Scoring Systems for Predicting Mortality for Severe Patients with COVID-19

Background: Coronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, mainly in patients with severe COVID-19.Methods: Patients with COVID-19 were retrospectively analyzed. Clinical characteristics were compared, and LASSO regression as well as multivariate analysis were used to screen variables and establish prediction model. Findings: A total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66·0 years while it was 73·0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. About 60.2% of severe patients had complications. Acute myocardial injury was the earliest injured organ, whereas the time from acute kidney injury to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariate analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established and divided into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (P =0·264). The complications in low-risk and high-risk groups were significantly different. We also found that the use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (P =0·036) and durations of disease by 7·5 days (P =0 · 012) compared with no corticosteroids.Interpretation: Old age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis.Funding Statement: This work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500). Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: This study was conducted according to the principles of Helsinki and approved by the Ethics Committee of Zhongnan Hospital of Wuhan University (No.2020063).