Does the Use of Erythropoietin Reduce the Risk of Exposure to Allogeneic Blood Transfusion in Cardiac Surgery? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The use of blood conservation techniques is important in cardiac surgery as postoperative bleeding is common and allogeneic blood transfusion carries the risk of transfusion reactions and infection transmission. Erythropoietin with and without preoperative autologous blood donation is one of the modalities to avoid allogeneic blood transfusion. The objective of this review was to assess the effectiveness of erythropoietin in reducing the risk of exposure to allogeneic blood transfusion during or after cardiac surgery. METHODS: A meta-analysis of 11 identified randomized controlled trials, reporting comparisons between erythropoietin and control, was undertaken. The primary outcome was the number of patients exposed to allogeneic blood transfusion during or after cardiac surgery. RESULTS: Eleven studies, involving 708 patients, met the inclusion criteria for this review. In total, 471 patients were given erythropoietin, and 237 patients formed the control group. The administration of erythropoietin with and without preoperative autologous blood transfusion prior to cardiac surgery is associated with a significant risk reduction: RR = 0.28 (95% CI 0.18-0.44, P < 0.001) and RR = 0.53 (95% CI 0.32-0.88, P < 0.01), respectively. CONCLUSION: The administration of erythropoietin before cardiac surgery is associated with a significant reduction in the risk of exposure to allogeneic blood transfusion. Further studies are warranted to define the patients' subgroups that may benefit the most from EPO administration.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".