Literature DB >> 33302889

Risk factors and clinical features of deterioration in COVID-19 patients in Zhejiang, China: a single-centre, retrospective study.

Ping Yi1, Xiang Yang2, Cheng Ding1, Yanfei Chen1, Kaijin Xu1, Qing Ni1, Hong Zhao1, Yongtao Li1, Xuan Zhang1, Jun Liu1, Jifang Sheng1, Lanjuan Li3.   

Abstract

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection swept through Wuhan and spread across China and overseas beginning in December 2019. To identify predictors associated with disease progression, we evaluated clinical risk factors for exacerbation of SARS-CoV-2 infection.
METHODS: A retrospective analysis was used for PCR-confirmed COVID-19 (coronavirus disease 2019)-diagnosed hospitalized cases between January 19, 2020, and February 19, 2020, in Zhejiang, China. We systematically analysed the clinical characteristics of the patients and predictors of clinical deterioration.
RESULTS: One hundred patients with COVID-19, with a median age of 54 years, were included. Among them, 49 patients (49%) had severe and critical disease. Age ([36-58] vs [51-70], P = 0.0001); sex (49% vs 77.6%, P = 0.0031); Body Mass Index (BMI) ([21.53-25.51] vs [23.28-27.01], P = 0.0339); hypertension (17.6% vs 57.1%, P < 0.0001); IL-6 ([6.42-30.46] vs [16.2-81.71], P = 0.0001); IL-10 ([2.16-5.82] vs [4.35-9.63], P < 0.0001); T lymphocyte count ([305-1178] vs [167.5-440], P = 0.0001); B lymphocyte count ([91-213] vs [54.5-163.5], P = 0.0001); white blood cell count ([3.9-7.6] vs [5.5-13.6], P = 0.0002); D2 dimer ([172-836] vs [408-953], P = 0.005), PCT ([0.03-0.07] vs [0.04-0.15], P = 0.0039); CRP ([3.8-27.9] vs [17.3-58.9], P < 0.0001); AST ([16, 29] vs [18, 42], P = 0.0484); artificial liver therapy (2% vs 16.3%, P = 0.0148); and glucocorticoid therapy (64.7% vs 98%, P < 0.0001) were associated with the severity of the disease. Age and weight were independent risk factors for disease severity.
CONCLUSION: Deterioration among COVID-19-infected patients occurred rapidly after hospital admission. In our cohort, we found that multiple factors were associated with the severity of COVID19. Early detection and monitoring of these indicators may reduce the progression of the disease. Removing these factors may halt the progression of the disease. In addition, Oxygen support, early treatment with low doses of glucocorticoids and artificial liver therapy, when necessary, may help reduce mortality in critically ill patients.

Entities:  

Keywords:  Coronavirus disease 2019 (COVID-19); Predictors; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Severe illnesses

Mesh:

Substances:

Year:  2020        PMID: 33302889      PMCID: PMC7726595          DOI: 10.1186/s12879-020-05682-4

Source DB:  PubMed          Journal:  BMC Infect Dis        ISSN: 1471-2334            Impact factor:   3.090


Background

In December 2019, a new type of coronavirus disease (COVID-19) occurred in Wuhan, China, which was caused by a novel enveloped RNA betacoronavirus 2 [1]. Due to its phylogenetic similarity to severe acute respiratory syndrome coronavirus (SARS-CoV), COVID-19 was also named SARS-CoV-2 [2]. SARS-CoV-2 infection has become a public health emergency of international concern since it has rapidly spread within China and in several other countries. As of 1 pm 19 February 2020, a total of 74,280 cases of COVID-19 were confirmed in China, with a total of 2008 deaths [3]. The latest mortality was approximately 2.3% [4]. The infection is highly contagious and can be transmitted from person to person, leading to cluster transmission in families [5]. However, Clinical manifestations of SARS-CoV-2 range from asymptomatic to severe acute respiratory syndrome [1]. Due to these unknown factors, there is currently no specific treatment for SARS-CoV-2, and the risk of death for severely ill patients is very high. Early detection of severe cases can reduce mortality. In this paper, a retrospective analysis of inpatients diagnosed with SARS-CoV-2 in Hangzhou, Zhejiang, China, over the period from January 19, 2020, to February 19, 2020, was performed to explore the relevant factors that may predict the risk factors for disease severity.

