Management of the Wound Complications in De Novo Renal Transplant Recipients: US92 12-Month Randomized Study.
Bibliographic record
Abstract
Background: In renal transplant recipients (RTxR), reducing CNI exposure with mTOR inhibitor can have a positive impact on renal function (RF). However, the use of higher dose of mTORi-based immunosuppression may be associated with an increased risk for wound complications (WC). Here we report the incidence of WC and management in the US92 study. Methods: In this 12 month (M), multicenter (52 US & Canadian centers), randomized, open-label non-inferiority study, 613 de novo RTxR received everolimus (EVR) (starting at 0.75 mg bid, adjusted to trough level 3-8 ng/ml)+low-dose tacrolimus (LTac; n=309) or mycophenolate mofetil (MMF) (2 g/day)+standard-dose Tac (STac; n=304). All RTxR received induction therapy (basiliximab if PRA<20%; or Rabbit ATG if PRA ≥20%) and steroids per local practice. Primary endpoint was composite efficacy failure rate at 12M (tBPAR, graft loss, death, or lost to follow-up). Incidence of WC with EVR+LTac vs MMF+STac throughout 12M were reported descriptively. Influence of BMI, recipient age, induction agent, and history of diabetes on WC was also analyzed by logistic regression. Results: At 12M, the composite efficacy failure rate with EVR+LTac vs MMF+STac was 24.6%; n=76 vs 20.4%; n=62. In the logistic regression analysis, history of diabetes was the only significant risk factor for WC (OR=1.56, 95% CI [1.04, 2.34], p=0.031) and more recipients had a history of diabetes in EVR+LTac (n=111, 36.3%) vs MMF+STac (n=95, 31.3%). Delayed or impaired transplant incision healing was the most frequently reported WC (Table) and despite the imbalance in baseline characteristics, there was no significant difference between the treatment groups. Similar proportions of patients in both EVR+LTac vs MMF+STac needed surgical repair (7.2% vs 5.6%; p=0.5087) or interventional radiology (4.6% vs 4.3%; p=0.9999).Table: No Caption available.Conclusions: Incidence rate of each WC was not statistically different in EVR+LTac vs MMF+STac. A higher number of patients in EVR+LTac group had history of diabetes that was a significant factor for WC. Management required for WC was similar between the two treatment groups. DISCLOSURES:Shaffer, D.: Grant/Research Support, Novartis. Mulgaonkar, S.: Grant/Research Support, Novartis, Other, Novartis, Advisor. Shihab, F.: Other, Novartis, Consultant and Speaker, Astellas, Consultant. Tomlanovich, S.: Grant/Research Support, Novartis, Genzyme, Genentech, Astellas, Alexion, Pfizer. Patel, D.: Employee, Novartis Pharmacutical Corporation. McCague, K.: Employee, Novartis Pharmacutical Corporation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".