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Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and
Aminreza Abkhoo1, Elaheh Shaker1,2, Mohammad-Mehdi Mehrabinejad1
1Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Insights
Predicting intensive care unit (ICU) mortality in coronavirus disease 2019 (COVID-19) patients is possible using a model incorporating oxygen saturation, hypertension, and pericardial effusion. This model aids in identifying high-risk COVID-19 patients for better outcomes.
Area of Science:
- Medicine
- Radiology
- Critical Care Medicine
Background:
- Coronavirus disease 2019 (COVID-19) poses significant mortality risks, particularly for patients requiring intensive care.
- Identifying predictive factors for mortality in severe COVID-19 cases is crucial for timely intervention.
Purpose of the Study:
- To determine factors associated with mortality in intensive care unit (ICU)-admitted COVID-19 patients.
- To develop a predictive model for COVID-19 patient mortality in the ICU.
Main Methods:
- Retrospective analysis of medical records and chest CT scans from 121 ICU-admitted COVID-19 patients.
- Comparison of demographic, clinical, laboratory, and radiologic findings between survivors and nonsurvivors.
- Development of a logistic regression model to predict in-ICU mortality.
Main Results:
- Nonsurvivors more frequently exhibited cardiomegaly, pleural effusion, pericardial effusion, advanced age, lower oxygen saturation, and hypertension.
- Ground-glass opacity was the most frequent radiologic finding; however, pulmonary involvement extent did not differ significantly between groups.
- A predictive model incorporating oxygen saturation, pericardial effusion, and hypertension achieved 75.5% accuracy in predicting in-ICU mortality.
Conclusions:
- A combination of clinical factors (age, oxygen saturation, hypertension) and radiologic findings (pericardial effusion) can predict mortality in ICU-admitted COVID-19 patients.
- The developed model demonstrates potential for prognostic value in managing severe COVID-19 cases.
- Further research may refine this model for broader clinical application.
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 (p : 0.04), pleural effusion (p : 0.02), and pericardial effusion (p : 0.03) 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 (p : 0.002), lower O2 saturation (p : 0.01), diastolic blood pressure (p : 0.02), and hypertension (p : 0.03) 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.
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