Method

Study design and participants

We included in the study all patients diagnosed with SARS-CoV-2 who were admitted to the First Affiliated Hospital of Zhejiang University School of Medicine between January 19, 2020, and February 19, 2020. This study was approved by the ethics review committee of the First Affiliated Hospital of Zhejiang University and conforms to the code of ethics of the Helsinki declaration in 2013. The degree of severity of SARS-CoV-2 (severe vs non-severe) at admission was assessed using the latest guidelines for SARS-CoV-2 diagnosis from the National Health Commission of China [6]. As outlined in the guidelines, patients were classified into four types: mild, moderate, severe, and critical illness. Mild cases included patients with mild clinical symptoms and no signs of pneumonia on imaging. Moderate cases included patients with fever, respiratory symptoms, and imaging manifestations of pneumonia. Severe cases included patients who met one of the following criteria: respiratory rate ≥ 30 breaths per minute; arterial oxygen saturation (SaO2) ≦ 93% at rest; or partial pressure of oxygen (PaO2)/ Fraction of Inspiration O2 (FiO2) ≦300 mmHg. Critical illness cases included patients who met any of the following criteria: respiratory failure requiring mechanical ventilation, occurrence of shock, and complications with other organ failure requiring care in an intensive care unit. Mild and moderate cases were categorized as non-severe cases, while severe and critical illness cases were defined as severe cases in this study.

Cytokine and lymphocyte measurement

Plasma cytokines (IL6, IL10) were measured in all patients using the Human Cytokine standard 27-plex assay panel and the Bio-PLEX 200 system (Bio-RAD, Hercules, CA, USA) according to the manufacturer’s instructions. Lymphocyte subset counts in peripheral blood [CD45+, CD3+ CD4+, CD3-CD19+ and CD3-CD16+ CD56+]were measured by flow cytometry in accordance with the manufacturer’s protocol. (BD FACS Calibur, USA). They were completed by the laboratory department of our hospital and the State Key Laboratory of Diagnosis and Treatment of infectious diseases.

Data collection

Data were collected from electronic medical records for the following parameters: demographics, underlying medical comorbidities, clinical symptoms and signs, laboratory tests, treatment. Laboratory tests consisted of IL-6, IL-10, lymphocyte subset count, white blood cell count, D2 dimer, PCT, CRP, and AST. Underlying medical comorbidities mainly included hypertension, diabetes and coronary heart disease. Treatment included antiviral, ambroxol, glucocorticoid, and artificial liver therapy.

SARS-CoV-2 RT-PCR test

Respiratory tract samples, such as nasopharyngeal, sputum, or throat swab samples, from all included patients were collected daily (Nasopharyngeal or throat swab sampling is less than 10% of all samples). Using SARS-CoV-2 nucleic acid detection kits (Shanghai Biogerm Medical Technology Co Ltd), we detected SARS-CoV-2 viral RNA by polymerase chain reaction analysis in accordance with the manufacturer’s protocol. The diagnosis was confirmed by the criteria recommended by the National Institute for Viral Disease Control and Prevention (China). A viral cycle threshold (CT) value less than 37 was defined as positive, and a CT value greater than 40 was defined as negative. A CT value between 37 and 40 was confirmed by retesting.

Statistics analyses

Continuous variables are presented as the median (Interquartile range IQR) and were compared by the Kruskal-Wallis test between the non-severe group and the severe group. Categorical variables were presented as numbers (%) and compared by the chi-square (χ2) test or Fisher’s exact test. The Kaplan-Meier (product-limit) method was used to evaluate the time periods from glucocorticoid treatment and artificial liver therapy to different clinical outcomes between the two groups. All statistical analyses were performed with SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). The significance level of the hypotheses tests was set at 0.05 (two-sided).

