Clinical Risk Factors of Need for Intensive Care Unit Admission of COVID-19 Patients; a Cross-sectional Study
Archives of Academic Emergency Medicine,
Vol. 11 No. 1 (2023),
15 November 2022
,
Page e15
https://doi.org/10.22037/aaem.v11i1.1853
Abstract
Introduction: It could be beneficial to accelerate the hospitalization of patients with the identified clinical risk factors of intensive care unit (ICU) admission, in order to control and reduce COVID-19-related mortality. This study aimed to determine the clinical risk factors associated with ICU hospitalization of COVID-19 patients.
Methods: The current research was a cross-sectional study. The study recruited 7182 patients who had positive PCR tests between February 23, 2020, and September 7, 2021 and were admitted to Afzalipour Hospital in Kerman, Iran, for at least 24 hours. Their demographic characteristics, underlying diseases, and clinical parameters were collected. In order to analyze the relationship between the studied variables and ICU admission, multiple logistic regression model, classification tree, and support vector machine were used.
Results: It was found that 14.7 percent (1056 patients) of the study participants were admitted to ICU. The patients’ average age was 51.25±21 years, and 52.8% of them were male. In the study, some factors such as decreasing oxygen saturation level (OR=0.954, 95%CI: 0.944-0.964), age (OR=1.007, 95%CI: 1.004-1.011), respiratory distress (OR=1.658, 95%CI: 1.410-1.951), reduced level of consciousness (OR=2.487, 95%CI: 1.721-3.596), hypertension (OR=1.249, 95%CI: 1.042-1.496), chronic pulmonary disease (OR=1.250, 95%CI: 1.006-1.554), heart diseases (OR=1.250, 95%CI: 1.009-1.548), chronic kidney disease (OR=1.515, 95%CI: 1.111-2.066), cancer (OR=1.682, 95%CI: 1.130-2.505), seizures (OR=3.428, 95%CI: 1.615-7.274), and gender (OR=1.179, 95%CI: 1.028-1.352) were found to significantly affect ICU admissions.
Conclusions: As evidenced by the obtained results, blood oxygen saturation level, the patient's age, and their level of consciousness are crucial for ICU admission.
- COVID-19
- intensive care units
- logistic models
- decision trees
- support vector machine
How to Cite
References
Shayganfar A, Sami R, Sadeghi S, Dehghan M, Khademi N, Rikhtehgaran R, et al. Risk factors associated with intensive care unit (ICU) admission and in-hospital death among adults hospitalized with COVID-19: a two-center retrospective observational study in tertiary care hospitals. Emerg Radiol. 2021;28(4):691-7.
Vanhems P, Gustin MP, Elias C, Henaff L, Dananché C, Grisi B, et al. Factors associated with admission to intensive care units in COVID-19 patients in Lyon-France. PLoS One. 2021;16(1):e0243709.
Kim L, Garg S, O'Halloran A, Whitaker M, Pham H, Anderson EJ, et al. Risk Factors for Intensive Care Unit Admission and In-hospital Mortality Among Hospitalized Adults Identified through the US Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET). Clin Infect Dis. 2021;72(9):e206-e14.
Cairns CB, Niemann JT. Intravenous adenosine in the emergency department management of paroxysmal supraventricular tachycardia. Ann Emerg Med. 1991;20(7):717-21.
Zhao Z, Chen A, Hou W, Graham JM, Li H, Richman PS, et al. Prediction model and risk scores of ICU admission and mortality in COVID-19. PLoS One. 2020;15(7):e0236618.
Zeng Z, Ma Y, Zeng H, Huang P, Liu W, Jiang M, et al. Simple nomogram based on initial laboratory data for predicting the probability of ICU transfer of COVID-19 patients: Multicenter retrospective study. J Med Virol. 2021;93(1):434-40.
Sadeghi A, Eslami P, Dooghaie Moghadam A, Pirsalehi A, Shojaee S, Vahidi M, et al. COVID-19 and ICU admission associated predictive factors in Iranian patients. Caspian J Intern Med. 2020;11(Suppl 1):512-9.
Huang Y, Lyu X, Li D, Wang L, Wang Y, Zou W, et al. A cohort study of 676 patients indicates D-dimer is a critical risk factor for the mortality of COVID-19. PLoS One. 2020;15(11):e0242045.
Tadic M, Cuspidi C, Sala C. COVID-19 and diabetes: Is there enough evidence? J Clin Hypertens (Greenwich). 2020;22(6):943-8.
Frydman GH, Boyer EW, Nazarian RM, Van Cott EM, Piazza G. Coagulation Status and Venous Thromboembolism Risk in African Americans: A Potential Risk Factor in COVID-19. Clin Appl Thromb Hemost. 2020;26:1076029620943671.
Halacli B, Kaya A, Topeli A. Critically-ill COVID-19 patient. Turk J Med Sci. 2020;50(Si-1):585-91.
