In the latest decades, an important change has been registered in liver surgery related to the progress of surgical techniques, critical care, and postoperative treatment, allowing a sharp decrease in mortality and morbidity. However, management of post-hepatectomy liver failure (PHLF) still remains a challenge and no supportive treatment has been found to be generally effective. The present study is a reappraisal of plasmapheresis as a potential supportive measure in patients with PHLF following major liver resection.
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http://dx.doi.org/10.7759/cureus.884 | DOI Listing |
Ann Gastroenterol Surg
January 2025
Division of Gastroenterological, Hepato-Biliary-Pancreatic, Transplantation and Pediatric Surgery, Department of Surgery Shinshu University School of Medicine Matsumoto Japan.
Background And Aim: Post-hepatectomy liver failure (PHLF) after major hepatopancreatoduodenectomy (HPD) is a challenge to overcome. However, the appropriate target proportion of the future liver remnant (pFLR) to prevent severe PHLF in major HPD remains uncertain. This study aimed to determine the minimum pFLR required for safe major HPD.
View Article and Find Full Text PDFHPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. Electronic address:
Background: We sought to define textbook outcome in liver surgery (TOLS) for intrahepatic cholangiocarcinoma (ICC) by considering the implications of perioperative outcomes on overall survival (OS).
Methods: Using a multi-institutional database, TOLS for ICC was defined by employing novel machine learning (ML) models to identify perioperative factors most strongly predictive of OS ≥ 12 months. Subsequently, clinicopathologic factors associated with achieving TOLS were investigated.
HPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States. Electronic address:
Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.
Sci Rep
January 2025
Department of Gastroenterological Surgery I, Hokkaido University Graduate School of Medicine, N15 W7 Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
Sci Adv
January 2025
School of Basic Medicine, Qingdao University, 308 Ningxia Road, Qingdao 266071, China.
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