Blood transfusion and acute kidney injury after cardiac surgery: a retrospective observational study
Bibliographic record
Abstract
PURPOSE: Cardiac surgery-associated acute kidney injury (AKI) is linked to poor outcomes. An observational study from Copenhagen, Denmark identified perioperative red blood cell (RBC) transfusion as a modifiable risk factor for AKI, with a dose-dependent relationship between the number of RBC units transfused and the occurrence and severity of AKI. We aimed to externally validate those findings in a larger population. METHODS: We conducted a retrospective observational study of adult patients undergoing nonemergent on-pump cardiac surgery at Toronto General Hospital (Toronto, ON, Canada) between 2016 and 2021. Acute kidney injury was classified using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Data were analyzed using inverse probability weighted logistic regression. RESULTS: Among 5,204 patients, 798 developed AKI, with 77% classified as stage 1, 11% as stage 2, and 12% as stage 3. Patients with AKI were older, had lower preoperative hemoglobin levels and estimated glomerular filtration rates, longer cardiopulmonary bypass duration, and lower intraoperative hemoglobin levels. Red blood cells were administered to 37% of patients, with 14% receiving plasma and 32% platelets. Only RBC transfusion, alone or combined with other blood products, was significantly associated with AKI. The transfusion of 1-2 RBC units increased the probability of stage 1 AKI by 4% and stage 2-3 AKI by 2% compared with patients not receiving RBCs. The risk was especially pronounced with the transfusion of > 2 units of RBCs, which raised the probability of stage 1 AKI by 12% and stage 2-3 AKI by 9%. CONCLUSIONS: This study confirms previous findings that RBC transfusion is associated with postoperative AKI in cardiac surgery patients. The association was strongest among patients who received > 2 units of RBCs. Prospective studies are needed to determine the optimal strategies for transfusion in these patients and evaluate potential alternatives.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".