Overweight and Obesity in Pediatric Kidney Transplant Recipients Are Not Associated With Higher Risks of Early Postoperative Complications
Bibliographic record
Abstract
BACKGROUND: Childhood overweight and obesity have been hypothesized to pose challenges in pediatric kidney transplantation due to potential complications and resultant worse outcomes. Herein, we aimed to determine if pediatric patients with an elevated body mass index (BMI) (> 85th percentile) are at higher risk for short-term complications following kidney transplantation. METHODS: Following a review of our transplant database (2010-2020), patients > 2-18 years were assigned to groups based on BMI percentiles at the time of surgery: normal (5th-85th percentile; n = 120) and obese/overweight (> 85th percentile; n = 60). Patients were matched using a 1-to-2-ratio nearest-neighbor propensity score matching adjusting for underlying diagnosis, sex, and age. The primary outcome was postoperative complications classified according to Clavien-Dindo, and secondary outcomes included postoperative creatinine and kidney graft rejection. RESULTS: One hundred eighty patients (n = 120 in the control and n = 60 in the overweight and obese group) were included. There was no significant difference in postoperative complications (72/120 vs. 40/60, p = 0.42). We noted no difference in nadir creatinine (7 days, [interquartile range (IQR) 1, 679] and 9 days, [IQR 1, 690], p = 0.11), postoperative creatinine levels at 3-, 6-, and 12-months post-transplantation, or rejection rates (8.4% and 8.4%, p > 0.99) between normal weighed and obese/overweight patients. However, more overweight and obese patients required intraoperative blood transfusions (55% and 33.4%, p = 0.006) and longer hospital stays (18.0 [IQR 9, 133] and 15.0 [IQR 7, 49], p = 0.02). CONCLUSIONS: It appears as though overweight and obese pediatric kidney transplant patients do not experience more postoperative complications. We propose that overweight and obesity should not be considered criteria to exclude pediatric patients from undergoing kidney transplantations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".