Development and validation of an early warning score (EWAS) for predicting clinical deterioration in patients with coronavirus disease 2019

Yabing Guo; Yingxia Liu; Jiatao Lu; Rong Fan; Fuchun Zhang; Xueru Yin; Zhihong Liu; Qinglang Zeng; Jing Yuan; Shufang Hu; Qiongya Wang; Baolin Liao; Mingxing Huang; Sichun Yin; Xilin Zhang; Rui Xin; Zhanzhou Lin; Changzheng Hu; Boliang Zhao; Ridong He; Minfeng Liang; Zheng Zhang; Li Liu; Jian Sun; Lu Tang; Lisi Deng; Jinyu Xia; Xiaoping Tang; Lei Liu; Jinlin Hou

The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.04.17.20064691 doi: medRxiv preprint directly contributing to plaque rupture through local inflammation. 25, 26 Management decisions for the high-risk patients with COVID-19 must be made quickly and are often based upon scant evidence. Under these pressing clinical circumstances, high quality and timely evidence-based guidance becomes especially crucial. EWAS, with a high level of differentiating power, can be regarded as such quality evidence in providing an efficient, feasible and practical approach to establish a hierarchical management system of COVID-19 in highly endemic SARS-CoV-2 areas where medical resources are extremely limited. Specifically, it is suggested that, by applying the EWAS, patients classified in the low-risk group should be isolated and treated in "mobile cabin hospitals", an isolation facility converted from sports stadiums, convention centers, etc. with structures easily installed which can accommodate large numbers of patients. Patients in the medium-risk group should be treated in the general wards, whereas patients in the high-risk group should be transferred to the intensive care unit for more comprehensive treatment due to the high risk of clinical deterioration within 14 days of admission being as high as approximately 40% in this group. It is predicted that this approach would benefit a considerable number of patients with COVID-19 by directing the appropriate level of medical resources according to the severity of the disease, thus reducing mortality and saving valuable medical and socioeconomic resources.