key: cord-0753081-vitq4niq authors: Zhang, Jin‐jin; Cao, Yi‐yuan; Tan, Ge; Dong, Xiang; Wang, Bin‐chen; Lin, Jun; Yan, You‐qin; Liu, Guang‐hui; Akdis, Mübeccel; Akdis, Cezmi A; Gao, Ya‐dong title: Clinical, radiological and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID‐19 patients date: 2020-07-14 journal: Allergy DOI: 10.1111/all.14496 sha: 284d6d07a599d59d419361745267949e54f55f39 doc_id: 753081 cord_uid: vitq4niq BACKGROUND: The coronavirus disease 2019 (COVID‐19) has become a global pandemic, with 10‐20% of severe cases and over 508,000 deaths worldwide. OBJECTIVE: This study aims to address the risk factors associated with the severity of COVID‐19 patients and the mortality of severe patients. METHODS: 289 hospitalized laboratory‐confirmed COVID‐19 patients were included in this study. Electronic medical records, including patient demographics, clinical manifestation, comorbidities, laboratory tests results, and radiological materials were collected and analyzed. According to the severity and outcomes of the patients, they were divided into three groups: non‐survived (n=49), survived severe (n=78), and non‐severe (n=162) groups. Clinical, laboratory and radiological data were compared among these groups. Principal component analysis (PCA) was applied to reduce the dimensionality and visualize the patients on a low‐dimensional space. Correlations between clinical, radiological and laboratory parameters were investigated. Univariate and multivariate logistic regression methods were used to determine the risk factors associated with mortality in severe patients. Longitudinal changes of laboratory findings of survived severe cases and non‐survived cases during hospital stay were also collected. RESULTS: Of the 289 patients, the median age was 57 years (range, 22 ‐ 88) and 155 (53.4%) patients were male. As of the final follow‐up date of this study, 240 (83.0%) patients were discharged from the hospital and 49 (17.0%) patients died. Elder age, underlying comorbidities, and increased laboratory variables, such as leucocyte count, neutrophil count, neutrophil‐to‐lymphocyte ratio (NLR), C‐reactive protein (CRP), procalcitonin (PCT), D‐dimer, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and blood urea nitrogen (BUN) on admission were found in survived severe cases compared to non‐severe cases. According to the multivariate logistic regression analysis, elder age, a higher number of affected lobes, elevated CRP levels on admission, increased prevalence of chest tightness/dyspnea and smoking history were independent risk factors for death of severe patients. A trajectory in PCA was observed from "non‐severe" towards "non‐survived" via "severe and survived" patients. Strong correlations between the age of patients, the affected lobe numbers and laboratory variables were identified. Dynamic changes of laboratory findings of survived severe cases and non‐survived cases during hospital stay showed that continuing increase of leucocytes and neutrophil count, sustained lymphopenia and eosinopenia, progressing decrease in platelet count, as well as high levels of NLR, CRP, PCT, AST, BUN, and serum creatinine were associated with in‐hospital death. CONCLUSIONS: Survived severe and non‐survived COVID‐19 patients had distinct clinical and laboratory characteristics, which were separated by principle component analysis. Elder age, increased number of affected lobes, higher levels of serum CRP, chest tightness/dyspnea, and smoking history were risk factors for mortality of severe COVID‐19 patients. Longitudinal changes of laboratory findings may be helpful in predicting disease progression and clinical outcome of severe patients. The coronavirus disease 2019 (COVID- 19) pandemic, an infectious disease caused by a novel strain of human coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), 1 has become the focus of attention worldwide. Since its first report in late December 2019 in Wuhan, China, 2 has aggressively spread across the world and dramatically impacted people's health and daily life. As of July 1, 2020, according to the Situation Report issued by the World Health Organization (WHO), the number of confirmed COVID-19 cases reported in over two hundred regions exceeded 10.3 million with around 508,000 deaths. 3 The clinical patterns of COVID-19 ranged from asymptomatic cases to critically ill patients. 