Characteristics and Early Predictors of Intensive Care Unit Admission among COVID-19 Patients in Qatar

Abstract

Purpose: This study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with Coronavirus disease (COVID-19). Methods & Materials: This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender. Univariate and multivariate logistic regression models were used to identify the predictors for ICU admission and in-hospital mortality among the COVID‐19 patients. Results: A total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range, IQR=18). Predictors independently associated with ICU admission included having cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p= 0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), body mass index ≥30 kg/m2 (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphocytes ≤0.8 × 103/μL (aOR=2.69, 95% CI: 1.80-4.02, p120 U/L (aOR= 2.59, 95% CI: 1.53-4.36, p600 μg/L (aOR=1.96, 95% CI: 1.40-2.74, p100 mg/L (aOR=4.09, 95% CI: 2.81-5.96, p120 U/L (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036). Conclusion: Having CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were also associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention.