The Impact of Waiting List BMI Changes on the Short-term Outcomes of Lung Transplantation
Bibliographic record
Abstract
BACKGROUND: Obesity and underweight are associated with a higher postlung transplantation (LTx) mortality. This study aims to assess the impact of the changes in body mass index (BMI) during the waiting period for LTx on early postoperative outcomes. METHODS: Medical records of 502 consecutive cases of LTx performed at our institution between 1999 and 2015 were reviewed. Patients were stratified per change in BMI category between pre-LTx assessment (candidate BMI) and transplant BMI as follows: A-candidate BMI, less than 18.5 or 18.5 to 29.9 and transplant BMI, less than 18.5; B-candidate BMI, less than 18.5 and transplant BMI, 18.5 to 29.9; C-candidate BMI, 18.5 to 29.9 and transplant BMI, 18.5 to 29.9; D-candidate BMI, 30 or greater and transplant BMI, 18.5 to 29.9; and E-candidate BMI, 30 or greater or 18.5 to 29.9 and transplant BMI, 30 or greater. Our primary outcome was in-hospital mortality and secondary outcomes were length of mechanical ventilation, intensive care unit length of stay (LOS), hospital LOS and postoperative complications. RESULTS: BMI variation during the waiting time was common, as 1/3 of patients experienced a change in BMI category. Length of mechanical ventilation (21 days vs 9 days; P = 0.018), intensive care unit LOS (26 days vs 15 days; P = 0.035), and rates of surgical complications (76% vs 44%; P = 0.018) were significantly worse in patients of group E versus group D. Obese candidates who failed to decrease BMI less than 30 by transplant exhibited an increased risk of postoperative mortality (odds ratio, 2.62; 95% confidence interval, 1.01-6.48) compared with patients in group C. Pre-LTx BMI evolution had no impact on postoperative morbidity and mortality in underweight patients. CONCLUSIONS: Our results suggest that obese candidates with an unfavorable pretransplant BMI evolution are at greater risk of worse post-LTx outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".