A European Multi-Center Analysis of Extracorporeal Photopheresis as Therapy for Chronic Lung Allograft Dysfunction
Bibliographic record
Abstract
Extracorporeal photopheresis (ECP) is used by few lung transplant centers to treat chronic lung allograft dysfunction (CLAD). Although reported results suggest a beneficial effect on CLAD progression, evidence is limited to single center experiences. The aim of this study is to analyze outcomes of ECP in a large multicenter European cohort. The primary endpoint was patient survival after initiation of ECP. This study included 631 patients, 87% suffered from bronchiolitis obliterans syndrome (BOS), and 13% had restrictive allograft syndrome (RAS). Long-term stabilization was achieved in 42%, improvement in 9%, and no response in 26%. Within the first 12 months of therapy, 23% of patients died. Patients’ survival after initiation of ECP at 5 years was 56% in stable, 70% in responders, and 35% in non-responders (p= 0.001). In multivariable Cox regression, both stabilization (HR: 0.48, CI: 0.27–0.86,p= 0.013) and response (HR: 0.11, CI: 0.04–0.35,p< 0.001) to ECP were associated with survival. Absolute FEV1 at baseline was also protective (HR: 0.09, CI: 0.01–0.94,p= 0.046). RAS phenotype was the only risk factor for mortality (HR: 2.11, 1.16–3.83,p= 0.006). This study provides long-term outcomes of ECP use in CLAD patients in the largest published cohort to date. Two-thirds of the cohort had a sustained response to ECP with excellent long-term results.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".