DEMOGRAPHIC AND CLINICAL CHARACTERISTICS OF PATIENTS WITH SLE ACROSS 5 REGISTRIES – THE LUPUSNET FEDERATED DATA NETWORK
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".