Development and Validation of a Nomogram Based on Perioperative Factors to Predict Post-hepatectomy Liver Failure

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 undergoing hepatectomy.

We retrospectively analyzed patients undergoing hepatectomy at Zhongshan Hospital, Fudan University from July 2015 to June 2018, and randomly divided them into development and internal validation cohorts. External validation was performed in an independent cohort. Least absolute shrinkage and selection operator (commonly referred to as LASSO) logistic regression was applied to identify predictors of PHLF, and multivariate binary logistic regression analysis was performed to establish the predictive model, which was visualized with a nomogram.

A total of 492 eligible patients were analyzed. LASSO and multivariate analysis identified three preoperative variables, total bilirubin (p=0.001), international normalized 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 (referred 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 predict PHLF accurately and distinguish high-risk patients.

PHLF can be accurately predicted by this model in patients undergoing hepatectomy, which may significantly contribute to the postoperative care of these patients.