Published online Aug 16, 2022. doi: 10.12998/wjcc.v10.i23.8161
Peer-review started: April 6, 2022
First decision: May 11, 2022
Revised: May 15, 2022
Accepted: July 11, 2022
Article in press: July 11, 2022
Published online: August 16, 2022
Processing time: 116 Days and 22.3 Hours
Coronavirus disease 2019 (COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60ºC, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction. The demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China with such climatic characteristics are still not clearly illustrated.
To illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases.
COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups.
Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Neutrophil percentage (NEUT%), platelet (PLT), C-reactive protein (CRP), creatine kinase isoenzyme (CK-MB), serum troponin I (TNI) and brain natriuretic peptides (BNP) showed significant differences among the groups (P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P < 0.001, respectively). The increase of CRP, D-dimer and NEUT% levels, as well as the decrease of lymphocyte count (LYMPH) and PLT counts, showed significant correlation with death of COVID-19 patients (P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively).
Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.
Core Tip: Early detection and intervention of coronavirus disease 2019 (COVID-19) patients with a higher risk of death will contribute to rationally allocate limited medical resources and reduce the case-fatality rate. Our results illustrated that some laboratory parameters, including neutrophil percentage (NEUT%), platelet (PLT), C-reactive protein (CRP), creatine kinase isoenzyme, serum troponin I and brain natriuretic peptides showed significant differences in COVID-19 deceased patients compared with surviving severe and critically ill cases. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower lymphocyte count and PLT counts. Our study added evidence to the notion that the pathogenesis of COVID-19 deceased patients was related to the superimposed bacterial or fungal infection, cellular immune deficiency, coagulation disorder, activation of inflammatory cytokine responses, and impaired organ function, which in turn could interact with each other, forming a complicated network.
- Citation: Wang L, Gao Y, Zhang ZJ, Pan CK, Wang Y, Zhu YC, Qi YP, Xie FJ, Du X, Li NN, Chen PF, Yue CS, Wu JH, Wang XT, Tang YJ, Lai QQ, Kang K. Comparison of demographic features and laboratory parameters between COVID-19 deceased patients and surviving severe and critically ill cases. World J Clin Cases 2022; 10(23): 8161-8169
- URL: https://www.wjgnet.com/2307-8960/full/v10/i23/8161.htm
- DOI: https://dx.doi.org/10.12998/wjcc.v10.i23.8161
Coronavirus disease 2019 (COVID-19) has been far more devastating than expected, showing no signs of slowing down at present. COVID-19 has led to more deaths than the sum of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome CoV infection. The case-fatality rate of COVID-19 as reported previously varied from 3.77% to 28% in Wuhan (epicenter area)[1-8], and this percentage was significantly higher than that in other non-epicenter areas of China[7,9]. The two consecutive outbreaks of the epidemic resulted in a total of 559 locally confirmed cases and 13 deceased patients in the Heilongjiang Province, China, with a crude case-fatality rate of about 2.3%, which is lower than the national average of 5.58% (4634/83027). This once again suggested that continuously enriching the management of COVID-19 and gradually alleviating the temporary shortage of public health capacity could effectively reduce the case-fatality rate[9], although its reasons might be manifold[7]. Studies on COVID-19 deceased patients were of great significance as they contribute to better understand the underlying pathogenesis of it, especially in other regions of China with different demographic characteristics, except Hubei Province.
Angiotensin converting enzyme 2 (ACE2) is the functional host receptor for SARS-CoV-2 and route of viral entry. It is mainly distributed in the alveolar epithelial type II cells[10,11], and so a high prevalence of pneumonia is observed in COVID-19 patients clinically rather than upper respiratory symptoms. ACE2 had stronger binding affinity with SARS-CoV-2 than SARS-CoV, and this might account for its greater pathogenicity[12]. Heilongjiang Province is the most northeastern province of China, and has cold weather for nearly half a year and an annual temperature difference of more than 60ºC, which increases the underlying morbidity associated with pulmonary diseases, and thus leads to lung dysfunction[13,14]. Chronic pulmonary disease plays an important role in predicting the in-hospital mortality in critically ill patients and even contributes to the case-fatality rate of COVID-19 patients[15,16]. What are the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province with such climatic characteristics remains a question.
