Malignancy history had no marked effect on the prognosis of COVID-19: A cohort study

doi:10.21203/rs.3.rs-44171/v1

Download PDF

Research

Malignancy history had no marked effect on the prognosis of COVID-19: A cohort study

https://doi.org/10.21203/rs.3.rs-44171/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Primary disease influenced the prognosis of coronavirus disease 2019 (COVID-19), but the clinical characters of patients accompanied with tumor were few reported.

Methods

We enrolled 528 COVID-19 patients. Date of laboratory tests and outcome were divided to corresponding groups to compare the risk factors of disease severity, progression and survival.

Results

The disease severity at hospitalization, progression rate (51.43% vs 54.42%) and mortality (19.51% vs 11.91%) were equal between tumor and non-tumor group. In both groups, lymphopenia was negatively related to the severity grading (OR = 0.019 and 0.168 separately), NLR was positively correlated with the poor outcome (OR = 1.371 and 1.155 separately), and CRP was relevant to the disease progression and survival (OR = 1.334 and 1.303 separately).

Conclusions

Malignancy history may have no marked effect on the severity and prognosis of COVID-19. Lymphopenia, NLR and CRP levels could be regarded as indicators to determine severe cases, and predict progression and survival.

Infectious Diseases

General Microbiology

COVID-19

malignancy

laboratory finding

survival

progression

Since December 2019, the Wuhan city, in the center of China, was attacked by a novel coronavirus which was later designated as severe acute respiratory syndrome coronavirus 2, (SARS-CoV-2). This coronavirus Infect patients of all ages, exhibiting multiple systemic inflammations, included severe viral pneumonia with respiratory failure and even death, called coronavirus disease 2019 (COVID-19) by WHO^1–3 (Huang C, Chen N, Guan WJ). Until July 21th 2020, there were 12579569 confirmed cases of SARS-CoV-2 infection worldwide, including 559049 deaths⁴.

With increasing understanding of this pandemic, a large amount of publications have precisely described the demographic, clinical features, illness severity grading and prognosis of COVID-19. Noteworthily, comorbidities were frequently present in 30–40% of patients, while the most common were hypertension, diabetes and coronary heart disease⁵. These comorbidities were confirmed as predictors to assess the clinical severities. Among which, malignancy was little mentioned and rarely reported, as their minor proportion in hospitalized COVID-19 patients. As Guan at al. reported, together with chronic obstructive pulmonary disease (COPD), hypertension and diabetes, malignancy was one of the risk factors of disease severity⁶. While another Meta-analysis suggested that there was no correlation between malignant tumor and COVID-19 patients' aggravation⁷. Few studies only reported the epidemiological data or overall outcome of this particular population^8–10. Here we focused on this minor population to explore the distinguishing laboratory feature in SARS-CoV-2 infected patients with tumor.

Study design and participants

This retrospective cohort study included 528 COVID-19 patients from February 5 to March 10, 2020, admitted to the Central Hospital of Wuhan, one of COVID-19 designated hospitals in Wuhan. The data cutoff is April 10, 2020. Patients diagnosed as COVID-19 according to Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia recommended by the National Health Commission(NHC) of China (version 7.0)¹¹were included. The study was approved by The Central Hospital of Wuhan Hospital Ethics Committee and written informed consent was waived by the Ethics Commission of the designated hospital for emerging infectious diseases.

Data Collection

Demographic, clinical, laboratory and outcome data were extracted from electronic medical records using a standardized data collection form. All data were collected by experienced clinicians and checked by two researchers independently.

Procedures

SARS-CoV-2 infection was confirmed by positive next-generation sequencing or real-time RT-PCR methods of respiratory specimens as previously described¹². Routine laboratory examinations included blood examinations, coagulation and biochemical tests. The clinical outcomes were evaluated by two experienced clinicians.

Definitions

The disease severity of COVID-19 was defined according to the guideline of Chinese NHC¹¹. Briefly, moderate grade was defined as patients with fever, respiratory symptoms and lung CT changed, but oxygen saturation exceeded 93% without oxygen; severe grade signifies respiratory frequency ≥ 30 times/minute, blood oxygen saturation ≤ 93%, oxygenation index ≤ 300 mmHg, and/or lung infiltration progression > 50% within 24 to 48 hours; and critical grade was defined as appearance of respiratory failure, septic shock, and/or multiple organ dysfunction or failure. Poor progression included moderate grade progressed to severe or critical grades and even death.