Patient and public participation

This was a retrospective cohort study in which the patients were not involved in the design of the study, the setting of the study questions, or the direct measurement of the results.

Result

Patient characteristics on admission

Of the 100 hospitalized SARS-CoV-2 patients between January 19, 2020, and February 19, 2020, 49 (49%) patients were critically ill. The demographic and clinical characteristics are shown in Table 1. The median age was 54 years. The age (median) of the severe group was significantly higher than that of the non-severe group (61 [51-70] vs 50 [36-58]; P = 0.0001]). Sixty-three percent of the patients were older than 50 years of age. A total of 63% of patients were male. In the severe group, the proportion of males was significantly higher than that of females (77.6% vs 49%; P = 0.0031). Overweight (BMI > 24) patients were common in both groups of COVID-19 patients. BMI was significantly higher in the severely ill group (24.44 (23.28–27.01) vs 24 (21.53–25.51); P = 0.0339). Underlying medical conditions such as hypertension (37%) and diabetes mellitus (11%) were the most common coexisting illnesses among SARS-CoV-2 patients. Moreover, the presence of hypertension was more common among patients with severe disease (57.1% vs 17.6; P < 0.0001). Fever was present in 71% of our patients, and there was no significant difference in fever between the two groups. In our study, the vast majority of patients (98%) had obvious pulmonary CT lesions. Especially in severe patients, pulmonary computed tomography (CT) lesions of different degrees were observed on admission, including patchy, nodular ground glass shadows and interstitial abnormalities.
Table 1

Clinical Characteristics of Patients With SARS-CoV-2 Infection

CharacteristicTotal (N = 100)Non-Severity (n = 51)Severity (n = 49)P Value*
Demographics, n (%)
 Age, median (IQR), y54 (42–64)50 (36–58)61 (51–70)0.0001
 ≥ 5063 (63)26 (51)37 (75.5)0.0111
 Male sex63 (63)25 (49)38 (77.6)0.0031
 BMI24.27 (22.14–25.97)24.01 (21.53–25.51)24.44 (23.28–27.01)0.0339
Mainly underlying conditions, n (%)
 Hypertension37 (37)9 (17.6)28 (57.1)< 0.0001
 Diabetes mellitus11 (11)3 (5.9)8 (16.3)0.3574
 Cardiac disease4 (4)1 (2)3 (6)0.118
Fever on admission-°C
 < 37.5 °C29 (29)19 (37.3)10 (20.4)0.0635
Fever during hospitalization
 Median highest temperature (IQR)38.25 (37.5–39)38.2 (37.6–39)38.3 (37.5–39)0.9669
Radiologic findings on admission
chest CT — no./total no. (%)
 Normal2 (2)2 (3.9)00.4952
Initial laboratory findings, median (IQR)
 WBC, 109/L6.4 (4.1–10.8)5.15 (3.9–7.6)9.1 (5.5–13.6)0.0002
 d-dimer452.5 (240–857.5)339 (172–836)604 (408–953)0.005
 ALT22 (15–40.5)20 (13–41)24 (15–40)0.2801
 AST20.5 (16–34)20 (16–29)25 (18–42)0.0484
 CRP24.665 (7.785–47.6)10.9 (3.8–27.9)39.78 (17.3–58.9)0.0001
 PCT0.06 (0.03–0.1)0.04 (0.03–0.07)0.07 (0.04–0.15)0.0039
 IL20.95 (0.855–1.82)0.95 (0.78–1.83)0.95 (0.95–1.81)0.5534
 IL620.425 (8.78–56.04)12.52 (6.42–30.46)38.22 (16.2–81.71)0.0001
 IL104.775 (2.91–8.03)3.45 (2.16–5.82)6.93 (4.35–9.63)< 0.0001
 TNF-a17.98 (10.945–59.085)23.29 (12.2–65.49)12.22 (10.77–36.44)0.1658
 IFN-r10.06 (5.12–33.56)11.4 (4.92–39.71)9.82 (5.19–28.57)0.6943
 ALC703 (402–1119)1119 (620–1554)543.5 (367.5–810.5)0.0002
 Total T cells348 (233–775)752 (305–1178)276.5 (167.5–440)0.0001
 Th cells176 (79–431)376 (176–618)115 (60.5–214.5)< 0.0001
 B cells115 (70–211)167 (91–213)93.5 (54.5–163.5)0.0189
 NK cells105 (64–183)154 (70–207)101 (61.5–155.5)0.1201