4 Fever, dry cough, and radiological changes in lungs tend to be common clinical manifestations in COVID-19 patients. Severe viral pneumonia with respiratory failure and the deterioration of underlying diseases are the main cause of death in severe patients. According to data provided by the China National Health Commission, the mortality rate of COVID-19 patients was 7.7% in Wuhan, 5 which was higher than the world average (4.9%). 3 As the number of infected and fatal cases are rising across the globe, there is a pressing need to investigate the clinical, radiological and laboratory characteristics, and more importantly, the mortality risk factors in severe COVID-19 patients. Our previous study found that higher levels of C-reactive proteins (CRP), D-dimer and procalcitonin (PCT) were associated with severe patients when compared to non-severe patients. 6 However, the mortality risk factors of COVID-19 patients have not yet been previously reported in detail. Elder age, comorbidities, leukocytosis, high levels of D-dimer, lactate dehydratase (LDH), and low platelet counts were reported to be risk factors associated with in-hospital death of severe patients. [7] [8] [9] [10] [11] Due to the distinct criteria used for severe and/or critically ill patients, the predictive value of these risk factors for death in severe patients may vary. The aim of this study is to compare the clinical, radiological and laboratory characteristics, and longitudinal variations in laboratory parameters of 289 hospitalized patients with COVID-19 with different severities and clinical outcomes. Potential risk factors and clinical findings associated with death in severe COVID-19 patients were analyzed. This article is protected by copyright. All rights reserved Adult hospitalized patients admitted to Zhongnan hospital of Wuhan University (n=178) and No.7 hospital of Wuhan (n=241) (admission date between Dec 29 th , 2019 and Feb 16 th , 2020) diagnosed as 'viral pneumonia' according to clinical symptoms and chest CT images were primarily enrolled in this study. 289 patients with positive real-time reverse transcription-polymerase chain reaction (rRT-PCR) results of SARS-CoV-2 nucleic acid test were diagnosed as COVID-19 and included in the analysis set. According to the disease severity and clinical outcome, these patients were divided into three groups: non-survived, survived severe and non-severe. All the patients were treated following the guidelines issued by the China National Health Commission (trial version [3] [4] [5] . 12 General treatments included supportive therapy, anti-viral agents (e.g., Arbidol, Oseltamivir and Lopinavir/Ritonavir) and oxygen supplementation. Critically ill patients were admitted to an intensive care unit (ICU) and supported by intubation and mechanical ventilation. In accordance with the criteria stated in the clinical guidelines for hospital discharge of a COVID-19 patient, 13 all the following four conditions should be met: 1) normal temperature lasting longer than 3 days; 2) significantly improved respiratory symptoms; 3) substantially improved acute exudative lesions on chest CT images; 4) two consecutive negative nucleic acid test results of respiratory tract samples (at least 24 hours apart). It should be noted that part of the clinical data of these patients has been previously reported as a Letter to Editor in the journal Allergy, but it only focused on the differences in clinical characteristics between patients with initially negative and then positive nucleic acid results for SARS-CoV-2. 6, 14 This study was approved by the Zhongnan Hospital of Wuhan University institutional ethics board (No.2020015 and No. 2020028). The electronic medical records of each patient were extracted and analyzed by four independent researchers with a standardized data collection form. Patients' demographic and baseline characteristics (including age, sex, exposure history, comorbidities, surgery history, and smoking history), symptomatic and radiological characteristics [including signs and symptoms, and chest computed tomography (CT) results], as well as the laboratory findings on admission (including complete blood cells counts and Accepted Article percentages, and biochemical parameters), and follow-up data of laboratory parameters in severe survived and non-survived patients were obtained and analyzed. The clinical outcome of each patient (i.e., non-survived, discharged, or remained in hospital) as of March 28 th , 2020 (final follow-up date) and the time intervals of initial onset of symptoms (i.e., the day when the symptoms were noticed) to hospital admission for each patient were also recorded. Information was collected regarding disease