Unbiased clustering of acute-on-chronic liver failure patients using machine learning in a real-world ICU cohort

Domain: neuroscience
Published: 2026-01-20
Editorial outcome: major_revision

Abstract
Acute-on-chronic liver failure is a complex condition with varied definitions, complicating risk stratification and targeted management. We apply unsupervised clustering to data from 1,256 patients with acute-on-chronic liver failure as defined by the North American Association for the Study of End-Stage Liver Disease. We systematically evaluate multiple algorithmic models to enable unbiased cohort stratification and to determine key clustering factors and clinical impacts across clusters. The optimal number of clusters is determined using the Partitioning Around Medoids with Ambiguous Clustering algorithm. The clusters from the best-performing nonnegative matrix factorization algorithm are selected, with the Lee’s algorithm demonstrating the best performance. Two distinct clusters are identified, showing markedly different 30-day mortality rates (70.35% vs 26.06%). Importantly, acid-base balance-related variables, including bicarbonate, pH, base excess, lactate, and anion gap, are among the primary clustering drivers. An external validation cohort, a decompensated cirrhosis cohort, and a European Association for the Study of the Liver-Chronic Liver Failure Consortium defined Acute-on-chronic liver failure patients cohort confirm consistent distributions of key clustering variables and divergence in 30-day mortality rates, supporting the role of acid-base balance variables. In summary, we show that the unbiased clustering approach successfully identifies distinct acute-on-chronic liver failure clusters with different mortality risks and emphasizes the critical role of metabolic regulation in acute-on-chronic liver failure outcomes, as well as consistent validation.

Introduction
Acute-on-chronic liver failure (ACLF) is a syndrome with high short-term mortality and complex mechanisms, often triggered by systemic inflammation and organ failures (OFs)1–3.

Different regions have varying definitions, with the European Association for the Liver-Chronic Liver Failure Consortium (EASL-CLIF)1, the North American Association for the Study of End-Stage Liver Disease (NACSELD), and the Asia Pacific Association for the Study of the Liver (APASL)4 being the most common. The NACSELD definition of ACLF is based on a North American cohort of patients hospitalized with acute decompensated cirrhosis and infections and evaluates four organ systems: circulatory (shock), respiratory (mechanical ventilation), renal (renal replacement therapy), and cerebral (grade III or IV hepatic encephalopathy)5.

Risk stratification of ACLF has predominantly been derived from clinical experience and diverse definition standards. The EASL-CLIF and NACSELD definitions emphasize the presence and number of OFs1,6, whereas the APASL classification focuses primarily on liver function impairment4. Predictive models, such as the CLIF-C ACLF score, have been developed to improve short-term mortality prediction in ACLF patients1,7. However, these models have been developed on selected patients in observational studies, not a real-world cohort, and rely on variables selected based on prior clinical and research knowledge, potentially missing complex interactions between factors that contribute to disease outcomes.

Unsupervised machine learning models offer an alternative for identifying unbiased patterns within data, independent of prior clinical assumptions in large heterogeneous, real-world populations8. Clustering analysis can systematically uncover distinct patient subgroups characterized by previously unrecognized combinations of clinical features, enhancing understanding of ACLF heterogeneity9.

In this study, we aimed to apply unsupervised clustering techniques to stratify mortality risk in real-world ICU settings NACSELD-defined ACLF patients without predefined clinical assumptions. We sought to compare the predictive performance of this model to traditional stratification based on the number of OFs, explored the contribution of various clinical and biochemical variables to mortality risk. We also validated the model using a cohort with decompensated cirrhosis, an EASL-CLIF-defined ACLF patients cohort, and an independent ICU NACSELD-defined ACLF patients cohort.

