Analysis of clinical characteristics, laboratory findings and therapy of 134 cases of COVID-19 in Wuhan, China: a retrospective analysis

Background ： As everyone knows, the pandemic COVID-19 is spreading in the whole world. The number of laboratory-confirmed cases reached 28,637,211 and that of the death cases was 917,404 in the world as of September 13 th , 2020. We sought to analyse the clinical characteristics, laboratory findings and therapy of some cases with COVID-19. Methods: In this retrospective study, we extracted the data on 134 patients with laboratory-confirmed COVID-19 in Wuhan Xinzhou District People's Hospita l from January 16 th to April 24 th , 2020. Cases were confirmed by real-time RT-PCR and abnormal radiologic findings. Outcomes were followed up until May 1 th , 2020. Results ： Co-infection and severe underlying diseases made it easier for a case with COVID-19 to develop to be a severe one or reach an outcome of death. Age above 60 years old, male and symptoms such as fever, cough, chest tightness, headaches and fatigue were related to severe COVID-19 and an outcome of death. In addition, higher temperature, blood leukocyte count, neutrophil count, C-reactive protein level, D-dimer level, alanine aminotransferase activity, aspartate aminotransferase activity, α -hydroxybutyrate dehydrogenase activity, lactate dehydrogenase activity and creatine kinase activity were also related to severe COVID-19 and an outcome of death, and so was lower lymphocyte count. Administration of gamma globulin seemed helpful for reducing the mortality of patients with severe COVID-19, however the P value was greater than 0.05 (P=0.180), which mean under the same condition, studies of larger samples are needed in the future. Conclusion: Multiple factors were related to severe COVID-19 and an outcome of death. Administration of gamma globulin seemed helpful for reducing the mortality of severe cases. More related studies are needed in the future.


Background
As everyone knows, the pandemic COVID-19 (coronavirus disease 2019), which is caused by the coronavirus SARS-CoV-2 (severe acute respiratory syn-drome coronavirus 2), is spreading in the whole world. The number of laboratory-confirmed cases reached 28,637,211 and that of the death cases was 917,404 in the world as of September 13 th , 2020 according to the information from the official website of World Health Organization.
In the past several months a large number of studies have described the clinical characteristics and laboratory findings of patients with COVID-19 [1,2,3]. When assessing the relationship between potential risk factors and the severity of COVID-19, some studies used the evaluating indicator that the highest or lowest level of candidates during hospitalization while other studies mainly used the evaluating indicator that the fixed value of candidates on admission [1,2,3]. What are the sphere of application and clinical significance of those two evaluating indicators? And what is the relationship between those two evaluating indicators? To answer these questions, we used both the fixed value on admission and the highest or lowest level of candidates during hospitalization to assess the relationship between potential risk factors and the severity of COVID-19 in our study. In the discussion section, we have pointed out the risk factors related to severe COVID-19 and an outcome of death, and expounded the clinical significance of these risk factors and the role of those two evaluating indicators. 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65 We performed a retrospective study on the clinical characteristics, laboratory findings and therapy of laboratory-confirmed cases with COVID-19.

Data sources
Inclusion criteria: 1. All cases were diagonosed with pneumonia based on the clinical manifestations and abnormal findings of chest X-ray or computed tomography.
2. A confirmed case with COVID-19 was defined as a positive result to high-throughput sequencing or real-time reverse-transcriptase polymerase-chain-reaction assay for nasal and pharyngeal swab specimens.
Exclusion criteria: 1. Patients with common bacteria or viruses associated with community-acquired pneumonia.
2. Patients with severe underlying disease.
3. Procalcitonin level ＞ 0.5 ng/ml. A flow chart, from the total number of patients up to the 134 patients of the study， was shown by Figure.1. 14 cases with severe underlying disease (i.e., chronic lung disease, chronic heart disease, chronic liver disease, chronic kidney disease) were excluded. 22 cases co-infected with COVID-19 and other respiratory pathogens (i.e., Bacteria, Chlamydia pneumoniae, Mycoplasma pneumoniae, adenovirus, and respiratory syncytial virus) associated with community-acquired pneumonia were excluded. 16 cases with both severe underlying diseases and other respiratory pathogens infection were excluded. Finally 134 confirmed cases with COVID-19 were included into our study.

