DIFFERENT PHENOTYPES OF SEVERE FLARES IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS (SLE): RESULTS OF A CLUSTERING ANALYSIS IN A MONOCENTRIC COHORT.
Notice bibliographique
Résumé
PV222 / #475 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose to identify and compare different phenotypes of severe flares in a monocentric cohort of SLE patients. Methods This is a retrospective study of prospectively collected data from a monocentric cohort of adult SLE patients (2019 EULAR-ACR classification criteria), hospitalized in the last 5 years, due to a severe flare (SELENA-SLEDAI flare index definition). Patients with concomitant infections, oncologic and onco-hematologic conditions were excluded. Hospitalization was defined as baseline (t0). At t0 demographics, clinical, laboratory and treatment data were collected. Disease activity was assessed with SLEDAI-2K and BILAG-2004. Disease outcomes (Lupus Low Disease Activity State (LLDAS) and DORIS remission) and treatment were evaluated at 3, 6, 12-months (t3, t6, t12) after the flare. Organ damage (SLICC Damage Index (SLICC-DI)) was assessed at baseline and t12. A clustering analysis was performed on SLE flares, with a hierarchical method. Post hoc elaborations (1-way analysis of variance with Bonferroni test for quantitative variables, and Chi-square test for qualitative variables) were performed to estimate any statistically significant differences between the clusters. Results 122 severe flares in 110 patients (female 83%, Caucasian 89%) were included. 3 clusters were identified, composed of 40, 34 and 48 flares respectively. Cluster 1 included flares that occurred in younger patients (mean age 38.2±12 vs 46.3±13.9 and 43.2±11.1 in clusters 2 and 3 respectively; p=0.007) with a shorter disease duration (9.1±6 years vs 17.5±11.5 and 15±8.7; p=0.0001), characterized by a higher frequency of BILAG A manifestations in the constitutional, cardiopulmonary and musculoskeletal domains (p= 0.0001). These flares presented hyperinflammatory stigmata (higher C-reactive protein, more severe lymphopenia, a tendency for higher ferritin values) and a richer autoantibody profile (anti-dsDNA, anti-Smith, anti-nucleosome, anti-hystone), compared to the other clusters. Cluster 2 included less severe flares with more BILAG B scores (59% vs 28% and 19%; p=0.0001) and mainly joint and skin manifestations. Cluster 3 was characterized by a clear predominance of renal flares (96%) (p=0.0001). 85% of flares in each cluster required adding/changing the immunsuppressant, mainly Mycophenolate in c lusters 1 and 3 (30% and 47% vs 9%; p=0.001). Glucocorticoid pulses were less frequently used in c luster 2 , accordingly to a milder flare phenotype (15% vs 50% and 83%; p= 0.0001). Belimumab was added in 25% and 30% of flares in cl uster 1 and 2 respectively, only in 8.5% in c luster 3 (p=0.02), as the majority of flares occurred before the approval of Belimumab for lupus nephritis. At t6 and t12, flares in clusters 1 and 3 presented a significantly higher cumulative glucocorticoid dose, compared to cluster 2 . At the different timepoints, c luster 1 and 3 presented a comparable and quite low percentage of patients that achieved LLDAS and remission (Table 1). No differences emerged among the clusters for SLICC-DI at t12. Table 1. *Cluster 2 vs 1 and 3 Conclusions different phenotypes of severe SLE flares exist. We identified a “hyperinflammatory” phenotype presenting with fever, arthritis and serositis, deserving similar aggressive therapeutic strategies as renal flares and burdened by a comparable proportion of unsatisfying response to treatment.
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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) | 0,001 | 0,001 |
| 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 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».