The Cost-Effectiveness of Hepatic Venous Pressure Gradient Monitoring in the Prevention of Recurrent Variceal Hemorrhage
Bibliographic record
Abstract
OBJECTIVE: Recurrent variceal hemorrhage is common following an initial bleed in patients with cirrhosis. The current standard of care for secondary prophylaxis is endoscopic band ligation (EBL). Combination of beta-blocker and nitrate therapy, guided by hepatic venous pressure gradient (HVPG) monitoring, is a novel alternative strategy. We sought to determine the cost-effectiveness of these competing strategies. METHODS: Decision analysis with Markov modeling was used to calculate the cost-effectiveness of three competing strategies: (1) EBL; (2) beta-blocker and nitrate therapy without HVPG monitoring (HVPG-); and (3) beta-blocker and nitrate therapy with HVPG monitoring (HVPG+). Patients in the HVPG+ strategy who failed to achieve an HVPG decline from medical therapy were offered EBL. Cost estimates were from a third-party payer perspective. The main outcome measure was the cost per recurrent variceal hemorrhage prevented. RESULTS: Under base-case conditions, the HVPG+ strategy was the most effective yet most expensive approach, followed by EBL and HVPG-. Compared to the EBL strategy, the HVPG+ strategy cost an incremental 5,974 dollars per recurrent bleed prevented. In a population with 100% compliance with all therapies, the incremental cost of HVPG-versus EBL fell to 5,270 dollars per recurrent bleed prevented. The model results were sensitive to the cost of EBL, the cost of HVPG monitoring, and the probability of medical therapy producing an adequate HVPG decline. CONCLUSIONS: Compared to EBL for the secondary prophylaxis of variceal rebleeding, combination medical therapy guided by HVPG monitoring is more effective and only marginally more expensive.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".