Laboratory test.
Patients usually received lab test every two days or when changing in health condition.

Therapy with gamma globulin.
Administration of gamma globulin was not used as conventional therapy method.
Whether gamma globulin was used depended on the will of patients and their relatives.

Statistical analysis
Continuous variables were expressed as the medians and interquartile ranges.
Categorical variables were summarized as the counts and percentages in each category.

Results
As shown by Figure 1, 52 cases with severe underlying disease or co-infected with COVID-19 and other respiratory pathogens associated with community-acquired pneumonia were excluded, and finally 134 cases were included. Thus all the 186 patients were divided into two groups, namely included cases group and excluded cases group, as shown by Table 1 Obviously co-infection and severe underlying disease were related to severe COVID-19 and an outcome of death.

Demographic and clinical characteristics
The demographic and clinical characteristics are shown in Table 2. And 134 cases were included.

Age
We grouped patients into three groups according to their age as shown by Table 2.
For＜30 years group, 12.5% of the patients were severe cases and none of the patients died. For 30-60 years group, 25.5% of the patients were severe cases and 2.1% of the patients died. For ＞60 years group, 33.3% of the patients were severe cases and 16.7% of the patients died. (P1=0.332, P2=0.006) Obviously, ＞60 years group was related to an outcome of death.

Fever
As shown by Table 2, 79.1% of the patients had a fever.
For with fever group, 30.2% of the fatients were severe cases, and 5.7% of the patients died. For without fever group, 7.1% of the patients were severe cases, and none of the patients died. (P1=0.014, P2=0.343) Obviously, with fever group was related to severe COVID-19. Obviously, higher temperature was related to severe COVID-19 and an outcome of death.

Cough
As shown by Table 2, 77.6% of the patients had a cough. For with cough group, 29.8% of the fatients were severe cases, and 5.8% of the patients died. For without cough group, 10.0% of the patients were severe cases, and none of the patients died. (P1=0.032, P2=0.337) Obviously, with cough group was related to severe COVID-19.

Chest tightness
As shown by Obviously, with chest tightness group was related to severe COVID-19 and an outcome of death.

Headaches
As shown by Table 2, 11.2% of the patients had headaches.
For with headaches group, 66.7% of the fatients were severe cases, and 20.0% of the patients died. For without headaches group, 20.2% of the patients were severe cases, and 2.5% of the patients died. (P1＜0.001, P2=0.019) Obviously, with headaches group was related to severe COVID-19 and an outcome of death.

Diarrhea
As shown by Table 2, 6.0% of the patients had diarrhea.
For with diarrhea group, 37.5% of the fatients were severe cases, and none of the patients died. For without diarrhea group, 24.6% of the patients were severe cases, and 4.8% of the patients died.
However, the P values were greater than 0.05 (P1=0.418, P2=1.000), which mean under the same condition, studies of larger samples are needed in the future.

Laboratory findings
The Laboratory findings are shown by Table 3 and Table 4.

Highest blood leukocyte count during hospitalization
As shown by Table 3, we grouped patients into three groups according to their highest blood leukocyte count during hospitalization.
For >10*10^9/L group, 92.6% of the patients were severe cases, and 22.2% of the patients died. For 4-10 * 10^9/L group, 7.5% of the patients were severe cases, and none of the patients died. For ＜ 4 * 10^9/L group, 14.3% of the patients were severe cases, and none of the patients died. (P1＜0.001, P2＜0.001) >10*10^9/L group was strongly related to severe COVID-19 and an outcome of death.

Blood leukocyte count on admission
As shown by Table 4, we grouped patients into three groups according to their blood leukocyte count on admission.
For >10*10^9/L group, 90.9% of the patients were severe cases, and 27.3% of the patients died. For 4-10 * 10^9/L group, 20.2% of the patients were severe cases, and >10*10^9/L group was strongly related to severe COVID-19 and an outcome of death.

Highest neutrophil count during hospitalization
As shown by Table 3, we grouped patients into three groups according to their highest neutrophil count during hospitalization.
For >7*10^9/L group, 83.9% of the patients were severe cases, and 19.4% of the patients died. For 2-7 * 10^9/L group, 8.5% of the patients were severe cases, and none of the patients died. For ＜ 2 * 10^9/L group, none of the patients were severe cases, and none of the patients died. (P1＜0.001, P2＜0.001) >7*10^9/L group was strongly related to severe COVID-19 and an outcome of death.

