Preoperative anemia and transfusion in cardiac surgery: a single-centre retrospective study
Bibliographic record
Abstract
BACKGROUND: Preoperative anemia and transfusion are associated with worse outcomes. This study aims to identify the prevalence of preoperative anemia, transfusion rates on surgery day, and predictors of transfusion in elective cardiac surgery patients at our centre. We also aim to evaluate our preoperative intervention program, and examine the intervention window for anemia before surgery. METHODS: This study included 797 adult patients who underwent elective cardiac surgery at a tertiary hospital. Multivariable logistic regression analysis was used to identify predictors of transfusion on surgery day. RESULTS: Preoperative anemia was present in 15% of patients. Anemic patients had a significantly higher transfusion rate at 53% compared to 10% in non-anemic patients. Hemoglobin concentration, estimated glomerular filtration rate (eGFR), body surface area (BSA), and total cardiopulmonary bypass time were predictive of transfusion on surgery day. Patients had a median of 7 days between initial visit and surgery day, however, referral to the blood conservation clinic was only done for 8% of anemic patients and treatment was initiated in 3% of anemic patients. Among the 3 anemic patients who received treatment, 2 did not require blood transfusion on surgery day. CONCLUSIONS: Preoperative anemia is present in 15% of patients at our centre and these patients have 53% transfusion rates on surgery day. Hemoglobin concentration, eGFR, BSA, and total cardiopulmonary bypass time were predictors of transfusion on surgery day. Patients had a median of 7 days between initial visit and surgery day. Referral and anemia treatment were infrequently initiated in preoperative anemic patient.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".