An Economic Evaluation of Venous Thromboembolism Prophylaxis Strategies in Critically Ill Trauma Patients at Risk of Bleeding
Bibliographic record
Abstract
BACKGROUND: Critically ill trauma patients with severe injuries are at high risk for venous thromboembolism (VTE) and bleeding simultaneously. Currently, the optimal VTE prophylaxis strategy is unknown for trauma patients with a contraindication to pharmacological prophylaxis because of a risk of bleeding. METHODS AND FINDINGS: Using decision analysis, we estimated the cost effectiveness of three VTE prophylaxis strategies-pneumatic compression devices (PCDs) and expectant management alone, serial Doppler ultrasound (SDU) screening, and prophylactic insertion of a vena cava filter (VCF) -- in trauma patients admitted to an intensive care unit (ICU) with severe injuries who were believed to have a contraindication to pharmacological prophylaxis for up to two weeks because of a risk of major bleeding. Data on the probability of deep vein thrombosis (DVT) and pulmonary embolism (PE), and on the effectiveness of the prophylactic strategies, were taken from observational and randomized controlled studies. The probabilities of in-hospital death, ICU and hospital discharge rates, and resource use were taken from a population-based cohort of trauma patients with severe injuries (injury severity scores >12) admitted to the ICU of a regional trauma centre. The incidence of DVT at 12 weeks was similar for the PCD (14.9%) and SDU (15.0%) strategies, but higher for the VCF (25.7%) strategy. Conversely, the incidence of PE at 12 weeks was highest in the PCD strategy (2.9%), followed by the SDU (1.5%) and VCF (0.3%) strategies. Expected mortality and quality-adjusted life years were nearly identical for all three management strategies. Expected health care costs at 12 weeks were Can$55,831 for the PCD strategy, Can$55,334 for the SDU screening strategy, and Can$57,377 for the VCF strategy, with similar trends noted over a lifetime analysis. CONCLUSIONS: The attributable mortality due to PE in trauma patients with severe injuries is low relative to other causes of mortality. Prophylactic placement of VCF in patients at high risk of VTE who cannot receive pharmacological prophylaxis is expensive and associated with an increased risk of DVT. Compared to the other strategies, SDU screening was associated with better clinical outcomes and lower costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".