Wang Menghan, Yu Dongping, Shang Yu, Zhang Xiaona, Yang Yi, Zhao Shuai, Su Dongju, Liu Lei, Wang Qin, Ren Juan, Li Yupeng, Chen Hong
Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Harbin Medical University, Harbin, 150081 China.
Department of Respiration, First Hospital of Harbin, Harbin, 150081 China.
Arab J Sci Eng. 2021 Jun 28:1-9. doi: 10.1007/s13369-021-05808-z.
The Coronavirus Disease 2019 (COVID-19) had become a Public Health Emergency of International Concern with more than 90 million confirmed cases worldwide. Therefore, this study aims to establish a predictive score model of progression to severe type in patients with COVID-19. This is a retrospective cohort study of 151 patients with COVID-19 diagnosed by nucleic acid test or specific serum antibodies from February 13, 2020, to March 14, 2020, hospitalized in a COVID-19-designed hospital in Wuhan, China. Of the 151 patients with average age of 63 years, 64 patients were male (42.4%), and 29 patients (19.2%) were classified as severe group. Multivariate analysis showed that age > 65 years (odds ratio [OR] = 9.72, 95%CI: 2.92-32.31, < 0.001), lymphocyte count ≤ 1.1 × 10/L (OR = 3.42, 95%CI: 1.24-9.41, = 0.017) and AST > 35 U/L (OR = 3.19, 95%CI: 1.11-9.19, = 0.032) were independent risk factors for the disease severity. The area under curve (AUC) of receiver operating characteristic curve of the probabilities of the composite continuous variable (age + lymphocyte + AST) is 0.796. Finally, a predictive score model called ALA was established, and its AUC was 0.83 (95%CI: 0.75-0.92). Using a cutoff value of 9.5 points, the positive and negative predictive values were 54.1% (38-70.1%) and 92.1% (87.2-97.1%), respectively. The ALA score model can quickly identify severe patients with COVID-19, so as to help clinicians to better choose accurate management strategy.
2019冠状病毒病（COVID-19）已成为国际关注的突发公共卫生事件，全球确诊病例超过9000万例。因此，本研究旨在建立COVID-19患者进展为重型的预测评分模型。这是一项回顾性队列研究，研究对象为2020年2月13日至2020年3月14日在中国武汉一家专门收治COVID-19患者的医院住院的151例经核酸检测或特异性血清抗体确诊的COVID-19患者。151例患者平均年龄63岁，其中男性64例（42.4%），29例（19.2%）被归为重症组。多因素分析显示，年龄>65岁（比值比[OR]=9.72，95%可信区间：2.92-32.31，P<0.001）、淋巴细胞计数≤1.1×10⁹/L（OR=3.42，95%可信区间：1.24-9.41，P=0.017）和谷草转氨酶>35 U/L（OR=3.19，95%可信区间：1.11-9.19，P=0.032）是疾病严重程度的独立危险因素。复合连续变量（年龄+淋巴细胞+谷草转氨酶）概率的受试者工作特征曲线下面积（AUC）为0.796。最后，建立了一个名为ALA的预测评分模型，其AUC为0.83（95%可信区间：0.75-0.92）。使用9.5分的截断值，阳性预测值和阴性预测值分别为54.1%（38-70.1%）和92.1%（87.2-97.1%）。ALA评分模型可以快速识别COVID-19重症患者，从而帮助临床医生更好地选择准确的管理策略。