Neutrophil count on admission
As shown by Table 4, we grouped patients into three groups according to their neutrophil count on admission.

Lowest lymphocyte count during hospitalization
For＜0.4* 10^9/L group, 80.0% of the patients were severe cases, and 20.0% of the patients died. For 0.4-0.8* 10^9/L group, 34.8% of the patients were severe cases, and 4.3% of the patients died. For ＞0.8* 10^9/L group, 8.2% of the patients were severe cases, and 1.4% of the patients died. (P1＜0.001, P2＜0.001) Lower lymphocyte count was strongly related to severe COVID-19 and an outcome of death.

Lymphocyte percentage on admission
As shown by Table 4, we grouped patients into three groups according to their lymphocyte count on admission.
For＜0.4* 10^9/L group, all of the patients were severe cases, and 50.0% of the patients died. For 0.4-0.8* 10^9/L group, 36.1% of the patients were severe cases, and 2.8% of the patients died. For ＞0.8* 10^9/L group, 18.1% of the patients were severe cases, and 3.2% of the patients died. (P1＜0.001, P2＜0.001) Lower lymphocyte count was strongly related to severe COVID-19 and an outcome of death.

C-reactive protein level on admission
As shown by Table 4, we grouped patients into four groups according to their C-reactive protein level on admission.
For＜20 mg/L groups, 4.4% of the patients were severe cases, and none of the patients died. For 20-90 mg/L group, 33.3% of the patients were severe cases, and 4.8% of the patients died. For 90-150 mg/L group, 63.6% of the patients were severe cases, and 18.2% of the patients died. For＞150 mg/L group, 76.9% of the patients were severe cases, and 15.4% of the patients died. (P1＜0.001, P2=0.009) Higher C-reactive protein level was strongly related to severe COVID-19.

Highest D-dimer level during hospitalization
As shown by Table 3, we grouped patients into two groups according to their highest D-dimer level during hospitalization.
For ≤1 mg/L group, 8.2% of the patients were severe cases, and none of the patients died. For＞1 mg/L group, 72.2% of the patients were severe cases, and 16.7% of the patients died. (P1＜0.001, P2＜0.001) Higher D-dimer level was strongly related to severe COVID-19 and an outcome of death.

D-dimer level on admission
As shown by Higher D-dimer level was strongly related to severe COVID-19 and an outcome of death.

Highest alanine aminotransferase activity during hospitalization
As shown by Table 3, we grouped patients into three groups according to their highest alanine aminotransferase activity during hospitalization.
For＜40 U/L group, 7.8% of the patients were severe cases, and 2.6% of the patients died. For 40-80 U/L group, 43.7% of the patients were severe cases, and 9.4% of the patients died. For＞80 U/L group, 56.0% of the patients were severe cases, and 4.0% of the patients died. (P1＜0.001, P2=0.295) Higher alanine aminotransferase activity was related to severe COVID-19.

Alanine aminotransferase activity on admission
As shown by Table 4, we grouped patients into three groups according to their alanine aminotransferase activity on admission.
For＜40 U/L group, 16.5% of the patients were severe cases, and 2.1% of the patients died. For 40-80 U/L group, 45.5% of the patients were severe cases, and 9.1% of the patients died. For＞80 U/L group, 75.0% of the patients were severe cases, and 25.0% of the patients died. (P1＜0.001, P2=0.032) Higher alanine aminotransferase activity was related to severe COVID-19 and an outcome of death.

Highest aspartate aminotransferase activity during hospitalization
As shown by Table 3, we grouped patients into three groups according to their highest aspartate aminotransferase activity during hospitalization. For＜40 U/L group, 10.5% of the patients were severe cases, and none of the patients died. For 40-80 U/L group, 48.6% of the patients were severe cases, and 13.5% of the patients died. For＞80 U/L group, 63.6% of the patients were severe cases, and 9.1% of the patients died. (P1＜0.001, P2=0.003) Higher aspartate aminotransferase activity was related to severe COVID-19.

