Pretransplant Serum Creatinine in Peritoneal Dialysis Patients Predicts Graft Outcomes
Bibliographic record
Abstract
Rationale & Objective: Although peritoneal dialysis (PD) as a pretransplant dialysis modality is associated with favorable outcomes after kidney transplant, it is unknown if pretransplant serum creatinine (Scr) level is associated with subsequent graft outcomes in candidates managed with PD. Our objective was to examine the association between Scr at the time of transplant and short-term and long-term outcomes posttransplant. Study Design: Retrospective cohort study. Setting & Participants: A total of 20,166 adult (≥18 years of age) patients who were receiving PD at the time of a first living or deceased donor kidney transplant in the United States between 2000 and 2017, identified using the Scientific Registry of Transplant Recipients database. Exposures: Primary exposure was final Scr level before transplant, categorized as <5, 5-8, 8-12, and >12 mg/dL. Sensitivity analyses for patient subgroups included recipient age (≥50 vs <50 years) and dialysis vintage (≥3 vs <3 years) at transplant. Outcomes: The primary outcome was death-censored graft loss (DCGL). Secondary outcomes included all-cause graft loss and delayed graft function (DGF). Results: Pretransplant Scr was significantly associated with DCGL (adjusted HR, 1.17; 95% CI, 1.02-1.34 for Scr >12 mg/dL [reference <5 mg/dL]) and DGF (adjusted OR, 2.71; 95% CI, 2.26-3.26 for Scr >12 mg/dL [reference <5 mg/dL]). There was no association with all-cause graft loss. The risk of DCGL and DGF associated with high pretransplant Scr was higher for those who were older (≥50 years) and those with longer dialysis vintage (≥3 years). Limitations: No access to potential predictors of pretransplant Scr including residual kidney function, dialysis adequacy and adherence; exact timing of Scr values pretransplant was unknown. Conclusions: To our knowledge, this is the first study to explore the association between pretransplant Scr level in PD patients and graft outcomes after kidney transplantation. The reason for this increased risk is unclear but may reflect reduced residual kidney function, among other factors.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".