Increased Recipient Body Mass Index Is Associated With Acute Rejection and Other Adverse Outcomes After Kidney Transplantation
Bibliographic record
Abstract
BACKGROUND: Outcomes of kidney transplant recipients with increased body mass index (BMI) remain controversial. We studied the relationship between BMI and clinically relevant outcomes among kidney transplant recipients at a large center. METHODS: We performed an observational cohort study of all recipients of kidney transplants at our center from January 1, 2000 to December 31, 2010 to determine if increased BMI at transplantation is associated with adverse outcomes, including delayed graft function and biopsy-proven acute rejection (BPAR). Recipient BMI was categorized as <20, 20 to 24.9 (reference), 25 to 29.9, 30 to 34.9, and ≥35 kg/m. Potential confounders were included in logistic and Cox proportional hazards models. RESULTS: A total of 1151 patients were studied. Recipient BMI of 30 to 34.9 and ≥35 kg/m were associated with an increased risk of delayed graft function (odds ratio [95% confidence interval [CI], 1.92 [1.16-3.19] and 4.49 [2.24-9.00], respectively). BMI≥35 kg/m was also associated with an increased risk of BPAR (hazard ratio [HR; 95% CI], 2.43 [1.48-3.99]), all-cause graft failure (HR [95% CI], 1.97 [1.09-3.56]), and death-censored graft failure (HR [95% CI], 2.43 [1.07-5.51]). Adjustment for acute rejection as a time-varying covariate significantly attenuated the association with graft failure endpoints. There was no significant relation between BMI and death with graft function. CONCLUSIONS: Increased BMI at kidney transplantation is a predictor of adverse outcomes, including BPAR. The role of pretransplantation weight reduction in improving graft and patient outcomes requires further study.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".