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Published on: December 19, 2020
Chest CT Scan Features to Predict COVID-19 Patients' Outcome and Survival
Mohammad-Mehdi Mehrabi Nejad1, Aminreza Abkhoo1, Faeze Salahshour1
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
Chest CT scans can predict COVID-19 patient outcomes. Higher pulmonary involvement scores and density indices on admission indicate a higher risk of mortality, aiding resource allocation for infectious coronavirus disease 2019 (COVID-19) patients.
Area of Science:
- Radiology
- Infectious Diseases
- Medical Imaging
Background:
- Efficient care for infectious coronavirus disease 2019 (COVID-19) patients necessitates tools for optimizing medical resource allocation.
- Identifying high-risk patients is crucial for managing healthcare resources effectively during pandemics.
Purpose of the Study:
- To evaluate the predictive capability of chest computed tomography (CT) characteristics upon admission for estimating the outcome and survival time of COVID-19 patients.
- To determine if specific imaging findings on initial chest CT scans correlate with patient prognosis.
Main Methods:
- A case-control study compared deceased COVID-19 patients (cases) with survivors (controls) using on-admission chest CT scans.
- Pulmonary involvement (PI) was semiquantitatively scored (0-25), and a PI density index was calculated.
- Demographic, clinical, and imaging variables were analyzed for their association with patient outcomes and survival time.
Main Results:
- The study included 186 COVID-19 patients (93 deceased, 93 survivors).
- Deceased patients showed significantly different PI scores (8.9±4.5 vs. 10.7±4.4) and PI density indices (2.0±0.7 vs. 2.6±0.8) compared to survivors.
- Axial distribution, cardiomegaly, pleural effusion, and pericardial effusion were more frequent in deceased patients. PI score ≥10 and PI density index ≥2.2 were linked to lower survival rates.
Conclusions:
- On-admission chest CT features, specifically the PI score and PI density index, serve as valuable predictors of clinical outcomes in COVID-19 patients.
- These imaging parameters can assist in stratifying patient risk and informing treatment strategies.
Background:
Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation to high-risk patients.
Purpose:
To assess the predictive value of on-admission chest CT characteristics to estimate COVID-19 patients' outcome and survival time.
Materials And Methods:
Using a case-control design, we included all laboratory-confirmed COVID-19 patients who were deceased, from June to September 2020, in a tertiary-referral-collegiate hospital and had on-admission chest CT as the case group. The patients who did not die and were equivalent in terms of demographics and other clinical features to cases were considered as the control (survivors) group. The equivalency evaluation was performed by a fellowship-trained radiologist and an expert radiologist. Pulmonary involvement (PI) was scored (0-25) using a semiquantitative scoring tool. The PI density index was calculated by dividing the total PI score by the number of involved lung lobes. All imaging parameters were compared between case and control group members. Survival time was recorded for the case group. All demographic, clinical, and imaging variables were included in the survival analyses.
Results:
After evaluating 384 cases, a total of 186 patients (93 in each group) were admitted to the studied setting, consisting of 126 (67.7%) male patients with a mean age of 60.4 ± 13.6 years. The PI score and PI density index in the case vs. the control group were on average 8.9 ± 4.5 vs. 10.7 ± 4.4 (p value: 0.001) and 2.0 ± 0.7 vs. 2.6 ± 0.8 (p value: 0.01), respectively. Axial distribution (p value: 0.01), cardiomegaly (p value: 0.005), pleural effusion (p value: 0.001), and pericardial effusion (p value: 0.04) were mostly observed in deceased patients. Our survival analyses demonstrated that PI score ≥ 10 (p value: 0.02) and PI density index ≥ 2.2 (p value: 0.03) were significantly associated with a lower survival rate.
Conclusion:
On-admission chest CT features, particularly PI score and PI density index, are potential great tools to predict the patient's clinical outcome.
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