Abbreviation: IQR Inter quartile range, BMI Body mass index, WBC White blood count, ALT Aspartate aminotransferase, AST Alanine aminotransferase, CRP C-reactive protein, PCT Procalcitonin, IL Interleukin, TNF-a Tumor necrosis factor a, IFN Interferon, Th T helper cells, ALC Absolute lymphocyte count, NK cells Natural killer cells

*, Chi-square (χ2) test or Fisher’s exact test was used with P < 0.05 as significant

Clinical Characteristics of Patients With SARS-CoV-2 Infection Abbreviation: IQR Inter quartile range, BMI Body mass index, WBC White blood count, ALT Aspartate aminotransferase, AST Alanine aminotransferase, CRP C-reactive protein, PCT Procalcitonin, IL Interleukin, TNF-a Tumor necrosis factor a, IFN Interferon, Th T helper cells, ALC Absolute lymphocyte count, NK cells Natural killer cells *, Chi-square (χ2) test or Fisher’s exact test was used with P < 0.05 as significant

Laboratory tests

Among laboratory indicators at admission, the white blood cell count ([3.9–7.6] vs [5.5–13.6], P = 0.0002), D-dimer ([172-836] vs [408-953], P = 0. 005), C-reactive protein ([3.8–27.9] vs [17.3–58.9], P < 0.0001), procalcitonin ([0.03–0.07] vs [0.04–0.15], P = 0.0039) and alanine aminotransferase ([16, 29] vs [18, 42], P < 0.0484) in the non-severe group were significantly lower than those in the severe group. In our study, we mainly focused on the laboratory detection of cytokines and immune cell subsets in patients with SARS-CoV-2 infection. We found that the expression of IL-6 in severe patients was significantly higher than that in the non-severe group ([6.42–30.46] vs [16.2–81.71], P = 0.0001), while the expression of IL-10 in severe patients was significantly lower than that in the non-severe group ([2.16–5.82] vs [4.35–9.63], P < 0.0001). The total number of T cells ([305-1178] vs [167.5–440], P < 0.0001), B cells ([91-213] vs [54.5–163.5], P = 0.0189), absolute number of lymphocytes ([620-1554] vs [367.5–810.5], P = 0.0002), Th ([176-618] vs [60.5–214.5], P < 0.0001) and Ts/Tc cells ([137–443)] vs [69–140.5], P < 0.0001) were significantly lower than those in non-severe cases.

Treatment and clinical outcomes

The treatment and clinical outcomes of COVID-19 patients are shown in Table 2. All 100 patients received antiviral treatment including lopinavir/ritonavir, interferon-α, darunavir/cobicistat, favipiravir and arbidol. The median time from symptom onset to antiviral regimen administration was 7 (4–9.5) days, with no significant difference between the non-severe and severe groups. The median time from antiviral regimen administration to negative results for SARS-CoV-2 RNA for the first time was 9 (5–14) days. Moreover, the time in the non-severe group was significantly shorter than that in the severe group (6 (4–12) vs 9 (7–15) days; P = 0.0142). The median duration from antiviral treatment to the time of a negative viral testing result was 10 (6–15) days, and the duration of the non-severe group was significantly shorter than that of the severe group (7 (4–13) vs 12 (9–18); P = 0.0006). In addition, our patients received supportive symptomatic treatments, including oxygen, glucocorticoid, ambroxol, antibiotic, and artificial liver therapy. Glucocorticoids were given to 80 (80%) patients, and the proportion was significantly higher in the severe group than in the non-severe group (98% vs 64.7%; P < 0.0001). Meanwhile, the maximum dosage of glucocorticoids in the severe group was significantly higher than that in the non-severe group (40[40-80] vs 40 [0-40]; P = 0.0001). Compared with the non-severe group, the severe group had a higher rate of antibiotic treatment (44.9% vs 3.9%; P < .0001). In the retrospective analysis, we found that the clinical outcome of our patients was very good. No patients died, and 86% of the patients recovered and were discharged from the hospital. The discharge rate of the non-severe group was significantly higher than that of the severe group (96.1% vs 75.5%; P = 0.003). The Intensive Care Unit (ICU) admission rate was 23%, which was higher in the severe group (42.9% vs 3.9%; P < 0.0001).
Table 2

