Zoster after Cyclophosphamide for Systemic Lupus Erythematosus or Vasculitis: Incidence, Risk Factors, and Effect of Antiviral Prophylaxis
Bibliographic record
Abstract
Objective. To assess the incidence and the risk factors for zoster in patients exposed to intravenous cyclophosphamide (CYC) for systemic vasculitis or systemic lupus erythematosus (SLE), as well as the protective effect of prophylaxis by valacyclovir (VCV). Methods. This retrospective study included all adults treated by intravenous CYC for SLE or systemic vasculitis between 2011 and 2015 at Toulouse University Hospital, France. Zoster occurrence was recorded using medical chart review, laboratory data, and patient interviews. Univariate Cox models were computed to assess the risk factors for zoster and the protective effect of prophylaxis by VCV. Results. The cohort consisted of 110 patients (81 systemic vasculitis and 29 SLE). During a mean followup of 3.4 years after CYC initiation, 10 cases of zoster occurred, leading to an overall incidence of 27.9/1000 patient-years (95% CI 15.2–50.6); it was 59.4/1000 patients (95% CI 27.5–123.6) during the year after CYC initiation. Four patients experienced persistent postherpetic neuralgia. Probable risk factors were lymphopenia < 500/µl at CYC initiation (HR 5.11, 95% CI 0.94–27.93) and female sex (HR 4.36, 95% CI 0.51–37.31). The incidence was higher in patients with SLE (HR as compared with systemic vasculitis patients = 2.68, 95% CI 0.54–13.26). None of the 19 patients exposed to VCV during the followup developed zoster. Conclusion. The incidence of zoster is high in systemic vasculitis and in patients with SLE exposed to intravenous CYC. CYC may favor postherpetic neuralgia. Prophylaxis by VCV should be considered, particularly in cases of lymphopenia < 500/µl at CYC initiation and during the year after.
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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 | 0.002 |
| 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 | 0.000 | 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".