Comparable kidney transplant outcomes in selected patients with a body mass index ≥ 40: A personalized medicine approach to recipient selection
Bibliographic record
Abstract
Abstract Introduction Many kidney transplant (KT) centers decline patients with a body mass index (BMI) ≥40 kg/m 2 . This study's aim was to evaluate KT outcomes according to recipient BMI. Methods We performed a single‐center, retrospective review of adult KTs comparing BMI ≥40 patients ( n = 84, BMI = 42 ± 2 kg/m 2 ) to a matched BMI < 40 cohort ( n = 84, BMI = 28 ± 5 kg/m 2 ). Patients were matched for age, gender, race, diabetes, and donor type. Results BMI ≥40 patients were on dialysis longer (5.2 ± 3.2 years vs. 4.1 ± 3.5 years, p = .03) and received lower kidney donor profile index (KDPI) kidneys (40 ± 25% vs. 53 ± 26%, p = .003). There were no significant differences in prevalence of delayed graft function, reoperations, readmissions, wound complications, patient survival, or renal function at 1 year. Long‐term graft survival was higher for BMI ≥40 patients, including after adjusting for KDPI (BMI ≥40: aHR = 1.79, 95% CI = 1.09–2.9). BMI ≥40 patients had similar BMI change in the first year post‐transplant (delta BMI: BMI ≥ 40 +.9 ± 3.3 vs. BMI < 40 +1.1 ± 3.2, p = .59). Conclusions Overall outcomes after KT were comparable in BMI ≥40 patients compared to a matched cohort with lower BMI with improved long‐term graft survival in obese patients. BMI‐based exclusion criteria for KT should be reexamined in favor of a more individualized approach.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".