To better address the above issue, the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province were compared with those of surviving severe and critically ill cases. This study was conducted in order to better understand the underlying pathogenesis of COVID-19 deceased patients, identify these patients as early as possible, guide clinical treatment regimens, and thus improve the clinical outcomes.
The COVID-19 deceased patients from different hospitals in Heilongjiang Province, China were included in this retrospective study and compared with the severe and critically ill cases who survived from the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were identified as severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters, including white blood cell count (WBC), neutrophil percentage (NEUT%), lymphocyte count (LYMPH), platelet (PLT) count, fibrinogen (FIB), D-Dimer, C-reaction protein (CRP), albumin (ALB), creatinine (CRE), creatine kinase isoenzyme (CK-MB), serum troponin I (TNI) and brain natriuretic peptides (BNP) levels were obtained from the medical records, and then were compared among the groups. This study was approved by the Ethics Committee of the First Affiliated Hospital of Harbin Medical University (IRB number: IRB-AF/SC-04).
Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. The respiratory samples of all enrolled COVID-19 patients were confirmed by SARS-CoV-2 nucleic acid detection. COVID-19 patients with incomplete medical records were excluded from the study.
Demographic data, including age, gender and number of comorbidities, and laboratory parameters, including WBC, NEUT%, LYMPH, PLT, FIB, D-dimer, CRP, ALB, CRE, CK-MB, TNI and BNP were collected and recorded from the medical records through dedicated personnel. The members of our research group were unaware of the patient's private information other than the data acquired for this study.
SPSS 22.0 (SPSS Inc., Chicago, IL, United States) was adopted for statistical analyses. Analysis of variance (ANOVA), χ2 test and Kruskal-Wallis rank sum test were employed for performing intergroup comparison of age, gender and number of comorbidities. Kruskal-Wallis rank sum test was used for intergroup comparison of CRP due to non-normal distribution, while one-way ANOVA was employed for intergroup comparison of other laboratory parameters with normal distribution. Pair-wise comparison was completed by least significance difference. Pearson correlation analysis was used to analyze the correlation between dynamic profile of laboratory parameters and death of COVID-19 patients. P values of < 0.05 were considered as statistically significant.
The ratio of COVID-19 deceased patients in men and women was 1:1, with a median age of 71.50 years. A quarter of these deceased patients demonstrated no comorbidities. COVID-19 deceased patients with 1, 2, 3, and 4 types of comorbidities accounted for 25.0%, 25.0%, 8.3% and 16.7% respectively. As shown in Table 1, there were no differences in age, gender, and number of comorbidities among the groups.
| COVID-19 deceased patients | Critically ill group | Severe group | F/χ2 | P value | |
| Age | 71.50 ± 10.41 | 63.78 ± 11.58 | 65.59 ± 11.75 | 1.978 | 0.147 |
| Gender | 0.053 | 0.974 | |||
| Female | 6 | 12 | 13 | ||
| Male | 6 | 14 | 14 | ||
| Number of comorbidities | 4.251 | 0.119 | |||
| 0 | 3 | 12 | 8 | ||
| 1 | 3 | 9 | 6 | ||
| 2 | 3 | 2 | 8 | ||
| 3 | 1 | 2 | 5 | ||
| 4 | 2 | 1 | 0 |
As shown in Table 2, laboratory parameters, including NEUT%, PLT, CRP, CK-MB, TNI and BNP showed significant differences among the groups (P = 0.020, P = 0.001, P < 0.001, P = 0.001, P < 0.001, P < 0.001, respectively), except for WBC, LYMPH, FIB, D-dimer, ALB and CRE (P = 0.131, P = 0.220, P = 0.809, P = 0.766, P = 0.306, P = 0.923, respectively).
