Screening and Prevention of Venous Thromboembolism in Critically Ill Patients: A Decision Analysis and Economic Evaluation
Bibliographic record
Abstract
RATIONALE: Venous thromboembolism is difficult to diagnose in critically ill patients and may increase morbidity and mortality. OBJECTIVES: To evaluate the cost-effectiveness of strategies to reduce morbidity from venous thromboembolism in critically ill patients. METHODS: A Markov decision analytic model to compare weekly compression ultrasound screening (screening) plus investigation for clinically suspected deep vein thrombosis (DVT) (case finding) versus case finding alone; and a hypothetical program to increase adherence to DVT prevention. Probabilities were derived from a systematic review of venous thromboembolism in medical-surgical intensive care unit patients. Costs (in 2010 $US) were obtained from hospitals in Canada, Australia, and the United States, and the medical literature. Analyses were conducted from a societal perspective over a lifetime horizon. Outcomes included costs, quality-adjusted life-years (QALY), and incremental cost-effectiveness ratios. MEASUREMENTS AND MAIN RESULTS: In the base case, the rate of proximal DVT was 85 per 1,000 patients. Screening resulted in three fewer pulmonary emboli than case-finding alone but also two additional bleeding episodes, and cost $223,801 per QALY gained. In sensitivity analyses, screening cost less than $50,000 per QALY only if the probability of proximal DVT increased from a baseline of 8.5-16%. By comparison, increasing adherence to appropriate pharmacologic thromboprophylaxis by 10% resulted in 16 fewer DVTs, one fewer pulmonary emboli, and one additional heparin-induced thrombocytopenia and bleeding event, and cost $27,953 per QALY gained. Programs achieving increased adherence to best-practice venous thromboembolism prevention were cost-effective over a wide range of program costs and were robust in probabilistic sensitivity analyses. CONCLUSIONS: Appropriate prophylaxis provides better value in terms of costs and health gains than routine screening for DVT. Resources should be targeted at optimizing thromboprophylaxis.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".