The Association of Postoperative Anaemia with Outcomes in Cardiac Surgical Patients Eligible for Patient Blood Management: A Single Institution Retrospective Cohort Study
Bibliographic record
Abstract
ABSTRACT Background Anaemia is prognostically important and affects 30-40% of cardiac surgical patients. The objective of this study was to examine the association of pre- and postoperative anaemia with outcomes in cardiac surgical patients. Methods This was a single-institution retrospective cohort study including cardiac surgical patients from October 26, 2020 to December 3, 2021. Patients were classified as preoperatively non-anaemic (hemoglobin ≥ 130 g/L), anaemic, or treated with IV Iron. The main predictors of interest were nadir haemoglobin on postoperative days 1-2 and preoperative anaemia and receipt of IV iron therapy. The primary outcome was number of red blood cell units (RBC) transfused on postoperative days 1-7. Secondary outcomes included acute kidney injury, hospital length of stay, and 30 day in-hospital mortality. Regression models, adjusted for demographics, comorbidities, and surgical characteristics, examined the association between predictors and outcomes. Results A total of 844 patients were included [528 (63%) non-anaemic, 276 (33%) anaemic, and 40 (5%) anaemic, treated with IV iron]. There was no difference between groups in RBC transfusion or mortality, however anaemic patients had a higher adjusted risk for acute kidney injury [aOR 2.69 (95% CI, 1.37 to 5.30), p=0.004] and longer hospital length of stay [aRR 1.38 (95% CI, 1.24 to 1.54), p<0.0001] compared to non-anaemic patients. Patients treated with IV iron did not have the same increased risk. A lower postoperative haemoglobin nadir was significantly associated with increased risk for all outcomes. Conclusions Postoperative anaemia confers additional risk regardless of preoperative anaemia status. Further research is needed to better clarify these associations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".