Predicting relapse in anti-neutrophil cytoplasmic antibody-associated vasculitis: a Systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Relapses affect 30-50% of patients with ANCA-associated vasculitis (AAV) over 5 years, necessitating long-term treatment. Although there have been studies looking at predictors of relapse in AAV, this research has yet to translate clinically into guidance on tailored therapy. The aim of this systematic review was to identify and meta-analyse existing risk factors from the literature and produce a model to calculate individualised patient risk of relapse. METHOD: A search strategy was developed to include all studies identifying predictors of AAV relapse using multivariate analysis. Individual risk factors were extracted and pooled hazard ratios (HRs) calculated. A model to predict the time to first relapse based on identified risk factors was tested retrospectively using a cohort of patients with AAV. RESULTS: The review of 2674 abstracts identified 117 papers for full text review, with 16 eligible for inclusion. Pooled HRs were calculated from significant risk factors, including anti-PR3 ANCA positivity [HR 1.69 (95% CI 1.46, 1.94)], cardiovascular involvement [HR 1.78 (95% CI 1.26, 2.53)], creatinine >200 µmol/l (relative to creatinine ≤100) [HR 0.39 (95% CI 0.22, 0.69)] and creatinine 101-200 µmol/l [HR 0.81 (95% CI 0.77, 0.85)]. Using data from 182 AAV patients to validate the model gave a C-statistic of 0.61. CONCLUSION: Anti-PR3 ANCA positivity, lower serum creatinine and cardiovascular system involvement are all associated with an increased risk of relapse, and a combination of these risk factors can be used to predict the individualised risk of relapse. In order to produce a clinically useful model to stratify risk, we need to identify more risk factors, with a focus on robust biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".