Results
Training cohort: patients with ACLF
Fig. 1a shows the selection process of ACLF patients. The final cohort of 1256 ACLF patients was included in the main analysis. The baseline characteristics of this cohort are depicted in Table 1. The median age of the cohort was 59 years (Q1–Q3: 51–67 years), and 62.8% of the cohort was male. The prevalence of OFs was as follows: respiratory failure (83.4%), circulatory failure (74.2%), brain failure (51.1%), and kidney failure (22.9%). The prevalence of triggering factors was as follows: infection (36.1%), alcohol (28.4%), and bleeding (40.5%).

Determination of the best clustering model
The optimal number of clusters was determined by testing values between 2 and 10 across five NMF clustering algorithms, utilizing the PAC algorithm to evaluate boundary regions in the clustering results. All five algorithms consistently identified two as the optimal number of clusters (Table 2). Among the five methods, the Lee algorithm performed best, as determined by six evaluation metrics of the PAC algorithm (Table 3).

Figure 2 presents the best model (Lee-2) clustering results. In the mixed coefficient matrix, each column represents a patient, and each row represents the probability of cluster membership, where darker colors indicate a higher probability (Fig. 2a). The basis matrix shows each feature’s weight within the clusters, with darker colors indicating greater weight (Fig. 2b). Fig. 2c presents the consistency matrix, a key diagnostic tool for evaluating the stability of NMF clustering results. In the symmetric matrix, both rows and columns represent individual patients. Each cell (i, j) indicates the proportion of times patients i and j were assigned to the same cluster across multiple NMF runs. The consistency matrix reveals clear and non-overlapping boundaries, demonstrating the stability of the clustering results.

Subgroup Analysis
t-SNE visualization of the Lee-2 clustering outcome indicated that, while there was some overlap between the two clusters, the majority of the data points were distinctly grouped, suggesting an effective clustering performance (Fig. 3a). Survival analysis revealed significant differences in mortality probability between the two clusters, with Cluster 1 having a higher 30-day mortality rate (70.35%) compared to Cluster 2 (26.06%) (Fig. 3b). Traditional classification by OFs grade also showed increasing mortality with higher OFs (Grade 1: 45.05%, Grade 2: 62.86%, Grade 3: 69.05%) (Fig. 3c). Multivariable Cox regression analysis demonstrated that the Lee-2 clustering model outperformed the traditional classification method based on the number of organ failures in mortality risk prediction, with Cluster 1 showing an adjusted mortality risk at 3.60 (95% CI 3.06–4.22) compared to Cluster 2 (Supplementary Fig. 1 and Supplementary Table 1).

Figure 3d shows the top 10 contributing characteristics of the two clusters. Statistical analysis revealed significant differences in the distribution of these features between the two clusters (Table 4 and Fig. 4). Cluster 1 demonstrated significantly higher vasopressor requirements, and creatinine levels. Notably, parameters related to acid-base balance, including bicarbonate, pH, base excess, and anion gap, also played a significant role in the clustering of ACLF patients. Cluster 1 had markedly lower bicarbonate levels, lower pH, a more pronounced base deficit, and a higher anion gap. Based on the t-SNE visualization results, we removed the overlapping patients and obtained the distribution of important features of clusters with more obvious differences (Supplementary Table 2).

Additionally, an analysis of OFs types demonstrated notable differences in organ involvement between the two clusters. Cluster 1 had a higher prevalence of kidney (Cluster 1 vs Cluster 2: 32.85% vs 10.92%) and circulatory (Cluster 1 vs Cluster 2: 84.01% vs 62.50%) failure, whereas respiratory failure (Cluster 1 vs Cluster 2: 77.33% vs 90.85%) was more common in Cluster 2, with brain failure (Cluster 1 vs Cluster 2: 48.55% vs 54.23%) observed in both clusters (Table 4). Supplementary Table 3 shows the distribution of OFs and triggering factors between clusters after removing the overlapping of t-SNE visualization, revealing a consistent distribution of OFs.

