Background and Aims: Post-hepatectomy liver failure (PHLF) is a severe complication and main cause of death in patients undergoing hepatectomy. The aim of this study was to build a predictive model of PHLF in patients under-going hepatectomy. Methods: We retrospectively analyzed patients undergoing hepatectomy at Zhongshan Hospital, Fudan University from July 2015 to June 2018, and ran-domly divided them into development and internal validation cohorts. External validation was performed in an independ-ent cohort. Least absolute shrinkage and selection operator (commonly referred to as LASSO) logistic regression was ap-plied to identify predictors of PHLF, and multivariate binary logistic regression analysis was performed to establish the predictive model, which was visualized with a nomogram. Results: A total of 492 eligible patients were analyzed. LAS-SO and multivariate analysis identified three preoperative variables, total bilirubin (p=0.001), international normal-ized ratio (p<0.001) and platelet count (p=0.004), and two intraoperative variables, extent of resection (p=0.002) and blood loss (p=0.004), as independent predictors of PHLF. The area under receiver operating characteristic curve (re-ferred to as AUROC) of the predictive model was 0.838 and outperformed the model for end-stage liver disease score, albumin-bilirubin score and platelet-albumin-bilirubin score (AUROCs: 0.723, 0.695 and 0.663, respectively; p<0.001 for all). The optimal cut-off value of the predictive model was 14.7. External validation showed the model could pre-dict PHLF accurately and distinguish high-risk patients. Con-clusions: PHLF can be accurately predicted by this model in patients undergoing hepatectomy, which may significantly contribute to the postoperative care of these patients.