High Incidence of Arterial and Venous Thrombosis in Antineutrophil Cytoplasmic Antibody–associated Vasculitis
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of arterial thrombotic events (ATE) and venous thromboembolism (VTE) in antineutrophil cytoplasmic antibody-associated vasculitis (AAV). METHODS: This is a retrospective cohort study presenting the incidence of ATE (coronary events or ischemic stroke) and VTE [pulmonary embolism (PE) or deep venous thrombosis (DVT)] in patients diagnosed with AAV between 2005 and 2014. RESULTS: There were 204 patients with AAV who were identified. Median followup for surviving patients was 5.8 (range 1-10) years, accounting for 1088 person-years (PY). The incidence of ATE was 2.67/100 PY (1.56 for coronary events and 1.10 for ischemic stroke) and for VTE was 1.47/100 PY (0.83 for DVT only and 0.64 for PE with/without DVT). On multivariate analysis, prior ischemic heart disease (IHD) and advancing age were the only independent predictors of ATE. Among patients without prior IHD or stroke, the incidence of ATE remained elevated at 2.32/100 PY (1.26 for coronary events and 1.06 for ischemic stroke). ATE, but not VTE, was an independent predictor of all-cause mortality. Event rates for both ATE and VTE were highest in the first year after diagnosis of AAV but remained above the population incidence during the 10-year followup period. In comparison to reported rates for the UK population, the event rates in our AAV patients were 15-times higher for coronary events, 11-times higher for incident stroke, and 20-times higher for VTE. CONCLUSION: Patients with AAV have a high incidence of arterial and venous thrombosis, particularly in the first year after 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".