severity, occurrence of complications (shock, respiratory failure or acute renal failure), and co-infection status with other pathogens during hospital stay. The available chest CT images of each patient were reviewed by a senior radiologist blinded to the clinical data, in order to confirm detailed abnormality of radiological characteristics of these patients. The presence or absence of the three following features was recorded for each patient: 1) ground-glass opacity (GGO); 2) subpleural lesions and pleural effusion; 3) number of affected pulmonary lobes. Exposure history was defined as any close contact with patients diagnosed with COVID-19 (e.g., familial cluster occurrence or occupational exposure of healthcare professionals) or visited Huanan wet market since December 2019. COVID-19 was diagnosed as severe if the patients met one of the following criteria: (a) respiratory distress with respiratory frequency ≥30/min; (b) pulse oximeter oxygen saturation ≤ 93% at rest, and (c) oxygenation index (artery partial pressure of oxygen/inspired oxygen fraction, PaO 2 /FiO 2 ) ≤300 mmHg. The SARS-CoV-2 viral nucleic acid test on the pharyngeal swab specimens of each patient from the two hospitals were processed by technicians from the Zhongnan Hospital of Wuhan University using a rRT-PCR assay. 15 According to the recommendations issued by the National Institute for Viral Disease Control and Prevention (China), 16 a cycle threshold value (Ct-value) less than 37 was defined as a positive test result, whereas a Ct-value of 40 or more was defined as negative. All rRT-PCR assays were performed with the same kit. Complete blood counts, biochemical parameters and variables reflecting hepatic and renal functions on admission and data of follow-up laboratory tests during hospital stay were collected for each patient, including leucocytes, platelets, neutrophils, lymphocytes, monocytes, eosinophils, basophils, C-reactive Accepted Article protein (CRP), serum amyloid A (SAA), procalcitonin (PCT), D-dimer, serum creatine kinase (CK), creatine kinase-MB (CK-MB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and serum creatinine. Serum and respiratory samples, such as pharyngeal swabs were collected to detect co-infection with other pathogens, such as Mycoplasma pneumoniae, Chlamydia pneumoniae, Coxsackie virus group B, adenovirus, echovirus, respiratory syncytial virus, Epstein-Barr virus, influenza A virus, influenza B virus, parainfluenza, cytomegalovirus, Gram-positive or Gram-negative bacteria, and fungi. Categorical variables were expressed as frequencies and percentages (%), and the frequencies of non-survived, survived severe, and non-severe patient groups (total patient number = 289) were compared by partition of chi-square test. Continuous variables were described as median and interquartile range (IQR) values. One-way ANOVA test and Kruskal-Wallis test were used, as appropriate, to compare the data from the three groups. A two-sided  of 0.0167 (after adjustment) was considered statistically significant for partition of chi-square test when comparing differences between categorical variables among the 3 groups. On the other hand, a two-sided α of 0.05 was considered statistically significant for one-way ANOVA test and Kruskal-Wallis test when comparing differences between continuous variables among the 3 groups. Principal Component Analysis (PCA) was used for dimensionality reduction and visualization of the patients after imputing missing values using the implementation "ppca" in pcaMethods package. 17 Euclidean distance and complete linkage were used for the heatmap between different laboratory parameters in three groups. Statistical analyses and figures were generated and plotted using GraphPad Prism version 7.00 software (GraphPad Software Inc.), SPSS statistical software (version 26.0, IBM) and R software (version 3.4.3, supported by the R Foundation for Statistical Computing). Potential risk factors for non-survived COVID-19 patients (n = 49) compared to survived severe patients (n = 78) were analyzed by a multivariate binary logistic model using Forward Stepwise (Wald) model This article is protected by copyright. All rights reserved method. Missing values of laboratory data for the logistic regression analysis, including affected lobe numbers, CRP, PCT, and D-dimer were replaced via multiple imputation. The cut-off value of neutrophil-to-lymphocyte ratio (NLR = 7.726) was calculated by receiver operating characteristics (ROC) analysis, with an area under the curve (AUC) of 