Aspartate aminotransferase activity on admission
As shown by Table 4, we grouped patients into three groups according to their aspartate aminotransferase activity on admission For＜40 U/L group, 16.2% of the patients were severe cases, and 1.0% of the patients died. For 40-80 U/L group, 50.0% of the patients were severe cases, and 3.3% of the patients died. For＞80 U/L group, 60.0% of the patients were severe cases, and 20.0% of the patients died. (P1＜0.001, P2=0.004) Higher aspartate aminotransferase activity was related to severe COVID-19 and an outcome of death.

Highest creatinine level during hospitalization
As shown by Table 3, we grouped patients into two groups according to their highest creatinine level during hospitalization.
For≤90 μmol/L group, 23.6% of the patients were severe cases, and 2.7% of the patients died. For＞90 μmol/L group, 33.3% of the patients were severe cases, and 12.5% of the patients died.

Creatinine level on admission
For≤90 μmol/L group, 25.4% of the patients were severe cases, and 4.6% of the patients died. For＞90 μmol/L group, 25.0% of the patients were severe cases, and none of the patients died.
However, the P values were greater than 0.05(P1=1.000, P2=1.000), which mean under the same condition, studies of larger samples are needed in the future.

Highest α-hydroxybutyrate dehydrogenase activity during hospitalization
As shown by Table 3, we grouped patients into four groups according to their highest α-hydroxybutyrate dehydrogenase activity during hospitalization. Higher α -hydroxybutyrate dehydrogenase activity was related to severe COVID-19 and an outcome of death.

Highest lactate dehydrogenase activity during hospitalization
As shown by Table 3, we grouped patients into four groups according to their highest lactate dehydrogenase activity during hospitalization. Higher lactate dehydrogenase activity was related to severe COVID-19 and an outcome of death.

Lactate dehydrogenase activity on admission
As shown by Table 4, we grouped patients into four groups according to their lactate dehydrogenase activity on admission.

Highest creatine kinase activity during hospitalization
As shown by Table 3, we grouped patients into three groups according to their highest creatine kinase activity during hospitalization.
For＜200 U/L group, 19.8% of the patients were severe cases, and 2.0% of the patients died. For 200-400U/L group, 38.5% of the patients were severe cases, and 7.7% of the patients died. For＞400 U/L group, 45.0% of the patients were severe cases, and 15.0% of the patients died. (P1=0.032, P2=0.031) Higher creatine kinase activity was related to severe COVID-19 and an outcome of death.

Creatine kinase activity on admission
As shown by Table 4, we grouped patients into three groups according to their creatine kinase activity on admission.
For＜200 U/L group, 20.4% of the patients were severe cases, and 1.9% of the patients died. For 200-400U/L group, 37.5% of the patients were severe cases, and 12.5% of the patients died. For＞400 U/L group, 50.0% of the patients were severe cases, and 16.7% of the patients died. (P1=0.020, P2=0.010) Higher creatine kinase activity was related to severe COVID-19 and an outcome of death.

Administration of gamma globulin
Administration of gamma globulin was not used as conventional therapy method.
Whether gamma globulin was used depended on the will of patients and their relatives. Table 5, 34 severe cases were divided into two groups according to wether gamma globulin was used.

As shown by
For severe cases treated with gamma globulin group, 93.8% of the patients survived while 6.2% of the patients died. For severe cases treated without gamma globulin group, 72.2% of the patients survived while 27.8% of the patients died. It seemed that administration of gamma globulin was helpful for reducing the mortality of severe cases. However the P value was greater than 0.05 (P=0.180), which mean under the same condition, studies of larger samples are needed in the future.

Underlying diseases and co-infection.
33.9% of all the 186 patients had one or more severe undelying diseases (i.e., chronic lung disease, chronic heart disease, chronic liver disease, chronic kidney disease) or co-infected with COVID-19 and other respiratory pathogens (i.e., Bacteria, Chlamydia pneumoniae, Mycoplasma pneumoniae, adenovirus, and respiratory syncytial virus) associated with community-acquired pneumonia, as shown by Table 1.
Compared with cases without undelying disease or co-infection, cases with severe undelying diseases or co-infection developed to be severe cases more easily and had much higher mortality rates, as shown by Table 1.
Next, to fully assess the role of SARS-CoV-2 in COVID-19 without too much interference, we excluded 52 cases with severe underlying diseases or co-infected with COVID-19 and other respiratory pathogens associated with community-acquired pneumonia from our study, and finally 134 cases with only COVID-19 were included, as shown by Figure 1.

