Global burden of disease: acute-on-chronic liver failure, a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Acute-on-chronic liver failure (ACLF) is characterised by acute decompensation of cirrhosis associated with organ failures. We systematically evaluated the geographical variations of ACLF across the world in terms of prevalence, mortality, aetiology of chronic liver disease (CLD), triggers and organ failures. METHODS: We searched EMBASE and PubMed from 3/1/2013 to 7/3/2020 using the ACLF-EASL-CLIF (European Association for the Study of the Liver-Chronic Liver Failure) criteria. Two investigators independently conducted the abstract selection/abstraction of the aetiology of CLD, triggers, organ failures and prevalence/mortality by presence/grade of ACLF. We grouped countries into Europe, East/South Asia and North/South America. We calculated the pooled proportions, evaluated the methodological quality using the Newcastle-Ottawa Scale and statistical heterogeneity, and performed sensitivity analyses. RESULTS: We identified 2369 studies; 30 cohort studies met our inclusion criteria (43 206 patients with ACLF and 140 835 without ACLF). The global prevalence of ACLF among patients admitted with decompensated cirrhosis was 35% (95% CI 33% to 38%), highest in South Asia at 65%. The global 90-day mortality was 58% (95% CI 51% to 64%), highest in South America at 73%. Alcohol was the most frequently reported aetiology of underlying CLD (45%, 95% CI 41 to 50). Infection was the most frequent trigger (35%) and kidney dysfunction the most common organ failure (49%). Sensitivity analyses showed regional estimates grossly unchanged for high-quality studies. Type of design, country health index, underlying CLD and triggers explained the variation in estimates. CONCLUSIONS: The global prevalence and mortality of ACLF are high. Region-specific variations could be explained by the type of triggers/aetiology of CLD or grade. Health systems will need to tailor early recognition and treatment of ACLF based on region-specific data.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".