Cluster Analysis To Explore Clinical Subphenotypes Of Eosinophilic Granulomatosis With Polyangiitis (Churg–Strauss)
Bibliographic record
Abstract
Objective Previous studies suggested that distinct phenotypes of eosinophilic granulomatosis with polyangiitis (EGPA) could be determined by presence or absence of antineutrophil cytoplasmic antibodies (ANCA), reflecting predominant vasculitic or eosinophilic processes, respectively. This study explored whether ANCA-based clusters or other clusters can be identified in EGPA. Methods This study used standardized data of 15 European centers for patients with EGPA fulfilling widely accepted classification criteria. We used multiple correspondence analysis, hierarchical cluster analysis, and a decision tree model. The main model included 10 clinical variables (musculoskeletal, mucocutaneous, ophthalmological, ENT, cardiovascular, pulmonary, gastrointestinal, renal, central or peripheral neurological involvement); a second model also included ANCA results. Results The analyses included 489 patients diagnosed in 1984–2015. ANCA were detected in 37.2% of patients, mostly P-ANCA (85.4%) and/or anti-myeloperoxidase (87.0%). Compared with ANCA-negative patients, those with ANCA had more renal (P<0.001) and peripheral neurological involvement (P=0.04), fewer cardiovascular signs (P<0.001) and fewer biopsies with eosinophilic tissue infiltrates (P=0.001). The cluster analyses generated four (model without ANCA) and five clusters (model with ANCA). Both models identified three identical clusters of 34, 39 and 40 patients according to the presence or absence of ENT, CNS and ophthalmological involvement. Peripheral neurological and cardiovascular involvement were not predictive characteristics. Conclusion Although reinforcing the known association of ANCA status with clinical manifestations, cluster analysis does not support a complete separation of EGPA in ANCA-positive and –negative subsets. Collectively, these data indicate that EGPA should be regarded as a phenotypic spectrum rather than a dichotomous disease.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".