Association of postoperative haemoglobin with adverse outcomes in patients undergoing cardiac surgery: a retrospective single centre cohort study
Bibliographic record
Abstract
Background Preoperative anaemia is an important risk factor for adverse outcomes in cardiac surgery, however data on postoperative anaemia is sparse. The aim of this study is to characterise the association of postoperative haemoglobin with 30-day mortality and morbidity after cardiac surgery. Methods We performed a retrospective cohort study of adults (age ≥18 yr) undergoing coronary revascularisation, valve surgery, or a combination at Toronto General Hospital from 2016 to 2020. We analysed the association between nadir postoperative day 1 (POD1) haemoglobin as a continuous and binary variable (haemoglobin ≤80 g L −1 ), with a primary composite outcome of 30-day mortality, stroke, myocardial infarction, acute kidney injury, sternal wound infection, or a combination. The secondary outcome was the incidence of adverse events. The primary outcome was analysed using logistic regression, secondary using Poisson regression; adjusted models accounted for clustering and confounders. Results We included 5960 patients. On POD1, mean haemoglobin was 90.1g L −1 (standard deviation 15.2) and 1794 patients (30%) had haemoglobin ≤80 g L −1 . Red blood cells were transfused to 49% of the cohort, and to 90% of patients with POD1 haemoglobin ≤80 g L −1 . Each 10 g L −1 decrease in POD1 haemoglobin increased the odds of the primary outcome (adjusted odds ratio [OR] 1.15 [1.05–1.25], P <0.001), as did haemoglobin ≤80 g L −1 (adjusted OR 1.44 [1.19–1.75], P <0.001). For adverse events, each 10 g L −1 decrease in haemoglobin was associated with an increased incidence rate ratio (IRR) (adjusted IRR 1.14 [1.07–1.20], P <0.001), as was haemoglobin <80 g L −1 (adjusted IRR 1.33 [1.16–1.54], P <0.001). Conclusions In postoperative cardiac surgical patients, progressive decreases in postoperative haemoglobin are associated with increased risk of mortality and major morbidity at 30 days.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".