Author: Xu, Jingbo; Wang, Weida; Ye, Honghui; Pang, Wenzheng; Pang, Pengfei; Tang, Meiwen; Xie, Feng; Li, Zhitao; Li, Bixiang; Liang, Anqi; Zhuang, Juan; Yang, Jing; Zhang, Chunyu; Ren, Jiangnan; Tian, Lin; Li, Zhonghe; Xia, Jinyu; Gale, Robert P.; Shan, Hong; Liang, Yang
Title: A predictive score for progression of COVID-19 in hospitalized persons: a cohort study Cord-id: e7i1q3cl Document date: 2021_6_3
ID: e7i1q3cl
Snippet: 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 r
Document: 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.
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