Risk factors for relapse of antineutrophil cytoplasmic antibody–associated vasculitis
Bibliographic record
Abstract
OBJECTIVE: To determine the association between characteristics at diagnosis and the time to first relapse in a large cohort of patients with antineutrophil cytoplasmic antibody-associated vasculitis (AAV). METHODS: We studied long-term followup data from 4 clinical trials that included newly diagnosed patients with a broad spectrum of AAV severity and manifestations. Patient and disease characteristics at baseline were used in competing risk regression models with relapse as the event of interest and death as the competing event. RESULTS: We assessed 535 patients with 1,804 patient-years at risk of relapse. At diagnosis, the median age was 60.7 years (interquartile range [IQR] 48.8-69.1 years), 284 patients (53%) had granulomatosis with polyangiitis (Wegener's), and the median creatinine level was 203 μmoles/liter (IQR 97-498). A total of 201 patients (38%) experienced a relapse and 133 patients (25%) died, 96 of whom had not had prior relapse. Anti-proteinase 3 antibodies (subhazard ratio [sHR] 1.62 [95% confidence interval 1.39-1.89]) and cardiovascular involvement (sHR 1.59 [95% confidence interval 1.07-2.37]) were independently associated with a higher risk of relapse. Compared with patients with a creatinine level ≤100 μmoles/liter, patients with higher creatinine levels had a lower risk of relapse (sHR 0.81 [95% confidence interval 0.77-0.85] for a creatinine level of 101-200 μmoles/liter; sHR 0.39 [95% confidence interval 0.22-0.69] for a creatinine level >200 μmoles/liter). CONCLUSION: Relapse of disease is common for patients with AAV. A creatinine level >200 μmoles/liter at the time of diagnosis is strongly associated with a reduced risk of relapse and may help guide monitoring and treatment of 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".