A novel online calculator based on noninvasive markers (ALBI and APRI) for predicting post-hepatectomy liver failure in patients with hepatocellular carcinoma

Background and aim: Post-hepatectomy liver failure (PHLF) remains the primary cause of in hospital mortality after hepatectomy. Identifying predictors of PHLF is important to improve surgical safety. We sought to identify the predictive accuracy of two noninvasive markers, albumin-bilirubin (ALBI) and aspartate aminotransferase to platelet count ratio index (APRI), to predict PHLF among patients with hepatocellular carcinoma (HCC), and to build up an online prediction calculator. Methods: Patients who underwent resection for HCC between 2013 and 2016 at 6 Chinese hos-pitals were retrospectively analyzed. The independent predictors of PHLF were identified using univariate and multivariate analyses; derivative data were used to construct preoperative and postoperative nomogram models. Receiver operating characteristic (ROC) curves for the two predictive models, and ALBI, APRI, Child-Pugh, model for end-stage liver disease (MELD) scores were compared relative to predictive accuracy for PHLF. Results: Among the 767 patients in the analytic cohort, 102 (13.3%) experienced PHLF. Multi -variable logistic regression analysis identified high ALBI grade (>-2.6) and high APRI grade (>1.5) as independent risk factors associated with PHLF in both the preoperative and postoperative models. Two nomogram predictive models and corresponding web-based calculators were sub-sequently constructed. The areas under the ROC curves for the postoperative and preoperative models, APRI, ALBI, MELD and Child-Pugh scores in predicting PHLF were 0.844, 0.789, 0.626, 0.609, 0.569, and 0.560, respectively. Conclusions: ALBI and APRI demonstrated more accurate ability to predict PHLF than Child -Pugh and MELD. Two online calculators that combined ALBI and APRI were proposed as useful preoperative and postoperative tools for individually predicting the occurrence of PHLF among patients with HCC. (c) 2020 Elsevier Masson SAS. All rights reserved.