Statistical Analysis

SPSS software (version 22.0) was used to analyze the data. Shapiro wilktest method was used to determine the distribution of continuous variables. Student-t test was used to test the score difference of each group in the normal distribution, and rank sum test was used to compare the difference in the normal distribution. The normal distribution measurement data is expressed by mean ± standard deviation (SD), and median (interquartile Range IQR) for the non-normal distribution data. Frequency (percentage) was used to express the counting data. Chi-square test was used to compare the distribution differences among groups. When the number of predicted cases was less than 5, Fisher accurate probability method was applicated. The significant factors of univariable analysis were included into multivariable logistic regression model. A two-sided α of less than 0.05 was considered statistically significant.

Demographic, clinical, and laboratory findings of COVID-19 patients

In this retrospective study, 528 patients diagnosed with COVID-19 were included, 41 patients with different tumor type (tumor group) and 487 patients without tumor (non-tumor group). Tumor type included 5 lung cancer, 5 gastrointestinal cancer, 5 blood cancer, 4 breast cancer, 4 head and neck cancer, 3 gynecological tumor, 3 liver cancer, 2 thyroid cancer, 2 urinary tumors, 2 prostate cancer, 2 esophagus cancer,1 pancreas cancer, 2 glioma and 1 osteosarcoma.

Compared with patients without tumor, patients with tumor were elder (median age 66 years [56-73] vs 58 years [39-68], p<0.001) (table 1). The gender distribution didn’t show significant difference. As for the Laboratory findings, tumor group showed significant higher levels of white blood count (WBC), neutrophil count (NEUT), neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) than non-tumor group, but lymphocyte count (LY) and the level of serum albumin were lower in tumor group (table 1). The level of D-dimer in tumor group was elevated than non-tumor group, however, it did not showed any significance in the multivariable regression analyze (data not shown). Of the 35 (85.37%) moderate patients in tumor group, 18 (51.43%) experienced poor progression during subsequent hospitalization; while in non-tumor group, this proportion was 240 (54.42%) progression in 441 (90.55%) moderate patients. We observed no obvious difference in disease progression between two groups. In terms of mortality, our data still showed no significant difference (19.51% vs 11.91%, p=0.16, table 1).

Different characteristics of moderate and severe patients on admission

As in the early stage of COVID-19, most patients were mild or moderate, and the majority of mild patients were admitted to the Fangcang shelter hospitals. So almost all the patients admitted in our hospital during this period were moderate or severe cases. We explored the differences between moderate and severe patients at admission separately in patients with tumors and without tumors (table 2). In tumor group, severe patients showed marked lower LY (0.95[0.67-1.28] vs 0.45[0.37-0.82], p=0.014) and higher NLR (4.51[2.16-7.23] vs 11.53 [11.34-13.30], p=0.031). Based on the results of univariable logistic regression, factors with p <0.05 were included for the multivariable logistic regression, only LY was negatively related to the severity (p=0.044). In non-tumor group, the levels of LY, NLR, D-dimer, CRP, and serum albumin were all significant different between moderate and severe patients. Then we did the multivariable regression and found only lower levels of LY and serum albumin were associated with the severity in COVID-19 patients without tumor.

Risk factors associated with poor progression among moderate patients

It was reported that almost 20% of COVID-19 patients with mild or moderate early presentation can develop severe or critical grade¹³. To further confirm the risk factors associated with poor progression among early moderate patients, we enrolled 476 moderate patients including 35 (7.35%) patients with tumor and 441 (92.65%) patients without tumor (table 1). According to the disease development, they were divided into two subgroups, stabilization and poor progression group (table 3). We found in patients with tumor, poor progression cases had higher CRP (5.19 [1.25-9.8] vs 1.47 [0.19-3.14], p=0.017) and lower serum albumin (32.0 [29.3-35.4] vs 37.7 [34.1-40.1], p=0.033) than stabilization patients, which also made sense in subsequent multivariable regression analysis (p=0.017 and p=0.033 separately). While in non-tumor moderate patients, older age, male, and higher levels of CRP were the risk factors associated with poor progression (table 3).

Risk factors associated with death in-hospital

We divided patients into survivor group and non-survivor group. The mortality was 19.51% in tumor group (table 1). The median age of non-survivors was 72 years old (IQR 71-80), elder than survivors (63, IQR 55-70) (p=0.006, table 4). But in multivariable logistic regression, this difference was meaningless; while higher NLR and CRP level were risk factors associated with death in-hospital among tumor patients (p=0.027 and 0.029 separately). In patients without tumor, older age, male, higher NLR and CRP were risk factors related to death in-hospital (table 4).