| Laboratory parameters | COVID-19 deceased patients | Critically ill group | Severe group | F/χ2 | P value |
| WBC | 7.65 ± 6.62 | 7.58 ± 2.32 | 5.77 ± 2.32 | 2.103 | 0.131 |
| NEUT% | 75.35 ± 11.41 | 82.19 ± 10.25 | 72.62 ± 14.131 | 4.151 | 0.020 |
| LYMPH | 1.08 ± 0.98 | 0.72 ± 0.48 | 0.89 ± 0.48 | 1.554 | 0.220 |
| PLT | 141.62 ± 59.88 | 261.69 ± 110.421 | 238.04 ± 119.171 | 5.301 | 0.001 |
| FIB | 4.28 ± 2.01 | 4.68 ± 2.23 | 4.78 ± 1.96 | 0.212 | 0.809 |
| D-Dimer | 3.22 ± 5.98 | 6.43 ± 8.18 | 6.25 ± 16.49 | 0.268 | 0.766 |
| CRP | 107.20(147.11) | 31.15(44.10)1 | 24.37(32.65)1 | 15.846 | < 0.001 |
| ALB | 31.10 ± 4.49 | 29.07 ± 3.95 | 30.44 ± 3.99 | 1.208 | 0.306 |
| CRE | 61.44 ± 23.04 | 63.26 ± 47.38 | 66.26 ± 28.08 | 0.081 | 0.923 |
| CK-MB | 45.74 ± 67.48 | 7.77 ± 8.651 | 8.07 ± 6.441 | 7.941 | 0.001 |
| TNI | 1.32 ± 1.97 | 0.03 ± 0.031 | 0.01 ± 0.011 | 13.504 | < 0.001 |
| BNP | 575.50 ± 484.94 | 164.80 ± 225.641 | 63.04 ± 66.251 | 10.614 | < 0.001 |
The increase in CRP, D-dimer and NEUT% levels, as well as the decrease of LYMPH and PLT counts showed significant correlation with the death of COVID-19 patients (P = 0.023, P = 0.008, P = 0.045, P = 0.020, P = 0.015, respectively) (Table 3).
As a highly pathogenic human CoV, SARS-CoV-2 had unprecedented pathogenicity and complex clinical manifestations that range from asymptomatic infection to fatal pneumonia. In China, about 15%-30% confirmed COVID-19 patients developed into severe and critically ill cases, usually presenting with acute respiratory distress syndrome and requiring some form of ventilatory support[1,2,17,18]. The case-fatality rate of critically ill patients with COVID-19 even exceeded 60%[19]. At present, the number of COVID-19 deceased patients worldwide has exceeded six million without any sign of slowing down. Moreover, the absence of available specific medications for treating COVID-19 was a clinical reality. Therefore, there is an urgent need to understand the demographic features and laboratory parameters of COVID-19 deceased patients in clinical practice so as to identify and intervene in the early stage and thus improve the clinical outcomes, and explore the underlying pathogenesis by comparing with those of surviving severe and critically ill cases.
At present, most of the studies on COVID-19 deceased patients in China were concentrated in Wuhan but lacked in other regions. Different generations of SARS-CoV-2 infection in patients with different demographic characteristics have inevitably led to different clinical characteristics[20]. Heilongjiang Province has unique climatic characteristics that affect lung function and the morbidity associated with respiratory diseases. The two consecutive outbreaks of COVID-19 in Heilongjiang Province were related to secondary or tertiary transmission of imported cases from Wuhan and the United States[21]. The question is that whether COVID-19 deceased patients caused by secondary or tertiary transmission of imported cases in Heilongjiang Province have special demographic features and laboratory parameters?
In our study, COVID-19 deceased patients in Heilongjiang Province included men and women in 1:1 ratio with a median age of 71.50 years. Contrary to the results of other studies, no differences were observed in age, gender, and number of comorbidities in COVID-19 deceased patients when compared to surviving severe and critically ill cases. The primary reason for this is that only COVID-19 deceased patients, and surviving severe and critically ill cases were collected in our study, lacking asymptomatic, mild, and moderate cases. We believed that comparing asymptomatic, mild, and moderate cases with COVID-19 deceased patients would expand the clinical characteristics that were associated with poor outcomes and confuse the true facts. COVID-19 patients included in our study were significantly older than those reported in other studies[3,22-24], and this might be a reason partly.
It has been widely accepted that SARS-CoV-2 infection causes a decrease in the absolute number of lymphocyte count, especially in severe and critically ill cases, and deceased patients[1,6,16,24,25]. The inhibited and delayed interferon (IFN) response signaling induced by SARS-CoV infection sensitized T cells to apoptosis via tumor necrosis factor-mediated pathway[26]. Furthermore, IFN weakens the T cell responses by up-regulating the expression of negative immune regulatory molecules[27]. It is speculated that due to high degree of homology, the mechanism on destruction of lymphocytes by SARS-CoV-2, as a similarly enveloped RNA virus, is known to be involved, but further studies are needed to confirm these. Therefore, a dynamic decrease in lymphocyte count is considered as an important sign of cellular immune deficiency and an indicator for disease progression[28]. As a prototypical acute phase serum protein, CRP is rapidly elevated in excessive host inflammatory response to virus invasion, becoming a useful marker for the severity of inflammatory response[29]. Complications from hypercoagulability induced by COVID-19 have been reported recently[30,31]. Due to wide distribution of ACE2 receptors in multiple organs[11], SARS-CoV-2 infection could cause multiple organ dysfunction[19,24,28,32], including heart damage in our results.
