Limited information exists on the clinical courses of COVID-19 cases in low-resource nations, such as Rwanda. Therefore, the objectives of this investigation were to evaluate 1) the clinical results for individuals hospitalized due to COVID-19 in Rwanda, and 2) the effectiveness of the Universal Vital Assessment (UVA) scoring system in forecasting death among these cases, in contrast to the sequential organ failure assessment (SOFA) and quick SOFA (qSOFA) systems. A retrospective analysis was performed on adults aged 18 years or older who were admitted with confirmed SARS-CoV-2 infection to the University Teaching Hospital of Butare (CHUB) in Rwanda between April 2021 and January 2022. UVA, SOFA, and qSOFA scores were computed for all subjects, and the area under the receiver operating characteristic curve (AUC) was assessed for each. Logistic regression models identified factors linked to fatal outcomes. Among the 150 enrolled cases, 83 (55%) were women, with a median age (interquartile range) of 61 (43–73) years. The typical duration of hospitalization (interquartile range) was 6 (3–10) days. Respiratory compromise affected 69 (46%) patients, of whom 34 (23%) developed acute respiratory distress syndrome (ARDS). In-hospital mortality reached 44%. Independent predictors of death comprised acute kidney injury (adjusted odds ratio [aOR] 7.99, 95% confidence interval [CI] 1.47–43.22, p = 0.016), critical COVID-19 illness (aOR 3.42, 95% CI 1.06–11.01, p = 0.039), and UVA score exceeding 4 (aOR 7.15, 95% CI 1.56–32.79, p = 0.011). The AUC values were 0.86 (95% CI 0.79–0.92) for UVA, 0.81 (95% CI 0.74–0.88) for qSOFA, and 0.84 (95% CI 0.78–0.91) for SOFA, showing no significant differences among them. Using a UVA threshold of >4, sensitivity was 0.58, specificity 0.93, positive predictive value 0.86, and negative predictive value 0.74 for predicting death. Individuals admitted to CHUB with COVID-19 experienced substantial mortality rates, effectively forecasted using the UVA system. Applying the UVA score for COVID-19 patients in resource-constrained settings could support healthcare providers in prioritization and treatment planning.
