Therapies for IgAN in the Cure Glomerulonephropathy Network (CureGN)
Bibliographic record
Abstract
Background: There is sparse literature on real-world use and impact of novel emerging therapies approved to treat IgAN and current trends in immunosuppressive therapy (IST) use. In North American adults with IgAN enrolled in CureGN within 6 months of their diagnostic kidney biopsy, we report treatment patterns and associated disease trajectories. Methods: CureGN is a prospective registry of participants with glomerular disease. Descriptive statistics estimated frequency of IgAN treatments, number of IST classes used, and time from biopsy to first IST/systemic corticosteroid (SCS). Cox proportional hazard models assessed risk of a composite 40% decline/kidney failure by IST and SCS use. Results: 152 adults with incident IgAN were followed for a median of 6.8 years (interquartile range [IQR]: 2.4, 8.1). A majority were male (64%), white (74%), and non-Hispanic (84%). IST were the most frequently used IgAN medications and 61% specifically used SCS. Post-enrollment, the most common IgAN medications were renin-angiotensin system (RAS) inhibitors, other ISTs, and sodium glucose cotransporter-2 inhibitors (SGLT2i), with 29% of participants receiving 2+ IST classes. Few participants (n=5) received newer IgAN therapies (eg, sparsentan, targeted delayed-release budesonide). (Table 1) Compared with participants with estimated glomerular filtration rate (eGFR) ≥60 at biopsy, those with eGFR <60 were more likely to have received 2+ IST classes during follow-up (37.1% vs 16.3%, p=0.03). Urine protein:creatinine ratio (UPCR) at biopsy was not associated with number of IST classes used or time to first IST. Adjusting for eGFR and UPCR at enrollment, IST use was not associated with a decreased risk of progression to kidney failure (Table 2). Conclusion: The impact oftraditional IST on long term kidney function requires further exploration. Despite availability of novel IgAN therapies, conservative management (RAS inhibition, SGLT2i) and traditional IST remain most used. Funding: Commercial Support - Calliditas NA Enterprises Inc.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".