An ordinal model to predict the risk of symptomatic liver failure in patients with cirrhosis undergoing hepatectomy.

Background & Aims: Selection criteria for hepatectomy in patients with cirrhosis are controversial. In this study we aimed to build prognostic models of symptomatic post-hepatectomy liver failure (PHLF) in patients with cirrhosis.

Methods: This was a cohort study of patients with histologically proven cirrhosis undergoing hepatectomy in 6 French tertiary care hepato-biliary-pancreatic centres. The primary endpoint was symptomatic (grade B or C) PHLF, according to the International Study Group of Liver Surgery's definition. Twenty-six preoperative and 5 intraoperative variables were considered. An ordered ordinal logistic regression model with proportional odds ratio was used with 3 classes: O/A (No PHLF or grade A PHLF), B (grade B PHLF) and C (grade C PHLF).

Results: Of the 343 patients included, the main indication was hepatocellular carcinoma (88%). Laparoscopic liver resection was performed in 112 patients. Three-month mortality was 5.25%. The observed grades of PHLF were: 0/A: 61%, B: 28%, C: 11%. Based on the results of univariate analyses, 3 preoperative variables (platelet count, liver remnant volume ratio and intent-to-treat laparoscopy) were retained in a preoperative model and 2 intraoperative variables (per protocol laparoscopy and intraoperative blood loss) were added to the latter in a postoperative model. The preoperative model estimated the probabilities of PHLF grades with acceptable discrimination (area under the receiver-operating characteristic curve [AUC] 0.73, B/C vs. 0/A; AUC 0.75, C vs. 0/A/B) and the performance of the postoperative model was even better (AUC 0.77, B/C vs. 0/A; AUC 0.81, C vs. 0/A/B; p <0.001).

Conclusions: By accurately predicting the risk of symptomatic PHLF in patients with cirrhosis, the preoperative model should be useful at the selection stage. Prediction can be adjusted at the end of surgery by also considering blood loss and conversion to laparotomy in a postoperative model, which might influence postoperative management.

Lay Summary: In patients with liver cirrhosis, the risk of a hepatectomy is difficult to appreciate. We propose a statistical tool to estimate this risk, preoperatively and immediately after surgery, using readily available parameters and on online calculator. This model could help to improve the selection of patients with the best risk-benefit profiles for hepatectomy.