In this retrospective study, we analyzed the data of 528 COVID-19 infected cases admitted in our hospital, which were divided into two groups, 41 patients accompanied with different tumors and 487 patients without tumor. The objective of this study was to compare the laboratory characteristics of these two groups and to explore risk factors for disease progression and survival separately.

Recent studies have indicated that lymphocyte count played a key role of severe or critical COVID-19 patients^14,15. Lymphopenia is a common feature and thought to be a critical factor associated with disease severity and mortality¹⁶. In our study, we found severe patients were more likely accompanied by lymphopenia on admission, confirmed by univariable and multivariable logistic regression analyze, which is consistent with the results of Qin et al¹⁷. Absolute counts of lymphocytes on admission were lower in severe patients when compared to moderate patients both in tumor group and non-tumor group. We found lymphopenia was valuable in determination the severity of COVID-19 patients. Besides, lymphocytes count was significant lower in tumor group related to non-tumor group.

The NLR in peripheral blood was a well-known marker of systemic inflammatory responses. Even though the underlying mechanisms are not fully known, it has been confirmed that circulating neutrophil counts were elevated above the normal range in tumor patients¹⁸. Elevated NLR has been observed in multiple solid cancers, including pancreas cancer¹⁹, non-small cell lung cancer²⁰, cervical adenocarcinoma²¹, glioma²², and thought to be related to poorer survival, as well as to predict tumor grade²³ and distinguish between tumor recurrence and pseudoprogression in high-grade gliomas²⁴.

As it has been reported that the increase of NLR was associated with more serious infections^17,25, and identified as an independent risk factor for COVID-19 patients with severe illness²⁶. Our results confirmed the increase of NLR was positively correlated with the poor outcome both in tumor and non-tumor group by multivariable logistic regression analysis. In general, the NLR in tumor group was significantly elevated in contrast to the other group.

Kaya et al. reported the inflammatory cytokines secreted by tumor cells caused the neutrophil count to increase, both in tumor stroma and in peripheral blood. The increase in the neutrophil count may cause a decrease in lymphocytes and lymphocyte apoptosis, as a result cellular immunity is depressed²⁷. This interaction between neutrophils and lymphocytes probably explained the increased neutrophil and NLR level with decreased lymphocyte count in tumor group.

Serum albumin is an indicator of nutritional status, and hypoalbuminemia reflects undernourished state in the elderly, patients with chronic diseases and cancer²⁸. In patients with inflammatory conditions, serum albumin levels were significantly reduced and negatively correlated with disease severity²⁹. We showed coherent result in non-tumor group by univariable and multivariable logistic regression analyze. Tumor patients are frequently combined with cachexia in various degrees according to tumor type and stage³⁰. One of the most characteristic biochemical indicators of cachexia is hypoalbuminemia. It has been reported that patient with serum-albumin < 35 g/L was associated with reduced quality of life and immune function, while serum-albumin < 32 g/L with shorter survival^29,31. Logically, we found reduced serum albumin in tumor group in our study, and albumin level was negatively related to inflammatory progression in COVID-19 patients with tumor.

Serum CRP, a sensitive and acute-phase protein synthesized by hepatocytes following stimulation by various cytokines, including tumor necrosis factor-α and interleukin (IL)-6, markedly increase within several hours of infection or inflammation. CRP ≥ 5 mg/dl was considered as an important evidence of severe inflammation. Elder patients with higher CRP had higher in-hospital mortality with a RR of 2 compared with lower CRP group³². Furthermore, CRP level is regarded as a useful marker of systemic inflammation and a key feature of cancer cachexia. CRP > 1 mg/dl was associated with reduced immune function and shorter survival in tumor patient³¹. The production of CRP was elevated in tumor patients with cachexia³⁰. Similarly in our results, CRP was positively associated with poor progression and death in both tumor and non-tumor group. CRP level of non-survivors and progression cases in tumor group was superior to 5 mg/dl (median of 10.44 and 5.19 separately). It's worth mentioning that tumor cells could also affect the production of CRP, which caused more incidence of hyper inflammation. Studies showed that pro-inflammatory cytokines are produced not only by inflammatory cells, but also by tumor cells. Among which, IL-6 was widely involved³³. In consequence, CRP level in tumor patients was logically elevated.

