A Prognostic Model to Predict Recovery of COVID-19 Patients Based on Longitudinal Laboratory Findings

The temporal change patterns of laboratory data may provide insightful clues into the whole course of COVID-19. This study aimed to evaluate longitudinal change patterns of key laboratory tests in patients with COVID-19, and identify independent prognostic factors by examining the associations between laboratory findings and outcomes of patients. This multicenter study included 56 patients with COVID-19 treated in Jilin Province, China, from January 21, 2020 to March 5, 2020. The laboratory findings, epidemiological characteristics and demographic data were extracted from electronic medical records. The average value of eosinophils and carbon dioxide combining power continued to significantly increase, while the average value of cardiac troponin I and mean platelet volume decreased throughout the course of the disease. The average value of lymphocytes approached the lower limit of the reference interval for the first 5 days and then rose slowly thereafter. The average value of thrombocytocrit peaked on day 7 and slowly declined thereafter. The average value of mean corpuscular volume and serum sodium showed an upward trend from day 8 and day 15, respectively. Age, sex, lactate dehydrogenase, platelet count and globulin level were included in the final model to predict the probability of recovery. The above parameters were verified in 24 patients with COVID-19 in another area of Jilin Province. The risk stratification and management of patients with COVID-19 could be improved according to the temporal trajectories of laboratory tests. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12250-020-00317-z) contains supplementary material, which is available to authorized users.