Prognosis and Risks for Probable Chronic Lung Allograft Dysfunction: A Prospective Multicenter Study
Bibliographic record
Abstract
Abstract Rationale Chronic lung allograft dysfunction (CLAD) hinders lung transplant success. A 2019 consensus refined CLAD diagnosis, introducing probable or definite CLAD based on persistence of lung function decline. Outcomes and risks for probable CLAD remain uncertain. Objectives We sought to determine the prognosis and clinical risks for probable CLAD in a prospective multicenter cohort. Methods Clinical Trials in Organ Transplantation–20 included 745 CLAD-eligible adult lung recipients at five centers and applied rigorous methods to prospectively adjudicate probable CLAD. The impact of probable CLAD on graft loss was determined using a Cox model that considered CLAD as a time-dependent covariate. Regularized Cox modeling with least absolute shrinkage and selection operator (LASSO) penalty was used to evaluate donor or recipient characteristics and the occurrence and timing of posttransplant events as probable CLAD risks. Similar analyses were performed for definite CLAD. Measurements and Main Results Probable CLAD occurred in 29.7% of patients at 3 years posttransplant and conferred a marked increase in risk for graft loss (unadjusted hazard ratio = 4.38, P < 0.001). Most patients (80%) with probable CLAD progressed to definite CLAD. Cytomegalovirus infection and, specifically, late presence (>90 d posttransplant) of donor-specific alloantibodies, acute rejection, acute lung injury, or organizing pneumonia contributed the greatest independent information about probable CLAD risk. Definite CLAD risks were similar. Conclusions Probable CLAD identifies patients at high risk for graft loss, supporting prospective identification of this condition for early initiation of CLAD-directed interventions. More effective strategies to prevent posttransplant cytomegalovirus, inhibit allospecific immunity, and reduce tissue injury are needed to reduce probable CLAD and improve lung recipient survival.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".