Our data represented that COVID-19 patients with tumor showed elevated NLR, CRP levels and more severe lymphopenia and hypoalbuminemia compared with patients without tumor. All these biochemical indicators signified higher probability of infection progression possibility and mortality. Actually in our study, we showed discordant results: we observed the tendency of increased mortality in tumor group (19.5% vs 11.9%), but was not statistically significant. Meanwhile, the severity grading and progression rate was similar in COVID-19 patients with and without tumors. One explanation might be that patients with cancer could experience systemic immunosuppressive states caused by both cancer and anticancer treatments³⁴. Studies have shown that development of cancer is frequently associated with inhibited immune status initiated by certain factors that inhibit effector T cell functions (TGF-β, IL-10, VEGF…) and recruit regulatory cells to generate an immunosuppressive microenvironment (IL-4, GM-CSF, IL-1β, VEGF…)^35,36. The interaction between systemic inflammation and tumor immunosuppressive state demands further investigation.

Our analyze showed the disease severity at hospitalization, progression rate and mortality were equal between tumor and non-tumor group. As one of combined diseases, malignancy history may have no marked effect on the severity and prognosis of COVID-19. Nevertheless, leucocyte count, neutrophil count, NLR, CRP and D-dimer levels were elevated in COVID-19 patients with cancer, with lower level of lymphocyte count and albumin. Lymphopenia was negatively related to the determination of severity at hospitalization both in patients with and without tumor; NLR was positively correlated with the poor outcome in either group. CRP was relevant to the disease progression and survival. ALB was related to disease progression in tumor group, and severity in non-tumor group.

There were some limitations in this study. First, due to the rapid pandemic outbreak and the shortage of medical workers, not all historical electronic medical records of patients were completely connected, thus a main limitation was the self-report of medical history by patients on admission. The clinical staging and precise treatment protocols were imprecise or even unavailable. We could not conclude the effect of forepassed application of radiotherapy (especially in lung cancer) or chemotherapy agent to the progression the COVID-19. Secondly, the time from onset to progression couldn’t be analyzed as a risk factor because of the incomplete data about the length of time before hospital admission. Besides, due to the retrospective study design, not all laboratory tests were performed in each patient, immunological indicators as IL-6, IL-17A, CD4 + T cell, and CD8 + T cell in particular. Therefore, profound immunological alterations were not concluded. Moreover, our study was single-central and small-sized with biased patient characteristics, these may make it difficult to generalize the result, larger sample population of multiple centers will be more representative.

COVID-19

coronavirus disease 2019

SARS-CoV-2

severe acute respiratory syndrome coronavirus 2

COPD

chronic obstructive pulmonary disease

NHC

National Health Commission

IQR

inter quartile range

standard deviation

WBC

white blood cell count

NEUT

neutrophil count

lymphocyte count

NLR

neutrophil-to-lymphocyte ratio

CRP

C-reactive protein

ALB

serum albumin

IL-6

interleukin-6

Ethics approval and consent to participate

The study was approved by The Central Hospital of Wuhan Hospital Ethics Committee and written informed consent was waived by the Ethics Commission of the designated hospital for emerging infectious diseases (No. 2020-183).

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

There was no funding for the study.

Authors' contributions

Drs Jiahao Hu and Haixia Ding contributed equally as co-first authors.

Concept and design: Haixia Ding, Jiahao Hu, Xiaowu Shi

Acquisition, analysis, or interpretation of data: Jiahao Hu, Shenglan Ye, Xiuwen Yang,

Drafting of the manuscript: Haixia Ding, Jiahao Hu

Critical revision of the manuscript for important intellectual content: Xiaowu Shi, shenglan Ye

Statistical analysis: Haixia Ding, Jiahao Hu

Administrative, technical, or material support: Xiaowu Shi, Xiuwen Yang

Supervision: Xiaowu Shi

Acknowledgement

We acknowledge all health-care workers involved in the diagnosis and treatment of patients with COVID-19 in Hubei Province, especially medical support teams from all over China.