The abnormalities in the levels of NEUT%, LYMPH, D-dimer, PLT, CRP, CK-MB, TNI and BNP usually indicated superimposed bacterial or fungal infection, cellular immune deficiency, coagulation disorder, activation of inflammatory cytokine responses, and impaired cardiac function. Close monitoring of the dynamic profile of the above laboratory parameters is considered essential for identifying COVID-19 patients who are at risk of poor outcomes in time. Our study added evidence to the notion that the pathogenesis of COVID-19 deceased patients was related to the superimposed bacterial or fungal infection, cellular immune deficiency, coagulation disorder, activation of inflammatory cytokine responses, and impaired organ function[33], which in turn could interact with each other, forming a complicated network.
However, there are several limitations in our study. Firstly, retrospective study with small sample size decreases the credibility of our conclusion, and should be further verified in larger sample size in the near future. Secondly, interventions to COVID-19 deceased patients from different hospitals in Heilongjiang Province are uneven, which might have impact on the results of our study. Thirdly, no further analysis of specific comorbidities was performed because of small sample size. Finally, the observational indicators included in our study are limited to demographic features and laboratory parameters, and lacked more comprehensive and in-depth indexes that reveal the pathogenesis of COVID-19 deceased patients.
In summary, the crude case-fatality rate of COVID-19 in Heilongjiang Province, which is the most northeastern province in China, was 2.3%. Our study added evidence to the notion that the pathogenesis of COVID-19 deceased patients was related to the superimposed bacterial or fungal infection, cellular immune deficiency, coagulation disorder, activation of inflammatory cytokine responses, and impaired organ function, which in turn could interact with each other, forming a complicated network. Further clinical or animal trials should focus on identification of specific pathogenesis after SARS-CoV-2 invasion.
The coronavirus disease 2019 (COVID-19) has been far more devastating than expected, however, the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province, China are still not clearly illustrated.
This study was conducted in order to better understand the underlying pathogenesis of COVID-19 deceased patients, identify these patients as early as possible, guide clinical treatment regimens, and thus improve the clinical outcomes.
In this study, we aimed to illustrate the demographic features and laboratory parameters of COVID-19 deceased patients in Heilongjiang Province by comparing with those of surviving severe and critically ill cases.
COVID-19 deceased patients from different hospitals in Heilongjiang Province were included in this retrospective study and compared their characteristics with those of surviving severe and critically ill cases in the COVID-19 treatment center of the First Affiliated Hospital of Harbin Medical University. The surviving patients were divided into severe group and critically ill group according to the Diagnosis and Treatment of New Coronavirus Pneumonia (the seventh edition). Demographic data were collected and recorded upon admission. Laboratory parameters were obtained from the medical records, and then compared among the groups.
Twelve COVID-19 deceased patients, 27 severe cases and 26 critically ill cases were enrolled in this retrospective study. No differences in age, gender, and number of comorbidities between groups were found. Some laboratory parameters showed significant differences among the groups. The increase of C-reactive protein (CRP), D-dimer and neutrophil percentage (NEUT%) levels, as well as the decrease of lymphocyte count (LYMPH) and platelet (PLT) counts, showed significant correlation with death of COVID-19 patients.
Compared with surviving severe and critically ill cases, no special demographic features of COVID-19 deceased patients were observed, while some laboratory parameters showed significant differences. COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.
COVID-19 deceased patients had higher CRP, D-dimer and NEUT% levels and lower LYMPH and PLT counts.
We are grateful to all colleagues who worked with us in the COVID-19 treatment center of Heilongjiang Province, and all those who provided selfless advice and help for this article. We pay tribute to the medical staff who lost their lives in the national fight against the COVID-19 epidemic.
