Assessment of Risks of Pulmonary Infection During 12 Months Following Immunosuppressive Treatment for Active Connective Tissue Diseases: A Large-scale Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Pulmonary infections (PI) are leading causes of death in patients with connective tissue diseases (CTD). The PREVENT study (Pulmonary infections in patients REceiving immunosuppressiVE treatmeNT for CTD) assessed risk of PI in patients with active CTD in the contemporary era of advanced immunosuppressive therapy. METHODS: In patients who started corticosteroids (n = 763), conventional immunosuppressants or biologics for active CTD were enrolled. Clinical and laboratory data, usage of drugs, and occurrence of PI were collected for 12 months. Baseline risk factors were investigated using Cox regression analysis. A nested case-control (NCC) study was performed with 1:2 matched case-control pairs to assess the risk for each drug category. RESULTS: During the observation period, 32 patients died (4.2%) and 66 patients were lost to followup (8.6%). Patients with PI (n = 61, 8%) had a significantly worse accumulated survival rate than patients without (p < 0.01). Cox hazard regression analysis using baseline data showed that these factors were significantly associated with PI: age ≥ 65 years (HR 3.87, 95% CI 2.22-6.74), ≥ 20 pack-years of smoking (2.63, 1.37-5.04), higher serum creatinine level (1.21, 1.05-1.41 per 1.0 mg/dl increase), and maximum prednisolone (PSL) dose during the first 2 weeks of treatment (2.81, 1.35-5.86 per 1.0 mg/kg/day increase). Logistic regression analysis by an NCC study revealed that maximum PSL dose within 14 days before PI (OR 4.82, 95% CI 1.36-17.01 per 1.0 mg/dl increase; 2.57, 1.28-5.16 if ≥ 0.5 mg/kg/day) was significantly associated with the events, while other immunosuppressants were not. CONCLUSION: Physicians should be aware of the higher risks for corticosteroids of PI than other immunosuppressants and assess these risk factors before immunosuppressive treatment, to prevent PI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".