Non-invasive biomarkers of disease activity and organ damage in ANCA-associated vasculitis: a systematic review
Bibliographic record
Abstract
BACKGROUND: In anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), histopathological assessment of affected tissue is often necessary for diagnosis and assessment of disease extent. There is a requirement for validated non-invasive biomarkers to avoid the need for serial tissue biopsies. METHODS: A systematic review of scientific databases from 2012 until present was performed to identify studies fulfilling the inclusion criteria. Studies were assessed for quality using the Strengthening the Reporting of Observational Studies in Epidemiology checklist for cohort, case-control and cross-sectional studies and the Risk of Bias Assessment tool for Non-randomised Studies, or the Cochrane Risk of Bias tool 2.0 for randomised controlled trials. A descriptive synthesis of the data for non-invasive (blood-based or urinary) biomarkers of AAV-related disease activity and organ damage was performed. RESULTS: Twenty-two high quality studies were included. These articles reported the value of blood-based and urinary biomarkers including anti-neutrophil cytoplasmic antibodies, immune cells, complement factors, gene expression profiles, cytokines, chemokines and other proteins in the assessment of disease activity and/or organ damage in patients with AAV. Many of these biomarkers involve the alternative complement pathway, neutrophil activation and macrophage activation. CONCLUSION: This is the first contemporary systematic review synthesising the value of non-invasive biomarkers of AAV-related disease activity and organ damage. The incorporation of individual markers in combined biomarker profiles might enhance clinical decision-making. Many unmet needs were identified; few studies involve oeosinophilic granulomatosis with polyangiitis and patients with childhood-onset AAV. Further validation of the candidate biomarkers is warranted in large prospective studies to bridge the existing knowledge gaps and apply precision health to systemic vasculitis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".