Postoperative outcomes of kidney transplant recipients undergoing non-transplant-related elective surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Reliable estimates of the absolute and relative risks of postoperative complications in kidney transplant recipients undergoing elective surgery are needed to inform clinical practice. This systematic review and meta-analysis aimed to estimate the odds of both fatal and non-fatal postoperative outcomes in kidney transplant recipients following elective surgery compared to non-transplanted patients. Methods Systematic searches were performed through Embase and MEDLINE databases to identify relevant studies from inception to January 2020. Risk of bias was assessed by the Newcastle Ottawa Scale and quality of evidence was summarised in accordance with GRADE methodology (grading of recommendations, assessment, development and evaluation). Random effects meta-analysis was performed to derive summary risk estimates of outcomes. Meta-regression and sensitivity analyses were performed to explore heterogeneity. Results Fourteen studies involving 14,427 kidney transplant patients were eligible for inclusion. Kidney transplant recipients had increased odds of postoperative mortality; cardiac surgery (OR 2.2, 95%CI 1.9–2.5), general surgery (OR 2.2, 95% CI 1.3–4.0) compared to non-transplanted patients. The magnitude of the mortality odds was increased in the presence of diabetes mellitus. Acute kidney injury was the most frequently reported non-fatal complication whereby kidney transplant recipients had increased odds compared to their non-transplanted counterparts. The odds for acute kidney injury was highest following orthopaedic surgery (OR 15.3, 95% CI 3.9–59.4). However, there was no difference in the odds of stroke and pneumonia. Conclusion Kidney transplant recipients are at increased odds for postoperative mortality and acute kidney injury following elective surgery. This review also highlights the urgent need for further studies to better inform perioperative risk assessment to assist in planning perioperative care.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".