Background: Post-hepatectomy liver failure (PHLF) represents the major determinant for death after liver resection. Early recognition is essential. Perioperative lactate dynamics for risk assessment of PHLF and associated morbidity were evaluated.
Methods: This was a multicentre observational study of patients undergoing hepatectomy with validation in international high-volume units. Receiver operating characteristics analysis and cut-off calculation for the predictive value of lactate for clinically relevant International Study Group of Liver Surgery grade B/C PHLF (clinically relevant PHLF (CR-PHLF)) were performed. Lactate and other perioperative factors were assessed in a multivariable CR-PHLF regression model.
Results: The exploratory cohort comprised 509 patients. CR-PHLF, death, overall morbidity and severe morbidity occurred in 7.7, 3.3, 40.9 and 29.3 per cent of patients respectively. The areas under the curve (AUCs) regarding CR-PHLF were 0.829 (95 per cent c.i. 0.770 to 0.888) for maximum lactate within 24 h (Lactate_Max) and 0.870 (95 per cent c.i. 0.818 to 0.922) for postoperative day 1 levels (Lactate_POD1). The respective AUCs in the validation cohort (482 patients) were 0.812 and 0.751 and optimal Lactate_Max cut-offs were identical in both cohorts. Exploration cohort patients with Lactate_Max 50 mg/dl or greater more often developed CR-PHLF (50.0 per cent) than those with Lactate_Max between 20 and 49.9 mg/dl (7.4 per cent) or less than 20 mg/dl (0.5 per cent; P < 0.001). This also applied to death (18.4, 2.7 and 1.4 per cent), severe morbidity (71.1, 35.7 and 14.1 per cent) and associated complications such as acute kidney injury (26.3, 3.1 and 2.3 per cent) and haemorrhage (15.8, 3.1 and 1.4 per cent). These results were confirmed in the validation group. Combining Lactate_Max with Lactate_POD1 further increased AUC (ΔAUC = 0.053) utilizing lactate dynamics for risk assessment. Lactate_Max, major resections, age, cirrhosis and chronic kidney disease were independent risk factors for CR-PHLF. A freely available calculator facilitates clinical risk stratification (www.liver-calculator.com).
Conclusion: Early postoperative lactate values are powerful, readily available markers for CR-PHLF and associated complications after hepatectomy with potential for guiding postoperative care.Presented in part as an oral video abstract at the 2020 online Congress of the European Society for Surgical Research and the 2021 Congress of the Austrian Surgical Society.
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http://dx.doi.org/10.1093/bjs/znab338 | 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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