Association of anti-neutrophil cytoplasmic antibody-associated vasculitis and cardiovascular events: a population-based cohort study
Bibliographic record
Abstract
ABSTRACT Background Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is implicated in elevating the risk for cardiovascular (CV) disease; whether the elevated risk applies to all types of CV diseases or specific types is unclear. This study examined the association of AAV and adverse CV outcomes compared with the non-AAV population. Methods We conducted a population-based, retrospective cohort study of adults (mean age 61 years, 51% female) with a new diagnosis of AAV in Ontario, Canada from 2007 to 2017. Weighted models were used to examine the association of AAV (n = 1520) and CV events in a matched (1:4) control cohort (n = 5834). The main outcomes were major adverse CV events (MACE), defined as myocardial infarction (MI), stroke or CV death, its components, atrial fibrillation (AF) and congestive heart failure (CHF). Results Over a mean follow-up of 3.8 years, AAV (compared with non-AAV) was associated with a higher risk of stroke: cumulative incidence 7.0% versus 5.2%, sub-distribution hazard ratio (sHR) 1.49 [(95% confidence interval (95% CI) 1.10–2.02]; AF: cumulative incidence 16.4% versus 11.5%, sHR 1.51, 95% CI 1.30–1.75; and CHF: cumulative incidence 20.8% versus 13.3%, sHR 1.41, 95% CI 1.22–1.62; but not for MACE, MI or CV death. The risks for all CV events, except CV death, were significantly elevated in the early period after AAV diagnosis, in particular AF (365-day sHR 2.06, 95% CI 1.71–2.48; 90-day sHR 3.33, 95% CI 2.66–4.18) and CHF (365-day sHR 1.75, 95% CI 1.48–2.07; 90-day sHR 2.65, 95% CI 2.15–3.26). Conclusion AAV is associated with a high risk of certain types of CV events, particularly in the early period following diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".