Preoperative anaemia is a risk factor for mortality and morbidity following aortic valve surgery
Bibliographic record
Abstract
OBJECTIVES: The impact of anaemia on patients undergoing aortic valve surgery has not been well studied. We sought to evaluate the effect of anaemia on early outcomes following aortic valve replacement (AVR). METHODS: All patients undergoing non-emergent aortic valve surgery (n = 2698) with or without other concomitant procedures between 1997 and 2010 were included. Preoperative anaemia was defined as per World Health Organization guidelines as haemoglobin (Hb) < 130 g/l in men and Hb < 120 g/l in women. Multivariable analyses were used to determine the association between preoperative anaemia and postoperative outcomes. RESULTS: The prevalence of preoperative anaemia was 32.2%. Patients with anaemia were older (71 ± 12 vs 66 ± 13 years, P < 0.001), more likely to have urgent surgery, recent MI, higher creatinine level and impaired preoperative left ventricular function. Overall unadjusted mortality was 2.8% in non-anaemic patients vs 8% in anaemic patients. Anaemic patients were more likely to require renal replacement therapy (11 vs 3%, P < 0.0001) and prolonged ventilation (24 vs 10%, P < 0.0001). Following multivariable adjustment, lower preoperative Hb was an independent predictor of mortality (odds ratio 1.19, 95% CI: 1.04-1.34, P = 0.007) and composite morbidity (odds ratio 1.36, 95% CI: 1.05-1.77, P = 0.02) after AVR. Mortality and composite morbidity were significantly higher with lower levels of preoperative Hb. CONCLUSIONS: Preoperative anaemia is a common finding in patients undergoing aortic valve surgery and is an important and potentially modifiable risk factor for postoperative morbidity and mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".