Immune dysfunction following COVID-19, especially in severe patients

The coronavirus disease 2019 (COVID-19) has been spreading worldwide. Severe cases quickly progressed with unfavorable outcomes. We aim to investigate the clinical features of COVID-19 and identify the risk factors associated with its progression. Data of confirmed SARS-CoV-2-infected patients and healthy participants were collected. Thirty-seven healthy people and 79 confirmed patients, which include 48 severe patients and 31 mild patients, were recruited. COVID-19 patients presented with dysregulated immune response (decreased T, B, and NK cells and increased inflammatory cytokines). Also, they were found to have increased levels of white blood cell, neutrophil count, and D-dimer in severe cases. Moreover, lymphocyte, CD4+ T cell, CD8+ T cell, NK cell, and B cell counts were lower in the severe group. Multivariate logistic regression analysis showed that CD4+ cell count, neutrophil-to-lymphocyte ratio (NLR) and D-dimer were risk factors for severe cases. Both CT score and clinical pulmonary infection score (CPIS) were associated with disease severity. The receiver operating characteristic (ROC) curve analysis has shown that all these parameters and scores had quite a high predictive value. Immune dysfunction plays critical roles in disease progression. Early and constant surveillance of complete blood cell count, T lymphocyte subsets, coagulation function, CT scan and CPIS was recommended for early screening of severe cases.


Scientific RepoRtS
| (2020) 10 SARS-CoV-2-infected patients have problems with liver and kidney function, showing elevated levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and creatinine (Cr). Comparing data between mild and severe patients (Table 2), more than half (62.0%) of all confirmed patients were male, and the proportion of males in severe group was higher than that in the mild group; however, there was no statistically significant difference (68.8% vs. 51.6%, P = 0.125). Patients in the severe group had a higher age distribution than in the non-severe group (

Laboratory results
White blood cell count (× 10 9 /L) 7.0 (4.3-10.7  Immune status of patients with COVID-19. Immune cell count and inflammatory factors were recorded to know the immune status of COVID-19 patients. They had a decreased immune cell count, including total T cell, CD4 + T cell, CD8 + T cell, and NK cell and B cell count as in comparison with healthy people (Fig. 2), and the levels of these immune cell count were also lower in severe than in mild cases (Fig. 3). There are higher levels of inflammatory factors, including IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ, found in COVID-19 patients than in healthy people (Fig. 4). However, these levels have no significant difference between severe and mild cases (Fig. 5). We also compared immune status between intubation group (n = 6) and non-intubation group. Intubated patients had lower CD4 + cell and CD8 + cell counts than patients without intubation. While, inflammatory factors had no significant differences between two groups.

Independent risk factors for severe COVID-19 cases.
After initial analysis, variables with P < 0.05 were selected, and multivariate logistic regression analysis was performed with the use of the forward stepwise method. Afterward, the CD4 + cell count (P = 0.015), NLR (P = 0.032) and D-dimer (P = 0.016) were considered the independent risk factors of the severe COVID-19 cases (Table 3)  www.nature.com/scientificreports/  To evaluate the predictive value of CT score, CPIS, and three independent risk factors, the ROC curve analysis was performed (Fig. 7). To better distinguish severe and non-severe patients, we have defined the new threshold value of these parameters by calculating the cut-off value. Table 4 has shown that CT score had the greatest predictive value with an AUC of 0.961 (95%CI, 0.925-0.997). CPIS as well as the combination of CD4 + T cell count, NLR, and D-dimer had an AUC of 0.828 (95% CI, 0.738-0.917) and 0.865 (95%CI, 0.784-0.946), respectively. The optimal cut-off values of the CT score and CPIS were 9.50 and 2.50, respectively. They all had a quite high sensitivity and specificity at the optimal cutoff value. These results have shown that the said parameters had quite a high predictive value.

