Prognostic models in acute liver failure-historic evolution and newer updates "prognostic models in acute liver...

Acute liver failure (ALF) is a rare and dynamic syndrome occurring as a sequela of severe acute liver injury (ALI). Its mortality ranges from 50% to 75% based on the aetiology, patients age and severity of encephalopathy at admission. With improvement in intensive care techniques, transplant-free survival in ALF has improved over time. Timely recognition of patients who are unlikely to survive with medical intervention alone is crucial since these individuals may rapidly develop multiorgan failure and render liver transplantation futile. Various predictive models, biomarkers and AI-based models are currently used in clinical practice, each with its fallacies. The King's College Hospital criteria (KCH) were initially established in 1989 to identify patients with acute liver failure (ALF) caused by paracetamol overdose or other causes who are unlikely to improve with conventional treatment and would benefit from a liver transplant. Since then, various models have been developed and validated worldwide. Most models include age, aetiology of liver disease, encephalopathy grade, and liver injury markers like INR, lactate, factor V level, factor VIII/V ratio and serum bilirubin. But none of the currently available models are dynamic and lack accuracy in predicting transplant free survival. There is an increasing interest in developing prognostic serum biomarkers that when used alone or in combination with clinical models enhance the accuracy of predicting outcomes in ALF. Genomics, transcriptomics, proteomics, and metabolomics as well as machine learning and artificial intelligence (AI) algorithms are areas of interest for developing higher-precision predictive models. Overall, the future of prognostic models in ALF is promising, with ongoing research paving the way for more accurate, personalized, and dynamic risk assessment tools that can potentially save lives in this challenging condition. This article summarizes the history of prognostic models in ALF and future trends.