Traditional and disease-related cardiovascular risk factors in ANCA-associated vasculitis: A prospective, two-centre cohort study
Bibliographic record
Abstract
OBJECTIVES: ANCA-associated vasculitis (AAV) has been associated with increased risk of cardiovascular (CV) events. The aim was to assess traditional and disease-related CV risk determinants in a two-centre prospective cohort of AAV patients. METHODS: Patients were recruited from centres in the Netherlands and Canada. A comprehensive CV risk assessment was performed at inclusion. Subjects were followed up yearly for 3-5 years until the first CV event, death or end of follow-up. Cox proportional hazards analyses were performed to relate baseline characteristics to the first CV event. RESULTS: A total of 144 patients were included (mean age 62 years, female sex 44%, median Framingham risk score 14.3%). Insulin resistance was present in 73% of patients tested at inclusion, independent of concurrent prednisone therapy. After a median follow-up of 2.90 years, 16 patients (11%) experienced a CV event (14 non-fatal and 2 fatal). The incidence of CV events was 5.45 per 100 patient-years. Age, Framingham risk score, HbA1c level, Diabetes Mellitus (DM), and previous CV event were significantly associated with CV events. Other factors, such as sex, impaired renal function, dyslipidemia, hypertension, smoking history and microalbuminuria, or disease-specific variables, like ANCA serotype or disease activity, were not significantly related to CV events in univariable or age-adjusted cox regression analysis. CONCLUSIONS: Determinants of an increased CV risk were identified. Disease-related factors and treatments can further modify individual risk factors, such as for steroids causing chronic insulin resistance and DM. Treatment of risk factors is essential to optimize long-term outcomes in AAV patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".