Trends and Impact of Pharmacological VTE Prophylaxis Timing for Traumatic Cervical Spinal Cord Injury Across North American Trauma Centers
Bibliographic record
Abstract
BACKGROUND: The aims of this study were to evaluate the timing and trend of venous thromboembolism (VTE) prophylaxis initiation following surgical intervention, and the impact of VTE prophylaxis timing on the occurrence of VTE complications, across North American trauma centers in patients with complete traumatic cervical spinal cord injury (SCI). METHODS: This retrospective, observational cohort study utilized data from the American College of Surgeons (ACS) Trauma Quality Improvement Program (TQIP) from 2013 to 2020. We identified surgically treated patients with complete traumatic cervical SCI. Patient variables included age, sex, race, insurance coverage, and comorbidity status. Outcomes of interest included time to VTE prophylaxis following surgery and the occurrence of VTE complications. Mixed-effect regression models were constructed to evaluate the adjusted estimate for each outcome accounting for patient-, injury-, and hospital-level covariates. RESULTS: The study included 5,325 patients treated across 463 trauma centers. The mean age in the cohort was 46.7 ± 18.9 years, with male predominance (81.1%). Race was predominantly White (62.3%) and Black (23.0%). The mean time to VTE prophylaxis initiation was 90 ± 112 hours, and the median time was 65 hours (interquartile range, 39 to 105 hours). The annual trend of VTE prophylaxis initiation after surgery was a decrease by 5.2 hours per year over the 8-year study interval. This was associated with an annual reduction of 6.2% in the odds of VTE complication occurrence. Multivariable mixed-effect regression models demonstrated a significant reduction in time to VTE prophylaxis (mean difference, -3.7 hours per year [95% confidence interval [CI], -5.3 to -2.1 hours per year]; p < 0.001) and VTE complications (odds ratio, 0.93 per year [95% CI, 0.88 to 0.98 per year]; p = 0.01) over the study period, after adjustment. CONCLUSIONS: This analysis provides insight into VTE prophylaxis practice patterns following surgery for complete cervical SCI across North American trauma centers from 2013 to 2020. The timing of VTE prophylaxis initiation consistently decreased, which appeared to be associated with a significant reduction found in VTE complications. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".