Bibliographic record
Abstract
Graft dysfunction continues to be a major challenge in the field of lung transplantation, with primary graft dysfunction (PGD) and chronic lung allograft dysfunction contributing substantially to mortality risk in the early and late phases of the posttransplant journey respectively.1 In recent years, a new conceptual phenotype of graft dysfunction has been introduced: baseline lung allograft dysfunction (BLAD), where lung function fails to reach a normal threshold—specifically, a forced expiratory volume in 1 s (FEV1) and a forced vital capacity of 80% of the predicted recipient reference values on 2 consecutive tests at least 3 wk apart.2 BLAD can be conceptualized as a physiologic state which can likely be the result of multiple different insults to the respiratory apparatus, including the lung tissue, the respiratory muscles, and the large airways, among others. It has now been associated with an increased risk of death in several single- and multicenter studies, and potentially with an increased risk of chronic lung allograft dysfunction as well.2-4 Defining “normal” in the initial studies, however, was done only in double-lung transplants, excluding single and lobar transplants given the lower donated tissue volumes given reference values for normal refer to a population of nontransplant recipients with 2 lungs free from disease. This has resulted in single-lung transplant recipients—who constitute up to one quarter of the world’s lung transplant recipients—being excluded from these discussions.1 In this issue of Transplantation, Gerckens et al5 from the University of Munich, Germany, attempt to address this issue by testing a different threshold in a pure cohort of single-lung transplant recipients from their center. Specifically, they selected a priori a threshold of 60% predicted for both the FEV1 and forced vital capacity, referencing previous publications showing that single-lung transplant recipients have tended to have roughly 20% less lung function compared with double lungs. They otherwise conducted an analogous analysis to the original publication from our group at the University of Alberta in Edmonton, Canada, assessing the survival association of failure to reach normal threshold and analyzing potential risk factors.2 The results paint a compelling picture that the 60% threshold in singles captures a similar physiologic risk state that 80% does in doubles, with an associated hazard ratio for death of 2.24 adjusted for age, sex at birth, and pretransplant disease category. Their noted prevalence of single-lung BLAD (43%) was also similar to previously cited estimates in double lungs, and a similar set of demographic and pretransplant factors was noted, most notably interstitial or restrictive lung disease as indication for transplant. An important observation here which is unique to single lungs is the relationship between native lung hyperinflation and BLAD. Native lung hyperinflation is a condition where patients who have undergone single-lung transplant for obstructive and hyperinflated lung diseases develop a posttransplant shift of the mediastinum toward the transplanted lung, resulting in compression of lung structure and function. The demonstration that this increases the risk of the single-lung BLAD construct is plausible and validates the authors’ proposed definition. One important relationship the authors were not able to analyze was the association between PGD and single-lung BLAD. Previous studies have shown an association between PGD and BLAD in double-lung transplant recipients, which could relate to uncorrectable tissue or airway damage from severe reperfusion injury, or potentially even long-term sequelae of critical illness.3,6 Testing this association in the unique context of single-lung transplants would have been interesting, but the authors were not able to retrieve this data element. As well, the authors note that although the 60% threshold is reasonable, it is not data-driven and it is possible that a different threshold may have dichotomized the population more effectively (the same is true of the double-lung threshold, even if 80% has a long history as a normal threshold in lung disease).7 Finally, as with the BLAD definition for double lungs, the use of a percent predicted approach has the desirable properties of familiarity and usability as well as defining abnormal and severity of abnormal with a single metric, but it has been criticized in pulmonary medicine for misclassifying patients and is not the main standard used in lung function interpretation now for normal thresholds.8,9 The Munich group’s approach to finding the normal baseline in single-lung transplants—defining a plausible threshold with a similar prevalence and survival association—is reasonable and will be an important piece of this puzzle but represents just 1 approach to the problem. Another approach may simply be to apply the same normal threshold to all lung transplant recipients, irrespective of the transplant type or the volume of donor lung (ie, lobar, nonanatomical size reduction, etc). Initially, this may seem illogical, but it may be more in line with the ultimate goal of lung transplantation: to restore normal lung function to restore quality and quantity of life irrespective of operation type. We do not expect most single-lung transplants to have the same function as double lungs and would therefore expect to see a higher prevalence of BLAD in single lungs, but viewed from another perspective, the generally poorer outcomes associated with nondouble-lung transplants may be in part explained by their increased risk of subnormal lung function.1,10 In essence, a unified threshold-based approach would be inclusive of single or lobar transplantation as a risk factor for BLAD, whereas the Munich group’s approach effectively tries to correct for the lower donor lung tissue volume in single-lung transplants. This study contributes meaningfully to the conversation about BLAD in lung transplant recipients and provides us insights into how to go about approaching this condition in single lung transplants. Ultimately, these questions will be best answered through international collaboration and consensus, including consideration of transplant type–specific thresholds, the role of lower limit of normal-based thresholds, understanding the causes and pathways that lead to BLAD, and most importantly, the path forward for BLAD patients in terms of investigations and treatments.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".