Prognostic Value of the Brixia Radiological Score in COVID-19 Patients: A Retrospective Study from Romania
The novel coronavirus pandemic, SARS-CoV-2, has a variable clinical spectrum, ranging from asymptomatic to critical forms. High mortality and morbidity rates have been associated with risk factors such as comorbidities, age, sex, and virulence factors specific to viral variants. Material and Methods...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Tropical Medicine and Infectious Disease |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2414-6366/10/5/130 |
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| Summary: | The novel coronavirus pandemic, SARS-CoV-2, has a variable clinical spectrum, ranging from asymptomatic to critical forms. High mortality and morbidity rates have been associated with risk factors such as comorbidities, age, sex, and virulence factors specific to viral variants. Material and Methods: We retrospectively evaluated imaging characteristics using the Brixia radiological score in relation to favorable or unfavorable outcomes in adult patients. We included COVID-19 cases, admitted between 2020 and 2022, in a specialized pulmonology hospital with no intensive care unit. We analyzed 380 virologically confirmed COVID-19 cases, with a mean age of 52.8 ± 13.02 years. The mean Brixia radiological score at admission was 5.13 ± 3.56, reflecting predominantly mild-to-moderate pulmonary involvement. Multivariate analysis highlighted the utility of this score as a predictive marker for COVID-19 prognosis, with values >5 correlating with other severity biomarkers, NEWS-2 scores, and a lack of vaccination and hospitalization delay of more than 6 days from symptom onset. Summarizing, the Brixia score is itself an effective tool for screening COVID-19 cases at risk of death for early recognition of clinical deterioration and for decisions regarding appropriate care settings. Promoting vaccination can reduce the severity of radiological lesions, thereby decreasing the risk of death. Technologies based on artificial intelligence could optimize diagnosis and management decisions. |
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| ISSN: | 2414-6366 |