To investigate the high prevalence of respiratory failure in Cluster 2, we analyzed variables related to respiratory function, vital signs, and brain failure (Supplementary Table 4). Notably, the proportion of patients with brain failure was higher in Cluster 2 (50.78%) compared to Cluster 1 (40.41%). However, markers of respiratory dysfunction were more prevalent in Cluster 1. Cluster 1 exhibited a lower median PO2/FiO2 ratio and a higher proportion of patients with PO2/FiO2 ratios between 100–200 mmHg (32.33% vs. 22.09%) and below 100 mmHg (19.55% vs. 10.66%).

To further explore the key characteristics contributing to mortality in each cluster, a Random Forest algorithm was applied (Supplementary Fig. 2 and Supplementary Table 5). Both overall and subgroup analyses identified INR, PT, PTT, total bilirubin, and temperature as significant predictors of mortality risk, highlighting the universal importance of coagulation dysfunction, liver function impairment, and temperature abnormalities across all patients. In subgroup analyses, lactate, PO2, SpO2, norepinephrine, and SBP were significant mortality risk factors in Cluster 1. Conversely, ALT, sodium, RBC, BUN, and RDW were identified as significant predictors in Cluster 2.

Validation EASL-CLIF definition ACLF cohort
We studied the performance of Lee-2 model on ACLF patients defined in EASL-CLIF, and screened 1411 ACLF patients who met the EASL-CLIF definition from MIMIC-IV database. Supplemental Table 6 shows that the median age of the cohort was 59 years (Q1-Q3: 52-67 years), and 63.36% of the cohort was male. The prevalence of OFs was as follows: liver failure (24.4%), kidney failure (51.5%), coagulation failure (35.9%), brain failure (44.6%), circulation failure (58.7%), lung failure (56.6%). The prevalence of triggering factors is as follows: infection (37.0%), alcohol (28.3%), and bleeding (38.6%). When applying the Lee-2 model, the 30-day mortality rates of Cluster 1 and Cluster 2 were 69.0% and 27.0%, respectively (Supplementary Fig. 3). The distribution of the top 10 clustering factors in ACLF patients defined by EASL-CLIF was similar to the distribution observed in ACLF cohorts defined by NACSELD (Supplementary Fig. 4 and Supplemental Table 7). It was worth noting that the incidence of liver failure, kidney failure, coagulation failure, circulation failure, lung failure, and infection in Group 2 was significantly lower, while there was no significant difference in the incidence of brain failure, alcohol, and bleeding between the two clusters.

Validation analysis: decompensated cirrhosis cohort
We expanded the study cohort to include all patients with decompensated cirrhosis admitted to the ICU. A total of 1334 decompensated cirrhosis patients were obtained (Fig. 1a, right panel). Supplemental Table 8 shows that the median age of the cohort was 59 years (Q1-Q3: 52–67 years), and 65.3% of the cohort was male. The prevalence of OFs was as follows: respiratory failure (50.8%), circulatory failure (44.5%), brain failure (39.3%), and kidney failure (13.9%). The prevalence of triggering factors was as follows: infection (35.7%), alcohol (27.5%), and bleeding (43.9%). When applying the Lee-2 model, the 30-day mortality rates for Clusters 1 and 2 were 73.4% and 31.7%, respectively (Supplementary Fig. 5). The distribution of the top 10 clustering-contributing factors in patients with decompensated cirrhosis closely mirrored those observed in the ACLF cohort (Supplementary Fig. 6 and Supplemental Table 9). Notably, Cluster 2 exhibited a significantly lower prevalence of kidney and circulatory failure, as well as infection, while there was no significant difference in the prevalence of brain failure, respiratory failure, alcohol, and bleeding between the two clusters.