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Critical care medicine
Country/Territory of origin: China
Peer-review report’s scientific quality classification
Grade A (Excellent): 0
Grade B (Very good): B, B
Grade C (Good): 0
Grade D (Fair): 0
Grade E (Poor): 0
P-Reviewer: Aguiar P, Spain; Vaquero L, Spain S-Editor: Wang JL L-Editor: A P-Editor: Wang JL
| 1. | Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395:497-506. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 32663] [Cited by in F6Publishing: 29334] [Article Influence: 7333.5] [Reference Citation Analysis (3)] |
| 2. | Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507-513. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 13871] [Cited by in F6Publishing: 12716] [Article Influence: 3179.0] [Reference Citation Analysis (1)] |
| 3. | Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395:1054-1062. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 17476] [Cited by in F6Publishing: 17738] [Article Influence: 4434.5] [Reference Citation Analysis (0)] |
| 4. | Zhang J, Wang X, Jia X, Li J, Hu K, Chen G, Wei J, Gong Z, Zhou C, Yu H, Yu M, Lei H, Cheng F, Zhang B, Xu Y, Wang G, Dong W. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect. 2020;26:767-772. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 358] [Cited by in F6Publishing: 388] [Article Influence: 97.0] [Reference Citation Analysis (0)] |
| 5. | Wang K, Zuo P, Liu Y, Zhang M, Zhao X, Xie S, Zhang H, Chen X, Liu C. Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China. Clin Infect Dis. 2020;71:2079-2088. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 146] [Cited by in F6Publishing: 191] [Article Influence: 47.8] [Reference Citation Analysis (0)] |
| 6. | Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, Zhao Y, Li Y, Wang X, Peng Z. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020;323:1061-1069. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 14113] [Cited by in F6Publishing: 14489] [Article Influence: 3622.3] [Reference Citation Analysis (0)] |
| 7. | Liang WH, Guan WJ, Li CC, Li YM, Liang HR, Zhao Y, Liu XQ, Sang L, Chen RC, Tang CL, Wang T, Wang W, He QH, Chen ZS, Wong SS, Zanin M, Liu J, Xu X, Huang J, Li JF, Ou LM, Cheng B, Xiong S, Xie ZH, Ni ZY, Hu Y, Liu L, Shan H, Lei CL, Peng YX, Wei L, Liu Y, Hu YH, Peng P, Wang JM, Liu JY, Chen Z, Li G, Zheng ZJ, Qiu SQ, Luo J, Ye CJ, Zhu SY, Cheng LL, Ye F, Li SY, Zheng JP, Zhang NF, Zhong NS, He JX. Clinical characteristics and outcomes of hospitalised patients with COVID-19 treated in Hubei (epicentre) and outside Hubei (non-epicentre): a nationwide analysis of China. Eur Respir J. 2020;55. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 209] [Cited by in F6Publishing: 190] [Article Influence: 47.5] [Reference Citation Analysis (0)] |
| 8. | Cao J, Hu X, Cheng W, Yu L, Tu WJ, Liu Q. Clinical features and short-term outcomes of 18 patients with corona virus disease 2019 in intensive care unit. Intensive Care Med. 2020;46:851-853. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 79] [Cited by in F6Publishing: 68] [Article Influence: 17.0] [Reference Citation Analysis (0)] |
| 9. | Sun Q, Qiu H, Huang M, Yang Y. Lower mortality of COVID-19 by early recognition and intervention: experience from Jiangsu Province. Ann Intensive Care. 2020;10:33. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 239] [Cited by in F6Publishing: 247] [Article Influence: 61.8] [Reference Citation Analysis (0)] |
| 10. | Zhang H, Penninger JM, Li Y, Zhong N, Slutsky AS. Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target. Intensive Care Med. 2020;46:586-590. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1522] [Cited by in F6Publishing: 1673] [Article Influence: 418.3] [Reference Citation Analysis (0)] |
| 11. | Zhou P, Yang XL, Wang XG, Hu B, Zhang L, Zhang W, Si HR, Zhu Y, Li B, Huang CL, Chen HD, Chen J, Luo Y, Guo H, Jiang RD, Liu MQ, Chen Y, Shen XR, Wang X, Zheng XS, Zhao K, Chen QJ, Deng F, Liu LL, Yan B, Zhan FX, Wang YY, Xiao GF, Shi ZL. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579:270-273. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 15248] [Cited by in F6Publishing: 13648] [Article Influence: 3412.0] [Reference Citation Analysis (0)] |