0.6614, and NLR was analyzed as categorical variables for the logistic regression analysis. All variables were subject to univariate logistic regression and odds ratios (ORs) were calculated between non-survived and survived severe groups, with a 95% confidence interval. Variables were included in binary logistic regression if corresponding p value was less than 0.05. Binary A total of 289 patients with COVID-19 were included in this study. Until 28 th March 2020, all 78 (27.0%) severe and 162 (56.0%) non-severe patients were discharged from the two hospitals and 49 (17.0%) patients died. The demographics, clinical symptoms and radiological characteristics of these patients on admission are shown in Table 1-3. Compared with survived severe patients (group B in Table 1 -3), non-survived patients (group A in Table 1 -3) were older in age (p=0.029) and had a higher prevalence of symptoms, including chest In comparison to survived severe patients (group B), non-severe patients (group C in Table 1 -3) were relatively younger (p < 0.001) and had less exposure history (p < 0.001), underlying comorbidities (p < 0.001), surgery history (p = 0.004), and gastrointestinal symptoms (p = 0.011). Leucocyte and neutrophil counts, and NLR were lower in non-severe patients than in survived severe patients. Non-severe patients had lower serum levels of CRP, PCT, D-dimer, CK-MB, ALT, AST, and BUN ( Fig. 1 , Table 1-3) . Lymphopenia, thrombocytopenia and elevated biochemical parameters including liver and renal function-related markers (all p<0.001) were found in significantly low or normal levels in non-severe patients ( Table 1-3) . Radiologically, ground-glass opacity [99 (46.3%)] and subpleural lesions [103 (48.1%)] were common CT signs and distributed in different numbers of lobes (Fig. S1 ). In addition, non-severe patients had a higher incidence of normal chest CT images or fewer infected pulmonary lobes compared to survived severe patients (p = 0.01). As expected, there were significant differences in the clinical characteristics, laboratory findings and CT images between non-survived and non-severe patients (Table 1 -3). To assess the similarities and differences between patients with different severities, principal component analysis (PCA) was applied to reduce the dimensionality and visualize the patients on a low-dimensional space. On the Fig. 2 biplot, a trajectory from "non-severe" towards "non-survived" via "severe and survived" patients was observed. These results are in agreement with the blood count and biochemical parameters as potential indicators of disease severity. Interestingly, the heterogeneity within the "non-survived" group is much larger than in the "non-severe" group, suggesting multiple reasons for disease severity. A heatmap of the overview of changes of laboratory results between the three groups is presented in Fig. S2 . 127 severe patients (49 non-survivors and 78 survivors) were included in the univariate and multivariate logistic regression analysis. In the univariate analysis, odds of in-hospital death were higher in patients with chest tightness/dyspnea and smoking history (Table 4 ). Additional findings associated with death included patients' age, affected lobe numbers, leucocyte counts, CRP levels, and elevated levels of NLR, PCT, BUN, and serum creatinine (Table 4 ). Multivariate analysis indicated that the age of patients (OR, 1.04; 95% CI, 1.00-1.08), smoking history (OR, 5.21; 95% CI, 1.39-19.52), chest tightness/dyspnea (OR, 3.03; 95% CI, 1.18-7.79), number of affected lobes on admission (OR, 1.71; 95% CI, 1.06-2.78) and CRP levels on admission (OR, 1.01; 95% CI, 1.00-1.02) were risk factors associated with death in cases with severe COVID-19 (Table 4 , Fig. 1 ). In these 289 patients, according to the heatmap of Spearman correlation of laboratory results on admission, together with age and affected lobe numbers (as shown in Fig. 3 ), three clusters of correlation were found. The first cluster had moderate correlations among lymphocyte, eosinophils, platelet, monocytes, leucocytes, and neutrophils in all patients. The second cluster had correlations among biochemical parameters including PCT, BUN, NLR, CRP, serum creatinine, CK, CK-MB, D-dimer, SAA, This article is protected by copyright. All rights reserved ALT, and AST, as well as age and numbers of affected pulmonary lobes. Many of these markers showed weak and moderate correlations, however some of them showed a strong correlation, such as NLR, neutrophils, ALT and AST. The third cluster had negative