Related Experiment Video
Updated: Sep 19, 2025

Author Spotlight: Advancing Liver Regeneration Research through ALPPS Mouse Model
Published on: January 19, 2024
MicroRNA based Prediction of Posthepatectomy Liver Failure and Mortality Outperforms Established Markers of
Anna Emilia Kern1, David Pereyra2, Jonas Santol3,4,5
1Medical University of Vienna, Vienna, Austria.
Background:
Posthepatectomy liver failure (PHLF) continues to be the most significant factor-determining outcome after hepatic resection, accounting for nearly half of postoperative mortality. In this study, we evaluated whether a newly developed commercially available test measuring circulating microRNAs (miRs) could predict PHLF and compared it with other established liver function tests.
Patients And Methods:
A total of 329 patients undergoing liver resection were included and postoperative outcome was assessed. Our previously described P-score, calculated on the basis of three circulating microRNAs (miR-122-5p, miR-192-5p, miR-151a-5p) using the hepatomiR® CE-IVD test, was evaluated and compared with other predictors of PHLF, namely indocyanine green (ICG)-clearance as well as the combined aspartate aminotransferase (AST)-to-platelet ratio index (APRI) and albumin-bilirubin grade (ALBI) score.
Results:
Compared with both other liver function tests, P-scores were superior in predicting PHLF and PHLF grades B and C (PHLF B + C) (PHLF B + C: hepatomiR® AUC = 0.835, APRI + ALBI AUC = 0.807; retention rate at 15 min (R15) AUC = 0.690; plasma disappearance rate (PDR) AUC = 0.691). We also documented a superior positive (77%) and negative predictive value (> 90%) for PHLF, along with a close association with postoperative overall survival. A health-economic analysis demonstrated the cost-effectiveness of hepatomiR® in terms of life-years gained due to improved patient risk stratification.
Conclusions:
The hepatomiR® P-score outperforms established liver function tests utilized in daily clinical practice for predicting PHLF and identifies patients possibly better served with alternative treatments. A health-economic assessment allowed us to demonstrate that optimized preoperative risk-assessment leads to a cost-effective improvement in patient outcomes.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
08:14MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017