Huang Q, Li F, Chen Y, Hong F, Wang H, Chen J. Prognostic factors and clinical outcomes in adult primary gliosarcoma patients: a Surveillance, Epidemiology, and End Results (SEER) analysis from 2004 to 2015. British Journal of Neurosurgery Published online December. 2019;12:1–7. doi:10.1080/02688697.2019.1699903.
Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet. 2020;395(10223):507–13. doi:10.1016/S0140-6736(20)30211-7.
Guan W, Ni Z, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. n engl j med. Published online 2020:13.
World Health Organization. Coronavirus disease (COVID-19) outbreak situation. https://www.who.int/emergencies/diseases/novel-coronavirus-2019.
Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet. 2020;395(10223):497–506. doi:10.1016/S0140-6736(20)30183-5.
Guan W, Liang W, Zhao Y, et al. Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis. Eur Respir J Published online March. 2020;26:2000547. doi:10.1183/13993003.00547-2020.
10.18632/aging.103000
Wang B, Li R, Lu Z, Huang Y. Does comorbidity increase the risk of patients with COVID-19: evidence from meta-analysis. aging. Published online April 8, 2020. doi:10.18632/aging.103000.
Liang W, Guan W, Chen R, et al. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. The Lancet Oncology. 2020;21(3):335–7. doi:10.1016/S1470-2045(20)30096-6.
Wang H, Zhang L. Risk of COVID-19 for patients with cancer. The Lancet Oncology. 2020;21(4):e181. doi:10.1016/S1470-2045(20)30149-2.
Xia Y, Jin R, Zhao J, Li W, Shen H. Risk of COVID-19 for patients with cancer. The Lancet Oncology. 2020;21(4):e180. doi:10.1016/S1470-2045(20)30150-9.
National Health Commission of China. Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia recommended. http://www.nhc.gov.cn/yzygj/s7653p/202003/ 46c9294a7dfe4cef80dc7f5912eb1989.shtml. Mar 5th 2020.
Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet. 2020;395(10229):1054–62. doi:10.1016/S0140-6736(20)30566-3.
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(13):1239. doi:10.1001/jama.2020.2648.
Liu K, Chen Y, Lin R, Han K. Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. Journal of Infection Published online March 2020:016344532030116X. doi:10.1016/j.jinf.2020.03.005.
Chen G, Wu D, Guo W, et al. Clinical and Immunologic Features in Severe and Moderate Forms of Coronavirus Disease 2019. Infectious Diseases (except HIV/AIDS); 2020. doi:10.1101/2020.02.16.20023903.
Chan JF-W, Yuan S, Kok K-H, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. The Lancet. 2020;395(10223):514–23. doi:10.1016/S0140-6736(20)30154-9.
Qin C, Zhou L, Hu Z, et al. Dysregulation of Immune Response in Patients with COVID-19 in Wuhan, China. SSRN Journal Published online. 2020. doi:10.2139/ssrn.3541136.
Fossati G, Ricevuti G, Edwards SW, Walker C, Dalton A, Rossi ML. Neutrophil infiltration into human gliomas. Acta Neuropathol. 1999;98(4):349–54. doi:10.1007/s004010051093.
Hata T, Mizuma M, Motoi F, et al. Diagnostic and Prognostic Impact of Neutrophil-to-Lymphocyte Ratio for Intraductal Papillary Mucinous Neoplasms of the Pancreas With High-Grade Dysplasia and Associated Invasive Carcinoma. Pancreas. 2019;48(1):99–106. doi:10.1097/MPA.0000000000001202.
Diem S, Schmid S, Krapf M, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer. 2017;111:176–81. doi:10.1016/j.lungcan.2017.07.024.
Jonska-Gmyrek J, Gmyrek L, Zolciak-Siwinska A, Kowalska M, Fuksiewicz M, Kotowicz B. Pretreatment neutrophil to lymphocyte and platelet to lymphocyte ratios as predictive factors for the survival of cervical adenocarcinoma patients. CMAR. 2018;10:6029–38. doi:10.2147/CMAR.S178745.
Zhang J, Zhang S, Song Y, et al. Prognostic role of neutrophil lymphocyte ratio in patients with glioma. Oncotarget. 2017;8(35). doi:10.18632/oncotarget.19484.
Wang P-F, Meng Z, Song H-W, et al. Preoperative Changes in Hematological Markers and Predictors of Glioma Grade and Survival. Front Pharmacol. 2018;9:886. doi:10.3389/fphar.2018.00886.
Huang Y, Ding H, Wu Q, et al. Neutrophil–lymphocyte ratio dynamics are useful for distinguishing between recurrence and pseudoprogression in high-grade gliomas. CMAR. 2019;11:6003–9. doi:10.2147/CMAR.S202546.
Lagunas-Rangel FA. Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. J Med Virol Published online April 8, 2020. doi:10.1002/jmv.25819.
10.1101/2020.02.10.20021584
Liu J, Liu Y, Xiang P, et al. Neutrophil-to-Lymphocyte Ratio Predicts Severe Illness Patients with 2019 Novel Coronavirus in the Early Stage. Infectious Diseases (except HIV/AIDS); 2020. doi:10.1101/2020.02.10.20021584.
kaya vildan, Yıldırım M, Yazıcı G, Yeşim Yalçın A, Orhan N, Güzel A. Prognostic Significance of Indicators of Systemic Inflammatory Responses in Glioblastoma Patients. Asian Pac J Cancer Prev. 2017;18(12). doi:10.22034/APJCP.2017.18.12.3287.
Law MR, Morris JK, Wald NJ, Hale AK. Serum Albumin and Mortality in the BUPA Study. Int J Epidemiol. 1994;23(1):38–41. doi:10.1093/ije/23.1.38.
Soeters PB, Wolfe RR, Shenkin A. Hypoalbuminemia: Pathogenesis and Clinical Significance. Journal of Parenteral Enteral Nutrition. 2019;43(2):181–93. doi:10.1002/jpen.1451.
Couch M, Lai V, Cannon T, et al. Cancer cachexia syndrome in head and neck cancer patients: Part I. Diagnosis, impact on quality of life and survival, and treatment. Head Neck. 2007;29(4):401–11. doi:10.1002/hed.20447.
Wallengren O, Lundholm K, Bosaeus I. Diagnostic criteria of cancer cachexia: relation to quality of life, exercise capacity and survival in unselected palliative care patients. Support Care Cancer. 2013;21(6):1569–77. doi:10.1007/s00520-012-1697-z.
Iwata M, Kuzuya M, Kitagawa Y, Iguchi A. Prognostic value of serum albumin combined with serum C-reactive protein levels in older hospitalized patients: continuing importance of serum albumin. Aging Clin Exp Res. 2006;18(4):307–11. doi:10.1007/BF03324664.
Babiuch K, Kuśnierz-Cabala B, Kęsek B, Okoń K, Darczuk D, Chomyszyn-Gajewska M. Evaluation of Proinflammatory, NF-kappaB Dependent Cytokines: IL-1α, IL-6, IL-8, and TNF-α in Tissue Specimens and Saliva of Patients with Oral Squamous Cell Carcinoma and Oral Potentially Malignant Disorders. JCM. 2020;9(3):867. doi:10.3390/jcm9030867.
Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015: Cancer Statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32. doi:10.3322/caac.21338.
Schreiber RD, Old LJ, Smyth MJ. Cancer Immunoediting: Integrating Immunity’s Roles in Cancer Suppression and Promotion. Science. 2011;331(6024):1565–70. doi:10.1126/science.1203486.
Vesely MD, Kershaw MH, Schreiber RD, Smyth MJ. Natural Innate and Adaptive Immunity to Cancer. Annu Rev Immunol. 2011;29(1):235–71. doi:10.1146/annurev-immunol-031210-101324.

