The Association Between Preoperative Anemia and 30-Day Mortality and Morbidity in Noncardiac Surgical Patients
Bibliographic record
Abstract
BACKGROUND: Anemia has been associated with increased postoperative morbidity and mortality. We used the American College of Surgeons National Surgical Quality Improvement Program database to retrospectively assess the relationship between preoperative anemia and 30-day postoperative mortality and morbidity in noncardiac surgical patients, careful to distinguish confounding variables from mediator variables. METHODS: Each patient with preoperative anemia was matched to one without anemia using propensity matching on potentially confounding baseline variables. Logistic regression was used to evaluate the relationship between preoperative anemia and 30-day postoperative mortality and morbidity. The primary hypothesis was evaluated after adjusting for covariables showing residual imbalance after matching. RESULTS: Within the database, 574,860 of 971,455 surgical cases met our inclusion criteria, and among those 145,218 (25.3%) were anemic at baseline. The unadjusted odds ratio (95% confidence interval) for 30-day mortality comparing anemic patients with nonanemic patients was 4.69 (4.01-5.49). Among the propensity-matched group of 238,596 patients, the total effect (i.e., not adjusting for mediator variables) of preoperative anemia was estimated as an odds ratio of 1.59 (1.42-1.78). After adjusting for suspected mediator variables, preoperative anemia was only weakly associated with an odds ratio of 1.24 (1.10-1.40) for 30-day mortality. CONCLUSION: Preoperative anemia appears to be associated with baseline diseases that markedly increase mortality. Anemia per se is a rather weak independent predictor of postoperative mortality. Our analysis also illustrates how analyzing large variable-rich registries challenges investigators to discriminate between confounding variables and mediator variables, i.e., factors that might be considered as "causal pathways" for the effect of the exposure or intervention on outcome.
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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 | 0.006 |
| 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 | 0.000 |
| 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".