Validation analysis: independent ACLF cohort
The screening process for the external validation cohort is presented in Fig. 1b. Supplemental Table 10 shows that the median age of the cohort was 57 years (Q1–Q3: 51–64 years), and 62.03% of the cohort was male. The prevalence of OFs was as follows: respiratory failure (94.9%), brain failure (82.9%), circulatory failure (68.4%), and kidney failure (28.5%). The analytical results for the external validation cohort are presented in Supplementary Fig. 7. The first validation method confirmed that the optimal number of clusters for NMF clustering in the external validation cohort is also two. Additionally, 7 of the top 15 important variables in this cohort aligned with the top 10 important variables identified in the original cohort. The second validation method demonstrated that the characteristics of the two clusters, obtained by directly applying the Lee-2 model to the external validation cohort, were consistent with those identified in the original cohort. These findings suggest that the Lee-2 model developed from the original cohort is stable and reliable.

Website Generation
In order to help healthcare providers better judge the subtype of patients so as to further treatment, we have established a website that can judge the subtype of patients after entering the relevant parameters of patients (https://aclf-clustering.streamlit.app/).

Methods
Data source
Data was obtained from the MIMIC-IV database (version 2.2)31–33. Due to the public availability and de-identification of the data, approval from the local institutional review board was not obtained. In this study, patients with cirrhosis (Supplemental Table 11) admitted to the intensive care units (ICUs) at the Beth Israel Deaconess Medical Center (BIDMC) between 2008 and 2019 who met the NACSELD-defined criteria (Supplemental Table 12) for ACLF were included5. For patients with multiple episodes of ACLF, only the most recent episode was included to avoid potential biases related to repeated measures. The study followed the TRIPOD guidelines.

Definition of clinical variables
A total of 50 variables were extracted, covering demographic characteristics, vital signs, laboratory results, microbial infections, and medication use. Specifically, demographic characteristics included age and sex. Vital signs included heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), respiratory rate, body temperature, SpO2, and blood glucose. Laboratory measurements comprised partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), pH, base excess, total CO2, lactate, anion gap, bicarbonate, blood urea nitrogen (BUN), calcium, chloride, creatinine, sodium, potassium, magnesium, phosphate. international normalized ratio (INR), prothrombin time (PT), partial thromboplastin time (PTT), hematocrit, hemoglobin, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), platelet count, red blood cell count (RBC), red cell distribution width (RDW), white blood cell count (WBC), alanine transferase (ALT), alkaline phosphatase (ALP), aspartate transferase (AST), total bilirubin. Respiratory parameters collected included total respiratory rate, minute ventilation volume, observed tidal volume, positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2), Microbial infection data observed in culture (blood, urine, sputum) from the microbiology events table. Finally, medication use included antibiotics (antibiotic prescriptions data from prescriptions table)34,35 and vasopressors (calculates norepinephrine equivalent dose for vasopressors based on formula36). The clinical and laboratory variables used in the study were collected within 24h prior to the diagnosis of ACLF in patients.

To handle missing data, K-Nearest Neighbors (KNN) algorithm37 was used for imputation (k=5). Patients with missing data in more than 20 variables were excluded. The number and proportion of missing values for each parameter were shown in Supplemental Table 13.

The study categorized the variables based on clinical experience and transformed all continuous variables into categorical variables (Supplemental Table 14).

Unsupervised clustering method
Nonnegative Matrix Factorization (NMF) is a matrix factorization technique38 that ensures a purely additive description of data by constraining all components to be nonnegative. We first determined the optimal number of cluster(s), followed by evaluating the top-performing clustering method.

Determination of the optimal number of clusters
The Partitioning Around Medoids with Ambiguous Clustering (PAC) algorithm was used to determine the optimal number of clusters, k39. The fundamental principle of the PAC method is to identify the number of clusters by calculating the proportion of samples that belong to the ambiguous region in the clustering outcome.

Evaluating the best clustering algorithm
Five commonly used NMF clustering methods were conducted and compared (Brunet40, Lee40, nsNMF41, snmf/r, and snmf/l42)

Brunet. This is a standard NMF algorithm based on the Kullback-Leibler divergence, using a simple multiplicative update rule proposed by Lee and Seung and enhanced to avoid numerical underflow.

