The Association of Airway Comorbidities With the Clinical Phenotypes and Outcomes of Patients With Antineutrophil Cytoplasmic Autoantibody–associated Vasculitis
Bibliographic record
Abstract
Objective. We investigated the association of airway comorbidities with the clinical phenotypes and outcomes of myeloperoxidase (MPO)–antineutrophil cytoplasmic antibodies (ANCA)–positive ANCA-associated vasculitis (AAV). Methods. An AAV patient multicenter cohort trial was established in 13 hospitals in western Japan between 2012 and 2018. We examined 143 of the new-onset MPO-ANCA–positive AAV patients. Their clinical characteristics and comorbidities at disease onset were compared based on clinical phenotypes. Multivariate analysis was performed to identify factors predictive of remission and death. Results. Twenty-seven cases with granulomatosis with polyangiitis (GPA), 10 with eosinophilic GPA (EGPA), 81 with microscopic polyangiitis (MPA), and 25 with unclassified AAV were identified. The average age of MPO-ANCA–positive patients was 71.4 years. Comorbidity (87.4%) and airway comorbidity (70.6%) were frequently observed in these patients. Examination of the clinical phenotypes revealed that the cases of GPA were frequently accompanied by infectious airway comorbidity (upper airway disease, bronchiectasis, pulmonary infections), and most of the cases of MPA and unclassified AAV were accompanied by fibrotic interstitial lung disease (fILD) or emphysema. Among MPO-ANCA–positive patients, infectious airway comorbidity was predictive of both remission (HR 1.58, P = 0.03) and mortality (HR 2.64, P = 0.04), and fILD was predictive of mortality (HR 7.55, P = 0.008). The combination of infectious airway comorbidities and fILD caused the worst survival outcomes in patients. Conclusion. MPO-ANCA–positive AAV was frequently accompanied by airway comorbidities. In addition to fILD, infectious airway comorbidities were closely associated with those clinical phenotypes and outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".