Developing and validating a pre-operative risk score to predict post-hepatectomy liver failure

Abstract

Background: Post hepatectomy liver failure (PHLF) is a serious complication in patients undergoing liver resection. This study hypothesized that a new pre-operative risk score developed through statistical modeling to predict PHLF could be used to stratify patients at higher risk of PHLF.

Methods: Patients who underwent hepatectomy between 2008 and 2016 were included in the derivation and validation cohorts. A multivariable binary logistic regression model was performed to identify predictors of PHLF, and a prognostic score was derived.

Results: A total of 1269 patients were included in the derivation cohort. PHLF was encountered in 13.1% and was associated with significantly increased 90-day mortality and prolonged post-operative hospital stay (both p < 0.001). Multivariable analysis identified the extent of surgery (p < 0.001) and pre-operative bilirubin (p = 0.015), INR (p < 0.001), and creatinine (p = 0.048) to be independent predictors of PHLF. A risk score derived from these factors returned an area under the ROC curve (AUROC) of 0.816 (p < 0.001) for an internal validation cohort (N = 453), significantly outperforming the MELD score (AUROC: 0.643).

Conclusion: The PHLF risk score could be used to stratify the risk of PHLF among patients planned for hepatectomy.