Clinical characteristics of 134 cases of COVID-19.
As shown by Table 2, fever, higher temperature, cough, chest tightness, headaches, fatigue were related to severe COVID-19. And age above 60 yeas old, male, higher temperature, chest tightness, headaches were related to an outcome of death.
Male and the the elderly had higher mortality rates compared with female and young people. Sex differences in the response to inflammation have been documented and can be attributed, at least in part, to sex steroid hormones [5]. Moreover, age-associated decreases in sex steroid hormones, namely, estrogen and testosterone, may mediate proinflammatory increases in older adults that could increase their risk of COVID-19 adverse outcomes [5].

Laboratory findings of 134 cases of COVID-19.
When assessing the relationship between potential risk factors and the severity of COVID-19, some studies used the evaluating indicator that the highest or lowest level of candidates during hospitalization while other studies mainly used the evaluating indicator that the fixed value of candidates on admission [1,2,3]. What are the sphere of application and clinical significance of those two evaluating indicators? And what is the relationship between those two evaluating indicators? To answer these questions, we used both the fixed value of candidates on admission and the highest or lowest level of candidates during hospitalization to assess the relationship between potential risk factors and the severity of COVID-19 in our study, as shown by Table 3 and Table 4. Table 3 and Table 4, higher blood leukocyte count, neutrophil count, C-reactive protein level, D-dimer level, alanine aminotransferase activity, aspartate aminotransferase activity, α -hydroxybutyrate dehydrogenase activity, lactate dehydrogenase activity and creatine kinase activity were related to svere COVID-19 and an outcome of death, and so was lower lymphocyte count. We could see that similar but 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65 not the same conclusions were reached when the evaluating indicator that the fixed value of candidates on admission or the evaluating indicator that the highest or lowest level of candidates during hospitalization were used. In our opinion, to find the risk factors related to severe COVID-19 and an outcome of death, the evaluating indicator that the highest or lowest level of candidates during hospitalization might be more appropriate compared with the evaluating indicator that the fixed value of candidates on admission.

