Dynamic changes in the immune response correlate with disease severity and outcomes during infection with SARS-CoV-2 (preprint)

Background:The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout China and all over the world. Little is known about the dynamic changes in the patient immune responses to SARS-CoV-2, and how different responses are correlated with disease severity and outcomes.Method:74 patients with confirmed COVID-19 were enrolled in this prospective research. The demographic information, medical history, symptoms, signs and laboratory results were analyzed and compared between severe and non-severe patients. The leukocytes, lymphocyte subsets and inflammatory cytokines were longitudinally collected.Results:Of the 74 patients included, 17 suffered from severe disease. The severe patients tended be older (65.29 ± 12.33 years vs. 45.37 ± 18.66 years), and had a greater degree of underlying disease (41.18% vs. 24.56%) , lower baseline lymphocytes counts (0.69 ± 0.36 × 10⁹ vs. 1.46 ± 0.75 × 10⁹) , higher neutrophil-lymphocyte-ratios (NLRs; 3.76 (3.15–5.51) vs. 2.07 (1.48–2.93)) and lower baseline eosinophil counts (0.01 ± 0.01 × 10⁹ vs. 0.05 ± 0.07 × 10⁹), than that in non-severe patients. The baseline helper T (Th) cells (335.47 vs. 666.46/mL), suppressor T(Ts) cells (158 vs. 334/mL), B cells (95 vs. 210/mL), and natural killer (NK) cells (52 vs. 122/mL) were significantly decreased in severe cases compared to that in non-severe cases. In addition, the baseline neutrophils and B cells were positively correlated with the severity of COVID-19 and the baseline lymphocytes and Th cells were negatively correlated with the severity of COVID-19. The dynamic change of T cells, Th cells and IFN-γ in the severe cases were parallel to the amelioration of the disease.Conclusions:Collectively, our study provides novel information on the kinetics of the immune responses in a cohort of COVID-19 patients with different disease severities. Furthermore, our study indicates that both innate and adaptive immune responses correlate with better clinical outcomes.

Nomogram Prediction of Severe Risk in Patients With COVID-19 Pneumonia: A Retrospective Study (preprint)

Background COVID-19 elicits a range of different responses in patients and can manifest into mild to very severe cases in different individuals, depending on many factors. We aimed to establish a prediction model of severe risk in COVID-19 patients, to help clinicians achieve early prevention, intervention, and aid them in choosing effective therapeutic strategy.Methods We selected confirmed COVID-19 patients who admitted to First Hospital of Changsha city between January 29 and February 15, 2020 and collected their clinical data. Multivariate logical regression was used to identify the risk factors associated with severe risk. These factors were incorporated into the nomogram to establish the model. The ROC curve, calibration plot and decision curve were used to assess the performance of model.Results 239 patients were enrolled and 45 (18.83%) patients developed severe pneumonia. Univariate and multivariate analysis showed that age, COPD, shortness of breath, fatigue, creatine kinase, D-dimer, lymphocytes and h CRP were independent risk factors for severe risk in COVID-19 patients. Incorporating these factors, the nomogram achieved good concordance indexes of 0.873 (95% CI: 0.819–0.927), and well-fitted calibration plot curves. The model provided superior net benefit when clinical decision thresholds were between 10–70% predicted risk.Conclusions Using the model, clinicians can intervene early, improve therapeutic effects and reduce the severity of COVID-19, thus ensuring more targeted and efficient use of medical resources.