Comparison of treatment responses and clinical outcomes of patients infected with SARS-CoV-2 between non-severe and severe group

VariableTotal (N = 100)Non-Severity (n = 51)Severity (n = 49)P Value
Treatments n (%)
Antivirus treatment100 (100)51 (100)49 (100)
median (IQR), days
Time from illness onset to Antivirus start7 (4–9.5)6 (3–9)7 (5–10)0.2948
Glucocorticoid treatment n (%)81 (81)33 (64.7)48 (98)< 0.0001
Maximum dosage80 (20–80)80 (0–40)80 (40–80)0.0001
(equivalent methylprednisolone), median (IQR), mg/d
Artificial liver support n (%)9 (9)1 (2)8 (16.3)0.0148
Antibiotic treatment n (%)24 (24)2 (3.9)22 (44.9)< 0.0001
Oxygen support< 0.0001
 Nasal cannular64 (64)46 (90.2)18 (36.7)
 high-flow nasal cannula13 (13)3 (5.9)10 (20.4)
 Invasive mechanical ventilation23 (23)2 (3.9)21 (42.9)
median (IQR), days
ΑAT to first virologic conversion9 (5–14)6 (4–12)9 (7–15)0.0142
 AT to stable virologic conversion10 (6–15)7 (4–13)12 (9–18)0.0006
 AT to radiologic recovery7 (4–10)7 (5–11)6.5 (4–9.5)0.3162
 AT to temperature recovery5 (2–8)3 (2–7)6.5 (2–9)0.0903
Clinical outcomes, n (%)
 Discharge from hospital86 (86)49 (96.1)37 (75.5)0.0030
ΒICU admission23 (23)2 (3.9)21 (42.9)< 0.0001
 Death0000

Abbreviation: ΑAT Antiviral therapy onset, ΒICU Intensive care unit

Comparison of treatment responses and clinical outcomes of patients infected with SARS-CoV-2 between non-severe and severe group Abbreviation: ΑAT Antiviral therapy onset, ΒICU Intensive care unit

Risk factors for SARS-CoV-2 severe illness

Using a multivariate logistic regression analysis, we identified the risk factors associated with exacerbation of SARS-CoV-2 (Table 3). Age and BMI were recognized as predictors (independent factors) of severe illness. However, sex, hypertension, IL-6, T lymphocyte count, B lymphocyte count, glucocorticoid treatment and artificial liver support were not recognized as independent factors.
Table 3

Multivariate Logistic Regression analysis of risk factor for disease severity among hospitalized patients with COVID19

VariableOdds Ratio (95% Confidence Interval)P Value
Age1.064 (1.007–1.124).027
BMI1.240 (1.006–1.528).044
IL61.005 (0.995–1.015).307
T cells1.003 (0.995–1.011).424
B cells1.005 (0.997–1.014).196
Artificial liver support0.985 (0.073–13.211).99
Multivariate Logistic Regression analysis of risk factor for disease severity among hospitalized patients with COVID19

