Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-13T13:18:30.375Z Has data issue: false hasContentIssue false

Predicting Intensive Care Unit Admissions for COVID-19 Patients in the Emergency Department

Published online by Cambridge University Press:  31 August 2021

Suphi Bahadirli*
Affiliation:
Department of Emergency Medicine, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
Erdem Kurt
Affiliation:
Department of Emergency Medicine, Istanbul Training and Research Hospital, Istanbul, Turkey
*
Corresponding author: Suphi Bahadirli, Email: drsuphibahadirli@gmail.com.

Abstract

Objective:

Determining the parameters that can predict the requirement of intensive care unit (ICU) admissions among the coronavirus disease 2019 (COVID-19) patients presented to the emergency departments (EDs).

Methods:

In adult consecutive patients admitted (March 15 - April 15, 2020) to the ED of a state hospital for COVID-19, we retrospectively analyzed demographic data, symptoms, laboratory tests, and chest computed tomography (CT) on arrival.

Results:

We included 458 patients [213 (46.5%) females, median age 48 y]. Body temperature, respiration rate, C-reactive protein (CRP), D-dimer, ferritin values, and the number of comorbidities were significantly higher in patients admitted to the ICU than others. Also, diffuse infiltration in chest CT is more common in patients who need ICU follow-up. As a result of the binary regression analysis, a statistically significant correlation was found between the presence of dyspnea (odds ratio [OR]: 12.55), tachypnea (relative risk [RR] ≥ 18) (OR: 14.54), multiple comorbidities (≥2) (OR: 23.39), diffuse infiltration in CT (OR: 14.52), and CRP (≥45 mg/L) (OR: 4.71); and the need for ICU admission.

Conclusion:

It has been concluded that the presence of dyspnea and tachypnea, elevated CRP, presence of multiple comorbidities, and diffuse infiltration in CT may predict the need for ICU admissions of the patients, who presented to the EDs.

Type
Original Research
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

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 CrossRefGoogle ScholarPubMed
Luo, Z, Ang, MJY, Chan, SY, et al. Combating the Coronavirus Pandemic: Early Detection, Medical Treatment, and a Concerted Effort by the Global Community. Research (Wash D C). 2020;2020. doi: 10.34133/2020/6925296 Google Scholar
Liang, W, Liang, H, Ou, L, et al. Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19. JAMA Intern Med. 2020;180(8):1-9. doi: 10.1001/jamainternmed.2020.2033 CrossRefGoogle ScholarPubMed
Wu, C, Chen, X, Cai, Y, et al. 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(7):934-943. doi: 10.1001/jamainternmed.2020.0994 CrossRefGoogle ScholarPubMed
Lippi, G, Plebani, M. Laboratory abnormalities in patients with COVID-2019 infection. Clin Chem Lab Med. 2020;58(7):1131-1134. doi: 10.1515/cclm-2020-0198 CrossRefGoogle ScholarPubMed
Francone, M, Iafrate, F, Masci, GM, et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol. 2020;30(12):6808-6817. doi: 10.1007/s00330-020-07033-y CrossRefGoogle ScholarPubMed
Doanay, F, Elkonca, F, Seyhan, AU, Yılmaz, E, Batırel, A, Ak, R. Shock index as a predictor of mortality among the Covid-19 patients. Am J Emerg Med. 2021;40:106-109. doi: 10.1016/j.ajem.2020.12.053 CrossRefGoogle Scholar
World Health Organization (2020). Coronavirus disease 2019 (COVID-19): Situation Report, 72. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed December 13, 2020.Google Scholar
Liu, S, Yao, N, Qiu, Y, He, C. Predictive performance of SOFA and qSOFA for in-hospital mortality in severe novel coronavirus disease. Am J Emerg Med. 2020;38(10):2074-2080. doi: 10.1016/j.ajem.2020.07.019 Google ScholarPubMed
Gidari, A, De Socio, GV, Sabbatini, S, Francisci, D. Predictive value of National Early Warning Score 2 (NEWS2) for intensive care unit admission in patients with SARS-CoV-2 infection. Infect Dis (Lond). 2020;52(10):698-704. doi: 10.1080/23744235.2020.1784457 Google ScholarPubMed
Haimovich, AD, Ravindra, NG, Stoytchev, S, et al. Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation. Ann Emerg Med. 2020;76(4):442-453. doi: 10.1016/j.annemergmed.2020.07.022 CrossRefGoogle ScholarPubMed
Sproston, NR, Ashworth, JJ. Role of C-Reactive Protein at Sites of Inflammation and Infection. Front Immunol. 2018;9. doi: 10.3389/fimmu.2018.00754 CrossRefGoogle ScholarPubMed
Luo, X, Zhou, W, Yan, X, et al. Prognostic value of C-reactive protein in patients with COVID-19. medRxiv. March 2020:2020.03.21.20040360. doi: 10.1101/2020.03.21.20040360 Google Scholar
Liu, F, Li, L, Xu, M, et al. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol. 2020;127:104370. doi: 10.1016/j.jcv.2020.104370 CrossRefGoogle ScholarPubMed
Chen, W, Zheng, KI, Liu, S, Yan, Z, Xu, C, Qiao, Z. Plasma CRP level is positively associated with the severity of COVID-19. Ann Clin Microbiol Antimicrob. 2020;19(1):18. doi: 10.1186/s12941-020-00362-2 CrossRefGoogle ScholarPubMed
Herold, T, Jurinovic, V, Arnreich, C, et al. Elevated levels of IL-6 and CRP predict the need for mechanical ventilation in COVID-19. J Allergy Clin Immunol. 2020;146(1):128-136.e4. doi: 10.1016/j.jaci.2020.05.008 CrossRefGoogle ScholarPubMed
Sanyaolu, A, Okorie, C, Marinkovic, A, et al. Comorbidity and its Impact on Patients with COVID-19. SN Compr Clin Med. June 2020:1-8. doi: 10.1007/s42399-020-00363-4 CrossRefGoogle ScholarPubMed
Richard Franki. Comorbidities the rule in New York’s COVID-19 deaths. https://www.mdedge.com/chestphysician/article/220457/coronavirus-updates/comorbidities-rule-new-yorks-covid-19-deaths. Accessed December 13, 2020.Google Scholar
Subodh Sharma Paudel. A meta-analysis of 2019 novel corona virus patient clinical characteristics and comorbidities. April 2020. doi: 10.21203/rs.3.rs-21831/v1 CrossRefGoogle Scholar
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. 2020;55(5). doi: 10.1183/13993003.00547-2020 CrossRefGoogle ScholarPubMed
Lei, J, Li, J, Li, X, Qi, X. CT Imaging of the 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology. 2020;295(1):18. doi: 10.1148/radiol.2020200236 CrossRefGoogle ScholarPubMed
Ai, T, Yang, Z, Hou, H, et al. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020;296(2):E32-E40. doi: 10.1148/radiol.2020200642 CrossRefGoogle ScholarPubMed