Traditional and Disease-Specific Risk Factors for Cardiovascular Events in Antineutrophil Cytoplasmic Antibody-Associated Vasculitis: A Multinational Retrospective Study
Bibliographic record
Abstract
OBJECTIVE: To investigate the occurrence of cardiovascular events (CVEs) in a large cohort of patients with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) across the European Union, China, Turkey, Russia, the United Kingdom, and the USA. METHODS: Patients with a definite diagnosis of AAV who were followed for ≥ 3 months and had sufficient documentation were included. Data on myocardial infarction (MI) and stroke were collected retrospectively from tertiary vasculitis centers. Univariate and multivariate Cox regression models were used to estimate hazard ratios (HRs) and 95% CIs. RESULTS: Over a median follow-up of 62.0 months (IQR 22.6-100.0), CVEs (mostly MIs) occurred in 245 (10.7%) of 2286 patients with AAV, with a higher frequency in China and the UK. On multivariate regression analysis, older age (55-64.9 yrs, HR 2.93, 95% CI 1.99-4.31), smoking (HR 1.98, 95% CI 1.48-2.64), Chinese origin (HR 4.24, 95% CI 3.07-5.85), and pulmonary (HR 1.50, 95% CI 1.09-2.06) and kidney (HR 3.02, 95% CI 2.08-4.37) involvement were independent variables associated with a higher occurrence of CVEs. CONCLUSION: We showed that geographic region and both traditional and disease-specific (kidney involvement in particular) factors were independently associated with CVEs. Proper assessment and management of modifiable cardiovascular (CV) risk factors are essential for prevention of CV morbidity in patients with AAV.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".