Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics

Abstract

Purpose. To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality rate. Method. We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients’ demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors. Results. Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly $\left(p:0.04\right)$ , pleural effusion $\left(p:0.02\right)$ , and pericardial effusion $\left(p:0.03\right)$ were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, $p:0.59$ ). Among nonradiologic factors, advanced age $\left(p:0.002\right)$ , lower O2 saturation $\left(p:0.01\right)$ , diastolic blood pressure $\left(p:0.02\right)$ , and hypertension $\left(p:0.03\right)$ were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84–0.97), $p:0.006$ ), pericardial effusion (6.56 (0.17–59.3), $p:0.09$ ), and hypertension (4.11 (1.39–12.2), $p:0.01$ ). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality. Conclusion. A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.