Novel therapies improve prognosis of IgAN and limit the applicability of the International IgA Nephropathy Prediction Tool
Bibliographic record
Abstract
ABSTRACT Background The International IgA Nephropathy Prediction Tools using clinical variables and the Oxford MEST scores were developed in outdated cohorts. External validation is required to assess the tool's applicability in predicting progression risk for patients on novel therapies. Methods We included 677 immunoglobulin A nephropathy (IgAN) patients (Peking University First Hospital, 2003–23) treated with endothelin receptor antagonists, Nefecon, sodium-glucose cotransporter 2 inhibitors, hydroxychloroquine or telitacicept, a BAFF/APRIL inhibitor. The primary outcome was defined as a 50% decline in estimated glomerular filtration rate or end-stage kidney disease. Discrimination (C-statistic), calibration [calibration slope, Integrated Calibration Index (ICI)], model fit (R2D) and risk stratification (Kaplan–Meier curves) were assessed. Results The median follow-up was 4.8 years (interquartile range 2.2, 8.1), and 190 (28.1%) patients experienced the primary outcome, with a 5-year risk of 9.8%. Compared with the median biopsy year of reported cohorts of original model, our cohort is more contemporary (2017). We validated both original and updated models (and for full model with and without race version). All versions showed adequate discrimination, poor calibration and model fit: C-statistic ∼0.74, calibration slope ∼0.50, R2D <20%, ICI >0.10, and poor separation of Kaplan–Meier curves, except for the highest-risk group. The tools consistently overestimated risk in patients receiving novel therapies. These findings further demonstrated that novel therapies can improved clinical outcomes for IgAN patients. Conclusions In this study, both versions of both models demonstrated limited performance and overestimated risks. Given the prognostic improvement with novel IgAN therapies, these prediction tools may need updating for use in currently treated patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".