Incidence of Cardiovascular Events Among Glomerular Disease Participants in the Cure Glomerulonephropathy Network (CureGN)
Bibliographic record
Abstract
Background: Cardiovascular (CV) events are known extra-renal complications of glomerular disease that cause increased rates of hospitalization, morbidity, and mortality. This analysis describes CV outcomes in CureGN, a prospective cohort study of adults and children with biopsy-proven minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), or IgA nephropathy/vasculitis (IgA) from 65 US and 6 international sites. Methods: Multivariable Cox regression was used to estimate associations with first post-enrollment CV event, among those without prior CV events. Results: The cohort included 2423 participants (566 MCD, 615 FSGS, 546 MN, 696 IgA). At enrollment, median age was 31 yrs (IQR 14-51), with 35% children, 57% male, 13% Hispanic, and 16% Black. At enrollment, median eGFR was 83 ml/min/1.73m2 (IQR 54-103) and median UPCR was 1.2 g/g (IQR 0.2-3.9). Median follow-up was 3.8 years (IQR 2.2-5.1). Overall, 96 participants (22 children, 74 adults) experienced at least one CV event post-enrollment, most commonly arrhythmia followed by heart failure. The cumulative incidence at 1-, 3-, and 5-years post-enrollment was 0.2%, 1.3%, and 1.9%, respectively, for children, and 1.4%, 4.4%, and 7.1%, respectively, for adults. Higher hazard of first CV event post-enrollment was associated with older age (HR=1.1 per 5 yrs), lower eGFR at enrollment (HR=0.88 per 10 mL/min/1.73 m2), and higher UPCR (HR=1.03 per g/g) [Figure]. Glomerular disease subtype was not associated with hazard of CV event (p=0.58). Conclusions: CV events were associated with older age and kidney disease severity, but not glomerular disease subtype, in this cohort. Further study and longer follow-up will improve understanding of CV risk in patients with glomerular disease and inform practice guidelines. Funding: NIDDK SupportSequential time from enrollment to first cardiovascular event Cox models
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".