Correspondence to editorial on “Carvedilol to prevent hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension stratified by new non-invasive model (CHESS2306)”

Article information

Clin Mol Hepatol. 2025;31(2):e155-e157

Publication date (electronic) : 2024 August 30

doi : https://doi.org/10.3350/cmh.2024.0723

Chuan Liu^1,2,*, Ling Yang^1,*, Hong You³, Gao-Jun Teng^2,4, Xiaolong Qi^,1,2

¹Liver Disease Center of Integrated Traditional Chinese and Western Medicine, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology (Southeast University), Nanjing, China

²Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University; State Key Laboratory of Digital Medical Engineering, Nanjing, China

³Liver Research Center, Beijing Key Laboratory of Translational Medicine in Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, China

⁴Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China

Corresponding author : Xiaolong Qi Liver Disease Center of Integrated Traditional Chinese and Western Medicine, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing 210000, China Tel: +86-18588602600, Fax: 025-83272121, E-mail: qixiaolong@vip.163.com

*These authors contributed equally to this work.

Editor: Han Ah Lee, Chung-Ang University College of Medicine, Korea

Received 2024 August 27; Accepted 2024 August 29.

Dear Editor,

We sincerely appreciate the editorial by Dr. Akhil and Dr. Rakhi [1], which provided insightful commentaries on our recent study published in Clinical and Molecular Hepatology [2] regarding the role of carvedilol in preventing hepatic decompensation of cirrhosis in patients with clinically significant portal hypertension (CSPH) stratified by a new non-invasive model [2]. Identifying predictors of decompensation in compensated patients is crucial, as the onset of decompensation is closely linked to mortality in cirrhosis [3]. The hepatic venous pressure gradient (HVPG) is the gold standard for assessing portal pressure and the foremost predictor of decompensation in cirrhotic patients [4,5]. As noted in the editorial, however, the invasive procedure and restricted accessibility of HVPG measurements impede its widespread use. Due to this reason, non-invasive tests are extensively employed to detect CSPH in patients with compensated cirrhosis.

The Baveno VII criteria provide specific thresholds for ruling in CSPH if liver stiffness measurement (LSM) ≥25 kPa or out if LSM≤15 kPa plus platelet count (PLT) ≥150×10⁹/L, yet a grey zone exists for patients whose test results fall between these thresholds [6]. In such cases, the ANTICIPATE model is applied [7], which considers a combination of LSM values and PLT to predict CSPH risk. Nonetheless, these non-invasive methods fail to account for all possible combinations of liver stiffness and platelet count values, leaving gaps in diagnostic frameworks. We thus developed a new non-invasive CSPH risk model for predicting CSPH in patients with compensated cirrhosis, which classifies them into low-risk, medium-risk and high-risk CSPH groups. Our new model narrowed down the grey zone to 22.5%, significantly lower than the 50.3% grey zone observed with the Baveno VII criteria.

We agree that when the current LSM values have been determined, there persists an element of ambiguity regarding the supplementary predictive capacity of LSM alterations. Semmler et al. [8] uncovered that repeating LSM enables an updated risk assessment for decompensation and liver-related mortality in ACLD, which is in agreement with Gawrieh et al. [9] who concluded that changes of LSM values are useful non-invasive surrogates for outcomes in patients with nonalcoholic fatty liver disease. These findings conflict with the discovery proposed by Wong et al. [10] that once the current LSM value is known, previous LSM values do not contribute to the prediction of liver-related events in patients with compensated advanced chronic liver disease (cACLD). As the quantity of LSM values increases, the outcomes are susceptible to alteration; however, the aggregation of such extensive data is challenging, exacerbated by the need for further investigation into the test-retest reliability of serial LSM measurements. It is undeniable that the variability in study designs, particularly in the number of LSM values considered, underscores the complexity in aggregating extensive data and the pressing need for additional investigation into the prognostic superiority of a single versus dynamic LSM value in predicting CD. Consequently, our ongoing research efforts are dedicated to investigating the impact of dynamic LSM values on the accuracy of risk assessment in the new predictive model, as well as elucidating their significance in directing carve-dilol treatment strategies.

Considering that our cohort primarily comprised patients with viral-related cirrhosis, it is imperative to acknowledge that there were also patients with non-viral cACLD, particularly in obese non-alcoholic steatohepatitis patients [11], for whom the accuracy of LSM remains to be considered. Consequently, it is essential to explore alternative non-invasive tests (NITs) beyond transient elastography across diverse etiologies and to identify novel efficient predictive models to stratify CSPH and decompensation.

Over the previous decade, apart from LSM, accumulating evidence [12] and meta-analytic studies [13] have underscored the significance of spleen stiffness measurement (SSM) as an indispensable marker for the diagnosis of CSPH. Elton Dajti and colleagues have demonstrated that the incorporation of spleen stiffness at a threshold of 40 kPa, in conjunction with LSM and PLT, constitutes the combined Baveno VII-SSM model, which can serve as an exemplary non-invasive method [14,15]. SSM remains a promising NIT with tremendous potential which warrants further investigation.

As the editorialists point out, despite the fact that numerous studies have evaluated a diverse range of NITs, limited number of investigations have been conducted into the utilization of NITs, either independently or in conjunction with additional biomarkers, for the evaluation of the therapeutic response to beta-blocker treatment. We took this into account and as such, our intention was not merely to identify patients with high-risk HVPG; rather, we aspired to intervene therapeutically, aiming to forestall further decompensation. Significantly, our findings elucidated that treatment with carvedilol markedly diminishes the 3-year cumulative incidence of decompensation, predominantly ascites, in patients with high-risk CSPH. This provides critical support for the utilization of carvedilol in the management of patients with compensated cirrhosis and CSPH. Given the promising results observed with carvedilol, it is vital that further studies be conducted using NITs to explore the evaluation of the therapeutic response to beta-blocker therapy to better tailor treatment strategies and improve patient outcomes.

Again, we thank Dr. Akhil and Dr. Rakhi for their invaluable review. In future research, we intend to employ randomized controlled trials to mitigate the potential biases arising from differences in baseline characteristics between carvedilol-treated and untreated patients in this study. Furthermore, we will delve into the impact of cofactors such as obesity and diabetes on disease progression and LSM values, aiming to refine our “CSPH risk model” and provide more precise guidance for the utilization of carvedilol in patients with compensated cirrhosis and CSPH. Despite the limitations of our investigation, our study has demonstrated for the first time that carvedilol treatment is associated with a significantly reduced incidence of hepatic decompensation in patients with high-risk CSPH stratified by the new model.