Prevalence and Complications of Post-Operative Transfusion for Cervical Fusion Procedures in Spine Surgery; an Analysis of 11,588 patients from the ACS-NSQIP Database
Bibliographic record
Abstract
Introduction Cervical spine fusion have gained interest in the literature since these procedures are now ever more frequently being performed in an outpatient setting with few complications and acceptable results. The purpose of this study was to assess the rate of blood transfusion after cervical fusion surgery, and its effect, if any on complication rates. Material and Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was used to identify patients that underwent cervical fusion surgery from 2010 to 2013. Univariate and multivariate regression analysis was used to determine post-operative complications associated with transfusion and cervical fusion. Results We identified 11,588 patients who had cervical spine fusion between 2010 and 2013. The overall rate of transfusion was found to be 1.47%. All transfused patients were found to have increased risk of: venous thromboembolism (TBE) (OR 3.19, CI: 1.16–8.77), myocardial infarction (MI) (OR 9.12, CI: 2.53–32.8), increased length of stay (LOS) (OR 28.03, CI: 14.28–55.01) and mortality (OR 4.14, CI: 1.44–11.93). Single level fusion had increased risk of: TBE (OR 3.37, CI: 1.01–11.33), MI (OR 10.5, CI: 1.88–59.89), and LOS (OR 14.79, CI: 8.2–26.67). Multilevel fusion had increased risk of: TBE (OR 5.64, CI: 1.15–27.6), surgical site infection (OR 16.29, CI: 3.34–79.49), MI (OR 10.84, CI: 2.01–58.55), LOS (OR 26.56, CI: 11.8–59.78) and mortality (OR 10.24, CI: 2.45–42.71). ACDF surgery had an increased risk of: TBE (OR 4.87, CI: 1.04–22.82), surgical site infection (OR 9.73, CI: 2.14–44.1), MI (OR 9.88, CI: 1.87–52.2), LOS (OR 28.34, CI: 13.79–58.21) and mortality (OR 6.3, CI: 1.76–22.48). Posterior fusion surgery had increased risk of: MI (OR 10.45, CI: 1.42–77.12) and LOS (OR 4.42, CI: 2.68–7.29). Conclusion Our results demonstrate that although cervical fusions can be done as outpatient procedures special precautions and investigations should be done for patients who receive transfusion after cervical fusion surgery. These patients are demonstrated to have higher rate of MI, DVT, wound infection and mortality when compared with those who do not receive transfusion.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".