Balancing the benefits and risks of blood transfusions in patients undergoing cardiac surgery: a propensity-matched analysis
Bibliographic record
Abstract
OBJECTIVES: Prior studies have found that cardiac surgery patients receiving blood transfusions are at risk for increased mortality and morbidity following surgery. It is not clear whether this increased risk occurs across all haematocrit (HCT) levels. The goal of this study was to compare operative mortality in propensity-matched cardiac surgery patients based on stratification of the preoperative HCT levels. METHODS: Between 1 August 2004 and 30 June 2011, 3516 patients had cardiac surgery. One thousand nine hundred and twenty-two (54.5%) required blood transfusion during or after surgery. A propensity score for transfusion was developed based on 22 baseline variables. One thousand seven hundred and fourteen patients were matched: 857 in the transfusion group (TG) and 857 in the non-transfused control group (CG). Univariate analyses demonstrated that, after propensity matching, the groups did not differ on any baseline factors included in the propensity model. Operative mortality was defined as death within 30 days of surgery. Preoperative HCT was stratified into four groups: <36, 36-39, 40-42 and ≥ 43. RESULTS: For HCT <36%, 30-day mortality was higher in the TG than that in the CG (3.0 vs 0.0%). For HCT 36-39, operative mortality was similar between TG (1.1%, N = 180) and CG (0.8%, N = 361; P = 0.748). For HCT 40-42, operative mortality was significantly higher in the TG compared with that in the CG (1.9 vs 0%, N = 108 and 218, respectively; P = 0.044). For HCT of ≥ 43, there was a trend towards higher operative mortality in the TG vs the CG (2.0 vs 0%, N = 102 and 152, respectively; P = 0.083). Other surgical complications followed the same pattern with higher rates found in the transfused group at higher presurgery HCT levels. HCT at discharge for the eight groups were similar, with an average of 29.1 ± 1.1% (P = 0.117). CONCLUSIONS: Our study indicates that a broad application of blood products shows no discernible benefits. Furthermore, patients who receive blood at all HCT levels may be placed at an increased risk of operative mortality and/or other surgical complications. Paradoxically, even though patients with low HCTs theoretically should benefit the most, transfusion was still associated with a higher complication and mortality rate in these patients. Our results indicate that blood transfusion should be used judiciously in cardiac surgery patients.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".