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

Suphi Bahadirli1, Erdem Kurt2

  • 1Department of Emergency Medicine, School of Medicine, Istanbul Medipol University, Istanbul, Turkey.

Insights

Key indicators like dyspnea, tachypnea, elevated C-reactive protein (CRP), multiple comorbidities, and diffuse lung infiltrates on CT scans can predict intensive care unit (ICU) admission for COVID-19 patients in the emergency department (ED).

Area of Science:

  • Emergency Medicine
  • Infectious Diseases
  • Radiology

Background:

  • Coronavirus disease 2019 (COVID-19) poses a significant challenge to healthcare systems, particularly in emergency departments (EDs).
  • Predicting the need for intensive care unit (ICU) admission is crucial for resource allocation and patient management.

Purpose of the Study:

  • To identify parameters that can predict ICU admission requirements for COVID-19 patients presenting to EDs.
  • To aid in early identification of high-risk COVID-19 patients.

Main Methods:

  • Retrospective analysis of 458 adult COVID-19 patients admitted to an ED.
  • Data collected included demographics, symptoms, laboratory tests, and chest computed tomography (CT) findings.
  • Binary regression analysis was used to determine predictors of ICU admission.

Main Results:

  • Patients admitted to the ICU showed higher body temperature, respiration rate, C-reactive protein (CRP), D-dimer, ferritin, and more comorbidities.
  • Diffuse infiltration on chest CT was more common in patients requiring ICU care.
  • Dyspnea (OR: 12.55), tachypnea (RR ≥ 18) (OR: 14.54), multiple comorbidities (≥2) (OR: 23.39), diffuse CT infiltration (OR: 14.52), and elevated CRP (≥45 mg/L) (OR: 4.71) were significant predictors of ICU admission.

Conclusions:

  • Dyspnea, tachypnea, elevated CRP, multiple comorbidities, and diffuse CT infiltrates are strong predictors for ICU admission in COVID-19 patients.
  • These findings can assist ED physicians in risk stratification and timely intervention for severe COVID-19 cases.
Abstract

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