 (95% confidence interval [CI], 0.883–0.937), greater than that of the lymphocyte-to-monocyte ratio (0.849, 95%CI, 0.812–0.880; p = 0.0007), lymphocyte percentage (0.808, 95%CI, 0.768–0.843; p < 0.0001), monocyte count (0.780, 95%CI, 0.739–0.817; p < 0.0001), or age (0.656, 95%CI, 0.610–0.699; p < 0.0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves. CONCLUSIONS: We found that age, lymphocyte percentage, and monocyte count are risk factors for the early-stage prediction of patients infected with the 2019 novel coronavirus. As such, our research provides a useful test for doctors to differentiate COVID-19 from influenza.