Blood transfusion in cardiac surgery does increase the risk of 5‐year mortality: results from a contemporary series of 1714 propensity‐matched patients
Bibliographic record
Abstract
BACKGROUND: Studies have found that cardiac surgery patients receiving blood transfusions are at risk for increased mortality during the first year after surgery, but risk appears to decrease after the first year. This study compared 5-year mortality in a propensity-matched cohort of cardiac surgery patients. STUDY DESIGN AND METHODS: Between July 1, 2004, and June 30, 2011, 3516 patients had cardiac surgery with 1920 (54.6%) requiring blood transfusion. Propensity matching based on 22 baseline characteristics yielded two balanced groups (blood transfusion group [BTG] and nontransfused control group [NCG]) of 857 patients (1714 in total). The type and number of blood products were compared in the BTG. RESULTS: Operative mortality was higher in BTG versus NCG (2.3% vs. 0.4%; p < 0.0001). Kaplan-Meier analysis of 5-year survival demonstrated no difference between groups in the first 2 years (BTG 96.3% and 93.0% vs. NCG 96.4% and 93.9%, respectively). There was a significant divergence during Years 3 to 5 (BTG 82.0% vs. NCG 89.3% at 5 years; p < 0.007). Five-year survival was significantly lower in patients who received at least 2 units of blood (79.6% vs. 88.0%; p < 0.0001). In multivariate Cox regression analyses, transfusion was independently associated with increased risk for 5-year mortality. Patients receiving cryoprecipitate products had a twofold mortality risk increase (adjusted hazard ratio, 2.106; p = 0.002). CONCLUSION: Blood transfusion, specifically cryoprecipitates, was independently associated with increased 5-year mortality. Transfusion during cardiac surgery should be limited to patients who are in critical need of blood products.
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 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 | 0.003 |
| 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 | 0.000 |
| 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".