Prevalence and Risk Factors for Major Infections in Patients with Antineutrophil Cytoplasmic Antibody–associated Vasculitis: Influence on the Disease Outcome
Bibliographic record
Abstract
OBJECTIVE: To analyze the role that infections play on the antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) outcome. METHODS: A retrospective study of adult patients with AAV diagnosed in a tertiary center. Clinical features, laboratory findings, treatment, relapses, major infections, and outcome were evaluated. RESULTS: Included were 132 patients [51 microscopic polyangiitis (MPA), 52 granulomatosis with polyangiitis (GPA), 29 eosinophilic GPA (EGPA)] with a mean followup of 140 (96-228) months. ANCA were positive in 85% of cases. A total of 300 major infections, mainly bacterial (85%), occurred in 60% patients during the followup. Lower respiratory tract (64%) and urinary tract infections (11%) were the most frequent, followed by bacteremia (10%). A total of 7.3% opportunistic infections were observed, most due to systemic mycosis. Up to 46% of all opportunistic infections took place in the first year of diagnosis, and 55% of them under cyclophosphamide (CYC) treatment. Bacterial infections were associated with Birmingham Vasculitis Activity Score (version 3) > 15 at the disease onset, a total cumulative CYC dose > 8.65 g, dialysis, and development of leukopenia during the followup. Leukopenia was the only factor independently related to opportunistic infections. Forty-four patients died, half from infection. Patients who had major infections had an increased mortality from any cause. CONCLUSION: Our results confirm that major infections are the main cause of death 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".