Cited by 6COVID-19 Clinical Research StudiesCOVID-19 epidemiological studiesCOVID-19 and healthcare impacts

Survival Rates and Prognostic Factors in Patients with Coronavirus Disease 2019: A Registry-Based Retrospective Cohort Study

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Shahbazi, Fatemeh, Karami, Manoochehr, Mirzaei, Mohammad, Mohammadi, Younes·2021·Journal of Research in Health Sciences

Abstract

Background: Coronavirus disease 2019 (COVID-19) is a contagious disease caused by a newly identified coronavirus. Our knowledge about the survival rate and prognostic factors of the disease is not established well. Therefore, this study aimed to identify the risk factors associated with the survival of COVID-19 cases in Hamadan province, West of Iran. Study design: A retrospective cohort study Methods: This retrospective cohort study was performed in Hamadan province, West of Iran. The study included patients that referred to the provincial hospitals from February 20 to September 20, 2020. The follow-up of each subject was calculated from the date of onset of respiratory symptoms to the date of death. Demographic and clinical characteristics were extracted from patients’ medical records. Kaplan-Meier method, Flemington-Harrington test, and Cox regression were used for data analysis. Results: The overall 1, 5, 10, 20, 30 and 49-day survival rates were estimated at 99.57%, 95.61%, 91.15%, 87.34%, 86.91%, and 86.74%, respectively. Furthermore, survival time showed a significant association with age, gender, history of traveling to contaminated areas, co-morbidity, neoplasms, chronic diseases, and hospital units. Conclusions: In conclusion, elderly people, male gender, and comorbidities presented a greater risk of death. Therefore, it is important to pay more attention to this group of people to reduce the incidence and consequences after infection.

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Crossref · 10.34172/jrhs.2021.47

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  • Other key innate cytokines elevated during COVID-19 include TNFα and IL-6, which is increased by IL-1β.

    “Other key innate cytokines that are elevated during COVID-19 include TNF α and IL-6, which is increased by IL-1 β . 137-142”

    From COVID-19, Myocarditis and Pericarditis (2023) · cites this paper as [139]

    The Discussion (page unavailable) mentions systemic inflammatory responses in COVID-19 but does not identify TNFα, IL-6, or IL-1β. Searches for those cytokines and the proposed IL-1β–IL-6 relationship found no passage addressing the claim. (LLM, conf=0.76)

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  • SARS-CoV-2 infection strongly activates complement and other innate immune pathways such as TLR4 and the inflammasome, leading to increased IL-1β and IL-18 levels.

    “SARS-CoV-2 infection has been documented to strongly activate complement and to activate other innate immune pathways such as TLR4 and the inflammasome, which leads to increased IL-1 β and IL-18 levels. 137-142”

    From COVID-19, Myocarditis and Pericarditis (2023) · cites this paper as [139]

    The Discussion mentions systemic inflammatory responses as a possible mechanism of adverse COVID-19 outcomes, but does not address complement, TLR4, the inflammasome, or IL-1β and IL-18. Searches for those pathways and cytokines found no passage supporting the claimed activation sequence; the paper instead focuses on survival and prognostic factors. (LLM, conf=0.76)

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  • Tim-3 and IL-10 upregulation is observed in patients with COVID-19.

    “Tim-3 and IL-10 upregulation is also observed in patients with COVID-19. 139”

    From COVID-19, Myocarditis and Pericarditis (2023) · cites this paper as [139]

    The Results and Discussion report COVID-19 survival, mortality, and risk factors, but do not address Tim-3 or IL-10 expression. Searches for “Tim-3 and IL-10 upregulation” and the expanded marker names found no relevant passage in our copy. (LLM, conf=0.76)

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  • The Tim-3/IL-10 pathway may be responsible for the inhibited T-cell responses reported during COVID-19 in some patients.

    “This pathway may be responsible for the inhibited T-cell responses that have been reported during COVID-19 in some patients. 138,139”

    From COVID-19, Myocarditis and Pericarditis (2023) · cites this paper as [139]

    The Discussion (page unavailable) mentions a weakened immune system as a possible contributor to adverse COVID-19 outcomes, but does not discuss Tim-3, IL-10, or inhibited T-cell responses. Searches for the Tim-3/IL-10 pathway and T-cell inhibition found no passage addressing the proposed mechanism. (LLM, conf=0.76)

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Research works printed but not linked: 18