| 12. | Gheblawi M, Wang K, Viveiros A, Nguyen Q, Zhong JC, Turner AJ, Raizada MK, Grant MB, Oudit GY. Angiotensin-Converting Enzyme 2: SARS-CoV-2 Receptor and Regulator of the Renin-Angiotensin System: Celebrating the 20th Anniversary of the Discovery of ACE2. Circ Res. 2020;126:1456-1474. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1325] [Cited by in F6Publishing: 1323] [Article Influence: 330.8] [Reference Citation Analysis (0)] |
| 13. | Lin Z, Gu Y, Liu C, Song Y, Bai C, Chen R, Chen S, Kan H. Effects of ambient temperature on lung function in patients with chronic obstructive pulmonary disease: A time-series panel study. Sci Total Environ. 2018;619-620:360-365. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 21] [Cited by in F6Publishing: 13] [Article Influence: 2.2] [Reference Citation Analysis (0)] |
| 14. | Hansel NN, McCormack MC, Kim V. The Effects of Air Pollution and Temperature on COPD. COPD. 2016;13:372-379. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 157] [Cited by in F6Publishing: 153] [Article Influence: 19.1] [Reference Citation Analysis (0)] |
| 15. | Shi Q, Zhang X, Jiang F, Hu N, Bimu C, Feng J, Yan S, Guan Y, Xu D, He G, Chen C, Xiong X, Liu L, Li H, Tao J, Peng Z, Wang W. Clinical Characteristics and Risk Factors for Mortality of COVID-19 Patients With Diabetes in Wuhan, China: A Two-Center, Retrospective Study. Diabetes Care. 2020;43:1382-1391. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 244] [Cited by in F6Publishing: 276] [Article Influence: 69.0] [Reference Citation Analysis (0)] |
| 16. | Sun H, Ning R, Tao Y, Yu C, Deng X, Zhao C, Meng S, Tang F, Xu D. Risk Factors for Mortality in 244 Older Adults With COVID-19 in Wuhan, China: A Retrospective Study. J Am Geriatr Soc. 2020;68:E19-E23. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 133] [Cited by in F6Publishing: 125] [Article Influence: 31.3] [Reference Citation Analysis (0)] |
| 17. | Qiu H, Tong Z, Ma P, Hu M, Peng Z, Wu W, Du B; China Critical Care Clinical Trials Group (CCCCTG). Intensive care during the coronavirus epidemic. Intensive Care Med. 2020;46:576-578. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 100] [Cited by in F6Publishing: 116] [Article Influence: 29.0] [Reference Citation Analysis (0)] |
| 18. | Wu Z, McGoogan JM. 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. JAMA. 2020;323:1239-1242. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 11409] [Cited by in F6Publishing: 11270] [Article Influence: 2817.5] [Reference Citation Analysis (0)] |
| 19. | Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, Yu T, Wang Y, Pan S, Zou X, Yuan S, Shang Y. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 2020;8:475-481. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 6231] [Cited by in F6Publishing: 6523] [Article Influence: 1630.8] [Reference Citation Analysis (0)] |
| 20. | Xu XW, Wu XX, Jiang XG, Xu KJ, Ying LJ, Ma CL, Li SB, Wang HY, Zhang S, Gao HN, Sheng JF, Cai HL, Qiu YQ, Li LJ. Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series. BMJ. 2020;368:m606. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1221] [Cited by in F6Publishing: 1247] [Article Influence: 311.8] [Reference Citation Analysis (0)] |
| 21. | Chen Q, Gao Y, Wang CS, Kang K, Yu H, Zhao MY, Yu KJ. Exploration of transmission chain and prevention of the recurrence of coronavirus disease 2019 in Heilongjiang Province due to in-hospital transmission. World J Clin Cases. 2021;9:5420-5426. [PubMed] [DOI] [Cited in This Article: ] [Cited by in CrossRef: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis (0)] |
| 22. | Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, Huang H, Zhang L, Du C, Zhang Y, Song J, Wang S, Chao Y, Yang Z, Xu J, Chen D, Xiong W, Xu L, Zhou F, Jiang J, Bai C, Zheng J, Song Y. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020;180:934-943. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 4960] [Cited by in F6Publishing: 5373] [Article Influence: 1343.3] [Reference Citation Analysis (1)] |
