Li Jingwen, Li Simin, Qiu Xiaoming, Zhu Wenyan, Li Linfeng, Qin Bo
Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland).
Data Processing Department, Yidu Cloud Technology Inc., Beijing, China (mainland).
Med Sci Monit. 2021 May 12;27:e932361. doi: 10.12659/MSM.932361.
BACKGROUND COVID-19 and influenza share many similarities, such as mode of transmission and clinical symptoms. Failure to distinguish the 2 diseases may increase the risk of transmission. A fast and convenient differential diagnosis between COVID-19 and influenza has significant clinical value, especially for low- and middle-income countries with a shortage of nucleic acid detection kits. We aimed to establish a diagnostic model to differentiate COVID-19 and influenza based on clinical data. MATERIAL AND METHODS A total of 493 patients were enrolled in the study, including 282 with COVID-19 and 211 with influenza. All data were collected and reviewed retrospectively. The clinical and laboratory characteristics of all patients were analyzed and compared. We then randomly divided all patients into development sets and validation sets to establish a diagnostic model using multivariate logistic regression analysis. Finally, we validated the diagnostic model using the validation set. RESULTS We preliminarily established a diagnostic model for differentiating COVID-19 from influenza that consisted of 5 variables: age, dry cough, fever, white cell count, and D-dimer. The model showed good performance for differential diagnosis. CONCLUSIONS This initial model including clinical features and laboratory indices effectively differentiated COVID-19 from influenza. Patients with a high score were at a high risk of having COVID-19, while patients with a low score were at a high risk of having influenza. This model could help clinicians quickly identify and isolate cases in the absence of nucleic acid tests, especially during the cocirculation of COVID-19 and influenza. Owing to the study's retrospective nature, further prospective study is needed to validate the accuracy of the model.
新型冠状病毒肺炎（COVID-19）和流感有许多相似之处，如传播方式和临床症状。无法区分这两种疾病可能会增加传播风险。快速便捷地鉴别COVID-19和流感具有重要的临床价值，特别是对于核酸检测试剂盒短缺的低收入和中等收入国家。我们旨在基于临床数据建立一个鉴别COVID-19和流感的诊断模型。
本研究共纳入493例患者，其中282例为COVID-19患者，211例为流感患者。所有数据均进行回顾性收集和审查。分析并比较了所有患者的临床和实验室特征。然后我们将所有患者随机分为训练集和验证集，采用多因素逻辑回归分析建立诊断模型。最后，我们使用验证集对诊断模型进行验证。
我们初步建立了一个鉴别COVID-19和流感的诊断模型，该模型由5个变量组成：年龄、干咳、发热、白细胞计数和D-二聚体。该模型在鉴别诊断方面表现良好。
这个包含临床特征和实验室指标的初始模型有效地鉴别了COVID-19和流感。得分高的患者患COVID-19的风险高，而得分低的患者患流感的风险高。在没有核酸检测的情况下，该模型可以帮助临床医生快速识别和隔离病例，特别是在COVID-19和流感共同流行期间。由于本研究的回顾性特点，需要进一步进行前瞻性研究以验证该模型的准确性。
