Evaluating the Long‐Term Benefits of Medications for Alcohol Use Disorder in Alcohol‐Associated Cirrhosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: The effectiveness and safety of medications for alcohol use disorder (MAUD) in treating alcohol-associated cirrhosis have not been fully elucidated. This study aims to explore the clinical benefits and risks of MAUD in patients with alcohol-associated cirrhosis and persistent alcohol use. METHODS: This retrospective cohort study utilised anonymised electronic health records (EHRs) from the TriNetX platform to evaluate the impact of MAUD on mortality, alcohol abstinence rates, hepatocellular carcinoma, hepatic decompensation and healthcare utilisation in patients with alcohol-associated cirrhosis. RESULTS: This study evaluated 200 054 patients with alcohol-associated cirrhosis; after propensity score matching, 17 548 patients (8774 in each group) were analysed comparing those treated with MAUD to those without. At 3 years, patients treated with MAUD exhibited significantly lower mortality (21.6% vs. 29.3%, HR 0.731, 95% CI 0.688-0.776, p < 0.001), reduced hepatocellular carcinoma (2.6% vs. 3.3%, HR 0.794, p = 0.010) and fewer hospitalisations (16.7% vs. 24.8%, HR 0.635, p < 0.001). Subgroup analyses revealed that among 5083 patients treated with naltrexone, 3-year mortality was reduced (19.8% vs. 30.3%, HR 0.645, 95% CI 0.596-0.699, p < 0.001), hepatocellular carcinoma incidence was lower (2.6% vs. 3.3%, HR 0.790, p = 0.047) and hepatic decompensation decreased (18.4% vs. 21.1%, HR 0.843, p = 0.004). Among 4108 patients treated with acamprosate, 3-year mortality was reduced (22.6% vs. 29.0%, HR 0.799, 95% CI 0.733-0.871, p < 0.001) and hospitalisations and emergency department visits were significantly lower, though no reduction in hepatocellular carcinoma was observed. CONCLUSION: This study demonstrates the significant benefits of MAUD in patients with alcohol-associated cirrhosis, including reduced mortality, lower healthcare utilisation and improved alcohol abstinence rates, supporting its integration into standard care protocols.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".