Discussion
The worldwide outbreak of COVID-19 has worsened 6 . As the main battlefield during the first stage, early detection and effective quarantine of patients and close contacts have allowed the epidemic in China so far to be under effective control. However, the mortality rate of COVID-19 patients in the severe group remains to be quite high because of the rapid progression of the disease and because there is no specific drug against the virus.  www.nature.com/scientificreports/ In-depth research on the characteristics of severe cases was urgently needed to identify severe individuals earlier and quicker. In our study, we compared clinical characteristics between healthy people and COVID-19 patients, and then compared these features between severe and mild cases. We found immune dysfunction in COVID-19 patients, and immunosuppression was more obvious in severe cases than mild cases. Parameters including CD4 + T cell count, NLR, and D-dimer, CT score, and CPIS had quite great value for predicting disease severity, which could be considered in early warning of severe patients. Severe cases usually have mild symptoms in the first week. The time point of aggravation was usually 9 days to 12 days after illness onset, after which the disease progressed quickly 5 . With the characteristics of the disease Figure 6. CT score and CPIS in mild and severe COVID-19 patients. CT score and CPIS were calculated and compared between mild and severe COVID-19 patients. CT score and CPIS were higher in severe patients and these two clinical scores were positively related to disease severity.  www.nature.com/scientificreports/ course, the first week was regarded as the early stage of the disease. So, we collected early clinical data 8 days from illness onset. In this study, we have identified the independent risk factors for severe cases, such as decrease of CD4 + T cell count and increase of NLR and D-dimer. NLR was a great indicator of the overall immune status 7 and was a widely used marker to assess the severity of bacterial infections and the prognosis of patients with pneumonia and tumors [8][9][10] . Several studies have shown that severe SARS-CoV-2-infected patients have a higher NLR 11,12 , an independent risk factor for mortality in COVID-19 patients 13 . D-dimer was a molecular marker of hypercoagulable state and hyperfibrinolysis, and it could be used in the prognosis of patients with infection or sepsis 14 . In patients with sepsis, inflammatory cells were activated, leading to the activation of the coagulation cascade and then causing the activation of the fibrinolytic system 15 . Increased coagulation could be found in COVID-19 patients, and increased D-dimer was associated with poor prognosis in COVID-19 patients 5,16 . In our study, we also found increased D-dimer was an independent risk factor for intubation. Consistent with a previous study, we found lymphocyte, CD4 + T cell, CD8 + T cell, NK cell, and B cell counts to be negatively correlated to the severity of COVID-19, suggesting that immune suppression could be more likely found in severe patients and SARS-CoV-2 may directly or indirectly damage the lymphocytes or NK cells and thus further aggravate the disease progression 17,18 . Even though inflammatory cytokine storms were thought to be a mechanism for COVID-19 progression [19][20][21] , there was only an increase in IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ when comparing pneumonia patients with healthy people, but no significant increase in severe patients compared to mild patients. Thus, the role of inflammatory cytokine storms in the progression of the disease was still unclear and controversial. However, these findings might also be due to the limitation in size and the large heterogeneity at the time points of the first detection. IL-1β is a key proinflammatory cytokine in pyroptosis, which played an important role in various infectious diseases. It was reported that IL-1β increased in COVID-19 patients and was associated with the disease severity 22,23 . However, this item was not detected in our hospital, thus, we could not explore the predictive value of IL-1β in COVID-19 patients. And it could be considered to detect the level of IL-1β in further clinical work or research in order to explore its potential clinical value.
There are similarities of other clinical features of patients in our medical center and those reported in previous studies [24][25][26][27][28] . Males were more susceptible to COVID-19 but had no significance to predict disease severity in our study which may be because of the small sample size, although the male-to-female ratio was quite high in the severe group (68. However, they were all in the normal range, so we did not select this parameter as a predictive factor. It did not mean oxygen saturation had no clinical value. In our study, all data of basic vital signs we selected was the first records on admission, and the first records of oxygen saturation (SpO 2 ) was detected by noninvasive pulse oximeter, which was easy to get but could be affected by various interference factors. So it might bring a slight error in this data. Continuous oxygen saturation monitoring or referring to arterial oxygen saturation (SaO 2 ) might better reflect the real hypoxemia status of the patient. As for the laboratory results, the higher levels of ESR, CRP, UN, LDH, and HBDH were found in severe COVID-19 patients, suggesting that there is an association between disease progression and the injury of cellular immunity, cardiomyocytes, the liver, and the kidney. Even though these parameters were not independent risk factors based on our analysis, they could be used in severe case screening, and their predicted value should be assessed by using a larger amount of data in further studies.
To investigate whether there was any clinical scoring tool used in early warning for severe cases, we have calculated the CT score and CPIS. Because of its good imaging data reflecting pulmonary inflammation, CT was often used to know whether the COVID-19 case was severe or not. In order to quantify the image data, we have chosen a commonly used CT scoring method to calculate the specific value. CPIS was initially developed as a diagnostic tool for ventilator-associated pneumonia, and it has been used as a predictor of prognosis recently as well 29 . Severe patients had higher CT score and CPIS, and there was a good correlation between these two clinical scores and disease severity. The ROC curve analysis has shown that CT score, CPIS, and combination of clinical parameters had a good predictive value of distinguished severe cases in early stage. Intubated patients also showed higher CT score and CPIS, which suggested these two clinical scoring tools can also be used in pre-intubation evaluation.
There are currently no specific antiviral therapies for SARS-CoV-2 infection. Since immune status play important role in disease severity, immunotherapies are used in severely ill patients 30 . Recent immunotherapies included drugs target specific Inflammatory molecules and pathways, intravenous immunoglobulin therapy, convalescent plasma infusion, and immune cell-targeted therapies (Treg cell and NK cell) 30 . Immunotherapies could regulate abnormal inflammatory response and prevent lung damage. Several researches have suggested immunotherapies can bring clinical benefits, including reduction of viral loads, and improved survival 31,32 . However, the evidence was limited, and the efficacy of these treatments was still not clear, and more researches are needed.
There were several limitations in our study which should be considered. Firstly, it was a retrospective study, which might contain selection bias, but we tried to avoid the bias by abiding strictly to the inclusion and exclusion criteria. Besides that, additional multicenter, multi-ethnic, and prospective studies are expected to revise our diagnostic model, and we also plan to have a multicenter study with a larger sample size so as to further validate and optimize the model. Moreover, it is our hope that better statistical algorithms will make the diagnostic model even more practical. Now, we are trying to develop a COVID-19-related database, making data share and management more efficient. Thus, data from the multicenter study could be used for further analysis.

