• Miao Luo, Jing Liu, Weiling Jiang, Shuang Yue, Huiguo Liu, and Shuang Wei.
• Department of Respiratory and Critical Care Medicine, Tongji Hospital, and.
• JCI Insight. 2020 Jul 9; 5 (13).

AbstractBACKGROUNDFatal cases of COVID-19 are increasing globally. We retrospectively investigated the potential of immunologic parameters as early predictors of COVID-19.METHODSA total of 1018 patients with confirmed COVID-19 were enrolled in our 2-center retrospective study. Clinical feature, laboratory test, immunological test, radiological findings, and outcomes data were collected. Univariate and multivariable logistic regression analyses were performed to evaluate factors associated with in-hospital mortality. Receiver operator characteristic (ROC) curves and survival curves were plotted to evaluate their clinical utility.RESULTSThe counts of all T lymphocyte subsets were markedly lower in nonsurvivors than in survivors, especially CD8+ T cells. Among all tested cytokines, IL-6 was elevated most significantly, with an upward trend of more than 10-fold. Using multivariate logistic regression analysis, IL-6 levels of more than 20 pg/mL and CD8+ T cell counts of less than 165 cells/μL were found to be associated with in-hospital mortality after adjusting for confounding factors. Groups with IL-6 levels of more than 20 pg/mL and CD8+ T cell counts of less than 165 cells/μL had a higher percentage of older and male patients as well as a higher proportion of patients with comorbidities, ventilation, intensive care unit admission, shock, and death. Furthermore, the receiver operating curve of the model combining IL-6 (>20 pg/mL) and CD8+ T cell counts (<165 cells/μL) displayed a more favorable discrimination than that of the CURB-65 score. The Hosmer-Lemeshow test showed a good fit of the model, with no statistical significance.CONCLUSIONIL-6 (>20 pg/mL) and CD8+ T cell counts (<165 cells/μL) are 2 reliable prognostic indicators that accurately stratify patients into risk categories and predict COVID-19 mortality.FundingThis work was supported by funding from the National Natural Science Foundation of China (no. 81772477 and 81201848).

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