Trends, Determinants, and Impact on Survival of Post-Lung Transplant Weight Changes: A Single-center Longitudinal Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Weight gain is commonly seen in lung transplant (LTx) recipients. Although previous studies have focused on weight changes at fixed time periods and relatively early after transplant, trends over time and long-term weight evolution have not been described in this population. The study objectives were to document weight changes up to 15 years post-LTx and assess the predictors of post-LTx weight changes and their associations with mortality. METHODS: Retrospective cohort study of LTx recipients between January 1, 2000, and November 30, 2016 (n = 502). Absolute weight changes from transplant were calculated at fixed time periods (6 mo, 1, 2, 5, 10, and 15 y), and continuous trends over time were generated. Predictors of weight changes and their association with mortality were assessed using linear and Cox regression analysis. RESULTS: LTx recipients experienced a gradual increase in weight, resulting from the combination of multiple weight trajectories. Interstitial lung disease diagnosis negatively predicted post-LTx weight changes at all time points, whereas transplant body mass index categories were significant predictors at earlier time points. Patients with a weight gain of >10% at 5 years had a better survival (hazard ratio [HR], 0.36; 95% confidence interval [CI], 0.20-0.66), whereas a 10% weight loss at earlier time points was associated with worse survival (1 y: HR, 2.04; 95% CI, 1.22-3.41 and 2 y: HR, 2.37; 95% CI, 1.22-4.58). CONCLUSIONS: Post-LTx weight changes display various trajectories, are predicted to some extent by individual and LTx-related factors, and have a negative or positive impact on survival depending on the time post-LTx. These results may lead to a better individualization of weight management after transplant.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".