Table 1: Demographic, laboratory findings, and outcomes of COVID-19 patients.

	Tumor (n=41)	Non-tumor (n=487)	P value
Age, median (IQR), years	66 (56-73)	58 (39-68)	0.001
Gender
Female (%)	21 (51.22)	264 (54.21)	0.71
Male (%)	20 (48.78)	223 (45.79)	0.71
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.89 (4.07-7.53)	5.07 (3.91-6.54)	0.044
NEUT, median (IQR), ×10⁹/L	4.28 (2.36-6.42)	3.20 (2.39-4.55)	0.012
LY, median (IQR), ×10⁹/L	0.88 (0.58-1.24)	1.16 (0.83-1.56)	0.001
NLR, median (IQR)	5.09(2.69-9.25)	2.70 (1.73-4.92)	< 0.001
CRP, median (IQR), mg/dl	3.05 (0.44-7.73)	0.92 (0.14-3.84)	0.003
ALB, median (IQR), g/L	35.2 (31.5-37.7)	39.1 (35.5-42.6)	< 0.001
Severity
Severe (%)	6 (14.6)	46 (9.45)	0.28
Moderate (%)	35 (85.37)	441 (90.55)	0.28
Progression among moderate patients
Stabilization (%)	17 (48.57)	201 (45.58)	0.73
Poor progression (%)	18 (51.43)	240 (54.42)	0.73
Outcomes
Survivor (%)	33 (80.49)	429 (88.09)	0.16
Non-survivor (%)	8 (19.51)	58 (11.91)	0.16

WBC = white blood cell count; NEUT = neutrophil count; LY = lymphocyte count; NLR = neutrophil-to-lymphocyte ratio; CRP = C-reactive protein; ALB = Serum albumin

Table 2: Different characteristics of moderate and severe patients on admission.

