Meta‐Analysis: Mortality Trends and Risk Factors in Severe Alcohol‐Associated Hepatitis
Bibliographic record
Abstract
ABSTRACT Background Severe alcohol‐associated hepatitis (sAH) is a life‐threatening condition. Despite advances in clinical management, prognosis remains poor and long‐term effectiveness of available therapies is uncertain. We conducted a systematic review and meta‐analysis to evaluate short‐term mortality (28, 60, and 90‐day) trends in sAH over the past five decades. Methods We searched PubMed, EMBASE, and Scopus from database inception to February 2024 for studies reporting 28, 60, and 90‐day mortality in patients with sAH. Pooled mortality estimates were calculated using a random‐effects meta‐regression model. We assessed heterogeneity using the I 2 statistic and explored sources of heterogeneity through subgroup and meta‐regression analyses. Separate Bayesian mixed‐effects binomial models were used to estimate the posterior distribution of mortality probability, updated sequentially across calendar time. Results 34 studies comprising 1586 patients with sAH were included. Pooled mortality rates were 26.8% (95% CI: 21.0%–33.5%) at 28 days, 35.1% (95% CI: 28.3%–42.5%) at 60 days, and 43.7% (95% CI: 34.6%–53.3%) at 90 days. Mortality increased steadily with follow‐up time. Substantial heterogeneity was observed, as expected in pooled proportion meta‐analysis (I 2 > 80%). Although cumulative Bayesian analysis showed that average 28‐day mortality declined from over 50% in the 1970s to ~25% after 2000, a formal decade‐based analysis indicated no statistically credible improvement in short‐term mortality was detected overall in the past four decades. In multivariable models adjusting for follow‐up time, the Model for End‐Stage Liver Disease (MELD) score was significantly associated with mortality. Conclusions Short‐term mortality in sAH remains high and has not improved in recent decades. These findings highlight the urgent need for effective therapies, improved patient selection for early liver transplantation, and better prognostic tools to guide clinical decision‐making.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.052 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".