CE-05 A longitudinal analysis of outcomes of lupus nephritis in an international inception cohort using a multistate model approach
Bibliographic record
Abstract
Background Patients with lupus nephritis (LN) may have improvement or deterioration in renal status over time. To capture bidirectional change we used a reversible multistate Markov model to study transitions in glomerular filtration rate (GFR) and proteinuria (PrU) in a prospective, international, inception cohort of SLE patients receiving standard of care. Materials and methods Patients were evaluated at enrolment and annually. GFR states were defined: state 1 (eGFR: >60 ml/min); state 2 (eGFR: 30–60 mL/min); and state 3 (eGFR: <30 ml/min). Similarly, PrU states were defined: state 1 (ePrU: <0.25 gr/day); state 2 (ePrU: 0.25–3.0 gr/day); and state 3 (ePrU: >3.0 gr/day). Multistate models provided estimates of relative transition rates and state occupancy probabilities. Results Of 1,826 SLE patients, 89% were female, 49.2% Caucasian with mean±SD age 35.1 ± 13.3 years. The mean disease duration at enrollment was 0.5 ± 0.3 years and follow-up was 4.6 ± 3.4 years. LN occurred in 700/1,826 (38.3%) patients. The likelihood of improvement in eGFR and ePrU (states 2→1 and 3→2) was greater than deterioration (states 1→2 and 2→3). After 5 years, the estimated transition to ESRD was 62% of patients initially in eGFR state 3 but only 11% from ePrU state 3. The probability of remaining in initial eGFR states 1, 2 and 3 was 85%, 11%, 3% and for ePrU was 62%, 29%, 4%. Male sex (p = 0.04) predicted improvement in eGFR states and older age (p < 0.001), race/ethnicity (p < 0.001), higher ePrU state (p < 0.001), higher renal biopsy chronicity score (p = 0.013) and baseline anticardiolipin antibodies (p = 0.039) predicted deterioration. For ePrU, race/ethnicity (p = 0.009), higher eGFR state (p = 0.011) and higher renal biopsy chronicity score (p = 0.015) predicted deterioration. Positive lupus anticoagulant (p = 0.006) and ISN/RPN class V nephritis (p = 0.013) were associated with lower improvement rates. Conclusions Multistate modelling in patients with LN generates probability estimates of transitions between disease states that reflect improvement or deterioration in renal outcomes. This approach identifies predictors of change in renal status and can inform clinical trial design by identifying outcomes that new therapeutic interventions for LN should meet or exceed. Acknowledgements Presented on behalf of the Systemic Lupus International Collaborating Clinics (SLICC)
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".