Cost-effectiveness analysis of deep brain stimulation for the treatment of alcohol use disorder and alcoholic liver disease
Bibliographic record
Abstract
BACKGROUND: Alcohol use disorder (AUD) is a major public health concern and cause of mortality and morbidity. Alcohol-associated liver disease (ALD) is a debilitating complication of AUD, mitigated by abstinence from alcohol use. Deep brain stimulation (DBS) is emerging as a potential treatment for AUD. However, its cost-effectiveness compared to the standard medical treatment is unclear. OBJECTIVE: To estimate the cost-effectiveness of DBS compared to medical management for patients with AUD and ALD. METHODS: We utilized a decision analytic model based on published literature to conduct a cost-effectiveness analysis of costs and health outcomes for DBS and medical management in patients with AUD and ALD. We also carried out a threshold analysis to determine the probability of success necessary for DBS to be cost-effective. Costs were measured in 2024 US dollars and effectiveness in quality-adjusted life years (QALYs). We used a time horizon of 1-2 years and adopted a societal perspective. RESULTS: Our results show that for AUD patients in general, DBS is not cost-effective at any DBS success rate. However, for advanced ALD patients, defined as fibrosis stage 3 or beyond DBS becomes cost-effective. For these patients, DBS is cost-effective over a two-year period at a $100,000 willingness-to-pay threshold at DBS success rates greater than 53 %. For advanced decompensated ALD patients, DBS is cost-effective over a one-year period at DBS success rate greater than 35 %. CONCLUSION: Should it prove efficacious, DBS may be cost-effective for patients with AUD and ALD. Thus, future randomized controlled trials to evaluate its efficacy are warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".