We sincerely appreciate the insightful Editorial by Sangam et al. regarding our study [1, 2]. We echo the call for harmonising definitions and prognostication in acute-on-chronic liver failure (ACLF) [3]. While the debate over definitions continues, the core concept of ACLF—a rapid deterioration due to an acute insult on chronic liver disease, with organ dysfunction and high short-term mortality—remains consistent. The Asia Pacific (APASL) definition highlights liver-dominant injury without prior decompensation, whereas the European (EASL) definition includes patients with or without prior decompensation of cirrhosis and focuses on multi-organ failures. However, the core concept of acute worsening in the setting of chronic liver disease remains the common denominator (aka. Acute Decompensation-AD).

Our study aimed to objectively group such patients with AD; ‘ACLF by APASL or EASL definition’ through machine learning, identifying distinct phenotypes based on their clinical profiles without a priori hypotheses. This approach uncovered four clusters with unique survival trajectories, demonstrating that cluster membership independently predicted prognosis beyond established scores like the CLIF-C-ACLF. We integrated the supervised learning to predict these clusters with simple decision trees. This phenotype-based strategy was described to enhance clinical decision-making. We utilised a systematic approach to select variables for clustering, balancing both qualitative and quantitative aspects of patient data. Baseline variables were first considered to capture initial clinical states, followed by dynamic data to reflect disease progression. The use of composite severity scores was debated and ultimately included based on their proven contribution to the model's accuracy utilising a titration-based empiric approach of data science.

Although the clustering results reflect our single-centre experience, they provide a foundation for external validation in diverse populations to ensure robustness. Harmonising data across global cohorts will be crucial to establishing an internationally accepted framework for diagnosing and prognosticating patients with AD and ACLF. As recent meta-analyses suggest [4], our cohort shares similarities with global studies using various ACLF definitions (APASL, EASL, NACSELD), further supporting the generalisability of our findings. While external validation is essential, we believe our well-characterised cohort from a large public sector hospital in India represents similar settings across Asia.

We also agree with the Editorial's emphasis on distinguishing clinical and biological heterogeneity. Our study is the first to apply machine learning to ACLF phenotyping, akin to the work of Nakano et al. in heart failure patients [5]. However, integrating biological data, such as multi-omics, will be crucial in refining these clusters and aligning them with underlying biological pathways. For instance, Cockrell et al. [6] demonstrated how integrating cytokine profiles and genetic algorithms improved model robustness in systemic inflammation, a strategy that could be adapted to ACLF.

In conclusion, our study offers a proof of concept that machine learning can capture the clinical heterogeneity among patients with AD and ACLF, potentially guiding personalised management plans. We propose a framework (Figure 1) that integrates global definitions, geographical variations and disease progression to inform future management strategies. We hope that these efforts will lead to more precise, targeted interventions in patients with AD and ACLF.

我们衷心感谢 Sangam 等人对我们研究的深刻见解 [1， 2]。我们响应了协调慢加急性肝衰竭 （ACLF） 定义和预后的呼吁 [3]。虽然关于定义的争论仍在继续，但 ACLF 的核心概念——由于慢性肝病的急性损伤而迅速恶化，伴有器官功能障碍和高短期死亡率——仍然一致。亚太地区 （APASL） 定义强调既往无失代偿的肝脏显性损伤，而欧洲 （EASL） 定义包括既往有或无肝硬化失代偿的患者，侧重于多器官衰竭。然而，在慢性肝病的情况下急性恶化的核心概念仍然是共同点（又名。急性失代偿 - AD）。

我们的研究旨在客观地对此类 AD 患者进行分组;通过机器学习“通过 APASL 或 EASL 定义进行 ACLF”，根据其临床特征识别不同的表型，而无需先验假设。这种方法发现了四个具有独特生存轨迹的集群，表明集群成员独立预测了超出 CLIF-C-ACLF 等既定评分的预后。我们集成了监督学习，通过简单的决策树来预测这些集群。这种基于表型的策略被描述为增强临床决策。我们利用系统方法来选择用于聚类的变量，平衡患者数据的定性和定量方面。首先考虑使用基线变量来捕捉初始临床状态，然后是反映疾病进展的动态数据。对综合严重性评分的使用进行了辩论，并最终根据它们利用基于滴定的数据科学经验方法对模型准确性的证明贡献而被纳入其中。

尽管聚类结果反映了我们的单中心经验，但它们为不同人群的外部验证提供了基础，以确保稳健性。协调全球队列中的数据对于建立国际公认的 AD 和 ACLF 患者诊断和预后框架至关重要。正如最近的荟萃分析所表明的那样 [4]，我们的队列与使用各种 ACLF 定义 （APASL、EASL、NACSELD） 的全球研究有相似之处，进一步支持了我们研究结果的普遍性。虽然外部验证是必不可少的，但我们相信，我们来自印度一家大型公共部门医院的特征明确的队列代表了整个亚洲的类似环境。

我们也同意社论强调区分临床和生物学异质性。我们的研究是首次将机器学习应用于 ACLF 表型分析，类似于 Nakano 等人在心力衰竭患者中的工作 [5]。然而，整合生物数据（例如多组学）对于提炼这些集群并将其与潜在的生物途径保持一致至关重要。例如，Cockrell 等人 [6] 展示了整合细胞因子谱和遗传算法如何提高全身炎症的模型稳健性，这是一种可以适应 ACLF 的策略。

总之，我们的研究提供了一个概念验证，即机器学习可以捕捉 AD 和 ACLF 患者之间的临床异质性，从而可能指导个性化的管理计划。我们提出了一个框架（图 1），该框架整合了全球定义、地理差异和疾病进展，为未来的管理策略提供信息。我们希望这些努力将导致对 AD 和 ACLF 患者进行更精确、更有针对性的干预。