Lee. This is a standard NMF algorithm based on Euclidean distances, using a simple multiplicative update rule proposed by Lee and Seung.

nsNMF. This is a non-smoothed NMF algorithm that uses a modified version of Lee and Seung’s multiplication update rules for extensions of NMF models based on Kullback-Leibler divergence, aiming to obtain more sparser results.

snmf/r, snmf/l. Both of these algorithms belong to the Alternate Least Squares (ALS) method, and they are very fast compared to other methods, where snmf/r with L2 regularization and snmf/l with L1 regularization.

The results of the PAC algorithm and the six evaluation indexes are used to determine the best clustering algorithm.

Residual sum of squares (RSS). This method measures the difference between the original data and the data reconstructed by the NMF model. A smaller RSS value indicates a better fit of the NMF model.

Explained variance (EVAR). This method represents the degree to which the NMF model explains the variance in the original data. A larger EVAR value indicates that the model can explain more variability in the data.

Silhouette coefficient (Sil). This method assesses the tightness and separation of clusters. A larger silhouette coefficient indicates better clustering results.

Cophenetic correlation coefficient (CCC). This method evaluates the consistency and stability of hierarchical clustering results by comparing the distances in the original data with the branch lengths in the clustering dendrogram.

Dispersion (Disp). This method refers to the degree of spread among observations within a cluster. A smaller dispersion value indicates that the observations are more tightly clustered, leading to better clustering results.

Silhouette consensus (SC). It is an ensemble method that assesses the consistency of clustering results by calculating the silhouette coefficient across multiple clustering outcomes. A higher value indicates greater consistency in the clustering results.

Statistical Analysis
All statistical analyses were performed using MySQL(8.0), Python (3.8.18) and R (4.3.2). Patients with ACLF, according to the NACSELD definition, were identified and characterized using descriptive analysis. Categorical variables were analyzed using the chi-square test. Normally distributed variables were compared using the Student’s t-test, whereas non-normally distributed variables were compared using the Mann-Whitney U test.

Visualization of clustering results
t-SNE (t-distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality reduction and visualization technique used to map high-dimensional data to a lower-dimensional space (usually two or three dimensions) to facilitate human understanding and interpretation43.

Analysis of clusters
Following clustering with the optimal NMF algorithm, mortality analyses were performed for the identified clusters. Survival probabilities were compared over time to assess differences in mortality risk between clusters. The predictive performance of the optimal NMF clustering model and the traditional classification based on the number of OFs was evaluated using the area under the receiver operating characteristic curve (AUROC). The two classification methods were further compared using the Akaike Information Criterion (AIC) from the corresponding Cox regression models and the DeLong test.

We subsequently used the Kim method42 to identify the contribution of each of the 50 variables to different patterns or topics in the model. After identifying the top ten important variables using the Kim method, we conducted statistical analyses to compare the distribution of these variables across subgroups. Additionally, we compared the proportion of patients with different numbers of OFs in each subgroup as well as the proportion of different precipitating factors (for definition, see Supplemental Table 15) and used the chi-square test to assess the differences between the subgroups, thereby observing the characteristics of each subgroup. We also used the Random Forest algorithm to analyze the variables most strongly related to mortality in each subgroup.

Classification of ACLF patients according to the EASL-CLIF definition
Due to the widespread application of the EASL-CLIF definition of ACLF, we conducted a validation analysis of the clustering model on the ACLF patient cohort defined by EASL-CLIF definition. We screened 1760 ACLF patients who met the EASL-CLIF definition from the MIMIC-IV database, excluded patients with 20 or more missing variables, and filled in the missing data using the KNN algorithm. Finally, we obtained a cohort of 1441 ACLF patients defined by EASL-CLIF definition. Subsequently, we applied the established clustering model to the cohort and conducted mortality analysis and feature importance analysis on the classified clusters to verify the clustering effect of the model on the EASL-CLIF definition cohort.

