Systemic Lupus Erythematosus Symptom Clusters and Their Association With Patient‐Reported Outcomes and Treatment: Analysis of Real‐World Data
Bibliographic record
Abstract
OBJECTIVE: To identify discrete clusters of systemic lupus erythematosus (SLE) patients based on symptoms and investigate differences across clusters. METHODS: Data were collected in the US and 5 European countries via the Adelphi Real World Lupus Disease Specific Programme, a cross-sectional survey. Rheumatologists provided data for 5 consecutively consulting adult patients with SLE, who were invited to participate. Identified SLE symptoms were reduced to factors based on commonly concurrent symptoms, using principal-component factor analysis. Factors were used as covariates in a latent-class cluster analysis to identify discrete patient clusters. Patient-reported outcomes and physician-reported data were compared across clusters. RESULTS: Among 1,376 patients, 87% were female and 74% were White. We identified 4 patient clusters (very mild, mild, moderate, and severe) based on 39 signs/symptoms. Physician-reported symptom burden, organ involvement, disease activity, and the number of flares increased with increasing cluster severity (P < 0.0001). Patient-reported impact (health status, fatigue, work productivity impairment, anxiety/depression, and emotional impact) increased with increasing cluster severity (P < 0.0001). Glucocorticoid and immunosuppressant use increased, and antimalarial use decreased, with increasing cluster severity. In all clusters, <20% of patients received biologics; >15% of patients not receiving biologics were considered eligible for treatment by their physician. The proportion of physicians and patients satisfied with treatment decreased with increasing cluster severity (P < 0.0001). CONCLUSION: Our large, international, real-world survey of SLE patients and physicians demonstrated strong associations between increased impairment, organ involvement, and humanistic burden in SLE, highlighting an unmet need for effective treatment options in patients with high disease activity.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".