Department of Hematology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong, China.
Department of Hematologic Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
NPJ Prim Care Respir Med. 2021 Jun 3;31(1):33. doi: 10.1038/s41533-021-00244-w.
Accurate prediction of the risk of progression of coronavirus disease (COVID-19) is needed at the time of hospitalization. Logistic regression analyses are used to interrogate clinical and laboratory co-variates from every hospital admission from an area of 2 million people with sporadic cases. From a total of 98 subjects, 3 were severe COVID-19 on admission. From the remaining subjects, 24 developed severe/critical symptoms. The predictive model includes four co-variates: age (>60 years; odds ratio [OR] = 12 [2.3, 62]); blood oxygen saturation (<97%; OR = 10.4 [2.04, 53]); C-reactive protein (>5.75 mg/L; OR = 9.3 [1.5, 58]); and prothrombin time (>12.3 s; OR = 6.7 [1.1, 41]). Cutoff value is two factors, and the sensitivity and specificity are 96% and 78% respectively. The area under the receiver-operator characteristic curve is 0.937. This model is suitable in predicting which unselected newly hospitalized persons are at-risk to develop severe/critical COVID-19.
在住院时，需要准确预测冠状病毒病 (COVID-19) 进展的风险。逻辑回归分析用于询问来自 200 万例散发病例地区每个住院患者的临床和实验室协变量。在总共 98 名患者中，有 3 名在入院时患有严重 COVID-19。在其余患者中，有 24 名出现严重/危急症状。预测模型包括四个协变量：年龄 (>60 岁；优势比 [OR] = 12 [2.3, 62])；血氧饱和度 (<97%；OR = 10.4 [2.04, 53])；C 反应蛋白 (>5.75 mg/L；OR = 9.3 [1.5, 58])；和凝血酶原时间 (>12.3 s；OR = 6.7 [1.1, 41])。截断值为两个因素，灵敏度和特异性分别为 96%和 78%。接收者操作特征曲线下的面积为 0.937。该模型适用于预测哪些未经选择的新住院患者有发展为严重/危急 COVID-19 的风险。
