Lymphopenia and Treatment-Related Infectious Complications in ANCA-Associated Vasculitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: ANCA-associated vasculitis (AAV) is treated with potent immunosuppressive regimens. This study sought to determine risk factors associated with infections during first-intention therapy. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This retrospective study involved two separate cohorts of consecutive cases of AAV seen from 2004 to 2011 at two university hospitals. The following were assessed: vasculitis severity; therapy; and periods with no, moderate (lymphocyte count, 0.3-1.0× 10(9)/L), or severe (lymphocyte count ≤ 0.3×10(9)/L) lymphopenia and neutropenia (neutrophil count ≤ 1.5×10(9)/L). RESULTS: One hundred patients had a mean age of 57±15 years and a Birmingham vasculitis activity score of 7.7±3.6. Therapy consisted of pulse methylprednisolone (59%), cyclophosphamide (85%), methotrexate (6%), and plasmapheresis (25%) in addition to oral corticosteroids. During follow-up, 53% of patients experienced infection and 28% were hospitalized for infection (severe infection). Only 18% experienced neutropenia, but 72% and 36% presented moderate and severe lymphopenia for a total duration of <0.1%, 73%, and 8% of the treatment follow-up, respectively. Lower initial estimated GFR, longer duration of corticosteroid use, and presence of lymphopenia were risk factors of infections. The rate was 2.23 events/person-year in the presence of severe lymphopenia compared with 0.41 and 0.19 during periods with moderate or no lymphopenia (P<0.001). Similarly, the rate of severe infections was 1.00 event/person-year with severe lymphopenia and 0.08 and 0.10 with moderate and no lymphopenia (P<0.001). This association remained independent of other risk factors. CONCLUSIONS: Lymphopenia is frequent during the treatment of AAV, and its severity is associated with the risk of infectious complications.
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 | 0.004 |
| 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 | 0.000 | 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".