Soluble urokinase plasminogen activator receptor and perioperative complications: a systematic review
Bibliographic record
Abstract
BACKGROUND: Better informative predictive tools are required to improve risk-stratification in patients undergoing surgery. Soluble urokinase-type plasminogen activator receptor (suPAR) has the potential to predict postoperative complications. The primary objective of this systematic review was to evaluate the association between suPAR measured around the time of surgery and perioperative complications. METHODS: We systematically reviewed studies indexed in Embase or PubMed from inception up to October 8, 2024. We included studies evaluating the association between suPAR measured around surgery and perioperative complications captured up to 90 days after surgery. We excluded case reports, reviews, editorials, studies with non-human participants, and clinical practice guidelines. We used the Quality in Prognostic Studies tool to assess the risk of bias, and Grading of Recommendations Assessment, Development and Evaluation to ascertain the certainty in the inference for reported associations. RESULTS: Eighteen of the 19 included studies provided evaluable data from 6410 patients on the association between suPAR and perioperative outcomes. There was moderate certainty that suPAR associates with acute kidney injury, low certainty of association with mortality, very low certainty of association with composites of multiple complications, resource utilization outcomes, and infectious complications. However, we were highly uncertain about the magnitude of association because studies were heterogeneous, poorly reported, mostly retrospective, and too small to provide robust estimates. CONCLUSIONS: We conclude that serum or plasma concentrations of suPAR measured around surgery is likely associated with perioperative complications, especially acute kidney injury. The magnitude of these associations and the clinical value of suPAR should now be compared to other clinical information and prognostic biomarkers in biobanks of large prospective studies.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".