Association Between Perioperative Hypotension and Graft Function in Kidney Transplantation
Bibliographic record
Abstract
Introduction: The impact of perioperative hypotension on graft function after kidney transplantation (KT) has not been well-described; however, it may be expected to negatively impact posttransplant outcomes. Methods: We conducted a retrospective cohort study of adult patients undergoing KT in a multiprovincial renal program from 2006 to 2019. Using multivariable logistic regression, we assessed the association of intraoperative hypotension (IOH; systolic blood pressure (sBP) ≤ 90 mm Hg within the final hour of surgery) and postoperative hypotension (POH; occurring within the first 2 postoperative days) with delayed graft function (DGF). In secondary analyses, we used adjusted logistic regression or Cox proportional hazards models to assess the impact of hypotension on prolonged length of stay (LOS), death-censored graft loss (DCGL), and all-cause graft loss (ACGL). Results: Of the 1020 patients included, 209 (20.5%) and 112 (11.0%) had IOH and POH, respectively. POH was associated with DGF (adjusted odds ratio [aOR]: 4.01, 95% CI: 2.24-7.19), LOS (aOR: 2.82, 95% CI: 1.69-4.71), DCGL (adjusted hazard ratio [aHR]: 3.37, 95% CI: 1.29-8.84), and ACGL (aHR: 2.21, 95% CI: 1.26-3.89). IOH was not associated with DGF (aOR: 1.02, 95% CI: 0.61-1.72) or LOS (aOR: 1.19, 95% CI: 0.81-1.76), but was associated with reduced DCGL (aHR: 0.32, 95% CI: 0.13-0.82) and ACGL (aHR: 0.59, 95% CI: 0.36-0.98). There was a trend toward greater susceptibility to POH in male than female recipients; however, this did not meet statistical significance. Conclusion: Overall, POH, but not IOH, was associated with an increased risk of DGF, prolonged LOS, DCGL, and ACGL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".