Decision-analysis modeling of effectiveness and cost-effectiveness of pharmacologic thromboprophylaxis for surgical inpatients using variable risk assessment models or other strategies
Bibliographic record
Abstract
BACKGROUND: Surgical inpatients are at a risk of venous thromboembolism (VTE), which can be life-threatening or result in chronic complications. Thromboprophylaxis reduces the VTE risk but incurs costs and may increase bleeding risk. Risk assessment models (RAMs) are currently used to target thromboprophylaxis at high-risk patients. OBJECTIVES: To determine the balance of cost, risk, and benefit for different thromboprophylaxis strategies in adult surgical inpatients, excluding patients who underwent major orthopedic surgery or were under critical care and pregnant women. METHODS: Decision analytic modeling was performed to estimate the following outcomes for alternative thromboprophylaxis strategies: thromboprophylaxis usage; VTE incidence and treatment; major bleeding; chronic thromboembolic complications; and overall survival. Strategies compared were as follows: no thromboprophylaxis; thromboprophylaxis for all; and thromboprophylaxis given according to RAMs (Caprini and Pannucci). Thromboprophylaxis is assumed to be given for the duration of hospitalization. The model evaluates lifetime costs and quality-adjusted life-years (QALYs) within England's health and social care services. RESULTS: Thromboprophylaxis for all surgical inpatients had a 70% probability of being the most cost-effective strategy (at a £20 000 per QALY threshold). RAM-based prophylaxis would be the most cost-effective strategy if a RAM with a higher sensitivity (99.9%) were available for surgical inpatients. QALY gains were mainly due to reduced postthrombotic complications. The optimal strategy was sensitive to several other factors such as the risk of VTE, bleeding and postthrombotic syndrome, duration of prophylaxis, and patient age. CONCLUSION: Thromboprophylaxis for all eligible surgical inpatients seemed to be the most cost-effective strategy. Default recommendations for pharmacologic thromboprophylaxis, with the potential to opt-out, may be superior to a complex risk-based opt-in approach.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".