An APRI+ALBI-Based Multivariable Model as a Preoperative Predictor for Posthepatectomy Liver Failure

Area of Science:

• Hepatobiliary Surgery
• Surgical Oncology
• Liver Function Assessment

Background:

• Posthepatectomy liver failure (PHLF B+C) is a major cause of mortality after major liver resections.
• Accurate preoperative prediction of PHLF is crucial for patient management and surgical planning.

Purpose of the Study:

• To develop and validate a multivariable model (MVM) for predicting clinically significant PHLF (B+C) using APRI+ALBI.
• To compare the predictive performance of the APRI+ALBI MVM against established liver function tests like indocyanine green clearance (ICG) and albumin-bilirubin grade (ALICE).

Main Methods:

• A large cohort (12,056 patients) from the National Surgical Quality Improvement Program database was used to develop the MVM via stepwise backwards elimination.
• Model performance was evaluated using receiver operating characteristic curve analysis and validated in an international cohort (2525 patients).
• Direct comparison of the APRI+ALBI MVM with ICG clearance and ALICE-based MVMs was performed in 620 patients.

Main Results:

• The APRI+ALBI MVM, incorporating age, sex, tumor type, and resection extent, predicted PHLF B+C with an AUC of 0.77 (validation AUC: 0.74).
• The APRI+ALBI MVM demonstrated comparable predictive performance to MVMs based on ICG clearance and ALICE.
• A smartphone application was developed for easy calculation of the APRI+ALBI MVM score.

Conclusions:

• The APRI+ALBI MVM provides a universally available and cost-effective method for preoperative risk assessment of PHLF B+C before hepatectomy.
• This model enhances preoperative predictive accuracy, aiding in better surgical decision-making.
• A freely available smartphone app facilitates the practical application of this risk assessment tool.