Lymphocyte subset alterations with disease severity, imaging manifestation, and delayed hospitalization in COVID-19 patients (preprint)

Background: COVID-19 continuously threated public health heavily. Present study aimed to investigate the lymphocyte subset alterations with disease severity, imaging manifestation, and delayed hospitalization in COVID-19 patients. Methods: Lymphocyte subsets was classified using flow cytometry with peripheral blood collected from 106 patients. Results: Multivariate logistic regression showed that chest tightness, lymphocyte count, and {gamma}-glutamyl transpeptidase were the independent predictors for severe COVID-19. The T cell, CD4+ T cell and B cell counts in severe patients were significantly lower than that in mild patients (p = 0.004, 0.003 and 0.046, respectively). Only the T cell count was gradually decreased with the increase of infiltrated quadrants of lesions in computed tomography (CT) (p = 0.043). The T cell, CD4+ T cell, and CD8+ T cell counts were gradually decreased with the increase of infiltrated area of the maximum lesion in CT (p = 0.002, 0.003, 0.028; respectively). The T cell count, as well as CD4+ T cell, CD8+ T cell, and NK cell counts were gradually decreased with the increased delayed hospitalization (p = 0.003, 0.002, 0.013, and 0.012; respectively). The proportion of T cell was gradually decreased but B cell was gradually increased with the increased delayed hospitalization (p = 0.031 and 0.003, respectively). Conclusion: T cell and CD4+ T cell counts negatively correlated with disease severity, CT manifestation and delayed hospitalization. CD4+ T cell was mainstay of immunity response in COVID-19 patients.

Development and Validation of the HNC-LL Score for Predicting the Severity of Coronavirus Disease 2019 (preprint)

Background: The severity of coronavirus disease 2019 (COVID-19) varies widely, ranging from asymptomatic to fatal. However, there is limited information regarding the risk factors associated with severe disease. In this study, we aimed to develop a model for predicting COVID-19 severity. Methods: A total of 690 patients with confirmed COVID-19 were recruited between January 1 and March 18, 2020 from hospitals in Honghu and Nanchang, and finally, 442 patients were analyzed. Data were partitioned into the training set and test sets to develop and validate the model, respectively. Results: A predictive HNC-LL (Hypertension–Neutrophil count–C-reactive protein– Lymphocyte count– Lactate dehydrogenase) score was established based on multivariate logistic regression analysis results. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC] = 0.861, 95% confidence interval [CI]: 0.800–0.922; P <0.001); the Honghu internal validation cohort (AUC = 0.871, 95% CI: 0.769–0.972; P <0.001); and the Nanchang external validation cohort (AUC = 0.826, 95% CI: 0.746–0.907; P <0.001), and outperformed other models including the CURB-65 score model, MuLBSTA score model, and neutrophil-to-lymphocyte ratio model. Moreover, the clinical significance of HNC-LL in accurately predicting patients with severe COVID-19 in the early phase was confirmed. Conclusions: We developed an accurate tool for predicting disease severity in patients with COVID-19. This model can potentially be used to identify patients at risk of developing severe disease in the early stage and therefore, guide treatment decisions.Funding Statement: This work was supported by the National Nature Science Foundation of China (Grant Nos. 81972897) and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015).Declaration of Interests: The authors declare that they do not have any conflicts of interest.Ethics Approval Statement: This retrospective analysis was approved by Medical Ethics committee of Nanfang Hospital of Southern Medical University, and the requirement for informed consent was waived by the ethics committee.