LONG-TERM INFECTION RISK IN MODERATE-SEVERE SYSTEMIC LUPUS ERYTHEMATOSUS FROM THE BRITISH ISLES LUPUS ASSESSMENT GROUP BIOLOGICS REGISTER (BILAG-BR): A PROSPECTIVE LONGITUDINAL STUDY
Notice bibliographique
Résumé
PT003 / #262 Topic: AS11 - Epidemiology and Public Health POSTER TOUR 01: CLINICAL OUTCOMES IN SLE 22-05-2025 10:00 AM - 10:40 AM Background/Purpose Patients with systemic lupus erythematosus (SLE) are at increased risk of infection relative to the general population. A previous analysis from our cohort found a crude incidence rate of serious infections at 117.7 (95% CI 98.3–141.0) per 1000 person-years in the first 12 months from cohort entry. We aimed to establish the long-term risk of serious infections in patients with moderate-to-severe SLE in a large national observational cohort. Methods The British Isles Lupus Assessment Group Biologics Register (BILAG-BR) is a UK-based prospective register of patients with SLE. We included patients starting a new biological (rituximab or belimumab) within the previous 12 months or a new standard of care DMARD drug within the last month. Our primary outcome was the long-term incidence of infections, and infections of special interest including herpes zoster. Infections occurring within 28 days of the initial infection were classified as relapses, whereas after 28 days were considered reinfections. Infections involving distinct organ systems or resulting in systemic dissemination within 28 days were recorded separately, unless pathogen identification confirmed they were related to a single infection event. Serious infections were those requiring intravenous antimicrobial treatment, hospital admission, or resulting in morbidity or death. Infection and mortality data were collected from study centers and the UK Office for National Statistics. Results Between July 2010, and January 2023, 1342 individuals contributed 7073.4 person-years of follow-up. This included 929 (69.2%) participants on rituximab, 209(15.6%) on belimumab, and 204 (15.2%) receiving standard of care. The median age at cohort entry was 45 years (IQR 35–55),1206 (89.9%) were women, 670/1172 (57.2%) were White, 207 (17.7%) were South Asian, 202 (17.2%) were Black, and 93 (7.9%) were of East Asian, mixed or other ethnic backgrounds. In total, 1471 infections occurred in 545 (40.6%) individuals. Of these, 303 infections in 186 (13.9%) were classified as serious (Table 1). The crude incidence rate of all infections and serious infections were 208.0 (95% CI 197.3 -218.6) and 42.8 (95% CI 38.0– 47.7) per 1000 person-years, respectively. In the 186 individuals with serious infection, 126 (67.7%) experienced 1 serious infection,42 (22.6%) had 2, and 18 (9.7%) had 3 or more (max 11) serious infections. Herpes zoster occurred in 4.7% of the 1,342 participants at risk (incidence rate 8.9 cases per 1000 person-years, (95% CI 6.96 – 11.4)). Two cases of tuberculosis were reported, both required hospitalization; 1 was a TB recurrence 153 days after the second rituximab cycle (cumulative dose 3 grams), the other was diagnosed during pretreatment screening. Bacterial pathogens were identified by culture in 54 cases (Figure 1A). Figure 1B shows the distribution of other infectious agents in the cohort. There were 22 infection-related deaths at a median of 2783 days (IQR 1343 – 3709) following initiation of therapy. There were no safety signals indicating an increased risk of atypical or opportunistic infections, as these occurrences were rare. Table 1: Distribution of 303 serious infections which occurred in 186 individuals. Figure 1. Pie charts showing A Proportion of causative bacterial infectious agents, B other infectious agents Conclusions Our longer-term analysis shows a lower crude incidence rate of serious infections than in the first 12 months of follow-up suggesting potential adaptation or mitigation of risk over time. We also noted the occurrence of pathogens that are potentially vaccine-preventable and so further work is required to understand vaccine protocols as well as the quality and durability of vaccine responses in this high-risk cohort.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».