Combined Body Mass Index and Body Surface Area to Predict Post Kidney Transplant Outcomes in Patients With Obesity
Bibliographic record
Abstract
Background. The prevalence of obesity is increasing in both the general and kidney failure populations. Severe obesity (body mass index [BMI] ≥ 40 kg/m2) is considered by many centers to be a barrier to kidney transplantation (KT). Obesity is typically defined using BMI. Body surface area (BSA) is not considered, though may also be important. Methods. We examined post-KT adverse outcomes associated with obesity defined using combined BMI-BSA parameters in a cohort of adult KT recipients (living/deceased donor) across the United States (Scientific Registry of Transplant Recipients: 2000–2017). Recipient obesity was defined as BMI ≥30 kg/m2, or BSA ≥1.94 m2 in women and ≥2.17 m2 in men. We used multivariable cox proportional hazards or logistic regression models as appropriate to assess the association between BMI-BSA-defined obesity with death-censored graft loss, all-cause graft loss, and delayed graft function. Results. The final study included 242 432 patients; 77 556 (32.0%) had obesity based on BMI and 67 312 (28.6%) had obesity based on BSA. Compared to patients with a nonobese BMI and BSA, the adjusted risk of death-censored graft loss, all-cause graft loss, and delayed graft function was greatest when both BMI and BSA indicated obesity (adjusted hazard ratio 1.23, 95% confidence interval [CI]: 1.20-1.27, adjusted hazard ratio 1.09, 95% CI: 1.07-1.11, adjusted odds ratio 1.58, 95% CI: 1.53-1.63, respectively); a significantly greater risk than when BMI and BSA were discordant. Conclusions. Currently only BMI is considered when evaluating obesity-related KT risk; however, combined BMI-BSA obesity may better identify individuals at high risk of poor outcomes posttransplant than BMI alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".