| 23. | Giacomelli A, Ridolfo AL, Milazzo L, Oreni L, Bernacchia D, Siano M, Bonazzetti C, Covizzi A, Schiuma M, Passerini M, Piscaglia M, Coen M, Gubertini G, Rizzardini G, Cogliati C, Brambilla AM, Colombo R, Castelli A, Rech R, Riva A, Torre A, Meroni L, Rusconi S, Antinori S, Galli M. 30-day mortality in patients hospitalized with COVID-19 during the first wave of the Italian epidemic: A prospective cohort study. Pharmacol Res. 2020;158:104931. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 194] [Cited by in F6Publishing: 176] [Article Influence: 44.0] [Reference Citation Analysis (0)] |
| 24. | Deng Y, Liu W, Liu K, Fang YY, Shang J, Zhou L, Wang K, Leng F, Wei S, Chen L, Liu HG. Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 in Wuhan, China: a retrospective study. Chin Med J (Engl). 2020;133:1261-1267. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 407] [Cited by in F6Publishing: 434] [Article Influence: 108.5] [Reference Citation Analysis (0)] |
| 25. | Sun Y, Dong Y, Wang L, Xie H, Li B, Chang C, Wang FS. Characteristics and prognostic factors of disease severity in patients with COVID-19: The Beijing experience. J Autoimmun. 2020;112:102473. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 137] [Cited by in F6Publishing: 153] [Article Influence: 38.3] [Reference Citation Analysis (0)] |
| 26. | Channappanavar R, Fehr AR, Vijay R, Mack M, Zhao J, Meyerholz DK, Perlman S. Dysregulated Type I Interferon and Inflammatory Monocyte-Macrophage Responses Cause Lethal Pneumonia in SARS-CoV-Infected Mice. Cell Host Microbe. 2016;19:181-193. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1056] [Cited by in F6Publishing: 1119] [Article Influence: 139.9] [Reference Citation Analysis (0)] |
| 27. | Teijaro JR, Ng C, Lee AM, Sullivan BM, Sheehan KC, Welch M, Schreiber RD, de la Torre JC, Oldstone MB. Persistent LCMV infection is controlled by blockade of type I interferon signaling. Science. 2013;340:207-211. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 618] [Cited by in F6Publishing: 588] [Article Influence: 53.5] [Reference Citation Analysis (0)] |
| 28. | Li X, Wang L, Yan S, Yang F, Xiang L, Zhu J, Shen B, Gong Z. Clinical characteristics of 25 death cases with COVID-19: A retrospective review of medical records in a single medical center, Wuhan, China. Int J Infect Dis. 2020;94:128-132. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 249] [Cited by in F6Publishing: 260] [Article Influence: 65.0] [Reference Citation Analysis (0)] |
| 29. | Marnell L, Mold C, Du Clos TW. C-reactive protein: ligands, receptors and role in inflammation. Clin Immunol. 2005;117:104-111. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 401] [Cited by in F6Publishing: 435] [Article Influence: 22.9] [Reference Citation Analysis (0)] |
| 30. | Danzi GB, Loffi M, Galeazzi G, Gherbesi E. Acute pulmonary embolism and COVID-19 pneumonia: a random association? Eur Heart J. 2020;41:1858. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 423] [Cited by in F6Publishing: 465] [Article Influence: 116.3] [Reference Citation Analysis (0)] |
| 31. | Magro C, Mulvey JJ, Berlin D, Nuovo G, Salvatore S, Harp J, Baxter-Stoltzfus A, Laurence J. Complement associated microvascular injury and thrombosis in the pathogenesis of severe COVID-19 infection: A report of five cases. Transl Res. 2020;220:1-13. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 1635] [Cited by in F6Publishing: 1570] [Article Influence: 392.5] [Reference Citation Analysis (1)] |
| 32. | Yang F, Shi S, Zhu J, Shi J, Dai K, Chen X. Analysis of 92 deceased patients with COVID-19. J Med Virol. 2020;92:2511-2515. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 121] [Cited by in F6Publishing: 121] [Article Influence: 30.3] [Reference Citation Analysis (0)] |
| 33. | Gao Y, Wang C, Kang K, Peng Y, Luo Y, Liu H, Yang W, Zhao M, Yu K. Cytokine Storm May Not Be the Chief Culprit for the Deterioration of COVID-19. Viral Immunol. 2021;34:336-341. [PubMed] [DOI] [Cited in This Article: ] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis (0)] |