correlations among blood cell components, biochemical parameters, age and affected lobe numbers. Most of the variables showed weak correlations, except a strong correlation between NLR and lymphocyte counts. Increased inflammatory parameters suggesting a cytokine storm and multiorgan injury showed a negative correlation, particularly with numbers of lymphocytes and eosinophils, but also platelets and basophils. Scatter plots of different correlations between age, affected lobe numbers and other laboratory parameters are shown in Fig. 4 , as well as Fig. S3 -S5. Differences in longitudinal trends of the laboratory findings between non-survived and survived severe patients were observed as the disease progressed. As shown in Fig. 5 , leucocyte and neutrophil counts increased in the early stage of hospitalization (3-7 days) and gradually decreased during the late stage of hospitalization (8-14 days) in severe survived patients, but continuously increased in non-survived patients. Even though lymphopenia was observed in both groups during hospitalization, the lymphocyte count was significantly lower in the non-survived group compared to the survived severe group. It should be noted that sustained eosinopenia and progressing thrombocytopenia were observed in non-survived patients, but both blood cell numbers were partially relieved in survived severe patients. Sustained high levels of NLR, CRP, PCT, AST, BUN, and serum creatinine were associated with fatal clinical outcome of severe patients. Of the 289 laboratory-confirmed COVID-19 cases in this study, most of the patients were more than 50 years old, with an almost 1:1 female-male ratio. The prevalence of underlying comorbidities, including hypertension, diabetes mellitus and coronary heart disease were similar to those demonstrated in previous studies. 14, [18] [19] [20] The mortality of the 289 hospitalized cases in the present study was 17.0% (49/289), which was lower than that reported by Zhou 21 The difference may be due to the different sample sizes and case inclusion criteria used in these studies. For the first time, we found that the number of affected lobes on CT scans were associated with disease severity. In accordance with previous reports, 18, 22 bilateral lung involvement was predominant in patients with abnormal chest CT images, mainly manifested as multiple ground glass opacities and subpleural lesions. Five pulmonary lobes were affected in more than half of the patients with abnormal chest CT images. Affected lobe numbers correlated with age, CRP, D-dimer and BUN, which also correlated with each other. Based on these observations, continuous monitoring of chest CT images is useful for the evaluation of the disease course. However, it is not easy to obtain a second chest CT scan in critically ill patients during hospitalization, especially in intubated and ventilated patients. In this situation, bedside X-ray can be an alternative option. Most of the non-survived patients in this study lacked a second CT scan. The mortality risk factors for severe patients identified in this study using a logistic regression model include elderly age, higher CRP levels, number of affected lobes, chest tightness/dyspnea, and smoking history. Although neutrophil counts and biochemical parameters were significantly different between survived severe and non-survived patients, these variables were not found to be an independent risk factor for mortality of COVID-19 patients. The present study supports the association of elderly age and increased mortality rate in COVID-19 patients, in accordance with a previous study. 19 Elderly age is known to be associated with a dampened immune function and more underlying comorbidities, which may lead to the poor outcome of these patients. 23 This article is protected by copyright. All rights reserved Several studies have reported that hypertension, hypoxia, leukocytosis, lymphopenia, and high serum LDH levels were independent predictors for in-hospital death. [7] [8] [9] [10] However, in the present study, only dyspnea and leukocytosis were found to be independent risk factors of death in critically ill COVID-19 patients. Previous reports identified that lower baseline levels and/or progressively decreasing platelet counts were associated with higher mortality of COVID-19 patients. 11 This is similar to our observations of non-survived patients in the current study. CRP is a widely used inflammatory marker in clinical studies. Elevated CRP levels indicate inflammation caused by various conditions, including infections. Elevated IL-6, which is the trigger of CRP synthesis in the liver was also observed in COVID-19 patients. 