Discussion

In December 2019, a new infectious disease, SARS-CoV-2, swept through Wuhan and quickly spread to all Chinese cities and dozens of countries overseas. Similar diseases are highly infectious and can spread through human-to-human transmission among close contacts and over a wide area [7, 8]. However, the source of SARS-CoV-2 is not fully clear, and the lack of specific treatments may cause the patient’s symptoms to progress from mild to severe or even result in death. Previous studies reported that the fatality rate among hospitalized SARS-CoV-2 patients was approximately 2.3%, which was much lower than those of SARS- and Middle East Respiratory Syndrome (MERS)-infected patients [4, 9]. For such patients, we need to remain vigilant and provide symptomatic supportive treatment early to reduce the occurrence of severe disease. In our retrospective cohort study, we included 100 SARS-CoV-2 patients from a single clinical centre in Hangzhou, Zhejiang Province. Our results showed that age and BMI were independent risk factors for severe illness. The median patient age was 54 years. Sixty-three percent of the patients were older than 50 years, which was consistent with multiple reports in the literature [10, 11]. Severe patients were much older than non-severe patients, which may be related to a lower immune response and a higher frequency of underlying conditions that are not beneficial for self-limited recovery after virus infection. In addition, we discovered that overweight (BMI > 24) was an important risk factor for severity in SARS-CoV-2 patients. Compared with the BMI of severe patients, non-severe patients had a lower BMI, with significant differences between the two groups (P = 0.0339). Similar to H7N9, the most common symptoms of SARS-CoV-2 patients were fever and cough [5, 12]. However, 26% of SARS-CoV-2 patients who were admitted to our hospital had a normal temperature. If these patients had been overlooked as having SARS-CoV-2, then more people might have been infected. This was similar to previous reports [13]. Coexisting disorders such as hypertension and diabetes mellitus were associated with severe illnesses. The proportions of hypertension and diabetes mellitus in severe illnesses were higher than those in non-severe cases. Furthermore, hypertension was significantly different between the two groups (P < 0.0001). This was consistent with H7N9 patients [14]. In addition, we also found that the laboratory parameters and supplementary treatment, including the white blood cell count, D-dimer, C-reactive protein, procalcitonin, alanine aminotransferase, IL-6, IL-10, CD3+ T-cell, CD4+ T-cell (CD3 + CD4+), CD8+ T-cell (CD3 + CD8+), B-cell (CD3 − CD19+) and NK-cell (CD3 − CD16 + CD56+), absolute lymphocyte number, and TS/Tc cells and the need for antibiotic, glucocorticoid and artificial liver therapy, were identified in single variable analysis as risk factors for severe illnesses but that these risk factors did not reach our criteria for significance in the multivariate analysis. Previous studies found that an inflammatory factor storm was one of the important factors for severe illness and even death of H7N9, SARS-CoV, MERS-CoV. The mechanism may be related to the overexpression of inflammatory factors and chemokines, which can lead to acute lung injury and ARDS [1, 15, 16]. In our retrospective study, we also found a significant increase in cytokine IL-6 in the patients with severe disease. Therefore, for patients with a gradual increase in the expression of such inflammatory factors, early low-dose glucocorticoid and artificial liver treatment may alleviate the progression of the disease and reduce the risk of death. The effectiveness and necessity of glucocorticoid use has been controversial in novel coronavirus infections. Large doses of glucocorticoids may cause significant side effects such as lymphocytopenia and femoral head necrosis [17]. In our cohort, the rates of treatments with glucocorticoids and artificial liver therapy in the severe group were greater than those in the non-severe group, and there were significant differences. However, there were no statistically significant differences in the treatment effects between the two groups (the days from treatment initiation to virus clearance) (supplementary figures 1–2), which may be related to our small sample size. Further research on larger cohorts is in progress. Our study found that the number of immune cells in most patients with mild disease is normal. The decrease in T lymphocytes, B lymphocytes and absolute lymphocyte counts are correlated with the severity of the disease. Therefore, early detection of related immune indicators may provide us with the ability to predict the severity of COVID19. There are several limitations to be considered when interpreting the findings. First, our study is a single-centre study of SARS-CoV-2 risk factors for critically ill patients in a hospital. Furthermore, when screening confirmed cases, the vast majority of tested samples were from lower respiratory tract specimens, but there were still a few pharyngeal swab specimens, and the pharyngeal swab false negative rate may have led to the lack of included patients. Finally, the number of children under 18 years in our sample was small, so no specific conclusions could be drawn for adolescents.

