Perioperative Hemoglobin and Delayed Graft Function in Kidney Transplant Recipients
Bibliographic record
Abstract
Background A Cochrane review of 31 trials reported no mortality or morbidity advantage with a liberal over restrictive transfusion strategy. Most centers use an absolute hemoglobin concentration (Hb) threshold of 70 g/L for transfusion, unless patients have active cardiac ischemia. However, recent studies have suggested that relative change in hemoglobin levels ([INCREMENT]Hb) is more predictive of patient outcomes. No such analysis has been done in patients undergoing a kidney transplantation, where procured grafts are at high risk of acute tubular necrosis due to ischemia. Given this, we aimed to analyze the relationship between delayed graft function (DGF), and Hb and [INCREMENT]Hb during transplant. Methods We conducted a single-center retrospective chart review of all adult, deceased donor kidney transplants between 2003 and 2017. Recipients of simultaneous multi-organ transplants and those that received a transfusion within the first 24 hours were excluded. Hb was captured at various time points within the first 24 hours post-transplant. [INCREMENT]Hb was defined as = [(pre-transplant Hb – nadir post-transplant Hb)/pre-transplant Hb] x 100. The outcome of interest was DGF, defined as the need for dialysis within the first week post-transplant. We excluded transplants that had primary non-function. Results Of the 934 transplants, 705 were eligible for analysis. Of these 25% (176) experienced DGF. Several baseline differences were noted in those that experienced DGF. Amongst those recipients that developed DGF, Hb values pre-transplant, the lowest Hb post-transplant or [INCREMENT]Hb were not statistically different from those that did not develop DGF (Table 1). In a univariate analysis, only pre-transplant Hb but not the lowest Hb post-transplant or [INCREMENT]Hb were predictive of DGF (Table 2). This effect lost significance in a multivariate analysis.Conclusion In those recipients that did not receive a blood transfusion perioperatively, the relative change in hemoglobin post-transplant was not predictive of DGF. Future work entails analyzing outcomes in those that received a transfusion. Given the risk of sensitization with blood transfusions, our work supports current restrictive transfusion practices in the kidney transplant population.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".