Comprehensive review of post-hepatectomy liver failure: definition, prediction, and intervention

Radical hepatectomy remains one of the most effective treatments for both early-stage and selected intermediate-to-advanced malignant liver tumors. It is also a definitive treatment option for certain benign liver tumors that are symptomatic or carry a risk of malignancy. Complications during the perioperative period of liver resection are critical factors affecting patients’ short- and long-term prognosis, and post-hepatectomy liver failure (PHLF) is one of the most common complications. PHLF is a major cause of perioperative death among liver resection patients, and the timely identification before surgery of patients at high-risk of PHLF is a pressing clinical issue and research focus. Traditional methods of liver function assessment are widely used and can identify patients at high-risk of PHLF, but their predictive accuracy is relatively low. In recent years, with the development of artificial intelligence, an increasing number of advanced algorithms and models incorporating more comprehensive risk factors have been applied to PHLF prediction. Researchers worldwide have constructed new PHLF-related prediction models using various statistical methods, demonstrating significant improvements in accuracy. Following an extensive literature review, this article summarizes the diagnosis, prediction, and prevention of PHLF, providing a comprehensive overview of research progress to facilitate a more thorough understanding among clinicians and researchers.