Influence of Erythrocyte Transfusion on the Risk of Acute Kidney Injury after Cardiac Surgery Differs in Anemic and Nonanemic Patients
Bibliographic record
Abstract
BACKGROUND: Acute kidney injury (AKI) after cardiac surgery is a major health issue. Two important risk factors for AKI are preoperative anemia and perioperative erythrocyte transfusion, and elucidating their relationship may help in devising preventive strategies. METHODS: In this cohort study of 12,388 adults who underwent cardiac surgery with cardiopulmonary bypass and received three units or less of erythrocytes on the day of surgery, the authors used propensity score methods and conditional logistic regression to explore the relationship between preoperative anemia (hemoglobin less than 12.5 g/dL), erythrocyte transfusion on the day of surgery, and AKI (more than 50% decrease in estimated glomerular filtration rate from preoperative to postoperative day 3-4). RESULTS: AKI occurred in 4.1% of anemic patients (n = 94/2,287) and 1.6% of nonanemic patients (n = 162 of 10,101) (P < 0.0001). In the 2,113 propensity-score matched pairs, anemic patients had higher AKI rates than nonanemic patients (3.8% vs. 2.0%; P = 0.0007). AKI rates increased in direct proportion to the amount of erythrocytes transfused, and this increase was more pronounced in anemic patients: in anemic patients, the rate increased from 1.8% among those not transfused to 6.6% among those transfused three units (chi-square test for trend P < 0.0001), whereas in nonanemic patients, it increased from 1.7% among those not transfused to 3.2% among those transfused three units (chi-square test for trend P = 0.1). CONCLUSIONS: Anemic patients presenting for cardiac surgery are more susceptible to transfusion-related AKI than nonanemic patients. Interventions that reduce perioperative transfusions may protect anemic patients against AKI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".