RISK FACTORS FOR DE NOVO LUPUS NEPHRITIS IN NON-RENAL SLE PATIENTS TREATED WITH AZATHIOPRINE
Notice bibliographique
Résumé
PV130 / #54 Poster Topic: AS15 - Lupus Nephritis-Clinical Background/Purpose Lupus nephritis (LN) is 1 of the most common, potentially organ threatening, and even fatal complications for patients living with systemic lupus erythematosus (SLE). The initial signs of lupus nephritis include persistent proteinuria > 0.5 g daily, microscopic hematuria with or without erythrocyte dysmorphism, cellular casts, and new-onset hypertension. The risk factors (eg, active extra-renal disease, male sex, smoking, and history of renal diseases) for LN were identified. Azathioprine (AZA) had been widely used for non-renal SLE and is 1 of the standard-of-care options for maintenance therapy for lupus nephritis. However, lupus nephritis develops even in patients already on immunosuppression. In this study, we attempted to identify the baseline risk factors for new LN flares in SLE patients receiving AZA for non-renal SLE. Methods In this retrospective, multicenter study, we identified SLE patients treated with azathioprine for non-renal manifestations. Individuals with previous or active LN at the AZA initiation were excluded. The demographics, systemic lupus erythematosus disease activity index-2K (SLEDAI-2K), and transient proteinuria with UPCR > 0.5 g/g for less than 1 month were identified. The LN flares were defined as either persistent proteinuria with UPCR > 0.5 g/g for 2 consecutive visits for more than 1 month or LN diagnosed on renal biopsy. The prognostic values of baseline SLEDAI score, serology (positive anti-dsDNA and low complement levels), active disease by organ domains, and transient proteinuria were analyzed. Results From 2006 to January 2023, 160 eligible patients were included in the analysis. 88% were female and the median age at enrollment was 37 (29-48) years. 96.9% patients received hydroxychloroquine concomitantly, and all were taking glucocorticoids. The SLEDAI score was 7.5 (4.0-11.0), and 5.6% patients had transient proteinuria at the time of AZA initiation. The median follow-up time was 6.1 (4.4-9.3) years. Through the observation period, LN flare occurred in 16% patients. The median time to LN flare was 4.4 (2.6-6.4) years. The LN flared patients had higher baseline SLEDAI score (10.5 vs. 6.0, p = 0.002), mucocutaneous disease (69% vs. 43%, p = 0.015), and transient proteinuria (27% vs. 1.5%, p < 0.001). The transient proteinuria at baseline was associated with decreased LN-free survival (Figure). On multivariable analyses, mucocutaneous (HR 2.88, p = 0.015), vasculitis (HR 6.81, p < 0.001), and transient proteinuria (HR 11.3, p < 0.001) were independent risk factors for LN flares (Table). Figure Table. Univariable and multivariable Cox regression analysis of baseline predictors for LN flare Conclusions In non-renal SLE patients treated with AZA, lupus nephritis flares were not uncommon. The baseline SLEDAI score, mucocutaneous disease, and vasculitis were associated with lupus nephritis development. The transient, low-level proteinuria was a strong predictor for lupus nephritis. Although low-level proteinuria might not be leading to renal biopsy, close monitoring and prompt diagnostic workup should be considered even in patients under immunosuppression.
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 | 0,000 | 0,001 |
| 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 | 0,000 | 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 ».