COMPARATIVE SAFETY OF COMMON IMMUNOSUPPRESSANTS IN SLE: A CLINICAL TRIAL EMULATION ON INFECTION RISK WITH ADJUSTMENT FOR GLUCOCORTICOID DOSE
Notice bibliographique
Résumé
O053 / #469 Topic: AS24 - SLE-Treatment ABSTRACT CONCURRENT SESSION 09: SLE THERAPY – REVISITING OLD DRUGS AND UNLOCKING HIDDEN POTENTIAL OF NEW MEDICATIONS 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Serious infections are among the most common causes of death in SLE. We performed a clinical trial emulation to assess the comparative safety of 4 commonly used immunosuppressants for treating SLE. Methods Using the OptumLabs Data Warehouse, we identified adults ≥18 years with SLE, defined as ≥3 ICD9/10 codes separated by ≥30 but ≤365 days, who initiated treatment with methotrexate, azathioprine, belimumab, or mycophenolate mofetil (MMF) between March 1, 2011, and September 30, 2023. We required a ≥6-month washout period during which patients could not have been treated with any of the drugs being compared. We excluded those with a prior history of lupus nephritis or organ transplant. The primary outcome was hospitalizations for infections. We emulated randomization using inverse probability weighting and stabilized weights for the 4 study groups, balancing baseline covariates, including glucocorticoid use and dose, hydroxychloroquine use, and disease severity (per the Garris algorithm; details in Table 1). Patients were followed for up to 3 years, death, or disenrollment. We applied 2 censoring approaches: intention-to-treat (ITT) and per-protocol (PP). In the ITT analysis, patients were followed based on their initial medication; in the PP analysis, patients were censored if they stopped or switched drugs. We first conducted analyses using inverse probability weighting with only baseline covariates, then further adjusted for post-baseline prednisone doses using a Marginal Structural Model. We repeated the analysis using traumatic injuries as a falsification outcome. Table 1. Baseline Characteristics of SLE Patients on Immunosuppressants After Weighting Demographics, comorbidities, SLE severity, and medication use for patients initiating azathioprine, belimumab, methotrexate, or MMF, with standardized mean differences (SMD) for group balance. Results After weighting, 738 patients initiated belimumab, 2,462 methotrexate, 1,113 MMF, and 1,459 azathioprine. The mean age was 48 years, and more than 90% were women. Most patients were on background therapy with hydroxychloroquine (66.0% to 70.8%) and glucocorticoids (72.6% to 74.3%), and around 50% had moderate SLE severity based on the Garris algorithm. Baseline covariates were balanced across groups after weighting (Table 1). Hospitalization rates for infections were highest with MMF, followed by azathioprine, methotrexate, and belimumab. In the baseline-only ITT analysis MMF showed significantly higher infection risk compared to belimumab (HR: 1.55, 95% CI 1.07-2.25, p = 0.02) and methotrexate (HR: 1.32, 95% CI 1.04-1.68, p = 0.02). The PP analysis confirmed higher infection risk with MMF compared to belimumab (HR: 2.27, 95% CI 1.11-4.62, p = 0.02). After adjusting for post-baseline prednisone doses, no significant differences were observed among the 4 drugs (Table 2). There were no differences in the cumulative incidence of traumatic injuries across all drugs, supporting internal validity. Table 2. Risk or Infection-Related Hospitalizations by Immunosuppressant in Non-Renal SLE Hazard ratios (HR) and 95% confidence interval (CI) for infection-relatad hospitalizations, with intention to treat (ITT) and per protocol (PP) analyses adjusted for baseline covariates and post-baseline prednisone doses. Significant p-values are bolded. Conclusions In this clinical trial emulation of comparative safety, adjusting for prednisone dose eliminated differences in infection-related hospitalization risk among the 4 studied immunosuppressants. These findings suggest that glucocorticoid dose, rather than the choice of immunosuppressant, may be the primary driver of serious infections in SLE
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 | 0,101 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,011 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».