CLUSTER ANALYSIS OF AUTOANTIBODIES IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Notice bibliographique
Résumé
PV195 / #239 Poster Topic: AS22 - SLE Heterogeneity Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune disease with diversity of autoantibodies and clinical manifestation. The identification of patient clusters by autoantibody profile can predict prognosis and mortality. The aim of this study is to define and describe serological clusters and their clinical and epidemiological characteristics, as well as their association with comorbidities, disease activity, severity and damage. Methods Descriptive, observational and multicenter study that includes patients with SLE from the Spanish national registry RELESSER. 1740 patients were included in the cross-sectional study and 718 in the prospective study (with annual follow-up during 5 years). The autoantibodies selected for cluster analysis were anti-DNA, anti-Sm, anti-RNP, anti-Ro, anti-La and antiphospholipid antibodies. Cluster analysis was carried out using Gower distance. To compare the distributions of categorical variables, Chi-square tests were used or Fisher’s exact test in cases of low expected frequencies. For continuous variables, nonparametric tests such as Kruskal-Wallis or ANOVA were applied, depending on the data distribution and homogeneity of variances. Results Four serological clusters were defined (Table 1). Cluster 1 (absence of anti-extractable nuclear antigen antibodies; 44.54% of the patients) was characterized by lower frequency of vasculitis (6.6%), leukopenia (49.1%) and lymphopenia (48.0%) (Table 2). Cluster 2 (antiphospholipid antibodies; 12.24%) was represented by higher frequency of high blood pressure (35.5%), hemolytic anemia (13.5%), thrombocytopenia (39.9%), vasculitis (12.5%), visual disturbances (9.1%) and higher use of immunoglobulins (10.3%) and oral anticoagulants (39.0%). Cluster 3 (anti-Ro and anti-La; 26.67%) had the lowest frequency of lupus nephritis (24.9%). Patients from cluster 4 (anti-Smith and anti-ribonuclear proteins; 16.55%) were younger at disease onset (median age of 29.2 years) and had the highest frequency of lupus nephritis (38.5%), leukopenia (66.0%), lymphopenia (62.2%), hypocomplementemia (89.5%), myositis (5.6%) and cutaneous manifestations. Besides, they had a higher frequency of osteoporosis (11.0%) and severe infections (26.4%) and a higher use of glucocorticoids (93.5%), azathioprine (42.0%), cyclophosphamide (25.5%) and mycophenolate mofetil (24.0%) (Table 3). Regarding disease activity assessed by SLEDAI (Systemic Lupus Erythematosus Disease Activity Index), at visit 1 of the longitudinal study, patients in cluster 4 had the highest scores: 2.5 ± 3.37 (1.70 ± 3.09 in cluster 1 (p=0.18), 2.18 ± 4.26 in cluster 2 (p=0.4), 1.86 ± 2.50 in cluster 3 (p=0.011). After 5 years of follow-up, no differences were observed between clusters. Concerning damage assessed by SLICC/ACR DI, patients in cluster 2 exhibited the highest scores at visit 1: 1.93 ± 2.30 (1.42 ± 1.81 in cluster 1 (p=0.018), 1.13 ± 1.72 in cluster 3 (p<0.001), 1.67 ± 1.92 in cluster 4 (p=0.4)). After 5 years of follow-up, a significant increase was observed across all clusters (p<0.001), with differences persisting at the end of follow-up (p=0.049). Regarding severity measured by Katz, patients in cluster 4 had the highest scores at visit 1: 5.24 ± 2.09 (4.45 ± 2.13 in cluster 1 (p<0.001), 4.57 ± 2.20 in cluster 2 (p=0.019), 4.35 ± 1.69 cluster 3 (p<0.001)). The differences persist between clusters after follow-up (p=0.005). As for mortality, 21 deaths were recorded: 5 in cluster 1 (1.74%), 6 in cluster 2 (5.50%), 6 in cluster 3 (2.97%) and 4 in cluster 4 (3.36%), with no significant differences between clusters (p=0.427). Table 1. Epidemiological characteristics and comorbidities Table 2. Clinical characteristics Table 3. Treatments Conclusions In our cohort, the serological profile constitutes a crucial factor for the clinical stratification of patients and predicting their prognosis. Nevertheless, further studies are required to facilitate a more precise identification and comprehensive understanding of these patients.
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) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 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) | 0,002 | 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 ».