The effect of pre–heart transplant body mass index on posttransplant outcomes: An analysis of the ISHLT Registry Data
Bibliographic record
Abstract
We evaluated the effect of pre-heart transplant body mass index (BMI) on posttransplant outcomes using the International Society for Heart and Lung Transplantation Registry. Kaplan-Meier analysis and a multivariable Cox proportional hazard regression model were used for all-cause mortality, and cause-specific hazard regression for cause-specific mortality and morbidity. We assessed 38 498 recipients from 2000 to 2014 stratified by pretransplant BMI. Ten-year survival was 56% in underweight, 59% in normal weight, 57% in overweight, 52% in obese class I, 54% in class II, and 47% in class III patients (P < 0.001). Mortality was increased in underweight (HR 1.29, 95% CI 1.24-1.35), obese class I (HR 1.19, 95% CI 1.13-1.26), class II (HR 1.20, 95% CI 1.08-1.32), and class III patients (HR 1.45, 95% CI 1.15-1.83). Obesity was independently associated with increased death from myocardial infarction, chronic rejection, infection, and renal dysfunction. An underweight BMI lead to increased death from infection, acute and chronic rejection, malignancy, and bleeding. Obese patients had a higher incidence of renal dysfunction, diabetes, stroke, acute rejection, cardiac allograft vasculopathy, and malignancy, and underweight recipients had increased acute rejection. We have shown that pretransplant obese and underweight patients have increased post-heart transplant mortality and morbidity. This has implications for candidate selection and posttransplant management.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".