24 A cytokine storm has been suggested as a culprit for poor prognosis of critically ill COVID-19 patients. In the current study, higher CRP levels were found to be associated with poor clinical outcome of severe patients. Wang et al. identified higher levels of CRP in non-survivors compared with survivors within 15 days of COVID-19 hospitalization. 10 Additionally, Wu et al. found that elevated high-sensitivity CRP was significantly associated with higher risks of acute respiratory distress syndrome (ARDS) in COVID-19 patients. 7 The data suggested that CRP was a marker of a developing cytokine storm in COVID-19 patients and was associated with disease mortality. In a recent study, 25 higher NLR was suggested to be independent risk factors of mortality in hospitalized COVID-19 patients. However, logistic regression analysis indicated that the odd ratio of in-hospital death was higher in patients with higher NLR but it was not an independent risk factor of death for severe patients in the current study. Interestingly, the prevalence of patients with smoking history is 9.7%, which is close to that reported in the latest scientific brief published by the World Health Organization (WHO). 26 The presence of smoking history was found to be a risk factor of death in critically ill patients. Similarly, Mehra et al. 27 demonstrated that current smokers had higher in-hospital death rate in COVID-19 patients. A systemic review also concluded that smoking is most likely associated with poor disease progression and adverse outcomes. 28 In view of these results, smoking should not be considered a preventive measure for COVID-19 and as a public health issue should be discouraged at all times. Although smoking is a major cause of chronic obstructive pulmonary disease (COPD), a recent study reported that COPD did not increase the risk of This article is protected by copyright. All rights reserved COVID-19 patients requiring admission to the intensive care unit (ICU). 29 This was in contrast to the recent findings in a meta-analysis which identified COPD as an independent risk factor of disease progression. 30 Angiotensinconverting enzyme 2 (ACE2) is highly expressed in airway epithelial cells and plays an important role in SARS-CoV-2 infection as it has been demonstrated to serve as a receptor for SARS-CoV-2. 31 The higher levels of ACE2 expression in the lower respiratory tract of current smokers may contribute to the increased risk of developing severe COVID-19. [32] [33] The strong positive correlations identified between the affected lobe numbers, patients' age, neutrophil and lymphocyte counts indicate that more severe pneumonia was associated with elderly age and higher degree of lymphopenia, indicating that the numbers of affected lobes could be a possible risk factor for severe cases and in-hospital mortality of severe COVID-19 patients. Leukocytosis, eosinopenia and lymphopenia may be associated with the progression of inflammatory status. More severe illness was associated with older patients, given the increased levels of CRP, SAA, PCT, and D-dimer. Comparison of the dynamic profile of laboratory findings in severe patients revealed a sustained increase in leucocyte count, neutrophil count, biological markers, as well as continued decrease in platelet count, lymphopenia and eosinopenia in these patients. As previously reported, eosinopenia may be an indicator for SARS-CoV-2 infection, 14 and the degree of eosinopenia was associated with the severity of COVID-19. [34] [35] Persistent eosinopenia may be a predictor of disease severity and adverse clinical outcome during hospitalization, which is consistent with the results reported by Xie et al. 34 The anti-viral effect exhausts eosinophils and may be the cause of eosinopenia in COVID-19 patients. 36 Moreover, the recovery from eosinopenia was associated with favorable clinical outcome in severe patients. Lymphopenia is prominent in adult COVID-19 patients, but not in pediatric patients. 37 In this study, the recovery from lymphopenia in survived severe patients was not as significant as that of eosinopenia (Fig. 5C, F) . Similarly, a recent study showed an increasing trend of eosinophils, lymphocyte, and platelet in severe survivors but remained constant at lower levels in non-survivors, which is consistent with our results. 