According to
That makes sense because the laboratory findings of patients on admission could be unrepresentative as the laboratory findings may vary depending on patient's health condition, and many cases that developed to be severe cases during hospitalization could be non-severe cases on admission. But why similar conclusions were also reached when the evaluating indicator that the fixed value of candidates on admission was used ? It's easy to understand because on admission some cases were already severe cases, and the blood test results of non-severe cases on admission that developed to be severe cases during hospitalization would keep increasing (i.e., blood leukocyte count, neutrophil count, C-reactive protein level, D-dimer level, alanine aminotransferase activity, aspartate aminotransferase activity, α-hydroxybutyrate dehydrogenase activity, lactate dehydrogenase activity and creatine kinase activity) or decreasing (i.e., lymphocyte count) before they reach the peak or the valley. This caused difference between non-severe group and severe group, and it made it possible for us to pick out the risk factors using statistical methods. That also mean the utility of the evaluating indicator that the fixed value of candidates on admission, was only a part of that of the evaluating indicator that the highest or lowest level of candidates during hospitalization, and under the same conditions the risk factors of Table 3 could account for more severe cases than the risk factors of Table 4, that is because under the circumstances, the difference between non-severe group and severe group of was bigger in Table 3. This view was proved by our study, for example, in Table 3 >10*10^9/L group accounted for 73.5% of all the 34 severe cases, however, in Table 4 >10*10^9/L group only accounted for 14.7% of all the 1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65 34 severe cases. So in theory, the conclusions would be more reliable when the evaluating indicator that the highest or lowest level of candidates during hospitalization was used, compared with the evaluating indicator that the fixed value of candidates on admission.
Furtherly, based on the above point of view, it is easy to understand that more attention should be paid to the patients whose blood test results keep increasing (i.e., blood leukocyte count, neutrophil count, C-reactive protein level, D-dimer level, alanine aminotransferase activity, aspartate aminotransferase activity, α -hydroxybutyrate dehydrogenase activity, lactate dehydrogenase activity and creatine kinase activity) or decreasing (i.e., lymphocyte count) during hospitalization.
Next, let us talk about the clinical significance of these risk factors.
Leukocyte such as neutrophil, lymphocyte and monocyte are part of immune system.
When human body is infected with viruses, lymphocyte count usually keeps normal or increases while neutrophil count decreases. However, in our study lymphopenia and neutrocytosis were common in patients with severe COVID-19. This abnormal phenomenon reflected the dysregulation of the immune response.
Neutrocytosis was related to Neutrophil extracellular traps (NETs), which originated from decondensed chromatin released to immobilize pathogens and could trigger immunothrombosis [6]. In addition, neutro-philic infiltration was found in a study that examined post-mor-tem biopsies from four COVID-19 patients [7]. As suggested by a previous study, the neutrocytosis might be partly caused by a dysregulated myeloid cell compartment. Severe COVID-19 was marked by occurrence of neutrophil precursors, as evidence of emergency myelopoiesis, dysfunctional mature neutrophils [8].
Lymphopenia, a marker of impaired cellular immunity, is a cardinal laboratory finding reported in 67-90% of patients with COVID-19, with prognostic association in the vast majority of studies published so far [1,2,3]. Several mechanisms likely contribute to the reduced number of T cells in the blood, including effects from the inflammatory cytokine milieu and T cell recruit-ment to sites of infection [9].  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60  61  62  63  64  65 C-reactive protein, α -hydroxybutyrate dehydrogenase activity and Lactate dehydrogenase belong to serum inflammatory markers. Elevation of serum inflammatory markers reflected excessive inflammation and is pre-dictive of subsequent critical illness and mortality in patients with COVID-19 [2,3,10]. D-dimer reflects coagulation function. The increased D-dimer level reflected a hypercoagulable state, which might promote thrombus formation. Thrombotic complications were first reported from intensive care units in China and the Netherlands in up to 30% of patients [11,12]. There is also emerging evidence of thrombosis in intra-venous catheters and extracorporeal circuits, and arterial vascular occlusive events, including acute myocardial infarction, acute limb ischemia, and stroke, in severely affected people in studies from the USA (United States of America), Italy and France [4].
Alanine aminotransferase activity and aspartate aminotransferase activity were used to evaluate liver function. In critically ill patients with COVID-19, a hepatocellular injury pattern is seen in 14-53% of hospitalized patients [4]. Aminotransferases are typically elevated but remain less than five times the upper limit of nor-mal. Rarely, severe acute hepatitis has been reported [4]. In our study, we didn't see an obvious difference between 40-80 U/L group and ＞80 U/L group, and most of the liver fucntion were temporary and reversible. That means the liver function injury might be caused by multiple factors: immunity, inflammation and drugs.
Creatine kinase activity was used to cardiac function. SARS-CoV-2 could cause both direct cardiovascular sequelae and indirect cardiovascular sequelae, including myocardial injury, acute coronary syndromes, cardiomyopathy, acute cor pulmonale, arrhythmias, and cardiogenic shock, as well as the aforementioned thrombotic com-plications [15,16].

Administration of gamma globulin
Administration of gamma globulin was not used as conventional therapy method.
Whether gamma globulin was used depended on the will of patients and their relatives.
As shown by Table 5, 34 severe cases were divided into two groups according to wether gamma globulin was used. It seemed that administration of gamma globulin was helpful for reducing the mortality of severe cases. However the P value was greater than 0.05 (P=0.180), which mean under the same condition, studies of larger samples are needed in the future. In fact, gamma globulin was probably useful as suggested by several studies [17,18].

Conclusion
Compared with cases with only COVID-19, cases with undelying diseases or co-infected with COVID-19 and other respiratory pathogens associated with community-acquired pneumonia developed to be severe or died more easily.
Multiple factors were related to severe COVID-19 and an outcome of death.
Administration of gamma globulin seemed helpful for reducing the mortality of severe cases. More related studies are needed in the future.

Statement of Ethics
Ethics Committee of Xinzhou District People's Hospital approved this study.

Consent for publication
Not applicable

Availability of data and materials
All data generated or analysed during this study are included in this published article [and its supplementary information files].

Competing interests
The authors declare that they have no competing interests

Funding
This study was not funded by anyone.

Author Contributions
JC and JZ designed the study. RZ and JZ participated in data collection and analysis. All authors have contributed to the last version of the manuscript. Finally 134 confirmed cases with COVID-19 were included into our study.