Establishing Surrogate Kidney End Points for Lupus Nephritis Clinical Trials: Development and Validation of a Novel Approach to Predict Future Kidney Outcomes
Bibliographic record
Abstract
OBJECTIVE: End points currently used in lupus nephritis (LN) clinical trials lack uniformity and questionably reflect long-term kidney survival. This study was undertaken to identify short-term end points that predict long-term kidney outcomes for use in clinical trials. METHODS: A database of 944 patients with LN was assembled from 3 clinical trials and 12 longitudinal cohorts. Variables from the first 12 months of treatment after diagnosis of active LN (prediction period) were assessed as potential predictors of long-term outcomes in a 36-month follow-up period. The long-term outcomes examined were new or progressive chronic kidney disease (CKD), severe kidney injury (SKI), and the need for permanent renal replacement therapy (RRT). To predict the risk for each outcome, hazard index tools (HITs) were derived using multivariable analysis with Cox proportional hazards regression. RESULTS: Among 550 eligible subjects, 54 CKD, 55 SKI, and 22 RRT events occurred. Variables in the final CKD HIT were prediction-period CKD status, 12-month proteinuria, and 12-month serum creatinine level. The SKI HIT variables included prediction-period CKD status, International Society of Nephrology (ISN)/Renal Pathology Society (RPS) class, 12-month proteinuria, 12-month serum creatinine level, race, and an interaction between ISN/RPS class and 12-month proteinuria. The RRT HIT included age at diagnosis, 12-month proteinuria, and 12-month serum creatinine level. Each HIT validated well internally (c-indices 0.84-0.92) and in an independent LN cohort (c-indices 0.89-0.92). CONCLUSION: HITs, derived from short-term kidney responses to treatment, correlate with long-term kidney outcomes, and now must be validated as surrogate end points for LN clinical trials.
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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 | 0.211 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".