Clinical implications of a dynamic nomogram for predicting sepsis in acute liver failure

Abstract

This letter comments on a web-enabled, dynamic nomogram developed for early sepsis-risk estimation in adults with acute liver failure (ALF) admitted to the intensive care unit. The study successfully established and validated the sepsis in ALF model using five routinely available variables: Age, total bilirubin, lactate dehydrogenase, albumin, and mechanical ventilation. Across cohorts, the model demonstrated strong discrimination and outperformed traditional scores. We commend the inclusion of both Western and Chinese intensive care unit cohorts, which enhances the cross-population generalizability of the findings. This letter highlights the strengths of the model, including its web-based dynamic calculator and effective risk stratification, while also acknowledging limitations such as reliance on baseline admission data, restriction to intensive care unit populations, and the absence of infection-related biomarkers. We encourage further prospective, multicenter investigations to refine the sepsis in ALF model and expand its clinical utility.

Keywords: Acute liver failure; Risk prediction; Predictive model; Intensive care; Biomarkers; Dynamic nomogram

Core Tip: This letter highlights the development and validation of a dynamic nomogram for predicting sepsis risk in patients with acute liver failure. The sepsis in acute liver failure model shows robust discrimination, consistently outperforming the sequential organ failure assessment and systemic inflammatory response syndrome scores, and has been validated across both Western and Chinese intensive care unit cohorts. Future work should incorporate longitudinal clinical variables, integrate infection-related biomarkers, and pursue multicenter validation to further enhance clinical utility.