Identification of four novel acute-on-chronic liver failure clusters with distinct clinical trajectories and mortality using machine learning methods

Background and aims: Machine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute-on-chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.

Methods: Clinical data of 1568 patients with ACLF from a tertiary care centre (2015-2023) were subjected to distance-, density- and model-based clustering algorithms. Final model was selected on best cluster separation, viz. Silhouette width and Dunn's index (for distance- or density-based algorithms) and minimum BIC (for model-based algorithms). Cluster assignments, patient trajectories and survival were analysed through inferential statistics. Supervised ML models were trained in 70% data that predicted clusters in remaining 30% data followed by an temporal validation.

Results: The cohort was male-predominant (87%), aged 44.3 years, with alcohol-associated hepatitis (62.9%) and survival of 50.5%. Due to poor performance of distance- and density-based algorithms and better explainability, the latent class model (LCM) was selected for exploration. LCM revealed four clusters with distinct trajectories, reversibility and survival (independent of MELD, CLIF-C ACLF and AARC scores). Cluster1 had patients with none/one organ failure and highest reversibility. Cluster2 had females with viral hepatitis and two organ failures. More-than-one acute precipitant, severity, infections, organ failures and irreversibility escalated from clusters 1 to 4. Circulatory and renal failures critically influenced cluster assignments. Incorporating clusters to CLIF-C ACLF, infection and ACLF definition improved the discriminative accuracy of CLIF-C-ACLF by 11%. Extreme gradient boost and decision trees could predict clusters with AUCs of 0.989 (0.979-0.995) and 0.875 (0.865-0.890). MELD, CLIF-C-OF, haemoglobin, lactate, CLIF-C-ACLF and ALT were critical variables for cluster prediction. Clusters with distinct survival were documented in a temporal validation cohort.

Conclusions: ML for the first time could identify clusters with distinct phenotypes, trajectories and outcomes in ACLF. Stratification into clusters can address heterogeneity, guide prognosis, recruitment in trials, resource allocation and liver transplant discussions in ACLF.