Editorial: Decoding ACLF—Sub-Phenotyping to Advance Precision Medicine in Acute-On-Chronic Liver Failure

Patients ‘lumped’ into the syndrome acute-on-chronic liver failure (ACLF) differ on several observable levels: underlying aetiology of cirrhosis, precipitants, number and severity of organ failures (OFs). Despite phenotypic heterogeneity, consensus definitions of ACLF have changed how the condition is understood, recognised and studied.

A key unmet need is identifying which elements of ACLF heterogeneity matter. There have been attempts to address this. For example, patients with Hepatitis B virus (HBV)-associated ACLF have distinct OF patterns and worse outcomes compared with non-HBV-associated ACLF [1]. Similarly, extrahepatic precipitating insults are associated with more extrahepatic OFs and worse outcomes, whereas hepatic insults predominantly cause liver and coagulation failure [2].

Verma et al. take a different approach, suggesting that latent or unobservable heterogeneity in the clinical characteristics of patients with ACLF—ACLF sub-phenotypes or clusters—may account for differences in patient trajectories and outcomes [3]. If ACLF sub-phenotypes exist, are identifiable, and sub-phenotypes respond differently to treatments, this approach could be used to match the right treatment to patient sub-phenotype: personalised medicine.

Using clinical data from a single-centre, Indian cohort of patients with ACLF, the authors tested several clustering algorithms. The number of ACLF clusters identified varied depending on the algorithm. The latent class analysis model was deemed most robust and identified four ACLF clusters with distinct survival profiles. Adding cluster assignment to the Chronic liver failure (CLIF-C) OF ACLF score improved prognostic accuracy. In a validation cohort, cluster membership could be predicted using a limited set of variables. This is a tentative step towards personalised ACLF management, but several unanswered questions remain.

First, are these clusters reproducible? Verma et al. [3] did not take a clinically informed approach to variable selection and included composite severity scores (derived from clinical data). Informed selection of variables and use of raw data may alter clustering results. Also, region-specific differences in ACLF aetiology and outcomes are well recognised [4], and external validation in a heterogenous global cohort is required.

Second, will absence of a universal consensus definition for ACLF hinder progress? There are several overlapping ACLF definitions [5-8], with important differences: nature of precipitating insult, compulsory inclusion of liver failure in ACLF diagnostic criteria, definitions and thresholds for organ failure. In this study, patients who met the EASL and/or APASL ACLF definitions were included. Sub-phenotypes will likely differ depending on definition; sub-phenotyping ACLF in variably defined populations could lead to confusion rather than clarity.

Third, and most important, does clinical heterogeneity represent biological heterogeneity? To deliver personalised medicine, we need to identify subgroups of ACLF patients with shared biological pathways, that is, endotypes. Studies to sub-phenotype sepsis and acute respiratory distress syndrome used biological data (‘omic’ data and biomarkers) to identify possible endotypes [9] and then determined if bedside identification of these endotypes—using clinical data—is feasible [10]. We do not know if the identified ACLF clusters capture underlying biological pathways of ACLF.

ACLF is complex and multifaceted. Current management is largely supportive with no disease-modifying treatments. There is an urgent need for biological and clinical phenotyping to develop targeted therapies.

Amy Sangam: writing – original draft, writing – review and editing. Banwari Agarwal: writing – review and editing, writing – original draft. Rohit Saha: writing – original draft, writing – review and editing.

The authors declare no conflicts of interest.

This article is linked to Verma et al papers. To view these articles, visit https://doi.org/10.1111/apt.18274 and https://doi.org/10.1111/apt.18364.