Progression of severity in coronavirus disease 2019 patients before treatment and a self-assessment scale to predict disease severity

Abstract Objectives This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings. Setting and participants Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China. Primary and secondary outcome measures We categorized COVID-19 disease severity, based on The Chinese Diagnosis and Treatment Protocol for COVID-19, as “nonsevere” (which grouped asymptomatic, mild, and ordinary disease) versus “severe” (grouping severe and critical illness). Results Twelve scale items—age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage—were identified and showed good predictive ability (area under the curve = 0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (= 0·71). Conclusions Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Summary Our study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance.