DEMOGRAPHIC AND CLINICAL CHARACTERISTICS OF PATIENTS WITH SLE ACROSS 5 REGISTRIES – THE LUPUSNET FEDERATED DATA NETWORK
Notice bibliographique
Résumé
PV193 / #375 Poster Topic: AS22 - SLE Heterogeneity Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune disease with a broad range of clinical manifestations and a high unmet need across patient populations. Real-world data on SLE are scattered across many registries worldwide, with heterogeneous data collection. The Lupus Federated Data Network (LupusNet) is an interdisciplinary international initiative that aims to combine and harmonize data from existing SLE registries to create a global, federated network of SLE databases with a larger number of patients, greater data consistency, and the potential to address gaps in the understanding of SLE. Methods Data from 5 registries representing > 10,000 patients with SLE from 4 regions contributed to LupusNet: APLC (Asia Pacific), RELESSER (Europe), FORWARD (North America), and Almenara and GLADEL (Central and South America). LupusNet uses a federated data network approach and a privacy-by-design method, where the data remains with the respective registries and the analysis occurs at the local center; only aggregated results are shared. Figure 1 illustrates the schedule of patient visits for clinical assessments (including the Systemic Lupus Erythematosus Disease Activity Index [SLEDAI]) in each registry. Demographic and clinical variables (eg, disease activity/severity, clinical events, biopsies/histology, biomarkers, treatment history, comorbidities, medications, and patient-reported outcomes) were mapped and harmonized to the Observational Medical Outcomes Partnership Common Data Model v5.4. This study describes the baseline demographics, patient characteristics, and disease activity based on the SLEDAI in LupusNet during ± 90 days of registration. Figure 1. Frequency of Patient Visits by Registry Results A total of 10,267 patients were included and mapped in LupusNet. Of those, 3,908 patients were in Asia Pacific, 1,806 in Europe, 3,066 in North America, and 1,487 in Central and South America. Select baseline demographics and characteristics of patients with SLE are presented in Table 1. Disease activity based on the SLEDAI questionnaire was assessed at registration from 4 registries that collected these data. Across registries, the majority of the patients were females; the duration from SLE diagnosis to the registry entry ranged from 5 to 10 years. Heterogeneity and variability in disease manifestations determined from SLEDAI responses were observed across registries, particularly in relation to arthritis, nephritis (ie, proteinuria, pyuria, hematuria, and urinary casts), increased anti-double-stranded deoxyribonucleic acid (anti-dsDNA) antibody, and leukopenia. Table 1. Baseline Demographics and Patient Characteristics in LupusNet Conclusions Mapping patient characteristics from LupusNet allows researchers to analyze a larger population of patients with SLE across different geographical regions. These findings demonstrate a high degree of variability in disease activity measured by SLEDAI across registries, likely due to differences in recruitment strategy, treatment strategy/access, healthcare system, and race/ethnicity. Compared to individual registries, this network of collective SLE databases allows further study to better understand disease heterogeneity, patient populations, and treatment patterns with the goal of improving outcomes for patients with SLE across the globe.
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 | 0,007 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».