conclusion
The combination of CD4 + T cell count, NLR, and D-dimer, CT score, and CPIS could be used as COVID-19 disease severity predictors. We have recommended that these parameters be surveyed earlier and constantly for early warning of severe COVID-19 patients. Immune dysfunction plays a critical role in disease progression. The

Scientific RepoRtS
| (2020) 10:15838 | https://doi.org/10.1038/s41598-020-72718-9 www.nature.com/scientificreports/ underlying mechanism of COVID-19 development and progression might be complex, and so further research was urgently needed to help better understand and control this epidemic. Laboratory confirmation. Laboratory confirmation was achieved using the real-time reverse transcription-polymerase chain reaction (RT-PCR) assay for SARS-CoV-2 in accordance to the protocol established by the WHO 34 . In our hospital, sputum samples were the sample of choice for RT-PCR assay within 3 h. Two target genes of SARS-CoV-2 were tested during the process: open reading frame 1ab (ORF1ab) and nucleocapsid protein (N).

Selection of participants. We included laboratory-confirmed SARS-CoV-2-infected patients in the First
CT score and CPIS. The CT scores were analyzed retrospectively by two radiologists without the knowledge of the patient's diagnosis and other clinical features. The first CT imaging on admission was selected and calculated using the method introduced by Casarini et al. 35 . In order to assess lung infection more rapidly, we have used the simplified version of CPIS 36 , and it was calculated by using the first clinical results in the hospital.
Statistical analysis. Continuous variables were expressed as medians with interquartile ranges (IQR), while categorical variables were expressed as numbers and percentages in each category. The Mann-Whitney U-test evaluated continuous data, and the chi-square test was used for categorical variables. Performed multivariate logistic regression analyses with forward stepwise method identified independent risk factors. Spearman's rank-order correlation investigated whether the two clinical scores (CT score, CPIS) and disease severity were associated. The predictive powers of these parameters and two clinical scores were known by calculating the area under the receiver operating characteristic curve (AUC). All statistical analyses were done by the SPSS statistical software package (version 25.0). A P value < 0.05 means statistically significant.
Ethics approval and written informed consent. This study was approved by the Ethical Committee of the First Affiliated Hospital, School of Medicine, Zhejiang University (code number IIT20200025A). Written informed consent was obtained from each patient or his/her authorized representatives following a full explanation of the study. All methods and procedures in this study were carried out in accordance with relevant guidelines and regulations.

Data availability
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.