Blood Transfusion in Primary Total Hip and Knee Arthroplasty. Incidence, Risk Factors, and Thirty-Day Complication Rates
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to analyze NSQIP (National Surgical Quality Improvement Program) data to better understand the incidence, risk factors, and thirty-day complication rates associated with transfusions in primary total hip and knee arthroplasty. METHODS: We identified 9362 total hip and 13,662 total knee arthroplasty procedures from the database and separated those in which any red blood-cell transfusion was performed within seventy-two hours after surgery from those with no transfusion. Patient demographics, comorbidities, preoperative laboratory values, intraoperative variables, and postoperative complications were compared between patients who received a transfusion and those who did not. Multivariate logistic regression was used to identify independent risk factors for receiving a transfusion as well as for associated postoperative complications (thirty-day incidences of infection, venous thromboembolism, and mortality). RESULTS: The transfusion rate after total hip arthroplasty was 22.2%. Significant risk factors for receiving a transfusion were age (OR [odds ratio] per ten years = 10.1), preoperative anemia (OR = 3.6), female sex (OR = 2.0), BMI (body mass index) of <30 kg/m(2) (OR = 1.4), and ASA (American Society of Anesthesiologists) class of >2 (OR = 1.3). Multivariate logistic regression analysis indicated that adjusted odds of infection, venous thromboembolism, and mortality did not differ significantly between patients who received a transfusion and those who did not. The transfusion rate after total knee arthroplasty was 18.3%. Risk factors for receiving a transfusion were age (OR per ten years = 10.2), preoperative anemia (OR = 3.8), BMI of <30 kg/m(2) (OR = 1.4), female sex (OR = 1.3), and ASA class of >2 (OR = 1.3). Multivariate logistic regression indicated that a transfusion was significantly associated with mortality (OR = 2.7) but not with infection or venous thromboembolism. CONCLUSIONS: We did not find a strong association between perioperative red blood-cell transfusion and thirty-day incidences of infection, venous thromboembolism, or mortality; however, the odds of mortality were higher in patients who received a transfusion during total knee arthroplasty. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.001 | 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".