Tumor patients	Moderate (n=35)	Severe (n=6)	P value	Multivariable (95% CI for OR)	P value
Age, median (IQR), years	65.5 (57-74)	66 (53-72)	0.577
Gender
Female (%)	16 (45.71)	4 (66.67)	0.41
Male (%)	19 (54.29)	2 (33.33)	0.41
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.89 (4.13-7.51)	6.29 (3.67-7.54)	>0.999
NEUT, median (IQR), ×10⁹/L	4.09 (2.24-6.27)	4.79 (3.10-6.58)	0.552
LY, median (IQR), ×10⁹/L	0.95 (0.67-1.28)	0.45 (0.37-0.82)	0.014	0.019 (0-0.094)	0.044
NLR, median (IQR)	4.51 (2.16-7.23)	11.53 (11.34-13.30)	0.031	1.03 (0.863-1.23)	0.743
CRP, median (IQR), mg/dl	2.97 (0.39-6.19)	6.09 (2.10-9.31)	0.24
ALB, median (IQR), g/L	34.8 (30.6-37.8)	35.55 (33.2-36.7)	0.957
Non-tumor patients	Moderate (n=441)	Severe (n=46)	P value	Multivariable (95% CI for OR)	P value
Age, median (IQR), years	58 (38-68)	62 (55-69)	0.05
Gender
Female (%)	200 (45.71)	23 (50)	0.547
Male (%)	241 (54.29)	23 (50)	0.547
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.11 (3.96-6.67)	4.76 (3.60-6.06)	0.084
NEUT, median (IQR), ×10⁹/L	3.19 (2.36-4.54)	3.34 (2.53-4.58)	0.427
LY, median (IQR), ×10⁹/L	1.21 (0.88-1.63)	0.70 (0.52-1.01)	<0.001	0.168 (0.061-0.462)	0.001
NLR, median (IQR)	2.59 (1.67-4.90)	5.00 (2.86-9.88)	<0.001	0.962 (0.889-1.04)	0.33
CRP, median (IQR), mg/dl	0.67 (0.12-3.19)	4.61 (2.94-6.47)	<0.001	1.08 (0.991-1.177)	0.08
ALB, median (IQR), g/L	39.6 (35.2-42.9)	33.5 (31.2-37.6)	<0.001	0.847 (0.782-0.918)	<0.001

WBC = white blood cell count; NEUT = neutrophil count; LY = lymphocyte count; NLR = neutrophil-to-lymphocyte ratio; CRP = C-reactive protein; ALB = Serum albumin.

Table 3: Risk factors associated with poor progression among moderate patients.

Tumor patients	Stabilization (n=17)	Poor progression (n=18)	P value	Multivariable (95% CI for OR)	P value
Age, median (IQR), years	62 (55-69)	70 (62-85)	0.074
Gender
Female (%)	8 (47.06)	11 (61.11)	0.505
Male (%)	9 (52.94)	7 (38.89)	0.505
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.80 (3.58-7.51)	5.90 (4.34-7.51)	0.463
NEUT, median (IQR), ×10⁹/L	4.09 (2.12-6.22)	4.47 (2.38-6.27)	0.382
LY, median (IQR), ×10⁹/L	0.96 (0.62-1.22)	0.85 (0.68-1.4)	0.804
NLR, median (IQR)	4.23 (2.16-7.24)	5.24 (3.20-7.24)	0.458
CRP, median (IQR), mg/dl	1.47 (0.19-3.14)	5.19 (1.25-9.82)	0.011	1.334 (1.052-1.690)	0.017
ALB, median (IQR), g/L	37.7 (34.1-40.1)	32.0 (29.3-35.4)	0.003	0.804 (0.658-0.983)	0.033
Non-tumor patients	Stabilization (n=201)	Poor Progression (n=240)	P value	Multivariable (95% CI for OR)	P value
Age, median (IQR), years	47 (33-65)	62 (47-70)	<0.001	1.023 (1.010-1.037)	0.001
Gender
Female (%)	133 (66.17)	108 (45)	<0.001	0.509 (0.326-0.794)	0.003
Male (%)	68 (33.83)	132 (55)	<0.001	0.509 (0.326-0.794)	0.003
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.18 (4.05-6.25)	4.96 (3.90-6.76)	0.864
NEUT, median (IQR), ×10⁹/L	3.03 (2.24-4.24)	3.26 (2.38-6.27)	0.053
LY, median (IQR), ×10⁹/L	1.40 (1.05-1.86)	1.03 (0.73-1.40)	<0.001	0.826 (0.616-1.108)	0.202
NLR, median (IQR)	2.00 (1.44-3.13)	3.30 (2.06-5.40)	<0.001	1.056 (0.961-1.161)	0.258
CRP, median (IQR), mg/dl	0.28 (0.06-1.00)	2.03 (0.34-4.73)	<0.001	1.303 (1.157-1.467)	0.003
ALB, median (IQR), g/L	40.6 (37.5-43.4)	38.6 (34.9-41.7)	<0.001	0.987 (0.935-1.042)	0.633

