The incidence and importance of anaemia in patients undergoing cardiac surgery in the UK – the first Association of Cardiothoracic Anaesthetists national audit
Bibliographic record
Abstract
The importance and variability of pre-operative anaemia in cardiac surgical patients across the UK is not known, and there is debate about its association with patient outcomes. The Association of Cardiothoracic Anaesthetists carried out its first national audit on anaemia and transfusion, and analysed data from 19,033 patients operated on in 12 cardiac surgical centres between 2010 and 2012; 5895 (31%) had pre-operative anaemia. Centre-specific prevalence of anaemia varied from 23% to 45%; anaemia was associated with older patients, diabetes and surgical risk (EuroSCORE). Nevertheless, controlling for these factors, regional variation remained an independent effect (p < 0.001). Multivariable analysis demonstrated an independent association of anaemia with transfusion (odds ratio (95% confidence interval) 2.75 (2.55-2.95), p < 0.001), mortality (1.42 (1.18-1.71), p < 0.001) and hospital stay (geometric mean ratio (95% confidence interval) 1.15 (1.13-1.17), p < 0.001). Haemoglobin concentration per se was also independently associated with worse outcomes; a 10 g.l(-1) decrease in haemoglobin was associated with a 43% increase (95% confidence interval 40-46%) in the odds of transfusion and a 16% increase (95% confidence interval 10-22%) in the odds of mortality (both p < 0.001). This large UK-wide audit has demonstrated marked regional variation in both anaemia and transfusion, with a consistently high incidence of both. The independent association between pre-operative anaemia and worse outcomes in UK practice has also been confirmed, and robust prospective study of anaemia treatment before cardiac surgery is required; these data will assist in designing such trials.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".