Allogeneic blood transfusions and infection risk in lumbar spine surgery: An American College of Surgeons National Surgery Quality Improvement Program Study
Bibliographic record
Abstract
BACKGROUND: Allogenic blood transfusions can lead to immunomodulation. Our purpose was to investigate whether perioperative transfusions were associated with postoperative infections and any other adverse events (AEs), after adjusting for potential confounding factors, following common elective lumbar spinal surgery procedures. STUDY DESIGN AND METHODS: We performed a multivariate, propensity-score matched, regression-adjusted retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program database between 2012 and 2016. All lumbar spinal surgery procedures were identified (n = 174,891). A transfusion group (perioperative transfusion within 72 h before, during, or after principal surgery; n = 1992) and a control group (no transfusion; n = 1992) were formed. Following adjustment for between-group baseline features, adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) were calculated using a multivariate logistic regression model for any surgical site infection (SSI), superficial SSI, deep SSI, wound dehiscence, pneumonia, urinary tract infection, sepsis, any infection, mortality, and any AEs. RESULTS: Transfusion was associated with an increased risk of each specific infection, mortality, and any AEs. Statistically significant between-group differences were demonstrated with respect to any SSI (aOR: 1.48; 95% CI: 1.01-2.16), deep SSI (aOR: 1.66; 95% CI: 0.98-2.85), sepsis (aOR: 2.69; 95% CI: 1.43-5.03), wound dehiscence (aOR: 2.27; 95% CI: 0.86-6.01), any infection (aOR: 1.46; 95% CI: 1.13-1.88), any AEs (aOR: 1.80; 95% CI: 1.48-2.18), and mortality (aOR: 2.17; 95% CI: 0.77-6.36). CONCLUSION: We showed an association between transfusion and infection in lumbar spine surgery after adjustment for various applicable covariates. Sepsis had the highest association with transfusion. Our results reinforce a growing trend toward minimizing perioperative transfusions, which may lead to reduced infections following lumbar spine surgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".