ANCA-associated Vasculitis Management in the United States: Data From the Rheumatology Informatics System for Effectiveness (RISE) Registry
Bibliographic record
Abstract
OBJECTIVE: The management of antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) has evolved substantially over the last 2 decades. We sought to characterize AAV treatment patterns in the United States. METHODS: We identified patients with AAV in the Rheumatology Informatics System for Effectiveness (RISE) registry who had at least 2 rheumatology clinician visits between January 1, 2015, and December 31, 2017. Demographics, medications, laboratory test results, and billing codes were extracted from the medical record. Demographic and prescription trends were assessed overall and across US regions. RESULTS: We identified 1462 patients with AAV, 259 (18%) with new or relapsing AAV. The majority were classified as having granulomatosis with polyangiitis (75%). The mean age was 59.8 years and 59% were female. The majority of patients were in the South (45%) followed by the Mid-West (32%), West (12%), and Northeast (8%). Patients had a median of 3 visits and follow-up of 579 days. The most commonly prescribed medications during the study period were glucocorticoids (86%) followed by rituximab (45%), methotrexate (33%), azathioprine (32%), and mycophenolate mofetil (18%); cyclophosphamide (CYC) was rarely used (7%). At the most recent visits in RISE, 47% of patients were on glucocorticoids. Prescription trends were similar across regions. CONCLUSION: To our knowledge, this is the first study to evaluate the demographics and management of AAV by rheumatologists outside of major referral centers. Management strategies vary widely, but CYC is rarely used. These observations can be used to inform future research priorities. Additional studies are needed to characterize AAV severity in RISE as well as patient and provider treatment preferences.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".