COVID-19 early warning score: a multi-parameter screening tool to identify highly suspected patients

Cong-Ying Song; Jia Xu; Jian-Qin He; Yuan-Qiang Lu

There were also many shortcomings in our study that should be considered. Firstly, it was a retrospective study, which might contain selection bias, although we tried to avoid the bias through sorting patients by "Z-A" based on Chinese first name and strictly abide the inclusion and exclusion criteria. In addition, because of the incomplete of potentially valuable information, such as smoking history and basic diseases, we cannot conduct a comprehensive analysis of these factors currently. Besides of that, additional multi-center, multi-ethnic and prospective studies are expected to revised our diagnostic model and COVID-19 EWS, and we also plan to implement a multi-center study with a larger sample size to further validate and optimize the model. More over, it is hoped that better statistical algorithms will make the diagnostic model more practical.