Conclusions

Patients with SARS-CoV-2 infection are likely to progress to critical illness. In our analysis, we found that pre-existing diseases and multiple laboratory indicators were associated with disease progression. Age and BMI may be independent risk factors for the development of severe SARS-CoV-2. Therefore, it is important to evaluate the severity of the newly diagnosed patients’ condition to provide individualized diagnosis and treatment and to improve the prognosis. Additional file 1.
  14 in total

1.  Comparison of patients hospitalized with influenza A subtypes H7N9, H5N1, and 2009 pandemic H1N1.

Authors:  Chen Wang; Hongjie Yu; Peter W Horby; Bin Cao; Peng Wu; Shigui Yang; Hainv Gao; Hui Li; Tim K Tsang; Qiaohong Liao; Zhancheng Gao; Dennis K M Ip; Hongyu Jia; Hui Jiang; Bo Liu; Michael Y Ni; Xiahong Dai; Fengfeng Liu; Nguyen Van Kinh; Nguyen Thanh Liem; Tran Tinh Hien; Yu Li; Juan Yang; Joseph T Wu; Yaming Zheng; Gabriel M Leung; Jeremy J Farrar; Benjamin J Cowling; Timothy M Uyeki; Lanjuan Li
Journal:  Clin Infect Dis       Date:  2014-01-31       Impact factor: 9.079

2.  Review of the Clinical Characteristics of Coronavirus Disease 2019 (COVID-19).

Authors:  Fang Jiang; Liehua Deng; Liangqing Zhang; Yin Cai; Chi Wai Cheung; Zhengyuan Xia
Journal:  J Gen Intern Med       Date:  2020-03-04       Impact factor: 5.128

3.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.

Authors:  Chaolin Huang; Yeming Wang; Xingwang Li; Lili Ren; Jianping Zhao; Yi Hu; Li Zhang; Guohui Fan; Jiuyang Xu; Xiaoying Gu; Zhenshun Cheng; Ting Yu; Jiaan Xia; Yuan Wei; Wenjuan Wu; Xuelei Xie; Wen Yin; Hui Li; Min Liu; Yan Xiao; Hong Gao; Li Guo; Jungang Xie; Guangfa Wang; Rongmeng Jiang; Zhancheng Gao; Qi Jin; Jianwei Wang; Bin Cao
Journal:  Lancet       Date:  2020-01-24       Impact factor: 79.321

4.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia.

Authors:  Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng
Journal:  N Engl J Med       Date:  2020-01-29       Impact factor: 176.079

5.  Expression of inflammation-related genes in the lung of BALB/c mice response to H7N9 influenza A virus with different pathogenicity.

Authors:  Meng Yu; Qingnan Wang; Wenbao Qi; Kaizhao Zhang; Jianxin Liu; Pan Tao; Shikun Ge; Ming Liao; Zhangyong Ning
Journal:  Med Microbiol Immunol       Date:  2016-07-11       Impact factor: 3.402

Review 6.  Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology.

Authors:  Rudragouda Channappanavar; Stanley Perlman
Journal:  Semin Immunopathol       Date:  2017-05-02       Impact factor: 9.623

7.  Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China.

Authors:  Zhiliang Hu; Ci Song; Chuanjun Xu; Guangfu Jin; Yaling Chen; Xin Xu; Hongxia Ma; Wei Chen; Yuan Lin; Yishan Zheng; Jianming Wang; Zhibin Hu; Yongxiang Yi; Hongbing Shen
Journal:  Sci China Life Sci       Date:  2020-03-04       Impact factor: 10.372

Review 8.  Characteristics of and Public Health Responses to the Coronavirus Disease 2019 Outbreak in China.

Authors:  Sheng-Qun Deng; Hong-Juan Peng
Journal:  J Clin Med       Date:  2020-02-20       Impact factor: 4.241

9.  A Novel Coronavirus from Patients with Pneumonia in China, 2019.

Authors:  Na Zhu; Dingyu Zhang; Wenling Wang; Xingwang Li; Bo Yang; Jingdong Song; Xiang Zhao; Baoying Huang; Weifeng Shi; Roujian Lu; Peihua Niu; Faxian Zhan; Xuejun Ma; Dayan Wang; Wenbo Xu; Guizhen Wu; George F Gao; Wenjie Tan
Journal:  N Engl J Med       Date:  2020-01-24       Impact factor: 91.245

10.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention.

