COVID-19 IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: A SINGLE-CENTER EXPERIENCE
Notice bibliographique
Résumé
PV139 / #320 Poster Topic: AS17 - Miscellaneous Background/Purpose The COVID-19 pandemic has posed significant challenges to peoples’ life worldwide. Patients with systemic lupus erythematosus (SLE) are particularly susceptible to infections. Emerging evidence suggests that the prevalence of COVID-19 may be higher among patients with rheumatic conditions. Various factors, including comorbidities, disease state, and treatment regimens, can influence outcomes. However, the specific impact of COVID-19 on lupus patients is not yet fully understood. More evidence-based knowledge is necessary to plan effective strategies for managing these patients in the future. This study aimed to investigate the effects of COVID-19 on patients with SLE. Methods This observational study was conducted at the Green Life Center for Rheumatic Care and Research from June 2021 to May 2022. Participants included previously diagnosed SLE patients who attended follow-up or new appointments and had a confirmed history of COVID-19 infection based on positive RT-PCR results for SARS-CoV-2 from oral or nasopharyngeal swabs. Patients with suspected but unconfirmed infections, as well as those with overlap syndromes or mixed connective tissue disease (MCTD), were excluded. The sample size was of 94. Data were collected through patient interviews and medical record reviews. The severity of COVID-19 was assessed according to national guideline: mild was defined as symptomatic, meets case definition, and no evidence of pneumonia or hypoxia; moderate was defined as clinical signs of pneumonia with saturation above 90% in room air; and severe was defined as presence of signs of pneumonia with either respiratory rate above 30 breaths per minute and or saturation less than 90% in room air. Data were collected on a preformed data sheet and analyzed using SPSS. Results Among the 94 patients, 89 (94.7%) were female and 5 were male, with a mean age of 36.7 ± 12.2 years. The majority (44%) of patients were over the age of 40. Severity assessments indicated that 66% had mild disease, while 12.8% had severe disease. The most common comorbidities reported were hypertension (42%), hypothyroidism (35.1%), and asthma (29.8%). Of the 76 available HRCT chest reports, 25 (32.8%) showed lung involvement. Eighteen (19.1%) patients had a history of hospitalization, with a mean duration of 9.1 days. Treatment data indicated that most patients (96.9%) received antibiotics, while 25.5% required oxygen, 17.2% received low molecular weight heparin (LMWH), 33% were administered rivaroxaban, 38% received dexamethasone, 18% received intravenous remdesivir, and 2 patients were treated with intravenous tocilizumab. Five patients required high dependency unit (HDU) or intensive care unit (ICU) support, and 2 required mechanical ventilation. The majority of patients (86.1%) were on immunosuppressants (33% on methotrexate, 10.6% on azathioprine, and 24.5% on mycophenolate mofetil), and 81.9% were taking hydroxychloroquine. During their illness, 31.9% of patients were on steroids, with a mean dose of 8.9 mg/day. Regression analysis revealed that both mycophenolate mofetil and steroids were associated with a higher risk of developing severe disease (odds ratios of 3.2, p-value 0.005, and 2.7, p-value 0.04, respectively). Methotrexate and hydroxychloroquine were associated with a lower probability of severe disease (odds ratios of 0.96, p-value 0.04, and 0.73, p-value 0.5, respectively). Conclusions Most lupus patients experienced mild to moderate COVID-19 infections. This study had found the widespread use of antibiotics during the pandemic. Immunosuppressive medications were associated with severity of infection. Mycophenolate mofetil and steroid were associated with a higher risk of severe infection, while methotrexate and hydroxychloroquine were linked to a lower risk.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».