Risk Associated With Preoperative Anemia in Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Preoperative anemia is an important risk factor for perioperative red blood cell transfusions, which are associated with postoperative morbidity and mortality. Whether preoperative anemia also is an independent risk factor for adverse outcomes after cardiac surgery, however, has not been fully elucidated. METHODS AND RESULTS: In this multicenter cohort study, data were collected on 3500 consecutive patients who underwent cardiac surgery during 2004 at 7 academic hospitals. The prevalence of preoperative anemia, defined as hemoglobin <12.5 g/dL, and its unadjusted and adjusted relationships with the composite outcome of in-hospital death, stroke, or acute kidney injury were obtained. The overall prevalence of preoperative anemia was 26%, with values ranging from 22% to 30% at the participating hospitals. After the exclusion of patients who had severe preoperative anemia (hemoglobin <9.5 g/dL) or preoperative kidney failure and those who underwent emergency surgery, the composite outcome was observed in 7.5% of patients (247 of 3286). The unadjusted odds ratio for the composite outcome in anemic versus nonanemic patients was 3.6 (95% confidence interval, 2.7 to 4.7). The risk-adjusted odds ratios, obtained by multivariable logistic regression and propensity-score matching to control for important confounders (including comorbidities, institution, surgical factors, and blood transfusion), were 2.0 (95% confidence interval, 1.4 to 2.8) and 1.8 (95% confidence interval, 1.2 to 2.7), respectively. CONCLUSIONS: Preoperative anemia is independently associated with adverse outcomes after cardiac surgery. Future studies should determine whether therapies aimed at treating preoperative anemia would improve the outcomes of patients undergoing cardiac surgery.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".