WBC = white blood cell count; NEUT = neutrophil count; LY = lymphocyte count; NLR = neutrophil-to-lymphocyte ratio; CRP = C-reactive protein; ALB = Serum albumin.

Table 4: Risk factors associated with death in-hospital.

Tumor patients	Survivor (n=33)	Non-survivor (n=8)	P value	Multivariable (95% CI for OR)	P value
Age, median (IQR), years	63 (55-70)	72 (71-80)	0.006	1.936 (0.814-4.606)	0.135
Gender
Female (%)	19 (57.58)	6 (75)	0.13
Male (%)	14 (42.42)	2 (25)	0.13
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.90 (4.34-7.51)	5.31 (3.53-9.82)	0.742
NEUT, median (IQR), ×10⁹/L	4.09 (2.33-6.22)	4.92 (3.53-8.24)	0.107
LY, median (IQR), ×10⁹/L	0.96 (0.68-1.26)	0.54 (0.39-0.67)	0.01	0.042 (0.001-5.772)	0.207
NLR, median (IQR)	4.46 (2.16-7.24)	11.61 (0.94-12.46)	0.004	1.371 (1.037-1.813)	0.027
CRP, median (IQR), mg/dl	2.17 (0.39-4.54)	10.44 (8.74-12.18)	0.003	1.344 (1.075-1.679)	0.009
ALB, median (IQR), g/L	36.7 (32.0-37.8)	31.7 (28.5-35.2)	0.029	0.430(0.128-1.450)	0.174
Non tumor patients	Survivor (n=429)	Non-survivor (n=58)	P value	Multivariable (95% CI for OR)	P value
Age, median (IQR), years	57 (37-67)	69 (58-83)	<0.001	1.062 (1.035-1.090)	<0.001
Gender
Female (%)	245 (47.06)	19 (61.11)	<0.001	0.939 (0.868-1.016)	0.022
Male (%)	184 (52.94)	39 (38.89)	<0.001	0.939 (0.868-1.016)	0.022
Laboratory findings
WBC, median (IQR), ×10⁹/L	5.01 (3.94-6.28)	5.86 (3.70-7.72)	0.13
NEUT, median (IQR), ×10⁹/L	3.16 (2.39-4.39)	4.12 (3.40-6.50)	0.018	0.829 (0.670-1.004)	0.055
LY, median (IQR), ×10⁹/L	1.21 (0.89-1.62)	0.65 (0.47-1.02)	<0.001	1.204 (0.896-1.617)	0.218
NLR, median (IQR)	2.51 (1.65-4.53)	4.89 (3.19-10.68)	<0.001	1.155 (1.057-1.262)	0.001
CRP, median (IQR), mg/dl	0.65 (0.12-3.04)	4.72 (2.66-8.09)	<0.001	1.183 (1.093-1.281)	<0.001
ALB, median (IQR), g/L	39.6 (36.2-42.9)	34.6 (32.7-39.1)	<0.001	0.939 (0.868-1.016)	0.116

WBC = white blood cell count; NEUT = neutrophil count; LY = lymphocyte count; NLR = neutrophil-to-lymphocyte ratio; CRP = C-reactive protein; ALB = Serum albumin

Download PDF

Version 1

posted

You are reading this latest preprint version

Malignancy history had no marked effect on the prognosis of COVID-19: A cohort study

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Status:

Version 1