A Cross-Sectional Study on Predictors of Covid-19 Infection, Admission, and Effect of Immunomodulating Treatments in Rheumatoid Arthritis
Bibliographic record
Abstract
Objectives COVID-19 infection frequently leads to a cytokine storm, which has successfully been treated with some immunomodulating therapies. Interestingly, treatments investigated for the management of COVID-19 significantly overlap with those used in rheumatoid arthritis (RA). We aimed to investigate the prevalence and predictors of COVID-19 infection, severe infection requiring emergency department (ED) visits, hospitalization, intensive care unit (ICU) admission, and intubation in RA. Methods This was a cross-sectional analysis by linking the RA patients from the Ontario Best Practices Research Initiative (OBRI) to Institute for Clinical Evaluative Sciences (ICES) administrative data containing all health care records for Ontarians to explore the COVID-19 infection, hospital/intensive care unit (ICU) admissions and mortality due to COVID-19 (between Jan 1 2020-Mar 31 2022). The primary outcome COVID-19 infection event was defined if patient had any positive COVID-19 test or diagnosis code ‘080’ in Ontario health insurance program (OHIP) during study period. We also looked at admission to hospital, ICU, and required intubation following COVID-19 infection. Characteristics of patients were compared between patients with and without covid-19 infection within 2 years before and 3 months after COVID-19 pandemic (15 March 2020). We also looked at the association of patient’s characteristics and risk of COVID-19 infection using multivariable logistic regression models. Results A total of 2969 patients were included. Among these, 596 (20.1%) were reported as having had COVID-19 infection. Females were significantly more likely to have infection (Covid-19 group: 81.9% vs non-covid-19: 76.5%). Patient-reported outcome (e.g. HAQ-DI, fatigue, and pain) and the number of comorbidities was significantly higher in patients with COVID-19 infection. Patients with COVID-19 were more likely to use biologic agents (52.5% vs 46.1%) and JAK inhibitors (13.4% vs 9.5%). There was a significant positive association between age younger than 50 years (adj ORs: 3.27; 95% CI: 1.79-5.99 and 1.77; 95% CI: 1.13-2.80 for 30-40 and 40-50 age group, respectively) and higher number of comorbidities (adj ORs: 1.19; 95% CI: 1.09-1.30) and risk of COVID-19 infection in multivariable analysis (Table 1). Out of 596 patients with covid-19 infection, 108 (18.1%) had a record of ED visit or hospitalization. Among 108 ED visits or hospitalization, 13 (12.0%) admitted to the ICU, 11 (10.2%) admitted to ICU and had intubation, 1 (0.93%) had only intubation during follow-up. Table 1. The association between sociodemographic, clinical, and treatment profile and covid-19 infection, univariable and multivariable logistic regression. Conclusion In this study we found that COVID-19 infection was higher in female patients, younger than 50 years old, and those with higher number of comorbidities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".