Pre‐operative anaemia, intra‐operative hepcidin concentration and acute kidney injury after cardiac surgery: a retrospective observational study
Bibliographic record
Abstract
Summary Acute kidney after cardiac surgery is more common in anaemic patients, whereas haemolysis during cardiopulmonary bypass may lead to iron‐induced renal injury. Hepcidin promotes iron sequestration by macrophages: hepcidin concentration is reduced by anaemia and increased by inflammation. We analysed the associations in 525 patients between pre‐operative anaemia (haemoglobin < 130 g.l −1 in men and < 120 g.l −1 in women), intra‐operative hepcidin concentration and acute kidney injury (dialysis or > 26.4 μmol.l −1 or > 50% creatinine increase during the first two days after cardiac surgery. Rates of pre‐operative anaemia and postoperative kidney injury were 109/525 (21%) and 36/525 (7%), respectively. The median ( IQR [range]) intra‐operative hepcidin concentration was 20 (10–33 [0–125]) μg.l −1 and was lower in anaemic patients than those who were not: 15 (4–28 [0–125]) μg.l −1 vs. 21 (12–33 [0–125]) μg.l −1 , respectively, p = 0.002. Four variables were independently associated with postoperative kidney injury, for which the beta‐coefficients ( SE ) were: minutes on cardiopulmonary bypass, 0.016 (0.004), p < 0.001; intra‐operative hepcidin concentration, 0.032 (0.008), p < 0.001; pre‐operative anaemia, 1.97 (0.56), p < 0.001; and Cleveland clinic risk score, 0.88 (0.35), p = 0.005. Contrary to generally increased rates of kidney injury in patients with higher hepcidin concentrations, rates of kidney injury in anaemic patients were lower in patients with higher hepcidin concentrations, beta‐coefficient ( SE ) −0.037 (0.01), p = 0.007. In cardiac surgical patients the rate of postoperative acute kidney injury predicted by the Cleveland risk score might be adjusted for pre‐operative anaemia and intra‐operative cardiopulmonary bypass time and hepcidin concentration. Pre‐operative correction of anaemia, reduction in intra‐operative bypass time and modification of iron homeostasis and hepcidin concentration might reduce acute kidney injury.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".