Advance Targeted Transfusion in Anemic Cardiac Surgical Patients for Kidney Protection
Bibliographic record
Abstract
INTRODUCTION: : Acute kidney injury (AKI) is a serious complication of cardiac surgery, and preoperative anemia and perioperative erythrocyte transfusion are important risk factors. Prophylactic erythrocyte transfusion in anemic patients may, therefore, protect against AKI. METHODS: : In this unblinded, parallel-group, randomized pilot trial, 60 anemic patients (hemoglobin 10-12 g/dL) undergoing cardiac surgery with cardiopulmonary bypass were randomized (1:1) to prophylactic transfusion (2 units of erythrocytes transfused 1 to 2 days before surgery (n = 29) or standard of care (transfusions as indicated; n = 31). Between-group differences in severity of perioperative anemia, transfusion, and AKI (more than 25% drop in estimated glomerular filtration rate) were measured. The relationships between transfusion, iron levels, and AKI were also measured. RESULTS: : Perioperative anemia and erythrocyte transfusions were lower in the prophylactic transfusion group--median (25th, 75th percentiles) for nadir hemoglobin was 8.3 (7.9, 9.1) versus 7.6 (6.9, 8.2) g/dL (P = 0.0008) and for transfusion was 0 (0, 2) versus 2 (1, 4) units (P = 0.0002)--but between-group AKI rates were comparable (11 patients per group). In 35 patients with iron studies, perioperative transfusions were directly related to postoperative transferrin saturation (correlation coefficient 0.6; P = 0.0002), and high (more than 80%) transferrin saturation was associated with AKI (5/5 vs. 8/30; P = 0.005), implicating transfusion-related iron overload as a cause of AKI. CONCLUSIONS: : In anemic patients, prophylactic erythrocyte transfusion reduces perioperative anemia and erythrocyte transfusions, and may reduce plasma iron levels. Adequately powered studies assessing the effect of this intervention on AKI are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".