Modelling long-term outcomes for patients with systemic lupus erythematosus
Bibliographic record
Abstract
BACKGROUND: New treatments for systemic lupus erythematosus (SLE) aim to improve tolerability and disease activity control over standard of care (SoC) treatment. SoC typically includes daily glucocorticoid (GC) which carries a risk of organ damage over time. This study sought to develop natural history models to identify predictors of long-term outcomes with current SoC SLE treatment. METHODS: Generalized linear and parametric accelerated failure time survival models (GLM) and parametric accelerated failure time (AFT) survival models were designed to identify predictors of disease activity, flare rate, GC use, organ damage, and mortality beyond the first year of treatment in patients with SLE. Models were run using a longitudinal retrospective analysis of prospectively collected Toronto Lupus Cohort (TLC) study data, collected between 1997 and 2020. Covariates of clinical and statistical significance were selected by bivariate- then multi-variate regression to find the model of best fit. FINDINGS: Of the 1255 subjects included, 89 % were female 89 % and 65 % Caucasian. Mean follow-up was 10·5 years. At first visit, 51 % of patients had moderate-to-severe disease activity (SLEDAI-2 K score ≥ 6). Mean organ damage scores gradually increased over the years following diagnosis. Median survival of the cohort was ∼35 years from study entry. In the GLM models, SLEDAI-2 K yearly average, and average GC dose were key for predicting change in SLEDAI-2 K, GC use/ dose, and flare (any/rate). Together, adjusted mean SLEDAI-2 K and GC dose were shown to be predictors of mortality and damage in at least 9 of 12 organ systems considered. INTERPRETATION: These comprehensive, longitudinal, predictive models show that disease activity and GC use are significant predictors of organ damage and mortality in a patient population with predominantly moderate to severe SLE. This deepens understanding of SLE natural history and underscores the need for new treatment approaches that reduce disease activity and GC use with an aim to improve long-term SLE outcomes. FUNDING: This study was funded by AstraZeneca.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".