Predictors of Disease Severity and Death in Patients Admitted with COVID-19 in Infectious Diseases Center, Qazvin University of Medical Sciences, Iran: A Cross-Sectional Study

Document Type : Original Article (s)

Authors

1 Department of Critical Care Nursing, School of Nursing and Midwifery, Qazvin University of Medical Sciences, Qazvin, Iran

2 Assistant Professor, Department of Emergency Medicine, Metabolic Disease Research Center, Qazvin University of Medical Sciences, Qazvin, Iran

3 Assistant Professor, Department of Community Medicine, Metabolic Diseases Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran

4 Associate Professor, Department of Critical Care Nursing, School of Nursing and Midwifery AND Metabolic Diseases Research Center, Qazvin University of Medical Sciences, Qazvin, Iran

Abstract

Background: The aim of this study was to determine the predictors of disease severity and death in patients hospitalized with coronavirus disease 2019 (COVID-19) in the Infectious Diseases Center of Qazvin University of Medical Sciences, Qazvin, Iran.Methods: A total of 228 patients with COVID-19 were included in this cross-sectional study. Of these, 114 patients were alive and 114 patients were dead, who were selected using the available sampling method. The logistic regression analysis method (Inter model) was used to predict the effective factors of severity of disease and mortality of patients and control of confounders.Findings: Multivariate logistic regression analysis showed that increase in C-reactive protein (CRP), blood urea nitrogen (BUN), and potassium(K), and decrease in the mean time from onset of symptoms to hospitalization could be predictors of death in patients with COVID-19. Moreover, the deceased patients were older than the surviving group. There was also a significant difference between the two groups of deceased and survivors in terms of the presence of underlying diseases of hypothyroidism and chronic kidney insufficiency (P < 0.001).Conclusion: According to the results of this study, measuring electrolytes at the beginning of hospitalization, and then serially, is recommended to take corrective measures in the field of disease control.

Keywords

References

  1. Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet 2020; 395(10223): 470-3.
  2. Gupta R, Ghosh A, Singh AK, Misra A. Clinical considerations for patients with diabetes in times of COVID-19 epidemic. Diabetes Metab Syndr 2020; 14(3): 211-2.
  3. Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, et al. Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity. Comput Mater Contin 2020; 63(1): 537-51.
  4. Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirusinfected pneumonia. N Engl J Med 2020; 382(13): 1-9.
  5. Shi Y, Yu X, Zhao H, Wang H, Zhao R, Sheng J. Host susceptibility to severe COVID-19 and establishment of a host risk score: Findings of 487 cases outside Wuhan. Crit Care 2020; 24(1): 108.
  6. Ruan Q, Yang K, Wang W, Jiang L, Song J. Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med 2020; 46(5): 846-8.
  7. Tan L, Wang Q, Zhang D, Ding J, Huang Q, Tang YQ, et al. Lymphopenia predicts disease severity of COVID-19: a descriptive and predictive study. Signal Transduct Target Ther 2020; 5(1): 33.
  8. Aggarwal S, Garcia-Telles N, Aggarwal G, Lavie C, Lippi G, Henry BM. Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): Early report from the United States. Diagnosis (Berl) 2020; 7(2): 91-6.
  9. Dreher M, Kersten A, Bickenbach J, Balfanz P, Hartmann B, Cornelissen C, et al. The characteristics of 50 hospitalized covid-19 patients with and without ARDS. Dtsch Arztebl Int 2020; 117(16): 271-8.
  10. Bi X, Su Z, Yan H, Du J, Wang J, Chen L, et al. Prediction of severe illness due to COVID-19 based on an analysis of initial fibrinogen to albumin ratio and platelet count. Platelets 2020; 31(5): 674-9.
  11. Sun Y, Dong Y, Wang L, Xie H, Li B, Chang C, et al. Characteristics and prognostic factors of disease severity in patients with COVID-19: The Beijing experience. J Autoimmun 2020; 112: 102473.
  12. Hussain A, do Vale Moreira NC. Clinical considerations for patients with diabetes in times of COVID-19 pandemic. Diabetes Metab Syndr 2020; 14(4): 451-3.
  13. Cilloniz C, Polverino E, Ewig S, Aliberti S, Gabarrus A, Menendez R, et al. Impact of age and comorbidity on cause and outcome in community-acquired pneumonia. Chest 2013; 144(3): 999-1007.
  14. Ladha KS, Zhao K, Quraishi SA, Kurth T, Eikermann M, Kaafarani HM, et al. The Deyo-Charlson and Elixhauser-van Walraven Comorbidity Indices as predictors of mortality in critically ill patients. BMJ Open 2015; 5(9): e008990.
  15. Pal R, Banerjee M. COVID-19 and the endocrine system: Exploring the unexplored. J Endocrinol Invest 2020; 43(7): 1027-31.
  16. Casas-Aparicio GA, Leon-Rodriguez I, Gonzalez-Navarro M, Alvarado-de la Barrera C, Avila-Rios S, Peralta-Prado A, et al. Acute kidney injury in patients with severe COVID-19. medRxiv 2020.
  17. Lin YF, Duan Q, Zhou Y, Yuan T, Li P, Fitzpatrick T, et al. Spread and Impact of COVID-19 in China: A systematic review and synthesis of predictions from transmission-dynamic models. Front Med (Lausanne) 2020; 7: 321.
  18. Cheng Y, Luo R, Wang K, Zhang M, Wang Z, Dong L, et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int 2020; 97(5): 829-38.
  19. Takaya Y, Yoshihara F, Yokoyama H, Kanzaki H, Kitakaze M, Goto Y, et al. Risk stratification of acute kidney injury using the blood urea nitrogen/creatinine ratio in patients with acute decompensated heart failure. Circ J 2015; 79(7): 1520-5.
  20. Murata A, Kasai T, Matsue Y, Matsumoto H, Yatsu S, Kato T, et al. Relationship between blood urea nitrogen-to-creatinine ratio at hospital admission and long-term mortality in patients with acute decompensated heart failure. Heart Vessels 2018; 33(8): 877-85.
  21. Zhang JJ, Cao YY, Tan G, Dong X, Wang BC, Lin J, et al. Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy 2021; 76(2): 533-50.
  22. Lippi G, South AM, Henry BM. Electrolyte imbalances in patients with severe coronavirus disease 2019 (COVID-19). Ann Clin Biochem 2020; 57(3): 262-5.
  23. Zhang J, Yu M, Tong S, Liu LY, Tang LV. Predictive factors for disease progression in hospitalized patients with coronavirus disease 2019 in Wuhan, China. J Clin Virol 2020; 127: 104392.
  24. Wang K, Zuo P, Liu Y, Zhang M, Zhao X, Xie S, et al. 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(16): 2079-88.
  25. Henry BM, de Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med 2020; 58(7): 1021-8.

Files

History

  • Receive Date: 08 December 2020
  • Revise Date:
  • Accept Date: 06 April 2022

Share

How to cite

Statistics