A cluster-based analysis evaluating the impact of comorbidities in fibrotic interstitial lung disease
Bibliographic record
Abstract
BACKGROUND: Comorbidities are frequent and have been associated with poor quality of life, increased hospitalizations, and mortality in patients with interstitial lung disease (ILD). However, it is unclear how comorbidities lead to these negative outcomes and whether they could influence ILD disease progression. The goal of this study was to identify clusters of patients based on similar comorbidity profiles and to determine whether these clusters were associated with rate of lung function decline and/or mortality. METHODS: Patients with a major fibrotic ILD (idiopathic pulmonary fibrosis (IPF), fibrotic hypersensitivity pneumonitis, connective tissue disease-associated ILD, and unclassifiable ILD) from the CAnadian REgistry for Pulmonary Fibrosis (CARE-PF) were included. Hierarchical agglomerative clustering of comorbidities, age, sex, and smoking pack-years was conducted for each ILD subtype to identify combinations of these features that frequently occurred together in patients. The association between clusters and change in lung function over time was determined using linear mixed effects modeling, with adjustment for age, sex, and smoking pack-years. Kaplan Meier curves were used to assess differences in survival between the clusters. RESULTS: Discrete clusters were identified within each fibrotic ILD. In IPF, males with obstructive sleep apnea (OSA) had more rapid decline in FVC %-predicted (- 11.9% per year [95% CI - 15.3, - 8.5]) compared to females without any comorbidities (- 8.1% per year [95% CI - 13.6, - 2.7]; p = 0.03). Females without comorbidities also had significantly longer survival compared to all other IPF clusters. There were no significant differences in rate of lung function decline or survival between clusters in the other fibrotic ILD subtypes. CONCLUSIONS: The combination of male sex and OSA may portend worse outcomes in IPF. Further research is required to elucidate the interplay between sex and comorbidities in ILD, as well as the role of OSA in ILD disease progression.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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 | 0.001 | 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".