Predicting Post-Hepatectomy Liver Failure in HCC Patients: A Review of Liver Function Assessment Based on Laboratory Tests Scores

Significance of Prediction Models for Post-Hepatectomy Liver Failure Based on Type IV Collagen 7s Domain in Patients with Hepatocellular Carcinoma

Background: Previous studies have attempted to establish predictive models for post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) undergoing liver resection. However, a versatile and useful predictive model for PHLF remains to be developed. Therefore, we aimed to develop predictive models for PHLF based on type IV collagen 7s domain (7s collagen) in patients with HCC. Methods: We retrospectively collected data from 972 patients with HCC who had undergone initial curative liver resection between February 2000 and December 2020 at our hospital. Multivariate logistic regression analysis using a restricted cubic spline was performed to evaluate the effect of 7s collagen on the incidence of PHLF. A nomogram was developed based on 7s collagen. Results: PHLF grades B or C were identified in 104 patients (11%): 98 (10%) and 6 (1%) PHLF grades B and C, respectively. Multivariate logistic regression analysis revealed that the preoperative serum level of 7s collagen was significantly associated with a proportional increase in the risk of PHLF, which was confirmed in both laparoscopic and open liver resections. A nomogram was developed based on 7s collagen, with a concordance index of 0.768. The inclusion of 7s collagen values in the predictive model increased the predictive accuracy. Conclusion: The findings highlight the efficacy of the serum level of 7s collagen as a predictive factor for PHLF. Our novel nomogram using 7s collagen may be useful for predicting the risk of PHLF.

Predictive factors of 90-day mortality after curative hepatic resection for hepatocellular carcinoma: a western single-center observational study

PURPOSE

The aim of this study was to identify predictive risk factors associated with 90-day mortality after hepatic resection (HR) in hepatocellular carcinoma (HCC).

METHODS

All patients undergoing elective resection for HCC from a single- institutional and prospectively maintained database were included. Multivariate regression analysis was conducted to identify pre- and intraoperative as well as histopathological predictive factors of 90-day mortality after elective HR.

RESULTS

Between August 2004 and October 2021, 196 patients were enrolled (148 male /48 female). The median age of the study cohort was 68.5 years (range19-84 years). The rate of major hepatectomy (≥ 3 segments) was 43.88%. Multivariate analysis revealed patient age ≥ 70 years [HR 2.798; (95% CI 1.263-6.198); p = 0.011], preoperative chronic renal insufficiency [HR 3.673; (95% CI 1.598-8.443); p = 0.002], Child-Pugh Score [HR 2.240; (95% CI 1.188-4.224); p = 0.013], V-Stage [HR 2.420; (95% CI 1.187-4.936); p = 0.015], and resected segments ≥ 3 [HR 4.700; (95% 1.926-11.467); p = 0.001] as the major significant determinants of the 90-day mortality.

CONCLUSION

Advanced patient age, pre-existing chronic renal insufficiency, Child-Pugh Score, extended hepatic resection, and vascular tumor involvement were identified as significant predictive factors of 90-day mortality. Proper patient selection and adjustment of treatment strategies could potentially reduce short-term mortality.

Probiotics are beneficial for liver cirrhosis: a systematic review and meta-analysis of randomized control trials

Introduction

Gut dysbiosis may play a pivotal role in the pathogenesis of cirrhosis and the severity of complications. Numerous studies have investigated the probiotics as treatments for cirrhosis. However, there is still a lack of definitive evidence confirming the beneficial effects of probiotics on cirrhosis.

Methods

Databases including PubMed, Embase, Web of Science, and the Cochrane Library were systematically searched for randomized controlled trials that compared the effects of probiotic intervention and control treatments, including placebo, no treatment, and active control, on cirrhosis, published from inception to February 2024. Outcomes included hepatic encephalopathy (HE) reversal, safety and tolerability of probiotics, liver function, quality of life, and other cirrhotic-related outcomes. A meta-analysis was conducted to synthesize evidence.

Results

Thirty studies were included. The quantitative synthesis results showed that compared with the control group, probiotics significantly reverse minimal hepatic encephalopathy (MHE) (risk ratio [RR] 1.54, 95% confidence interval [CI] 1.03 to 2.32) and improve HE (RR 1.94, 95% CI 1.24 to 3.06). Additionally, probiotics demonstrated higher safety and tolerability by causing a lower incidence of serious adverse events (RR 0.71, 95% CI 0.58 to 0.87). Probiotics could potentially improve liver function by reducing the Model for End-Stage Liver Disease (MELD) scores (standardized mean difference [SMD] -0.57, 95% CI -0.85 to -0.30), and displayed favorable changes in quality of life (SMD 0.51, 95% CI 0.27 to 0.75) and gut flora (SMD 1.67, 95% CI 1.28 to 2.06).

Conclusion

This systematic review and meta-analysis offers compelling evidence that probiotics are beneficial for cirrhosis by demonstrating reversal of HE, potential for liver function improvements, enhancements in quality of life, and regulation of gut dysbiosis. Furthermore, the apparent safety profile suggests that probiotics are a promising intervention for treating cirrhosis.

Clinical trial registration number

CRD42023478380.

Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence

(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.

Peri-interventional LiMAx test for liver ablation - A feasibility study

BACKGROUND

Local ablation is an important treatment for liver cancer in cases of cirrhosis. Liver failure may occur after ablation, especially in advanced liver diseases. Currently, there is no standardization for peri-interventional risk assessment. The liver maximum capacity test (LiMAx) is used to assess functional liver capacity, but there is a lack of exploration of its use in this context.

OBJECTIVE

The aim of this study was to retrospectively evaluate the usefulness of peri-interventional LiMAx measurements in patients with primary or secondary liver cancer who underwent ablation treatment.

METHODS

A LiMAx test was performed at 24 hours pre- and postablation in 49 patients. Blood parameters were collected to determine liver function using MELD and ALBI scores. The results of the LiMAx test were related with these scores and to critical postintervention LiMAx values.

RESULTS

LiMAx values correlated strongly with MELD and ALBI scores before the intervention and reflected the change in liver function, as shown by an increase in scores after the intervention. Notably, LiMAx values decreased during the intervention. AUC analysis for patients at risk of reaching a critical liver level after the intervention showed a cutoff value of 186μg/kg/h.

CONCLUSIONS

The LiMAx test may be a valuable tool in liver ablation for both peri-interventional monitoring of liver function and preintervention risk assessment.