Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Engineering (Beijing, China)

Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People's Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.

Yuan Ye, Sun Chuan, Tang Xiuchuan, Cheng Cheng, Mombaerts Laurent, Wang Maolin, Hu Tao, Sun Chenyu, Guo Yuqi, Li Xiuting, Xu Hui, Ren Tongxin, Xiao Yang, Xiao Yaru, Zhu Hongling, Wu Honghan, Li Kezhi, Chen Chuming, Liu Yingxia, Liang Zhichao, Cao Zhiguo, Zhang Hai-Tao, Ch Paschaldis Ioannis, Liu Quanying, Goncalves Jorge, Zhong Qiang, Yan Li

2020-Nov-28

COVID-19, Mortality risk prediction, Risk score