• Jaeyun J Wang, Jean Feng, Camilla Gomes, Lucia Calthorpe, Amir Ashraf Ganjouei, Fernanda Romero-Hernandez, Andrea Benedetti Cacciaguerra, Taizo Hibi, Mohamed Abdelgadir Adam, Adnan Alseidi, Mohammad Abu Hilal, Nikdokht Rashidian, and International Post-Hepatectomy Liver Failure Study Group.
• Department of Surgery, University of California, San Francisco, CA.
• Ann. Surg. 2023 Dec 1; 278 (6): 976984976-984.

ObjectiveThe study aim was to develop and validate models to predict clinically significant posthepatectomy liver failure (PHLF) and serious complications [a Comprehensive Complication Index (CCI)>40] using preoperative and intraoperative variables.BackgroundPHLF is a serious complication after major hepatectomy but does not comprehensively capture a patient's postoperative course. Adding the CCI as an additional metric can account for complications unrelated to liver function.MethodsThe cohort included adult patients who underwent major hepatectomies at 12 international centers (2010-2020). After splitting the data into training and validation sets (70:30), models for PHLF and a CCI>40 were fit using logistic regression with a lasso penalty on the training cohort. The models were then evaluated on the validation data set.ResultsAmong 2192 patients, 185 (8.4%) had clinically significant PHLF and 160 (7.3%) had a CCI>40. The PHLF model had an area under the curve (AUC) of 0.80, calibration slope of 0.95, and calibration-in-the-large of -0.09, while the CCI model had an AUC of 0.76, calibration slope of 0.88, and calibration-in-the-large of 0.02. When the models were provided only preoperative variables to predict PHLF and a CCI>40, this resulted in similar AUCs of 0.78 and 0.71, respectively. Both models were used to build 2 risk calculators with the option to include or exclude intraoperative variables ( PHLF Risk Calculator; CCI>40 Risk Calculator ).ConclusionsUsing an international cohort of major hepatectomy patients, we used preoperative and intraoperative variables to develop and internally validate multivariable models to predict clinically significant PHLF and a CCI>40 with good discrimination and calibration.Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

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