Risk of total and cause-specific mortality in patients with rheumatoid arthritis-associated interstitial lung disease or bronchiectasis
Bibliographic record
Abstract
OBJECTIVES: To investigate total and cause-specific mortality risk among rheumatoid arthritis-associated lung disease (RA-LD) compared with RA patients without lung disease (RA-no LD). METHODS: We conducted a retrospective cohort study using the Mass General Brigham Biobank (Boston, MA), comparing RA-LD cases to RA-no LD comparators matched by calendar date, age, sex and RA duration. RA-LD was verified by medical record review and chest imaging for clinically apparent RA-associated interstitial lung disease (RA-ILD) and/or RA-associated bronchiectasis (RA-BR). Outcomes included total and cause-specific mortality (respiratory, cancer, cardiovascular, infection). Total mortality was analysed using Cox regression, and cause-specific mortality was analyzed using Fine and Gray models estimating subdistribution hazard ratios (sdHR) and 95% confidence intervals (CI), adjusted for propensity scores. RESULTS: We analyzed 221 RA-LD cases (151 RA-ILD and 70 RA-BR) and 980 RA-no LD comparators. RA-LD patients had higher total mortality (34.0 vs 13.1 per 1000 person-years; HR 1.97, 95%CI 1.43-2.70). Infection-related mortality was more common in RA-LD patients (7% vs 3%, P = 0.002). RA-LD was associated with increased respiratory (13.1 vs 1.7 per 1000 person-years; sdHR 6.68, 95%CI 3.19-14.00) and lung infection-related mortality (6.3 vs 1.4 per 1000 person-years; sdHR 3.33, 95%CI 1.32-8.37), particularly in RA-ILD patients. RA-BR patients had higher cancer mortality (8.6 vs 2.3 per 1000 person-years; sdHR 2.89, 95%CI 1.11-7.47). CONCLUSION: RA-LD, particularly RA-ILD, is associated with higher mortality, especially respiratory and infection-related mortality. RA-BR is associated with increased respiratory and cancer mortality. While larger studies are needed, these findings emphasize the need for improved treatments for RA-LD, effective infection prevention, and vigilant cancer surveillance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".