38 Significant differences in levels of D-dimer on admission were identified between non-survived and survived severe patients. However, multivariate regression analysis did not identify D-dimer as an independent risk factor of the mortality in severe COVID-19 patients. This was consistent with a previous study conducted by Chen et al., 38 but different from the result from Zhou et al., which showed that elevated This article is protected by copyright. All rights reserved D-dimer was associated with poor prognosis of hospitalized COVID-19 patients. 19 High level of D-dimer is indicative of developing thrombosis. A recent study on COVID-19 patients in Wuhan revealed an extremely high incidence of thrombosis (41/48, 85.4%) in severe patients with a death rate of 31.7% (13/41) . 39 Endothelial damage is presumed to be an important mechanism of the development of thrombosis. 40 In addition, elevated D-dimer levels could occur in deep venous thrombosis (DVT) and in capillary microthrombi, secondary to pulmonary capillary endothelial injury, contributing to the death of severe COVID-19 patients. 41 Venous thromboembolism (VTE) had a higher incidence in ICU COVID-19 patients than those on the wards, and the incidence increased along with the duration of hospitalization. 42 A study of 184 ICU COVID-19 patients reported that computed tomography pulmonary angiography (CTPA) and/or ultrasonography confirmed VTE was 27% and arterial thrombotic events was 3.7%. 43 The study also concluded that pulmonary embolism (PE) was the most frequent thrombotic complication (81%) and that age and coagulopathy were independent predictors of thrombotic complications. 43 Poissy et al. found that PE occurred in 22 (20.6%) of 107 patients. However, only 1 DVT was identified in these patients, indicating that pulmonary thrombosis but not embolism is the cause for PE. 44 The contribution of capillary microthrombi to PE is unclear since it was difficult to identify capillary microthrombi in clinical practice. This study was limited to the relatively small number of patients which may limit the statistical power and the inclusion of hospitalized patients exclusively (non-hospitalized patients were not included in the analysis). These limitations may cause statistical bias and hence the significant difference identified in demographic and symptomatic characteristics, as well as the laboratory findings between the groups. Missing data on some variables, such as information of CT images and biochemical parameters may cause bias in the identification of risk factors for mortality in severe patients. In summary, this retrospective, bi-center study revealed that elder age, CRP levels, number of affected pulmonary lobes, clinical symptoms manifested as chest tightness/dyspnea, and smoking history were independent risk factors of mortality for non-survived patients compared with severe and survived patients. Assessment of these parameters may help to identify severe COVID-19 patients at a high risk of death. Earlier medical intervention and support on these patients with high risk may reduce the fatality of this disease. This article is protected by copyright. All rights reserved parameters including laboratory results on admission, age and affected lobe numbers were used in the analysis; results are represented by colored dots separated by three groups of severity. Despite no clear separation between the three groups, there was a clear trajectory from "non-severe" towards "non-survived" via " survived severe". CRP, C-reactive protein; PCT, procalcitonin; AST, aspartate aminotransferase; BUN, blood urea nitrogen. This article is protected by copyright. All rights reserved This article is protected by copyright. All rights reserved This article is protected by copyright. All rights reserved This article is protected by copyright. All rights reserved This article is protected by copyright. All rights reserved This article is protected by copyright. All rights reserved *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** Coronaviridae Study Group of the International Committee on Taxonomy of Viruses. 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All rights reserved Hepatic and renal function parameters Alanine aminotransferase Chi-square test or Fisher's exact test was used, as appropriate, to evaluate the difference between non-survived and severe & survived patients. OR, odds ratio; GI tract AST, aspartate aminotransferase ; BUN, blood urea nitrogen # Critically ill patients include non-survived (n=49) and severe & survived (n=78) patients This article is protected by copyright. All rights reserved