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dc.contributor.author | Gamboa-Cardenas, Rocio-, V | |
dc.contributor.author | Barzola-Cerron, Silvia | |
dc.contributor.author | Toledo-Neira, Denisse | |
dc.contributor.author | Reategui-Sokolova, Cristina | |
dc.contributor.author | Pimentel Quiroz, Víctor Roman | |
dc.contributor.author | Zevallos-Miranda, Francisco | |
dc.contributor.author | Alarcón, Graciela S. | |
dc.contributor.author | Ugarte Gil, Manuel Francisco | |
dc.date.accessioned | 2021-10-04T23:01:00Z | |
dc.date.available | 2021-10-04T23:01:00Z | |
dc.date.issued | 2021 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12866/9872 | |
dc.description.abstract | Objective: The identification of risk factors for COVID-19 adverse course in autoimmune rheumatic diseases (ARDs) is of the utmost importance when approaching patient management; however, data are scarce in relation to the Latin American population. The objective of this study was to determine predictors of hospitalization for COVID-19 patients from an ARD community cohort.Methods: A real setting longitudinal study (March to November 2020) in an ARD community cohort was carried out. Potential predictors of hospitalization for COVID-19 examined included (1) sociodemographic variables (age, gender, education, tobacco use, socioeconomic status, and co-inhabitants), (2) comorbidities, (3) time to COVID-19 diagnosis, and (4) ARD's features: clinical (disease type, disease duration, activity), treatment [corticosteroids use/doses, use of synthetic DMARDs (cDMARDs, tsDMARDs, and bDMARDs)], treatment schedule and non-adherence, and the Multidimensional Health Assessment Questionnaire (MDHAQ). Univariable and multivariable regression analysis were conducted; OR and 95% CI (p < 0.05) were determined.Results: One thousand and one hundred forty-eight patients with ARDs were included; 154 had COVID-19; of these 139 (90.3%) were women, aged 52.5 (13.7) years; 33.1% had hypertension and 61.0% an affected organ by ARD. Infection was detected 8.4 (10.1) days after symptoms started; there were 33 hospitalized patients (rate 21.4%). Predictors of hospitalization by multivariable analysis were age (OR: 1.06; CI: 1.01-1.10; p: 0.01), comorbidities: hypertension (OR: 3.95; 95% CI: 1.40-10.95, p: 0.01) and neoplasm (OR: 9.0; 95% CI: 1.6-52.3; p: 0.01), number of organs involved by ARD (OR: 2.26; 95% CI: 1.16-4.41; p: 0.02), and infection diagnosis delay (OR: 1.36; 95% CI: 1.03-1.80; p: 0.01).Conclusions: In our ARD patients with COVID-19, older age, comorbidities (neoplasm and hypertension), and a delay in COVID-19 diagnosis were predictors of hospitalization. The only ARD-associated predictor feature was the number of organs involved | en_US |
dc.language.iso | eng | |
dc.publisher | Springer | |
dc.relation.ispartofseries | Clinical Rheumatology | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es | |
dc.subject | Autoimmune rheumatic disease | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | COVID-19 adverse predictors | en_US |
dc.subject | SARS-CoV-2 | en_US |
dc.title | Predictors of hospitalization for COVID-19 in patients with autoimmune rheumatic diseases: results from a community cohort follow-up | en_US |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | https://doi.org/10.1007/s10067-021-05833-1 | |
dc.relation.issn | 1434-9949 |
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