A Cross-Sectional Study on Predictors of Covid-19 Infection, Admission, and Effect of Immunomodulating Treatments in Rheumatoid Arthritis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».