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Differentiating Between 2019 Novel Coronavirus Pneumonia and Influenza Using a Nonspecific Laboratory Marker–Based Dynamic Nomogram

机译:使用基于非特异性实验室标记的动态图形的2019年新型冠状病毒肺炎和流感之间的区分

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BackgroundThere is currently a lack of nonspecific 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.MethodsA nomogram was established using data collected from 457 patients (181 with COVID-19 and 276 with influenza A or B infection) in China. The nomogram used age, lymphocyte percentage, and monocyte count to differentiate COVID-19 from influenza.ResultsOur nomogram predicted probabilities of COVID-19 with an area under the receiver operating characteristic curve of 0.913 (95% confidence interval [CI], 0.883–0.937), greater than that of the lymphocyte:monocyte ratio (0.849; 95% CI, 0.812–0.880; P = .0007), lymphocyte percentage (0.808; 95% CI, 0.768–0.843; P .0001), monocyte count (0.780; 95% CI, 0.739–0.817; P .0001), or age (0.656; 95% CI, 0.610–0.699; P .0001). The predicted probability conformed to the real observation outcomes of COVID-19, according to the calibration curves.ConclusionsWe 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.
机译:背景技术目前缺乏非特异性实验室指标,作为区分2019年冠状病毒疾病(Covid-19)和流感A或B病毒感染的定量标准。因此,本研究的目的是建立一个探测器以检测Covid-19.Methodsa NOM图,使用从457名患者(181名与Covid-19和276具有流感A或B感染有流感A或B感染)的数据建立了NOM图。使用年龄,淋巴细胞百分比和单核细胞计数以区分Covid -19从流感区分化Covid-19的Covid-19的概率,接收器的接收器操作特性曲线为0.913(95%置信区间[CI],0.883-0.937 )大于淋巴细胞:单核细胞比(0.849; 95%CI,0.812-0.880; p = .0007),淋巴细胞百分比(0.808; 95%CI,0.768-0.843; p <.0001),单核细胞计数( 0.780; 95%CI,0.739-0.817; p <.0001)或年龄(0.656; 95%CI,0.610-0.699; P <.0001)。根据校准曲线,预测的概率符合Covid-19的真实观察结果。结论术指南,发现该年龄,淋巴细胞百分比和单核细胞计数是对2019年新型冠状病毒感染的患者的早期预测的危险因素。因此,我们的研究为医生提供了不同的考验,以区分Covid-19来自流感。
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