Incidence, Predictors, and Postoperative Complications of Blood Transfusion in Thoracic and Lumbar Fusion Surgery: An Analysis of 13,695 Patients from the American College of Surgeons National Surgical Quality Improvement Program Database
Bibliographic record
Abstract
Study Design Retrospective cohort study. Objective To identify predictive factors for blood transfusion and associated complications in lumbar and thoracic fusion surgeries. Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was used to identify patients who underwent lumbar or thoracic fusion from 2010 to 2013. Multivariate analysis was used to determine predictive factors and postoperative complications associated with transfusion. Results Out of 13,695 patients, 13,170 had lumbar fusion and 525 had thoracic fusion. The prevalence of transfusion was 31.8% for thoracic and 17.0% for lumbar fusion. The multivariate analysis showed that age between 50 and 60, age between 61 and 70, age > 70, dyspnea, American Society of Anesthesiologists class 3, bleeding disease, multilevel surgery, extended surgical time, return to operation room, and higher preoperative blood urea nitrogen (BUN) were predictors of blood transfusion for lumbar fusion. Multilevel surgery, preoperative BUN, and extended surgical time were predictors of transfusion for thoracic fusion. Patients receiving transfusions who underwent lumbar fusion were more likely to develop wound infection, venous thromboembolism, pulmonary embolism, and myocardial infarction and had longer hospital stay. Patients receiving transfusions who underwent thoracic fusion were more likely to have extended hospital stay. Conclusion This study characterizes incidence, predictors, and postoperative complications associated with blood transfusion in thoracic and lumbar fusion. Pre- and postoperative planning for patients deemed to be at high risk of requiring blood transfusion might reduce postoperative complications in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".