Predictive Factors for COVID-19 Severity in Patients with Axial Spondyloarthritis: Real-World Data from the Romanian Registry of Rheumatic Diseases
<i>Background and Objectives</i>: Coronavirus disease-2019 (COVID-19) posed unique challenges worldwide, underscoring important gaps in healthcare preparedness for patients receiving immunosuppressive therapies, such as the individuals with axial spondyloarthritis (axSpA), a subgroup of...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Medicina |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1648-9144/61/3/411 |
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| Summary: | <i>Background and Objectives</i>: Coronavirus disease-2019 (COVID-19) posed unique challenges worldwide, underscoring important gaps in healthcare preparedness for patients receiving immunosuppressive therapies, such as the individuals with axial spondyloarthritis (axSpA), a subgroup of spondyloarthritis (SpA) characterized by chronic inflammation and immune dysregulation. While global registry data exist for SpA, specific data on axSpA alone remain scarce, especially in Central and Eastern European populations. This study aims to identify predictive factors for severe COVID-19 outcomes and provide a descriptive analysis of axSpA patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), using real-world data from the Romanian Registry of Rheumatic Diseases (RRBR). <i>Materials and Methods</i>: This is a three-year retrospective observational cohort study that included 5.786 axSpA patients from the RRBR, of whom 183 (3.16%) were diagnosed with SARS-CoV-2 infection. Data were analyzed using R V4.4.1 and performing univariate and multivariate binary logistic regression to estimate associations using odds ratios (ORs), 95% confidence intervals (CIs), and <i>p</i>-values. A backward selection algorithm was applied to create the final predictive model, accounting for multicollinearity through variance inflation factors (VIFs). <i>Results</i>: The mean age of patients was 48.19 ± 12.26 years, with male predominance (64.5%). Serious COVID-19 (encompassing moderate to critical cases) occurred in 46 cases, with age ≥ 52.5 years (OR 2.64, 95% CI: 1.28–5.48, <i>p</i> = 0.009) and arterial hypertension (OR 2.57, 95% CI: 1.29–5.16, <i>p</i> = 0.007) identified as significant predictors. Individuals with advanced education levels had nearly three times lower odds of experiencing serious COVID-19 (OR 0.38, 95% CI: 0.18–0.76, <i>p</i> = 0.008). Furthermore, our findings confirm the lack of association between HLA-B27 and COVID-19 severity (<i>p</i> = 0.194), contributing to the ongoing discussion regarding its potential immunological role. Moreover, irrespective of the biological therapy administered, the likelihood of experiencing serious SARS-CoV-2 outcomes was not statistically significant (<i>p</i> = 0.882). In the final predictive model, only older age and higher education were deemed as predictive factors. <i>Conclusions</i>: This study highlights key predictors of COVID-19 severity in axSpA patients and emphasizes the protective role of higher education, an underexplored determinant of health outcomes in inflammatory diseases. The lessons learned during these last years can shape a more informed and compassionate healthcare system. |
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| ISSN: | 1010-660X 1648-9144 |