Editorial: Assessing the Prognosis of Patients With HBV and ACLF—Comorbidities Matter. Authors' Reply

Abstract

We extend our sincere gratitude to Dr. Francesco Paolo Russo and Alberto Ferrarese for their thorough evaluation and professional insights on our study [1]. We are gratified by their recognition of the potential of the age-adjusted Charlson Comorbidity Index for Hepatitis B Virus-Related Acute-on-Chronic Liver Failure (aCCI-HBV-ACLF) score in enhancing the accuracy of short-term and medium-term prognostic predictions, particularly in integrating comorbidity factors and reducing variability among clinicians [2].

Previous research has established that multiple comorbidities are strongly associated with poor prognosis, with extrahepatic complications such as chronic renal failure and diabetes significantly elevating the mortality risk in patients with liver disease [3-5]. However, the relatively low incidence of these comorbidities presents challenges in fully incorporating them into prognostic models. Although the aCCI was initially designed for long-term prognostic evaluation, study has underscored its relevance in evaluating the prognosis of liver disease patients [6]. Similarly, our further analysis demonstrated that in the short-term prognosis of patients with HBV-related ACLF, nearly all comorbidities included in the aCCI are significantly correlated with short-term survival outcomes (Table 1). For instance, cardiovascular diseases were associated with a 287% increase in the 28-day mortality risk and a 267% increase in the 90-day mortality risk. Additionally, patients with chronic obstructive pulmonary disease, connective tissue diseases, diabetes, moderate to severe renal disease, tumours and haematological diseases exhibited substantially increased mortality risks. In contrast, although peptic ulcer disease showed a certain increase in risk, it did not reach statistical significance (p > 0.05).

We fully concur with the concern regarding the exclusion of liver transplant patients from the study population. Liver transplantation is a crucial therapeutic option for ACLF, demonstrating significant improvements in patient outcomes [7, 8]. Studies have demonstrated that liver transplantation markedly enhances survival rates compared to patients not receiving transplantation, with only a small proportion succumbing to surgical complications or postoperative issues [9]. Including liver transplant recipients in this study could disproportionately elevate the incidence of end-point events, potentially leading to an overestimation of associated risks. Moreover, differences in medical expertise and resources across centres can significantly influence transplant success rates and post-transplant survival outcomes. Critical determinants of transplantation outcomes include the quality of donor organs, the overall health status of recipients and the expertise of the surgical team [10]. Not all patients awaiting transplantation are eligible, and therefore, including liver transplant recipients might compromise the predictive accuracy of the model when applied to other medical centres. Consequently, liver transplant recipients were excluded from the model's development.

We also acknowledge the potential influence of regional and racial variations on the generalisability of the aCCI-HBV-ACLF score. To address this, we aim to expand our data collection in future studies to validate the score in diverse populations. Once again, we are grateful for their in-depth review and constructive suggestions on our study. Their feedback has further illuminated the applicability and avenues for optimisation of the aCCI-HBV-ACLF score in different populations. We firmly believe that the aCCI-HBV-ACLF score can provide a more accurate prognostic assessment tool for the treatment and management of patients with ACLF in clinical practice.

Jiong Yu: conceptualization, writing – original draft. Xinyi Chen: data curation, writing – review and editing. Guoqiang Cao: data curation. Qiaoling Pan: data curation. Chenjie Huang: data curation. Rui Luo: data curation. Xiaoqing Lu: data curation. Xiaoxiao Chen: data curation. Tan Li: data curation. Haijun Huang: data curation, investigation, writing – review and editing. Jian Wu: investigation, writing – review and editing. Lanjuan Li: conceptualization. Hongcui Cao: conceptualization, funding acquisition, writing – review and editing.

The authors declare no conflicts of interest.

This article is linked to Chen et al papers. To view these articles, visit https://doi.org/10.1111/apt.18347 and https://doi.org/10.1111/apt.18361.