Authors:  Zunyou Wu; Jennifer M McGoogan
Journal:  JAMA       Date:  2020-04-07       Impact factor: 56.272

View more
  9 in total

1.  Clinical Characteristics and Factors Associated with Disease Progression of Mild to Moderate COVID-19 Patients in a Makeshift (Fangcang) Hospital: A Retrospective Cohort Study.

Authors:  Jia Liu; Jun-Fei Zhang; Han-Ning Ma; Ke Feng; Zhong-Wei Chen; Li-Shan Yang; Bin Mei; Jun-Jian Zhang
Journal:  Ther Clin Risk Manag       Date:  2021-08-16       Impact factor: 2.423

2.  Rescuing emergency cases of COVID-19 patients: An intelligent real-time MSC transfusion framework based on multicriteria decision-making methods.

Authors:  M A Alsalem; O S Albahri; A A Zaidan; Jameel R Al-Obaidi; Alhamzah Alnoor; A H Alamoodi; A S Albahri; B B Zaidan; F M Jumaah
Journal:  Appl Intell (Dordr)       Date:  2022-01-08       Impact factor: 5.019

3.  A new screening tool for SARS-CoV-2 infection based on self-reported patient clinical characteristics: the COV19-ID score.

Authors:  Pablo Diaz Badial; Hugo Bothorel; Omar Kherad; Philippe Dussoix; Faustine Tallonneau Bory; Majd Ramlawi
Journal:  BMC Infect Dis       Date:  2022-02-24       Impact factor: 3.090

4.  Associations between Serum Interleukins (IL-1β, IL-2, IL-4, IL-6, IL-8, and IL-10) and Disease Severity of COVID-19: A Systematic Review and Meta-Analysis.

Authors:  Yuanmin Chang; Mengru Bai; Qinghai You
Journal:  Biomed Res Int       Date:  2022-04-30       Impact factor: 3.246

5.  T-Cell Subsets and Interleukin-10 Levels Are Predictors of Severity and Mortality in COVID-19: A Systematic Review and Meta-Analysis.

Authors:  Amal F Alshammary; Jawaher M Alsughayyir; Khalid K Alharbi; Abdulrahman M Al-Sulaiman; Haifa F Alshammary; Heba F Alshammary
Journal:  Front Med (Lausanne)       Date:  2022-04-28

Review 6.  The Association between TNF-α, IL-6, and Vitamin D Levels and COVID-19 Severity and Mortality: A Systematic Review and Meta-Analysis.

Authors:  Ceria Halim; Audrey Fabianisa Mirza; Mutiara Indah Sari
Journal:  Pathogens       Date:  2022-02-01

7.  Digit ratios and their asymmetries as risk factors of developmental instability and hospitalization for COVID-19.

Authors:  A Kasielska-Trojan; J T Manning; M Jabłkowski; J Białkowska-Warzecha; A L Hirschberg; B Antoszewski
Journal:  Sci Rep       Date:  2022-03-17       Impact factor: 4.996

8.  Risk factors and early prediction of clinical deterioration and mortality in adult COVID-19 inpatients: an Australian tertiary hospital experience.

Authors:  Rowena Brook; Hui Yin Lim; Prahlad Ho; Kay Weng Choy
Journal:  Intern Med J       Date:  2022-04       Impact factor: 2.048

9.  The use of neutrophil-to-lymphocyte ratio (NLR) as a marker for COVID-19 infection in Saudi Arabia: A case-control retrospective multicenter study.

Authors:  Anwar A Sayed; Assem A Allam; Ayman I Sayed; Mohammed A Alraey; Mercy V Joseph
Journal:  Saudi Med J       Date:  2021-04       Impact factor: 1.484

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.