Prediction of the Severity of the Coronavirus Disease and Its Adverse Clinical Outcomes
Xiaohui Liu, Si Shi, Jinling Xiao, Hongwei Wang, Liyan Chen, Jianing Li, Kaiyu Han
Abstract
This study aims to investigate blood and biochemical laboratory findings in patients with severe coronavirus disease 2019 (COVID-19) and to develop a joint predictor for predicting the likelihood of severe COVID-19 and its adverse clinical outcomes and to provide more information for treatment. We collected the data of 88 patients with laboratory-confirmed COVID-19. Further, the patients were divided into a non-severe group and a critical group (including critically ill cases). Univariate analysis showed that the absolute lymphocyte count, albumin level, albumin/globulin ratio, lactate dehydrogenase level, interleukin-6 (IL-6) level, erythrocyte count, globulin level, blood glucose level, and age were significantly correlated with the severity of COVID-19. The multivariate binary logistic regression model revealed that age, absolute lymphocyte count, and IL-6 level were independent risk factors in patients with COVID-19. The receiver operating characteristic curve revealed that the combination of IL-6 level, absolute lymphocyte count, and age is superior to a single factor as predictors for severe COVID-19, regardless of whether it is in terms of the area under the curve or the prediction sensitivity and specificity. Early application is beneficial to early identification of critically ill patients and timing individual treatments to reduce mortality.