Back

Can the Chest X-ray Brixia Score predict COVID-19 disease severity and mortality in resource-limited settings? A retrospective cross-sectional study at a tertiary hospital in Northwestern, Tanzania

Mbwilo, D.; Byekwaso, Z.; Elisenguo, E.; Hyera, F.; Msaki, E.; Akrabi, A. M.; Seni, J.; Wajanga, B.; Ngoya, P. S.

2025-05-02 infectious diseases
10.1101/2025.05.01.25326785 medRxiv
Show abstract

BackgroundCOVID-19 primarily affects the lung, thus the chest x-ray (CXR) is the first line imaging modality of evaluating the disease. CXR scoring systems for quantifying the severity and progression of lung abnormalities in COVID-19 pneumonia have been introduced. However, a chest X-ray severity scoring system has never been implemented in Tanzania to assist in predicting COVID-19 patient outcomes. Therefore, this study was designed to determine whether the CXR Brixia Score can predict outcomes among patients with COVID-19 pneumonia. Materials and MethodsA retrospective cross-sectional hospital-based study conducted on COVID-19 pneumonia patients confirmed by nasopharyngeal swab RT-PCR assay at Bugando Medical Center (BMC) using data retrieved from the hospital database. Data retrieved included socio-demographics and clinical information. Baseline CXRs were reviewed and assigned a Brixia score by two experienced Radiologists. Disease severity, length of the hospital stay and in-hospital mortality were recorded as outcomes and correlated with the Brixia scores by logistic regression analysis after adjusting for potential cofounders. A p-value of <0.05 was significant. ResultsA total of 220 patients were enrolled with a mean age of 59 ({+/-}12.7) years. A severe Brixia score had a significant likelihood of severe form of disease (aOR=4.8, 95%CI=2.35 - 13.67, p=0.001) and death (aOR=5.39, 95%CI=1.65 - 24.73, p=0.041). A severe Brixia score did not predict a longer hospital stay (cOR=2.29, 95%CI=0.49 - 9.72, p=0.293). ConclusionCXR Brixia Score can predict disease severity and in-hospital mortality particularly in resource-limited settings. It may assist in timely identifying patients in need of aggressive management, and thereby avert adverse outcomes including mortality.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.