Berger E, Winkelmann J, Eckhardt H, Nimptsch U, Panteli D, Reichebner C, et al. A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe. Health Policy. 2022;126(5):373-81.
Dongelmans DA, Termorshuizen F, Brinkman S, Bakhshi-Raiez F, Arbous MS, de Lange DW, et al. Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the first and the consecutive upsurges of the second wave of the COVID-19 pandemic in the Netherlands. Ann Intensive Care. 2022;12(1):5.
Ahmadi Gohari M, Chegeni M, Haghdoost AA, Mirzaee F, White L, Kostoulas P, et al. Excess deaths during the COVID-19 pandemic in Iran. Infect Dis (Lond). 2022;54(12):909-917..
Population Stat: World Statistical Data; 2019 [updated 2019-09-05. Available from: https://populationstat.com/iran/kerman.
Daily reported of COVID-19: Kerman health sector news; 2022 [Available from: https://zil.ink/kmu.
Halpern NA, Goldman DA, Tan KS, Pastores SM. Trends in Critical Care Beds and Use Among Population Groups and Medicare and Medicaid Beneficiaries in the United States: 2000-2010. Crit Care Med. 2016;44(8):1490-9.
Moghadas SM, Shoukat A, Fitzpatrick MC, Wells CR, Sah P, Pandey A, et al. Projecting hospital utilization during the COVID-19 outbreaks in the United States. Proc Natl Acad Sci USA . 2020;117(16):9122-6.
Gottlieb M, Sansom S, Frankenberger C, Ward E, Hota B. Clinical Course and Factors Associated With Hospitalization and Critical Illness Among COVID-19 Patients in Chicago, Illinois. Acad Emerg Med. 2020;27(10):963-73.
Lashkari M, Yazdi-Feyzabadi V, Mohammadi M, Saberi H, Mehrolhassani MH. Designing a Financial Resource Allocation Model Using Goal Programming Approach: A Case Study of a hospital in Iran. Evid Based Health policy Maneg and Econ. 2018;2(2):70-9.
Ko JY, Danielson ML, Town M, Derado G, Greenlund KJ, Kirley PD, et al. Risk Factors for Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization: COVID-19-Associated Hospitalization Surveillance Network and Behavioral Risk Factor Surveillance System. Clin Infect Dis. 2021;72(11):e695-e703.
Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M. Logistic regression. New York: Springer; 2002.
Jewell NP. Statistics for epidemiology. New York: chapman and hall/CRC; 2003.
Rostami M, Garrusi B, Baneshi MR. A study on the use of bootstrap aggregation methods in estimation of stable parameters. J Biostat Epidemiol. 2016;2(2):104-10.
Alkhasawneh MS, Ngah UK, Tay LT, Mat Isa NA, Al-Batah MS. Modeling and testing landslide hazard using decision tree. J Appl Math. 2014;2014:929768.
Lu H, Xu Y, Ye M, Yan K, Gao Z, Jin Q. Learning misclassification costs for imbalanced classification on gene expression data. J BMC bioinformatics. 2019;20(25):681.
Mehrnaz HS, Ali MK. Calculating the best cut off point using logistic regression and neural network on credit scoring problem-A case study of a commercial bank. AFR J BUS MANAG . 2013;7(16):1414-21.
James G, Witten D, Hastie T, Tibshirani R. An introduction to statistical learning. New York: Springer; 2013.
Edureka: Support Vector Machine In R: Using SVM To Predict Heart Diseases
[updated May 15,2020. Available from: https://www.edureka.co/blog/support-vector-machine-in-r/.
Chen R-C, Dewi C, Huang S-W, Caraka RE. Selecting critical features for data classification based on machine learning methods. J Big Data. 2020;7(1):52.
Anguita D, Ridella S, Rivieccio F, editors. K-fold generalization capability assessment for support vector classifiers. Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.; 2005: IEEE.
Long WJ, Griffith JL, Selker HP, D'agostino RB. A comparison of logistic regression to decision-tree induction in a medical domain. Comput Biomed Res. 1993;26(1):74-97.
Subudhi S, Verma A, Patel AB, Hardin CC, Khandekar MJ, Lee H, et al. Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. NPJ Digit Med. 2021;4(1):87.
Pijls BG, Jolani S, Atherley A, Derckx RT, Dijkstra JI, Franssen GH, et al. Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies. J BMJ open. 2021;11(1):e044640.
Pranata R, Lim MA, Huang I, Raharjo SB, Lukito AA. Hypertension is associated with increased mortality and severity of disease in COVID-19 pneumonia: A systematic review, meta-analysis and meta-regression. J Renin Angiotensin Aldosterone Syst. 2020;21(2):1470320320926899.
Jakkula V. Tutorial on support vector machine (svm). J School of EECS, Washington State University. 2006;37(2.5):3.
Kumar A, Arora A, Sharma P, Anikhindi SA, Bansal N, Singla V, et al. Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis. Diabetes Metab Syndr . 2020;14(4):535-45.
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