Performance of non-invasive biomarkers compared with invasive methods for risk prediction of posthepatectomy liver failure in hepatocellular carcinoma. Issue 5 (10th February 2022)

• Record Type: Journal Article
• Authors: Hobeika, Christian; Guyard, Clémence; Sartoris, Riccardo; Maino, Cesare; Rautou, Pierre-Emmanuel; Dokmak, Safi; Bouattour, Mohamed; Durand, François; Weiss, Emmanuel; Vilgrain, Valérie; Beaufrère, Aurélie; Sepulveda, Ailton; Farges, Olivier; Paradis, Valérie; Luciani, Alain; Lim, Chetana; Sommacale, Daniele; Scatton, Olivier; Laurent, Alexis; Nault, Jean-Charles; Soubrane, Olivier; Ronot, Maxime; Cauchy, François
• Is Part Of: British journal of surgery. Volume 109:Issue 5(2022)
• Page Start: 455
• Page End: 463
• Publication Date: 2022-02-10
• Subjects: Surgery -- Periodicals; 617.005
• DOI: 10.1093/bjs/znac017
• Languages: English
• ISSNs: 0007-1323

Abstract

Abstract: Background: Posthepatectomy liver failure (PHLF) is a rare but dreaded complication. The aim was to test whether a combination of non-invasive biomarkers (NIBs) and CT data could predict the risk of PHLF in patients who underwent resection of hepatocellular carcinoma (HCC). Methods: Patients with HCC who had liver resection between 2012 and 2020 were included. A relevant combination of NIBs (NIB model) to model PHLF risk was identified using a doubly robust estimator (inverse probability weighting combined with logistic regression). The adjustment variables were body surface area, ASA fitness grade, male sex, future liver remnant (FLR) ratio, difficulty of liver resection, and blood loss. The reference invasive biomarker (IB) model comprised a combination of pathological analysis of the underlying liver and hepatic venous pressure gradient (HVPG) measurement. Various NIB and IB models for prediction of PHLF were fitted and compared. NIB model performances were validated externally. Areas under the curve (AUCs) were corrected using bootstrapping. Results: Overall 323 patients were included. The doubly robust estimator showed that hepatitis C infection (odds ratio (OR) 4.33, 95 per cent c.i. 1.29 to 9.20; P = 0.001), MELD score (OR 1.26, 1.04 to 1.66; P = 0.001), fibrosis-4 score (OR 1.36, 1.06 to 1.85; P = 0.001), liver surface nodularity score (OR 1.55, 1.28 to 4.29; P = 0.031), and FLR volume ratio (OR 0.99, 0.97 to 1.00; P = 0.014) were associated with PHLF. Their combination (NIB model) was fitted externally (2-centre cohort, 165 patients) to model PHLF risk (AUC 0.867). Among 129 of 323 patients who underwent preoperative HVPG measurement, NIB and IB models had similar performances (AUC 0.753 versus 0.732; P = 0.940). A well calibrated nomogram was drawn based on the NIB model (AUC 0.740). The risk of grade B/C PHLF could be ruled out in patients with a cumulative score of less than 160 points. Conclusion: The NIB model provides reliable preoperative evaluation with performance at least similar to that of invasive methods for PHLF risk prediction. Abstract : This case–control study with external validation assessed predictive performances of non-invasive tests in comparison with invasive methods (liver histology and hepatic venous pressure gradient). A combination of non-invasive tests (liver surface nodularity, fibrosis-4, Model for End-Stage Liver Disease scores) can be as valuable as invasive methods in anticipating posthepatectomy liver failure in patients with hepatocellular carcinoma.