Renal Histopathology Associated With Kidney Failure and Mortality in Patients With Lupus Nephritis: A Long-Term Real-World Data Study
Bibliographic record
Abstract
OBJECTIVE: Lupus nephritis (LN), a common manifestation of systemic lupus erythematosus, is associated with a higher risk of kidney failure and death. The renal pathology of LN helps elucidate the severity of inflammation and the extent of irreversible damage. We aimed to identify histologic variables that correlate with risks of kidney failure and mortality. METHODS: Between 2006 and 2019, a total of 526 patients with LN were enrolled. Renal pathology was classified according to the International Society of Nephrology/Renal Pathology Society classification. Components of activity and chronicity indices were analyzed to determine which variables correlated with an increased risk of kidney failure and death, with the adjustment of potential confounders. RESULTS: During the follow-up period (median 7.5, IQR 3.5-10.7 years), 58 patients progressed to kidney failure and 64 died. In the multivariate Cox regression analysis, tubular atrophy (hazard ratio [HR] 2.28, 95% CI 1.66-3.14) and tubulointerstitial inflammation (HR 3.13, 95% CI 1.34-7.33) predicted kidney failure. The renal outcome was even worse if tubular atrophy and tubulointerstitial inflammation coexisted (10-year kidney survival rate: 63.22%). The presence of cellular crescents was associated with an increased risk of death in male patients with LN (HR 1.91, 95% CI 1.02-3.57), whereas the presence of fibrous crescents predicted death in female patients with LN (HR 5.70, 95% CI 1.61-20.25). CONCLUSION: Histologic variables of renal biopsy in LN could be regarded as prognostic indicators for kidney failure and mortality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".