  1. Meng L, Hua F, Bian Z. Coronavirus Disease 2019 (COVID-19): Emerging and Future Challenges for Dental and Oral Medicine. J Dent Res. 2020; 99(5): 481-7.
  2. Zhang W, Zhao Y, Zhang F, Wang Q, Li T, Liu Z, et al., The use of anti-inflammatory drugs in the treatment of people with severe coronavirus disease 2019 (COVID-19): The experience of clinical immunologists from China. Clin Immunol. 2020; 214: 108393.
  3. Emami, A, Javanmardi F, Pirbonyeh N, Akbari A. Prevalence of underlying diseases in hospitalized patients with COVID-19: a systematic review and meta-analysis. Arch Acad Emerg Med. 2020; 8(1): e35.
  4. Mehra MR, Desai S, Kuy S, Henry TD, Patel AN. Cardiovascular disease, drug therapy, and mortality in COVID-19. N Engl J Med. 2020; 382(25): e102.
  5. Poyiadji N, Shahin G, Noujaim D, Stone M, Patel S, Griffith B. COVID-19-associated acute hemorrhagic necrotizing encephalopathy: CT and MRI features. Radiology. 2020; 296(2): E119-20.
  6. Zeng, JH, Liu YX, Yuan J, Wang FX, Wu WB, Li JX, et al. First case of COVID-19 complicated with fulminant myocarditis: a case report and insights. Infection. 2020; 48(5): 773-7.
  7. Jin M, Tong Q. Rhabdomyolysis as potential late complication associated with COVID-19. Emerg Infect Dis. 2020; 26(7): 1618- 20.
  8. Ahmed AE. Estimating survival rates in MERS-CoV patients 14 and 45 days after experiencing symptoms and determining the differences in survival rates by demographic data, disease characteristics and regions: a worldwide study. Epidemiol Infect. 2018. 146(4): 489-95.
  9. Choi KW, Chau TN, Tsang O, Tso E, Chiu MC, Tong WL, et al. Outcomes and prognostic factors in 267 patients with severe acute respiratory syndrome in Hong Kong. Ann Intern Med. 2003; 139(9): 715-23.
  10. Hong KH, Choi JP, Hong SH, Lee J, Kwon JS, Kim SM, et al. Predictors of mortality in Middle East respiratory syndrome (MERS). Thorax. 2018; 73(3): 286-9.
  11. 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. 2020; 80(6): e14-8.
  12. Jin JM, Bai P, He W, Wu F, Liu XF, Han DM, et al. Gender differences in patients with COVID-19: Focus on severity and mortality. Front Public Health. 2020; 8: 152.
  13. Beletew Abate B, Mengesha Kassie A, Wudu Kassaw M, Gebremeskel Aragie T, Adane Masresha S. Sex difference in coronavirus disease (COVID-19): a systematic review and meta- analysis. Diabetes and endocrinology. 2020; 10(10): e040129.
  14. Pradhan A, Olsson P E. Sex differences in severity and mortality from COVID-19: are males more vulnerable? Biol Sex Differ. 2020; 11(1): 1-11.
  15. Hooper G L. Health Seeking in Men: A Concept Analysis. Urologic Nursing. 2016; 36(4): 163-72.
  16. Xu RH, He JF, Evans MR, Peng GW, Field H E, Yu D W, et al. Epidemiologic clues to SARS origin in China. Emerg Infect Dis. 2004; 10(6): 1030.
  17. Shahbazi F, Khazaei S. Socio-economic inequality in global incidence and mortality rates from coronavirus disease 2019: an ecological study. New Microbes New Infect. 2020; 38: 100762.
  18. Xia, Y, Jin R, Zhao J, Li W, Shen H. Risk of COVID-19 for patients with cancer. Lancet Oncol. 2020; 21(4): e180.

Cited by

Sorted by citations

Authors
Shahbazi, F., Karami, M., Mirzaei, M., & Mohammadi, Y.
Affiliations
Hamedan University of Medical Sciences (IR)
Corresponding author
Mohammadi, Younes
Venue
Journal of Research in Health Sciences
Published
2021-04-24
Volume
21
Issue
2
Pages
e00515-e00515
ISSN
  • 2228-7795 (ISSN-L)
  • 2228-7809
OpenAlex
W3169548406
Cited by
6
References
1
Citations by year
2026: 1 · 2023: 1 · 2022: 1 · 2021: 3
FWCI
0.40
Type
journal article
Language
en
Open access
Locations
  • Journal of Research in Health Sciences (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • Hamadan University of Medical Sciences
  • Hamedan University of Medical Sciences
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
Keywords
COVID-19 pandemic, survival rate, prognostic factors, mortality risk factors, comorbidities, age, sex differences, travel history, hospitalized patients, Hamadan Province
MeSH terms
COVID-19, SARS-CoV-2, Adult, Age Factors, Aged, Chronic Disease, Female, Hospitalization, Hospitals, Humans, Iran, Male, Middle Aged, Neoplasms, Prognosis, Registries, Retrospective Studies, Risk Factors, Sex Factors, Travel, Comorbidity, Survival Rate, Survival Analysis, Hospital Mortality, Pandemics

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