Global burden of disease: acute-on-chronic liver failure, a systematic review and meta-analysis

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.