Histologic and Clinical Factors Associated with Kidney Outcomes in IgA Vasculitis Nephritis
Bibliographic record
Abstract
BACKGROUND: Nephritis is a common manifestation of IgA vasculitis and is morphologically indistinguishable from IgA nephropathy. While MEST-C scores are predictive of kidney outcomes in IgA nephropathy, their value in IgA vasculitis nephritis has not been investigated in large multiethnic cohorts. METHODS: Biopsies from 262 children and 99 adults with IgA vasculitis nephritis ( N =361) from 23 centers in North America, Europe, and Asia were independently scored by three pathologists. MEST-C scores were assessed for correlation with eGFR/proteinuria at biopsy. Because most patients ( N =309, 86%) received immunosuppression, risk factors for outcomes were evaluated in this group using latent class mixed models to identify classes of eGFR trajectories over a median follow-up of 2.7 years (interquartile range, 1.2-5.1). Clinical and histologic parameters associated with each class were determined using logistic regression. RESULTS: M, E, T, and C scores were correlated with either eGFR or proteinuria at biopsy. Two classes were identified by latent class mixed model, one with initial improvement in eGFR followed by a late decline (class 1, N =91) and another with stable eGFR (class 2, N =218). Class 1 was associated with a higher risk of an established kidney outcome (time to ≥30% decline in eGFR or kidney failure; hazard ratio, 5.84; 95% confidence interval, 2.37 to 14.4). Among MEST-C scores, only E1 was associated with class 1 by multivariable analysis. Other factors associated with class 1 were age 18 years and younger, male sex, lower eGFR at biopsy, and extrarenal noncutaneous disease. Fibrous crescents without active changes were associated with class 2. CONCLUSIONS: Kidney outcome in patients with biopsied IgA vasculitis nephritis treated with immunosuppression was determined by clinical risk factors and endocapillary hypercellularity (E1) and fibrous crescents, which are features that are not part of the International Study of Diseases of Children classification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".