Risk of Cardiovascular Events Is Higher in Patients with Glomerular Disease Compared with the General Population
Bibliographic record
Abstract
Background: Cardiovascular (CV) disease is a recognized cause of morbidity and mortality in chronic kidney disease; however, understanding of CV risk in patients with glomerular disease (GN) is limited. We sought to define CV risk in GN patients and compare incidence rates to the general population. Methods: A centralized kidney pathology registry (2000-2012) was used to capture all incident cases of focal segmental glomerulosclerosis (FSGS, n=540), IgA nephropathy (IgAN, n=759), membranous nephropathy (MN, n=387), and minimal change disease (MCD, n=226) in British Columbia, Canada. The primary outcome was a composite of major CV events, ascertained from a hospital discharge registry and evaluated using the Kaplan-Meier method. Hazard ratios (HR, 95% CI) were determined using Cox proportional hazards regression. Event rates were age and sex standardized to the general adult population to generate standardized incidence ratios (SIR, 95% CI). Results: Over a median follow-up of 6.8years there were 338 CV events; 10-year risk (95% CI) was 16.0% (13.8-18.3) and differed by GN type (Figure): IgAN=7.7% (5.4-10.4), MCD=13.2% (7.6-20.4), MN=19.4% (14.3-25.0), and FSGS=27.0% (21.9-32.4). Compared to IgAN, MN (HR=2.6, 1.7-3.9) and FSGS (HR=3.7, 2.6-5.3) had higher risk, but MCD (HR=1.3, 0.8-2.4) did not. Results were similar when comparing CV events before versus after ESKD. CV risk in GN patients was 2.5-fold higher than the general population (SIR 2.5, 2.1-2.8), and was higher in each GN subtype (IgAN=1.4, 1.0-1.8; MCD=1.8, 1.0-2.8; MN=3.0, 2.2-4.0; FSGS=4.0, 3.2-4.9). Conclusions: Patients with GN are at high risk of CV disease, both before and after ESKD onset. The CV risk for all GN subtypes was higher than the general population, including MCD and IgAN. This suggests CV preventive strategies should be considered in all patients with GN.Kaplan-Meier Curve by GN Type
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".