Classification of patients with decompensated cirrhosis
Given the challenges of diagnosing ACLF in clinical practice, we conducted a validation analysis by expanding the study population to include all patients with decompensated cirrhosis. Of 5994 patients with cirrhosis from the MIMIC-IV database, we identified patients with decompensated cirrhosis using ICD-9 and ICD-10 codes (Supplemental Table 16). We excluded patients with missing data in more than 20 variables and imputed the missing data using the KNN algorithm. Finally, we obtained a cohort of 1334 patients with decompensated cirrhosis. We subsequently applied the established clustering model to this broader cohort and conducted mortality analysis on the classified clusters. The model’s efficacy was validated by analyzing the distribution of key clustering variables, as well as the prevalence of OFs and precipitating factors.

External validation
Our external validation analysis was an external validation cohort obtained from the eICU Collaborative Research Database44. It contains data from many intensive care units in the continental United States and cover patients admitted to intensive care units in 2014 and 2015. A total of 588 ACLF patients using the NACSELD definition were identified. After excluding patients with missing data in more than 5 variables and imputing remaining missing values using the KNN algorithm, 158 patients were included in this validation cohort.

Since the eICU database lacks follow-up data on patient mortality, we were only able to compare clinical and laboratory characteristics between clusters during validation, without performing survival outcome analysis. We validated our unsupervised clustering results through two distinct approaches. First, we applied the same NMF clustering approaches used in the training cohort to the external validation cohort. This allowed for a comparison of the optimal clustering model and the key factors influencing clustering between the two cohorts. Second, we applied the optimal clustering model derived from the training cohort to the external cohorts to assess whether the resulting subtypes and their characteristics were consistent with those from the original analysis.

Website generation
To assist physicians in identifying and treating subtypes of ACLF patients, we developed an online classification tool after determining the best NMF clustering algorithm. By entering the relevant patient parameters, healthcare providers can instantly obtain the patient’s subgroup type online, allowing for a better understanding of the patient’s disease severity and mortality risk.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Zhang and colleagues describes a novel ML approach to phenotype patients admitted to the ICU with ACLF. Using an unsupervised clustering model on a cohort of 1,256 patients defined by NACSELD criteria, the authors identify two distinct clusters that correlate with prognosis and appear to be influenced by acid–base disturbances. The findings are supported by both internal and external validation cohorts. The manuscript is methodologically sound, comprehensive, and well-written. The authors should be acknowledged for their use of advanced analytical methods in addressing this complex clinical condition. However, several points still warrant clarification to strengthen the manuscript, despite some limitations already being acknowledged in the Discussion (e.g., the use of GCS instead of West Haven criteria for encephalopathy, defining respiratory failure based on the need for ventilation, and the absence of a dynamic assessment for ACLF).\nPlease find detailed comments below.\nMajor comments:\n1) The clustering model is based on NACSELD-defined ACLF, but it is unclear whether it would be applicable to patients classified using the EASL-CLIF criteria, which are more widely adopted internationally including also liver and coagulation failures (bilirubin and INR). NACSELD criteria, originally developed in the context of infection-driven ACLF, tend to capture patients with more advanced extrahepatic organ failure, whereas EASL-CLIF identifies earlier organ dysfunction, which still carries prognostic value. Incorporating a broader definition of ACLF could improve the model’s generalizability. Notably, the RF analysis (Suppl. Fig. 2) highlights liver and coagulation parameters among the most influential variables, further supporting their relevance. This limitation should be more clearly acknowledged in the Discussion as it may not yet represent a truly ‘holistic approach’.\n2) The manuscript does not account for liver transplantation, a key competing event in the natural course of ACLF. Excluding transplant data may bias survival analysis, especially if transplant eligibility differed between clusters (e.g., patients with respiratory failure may be less likely to undergo transplantation). The authors should clarify whether transplant information was available and, if not, address this as a limitation.\n3) In real-world settings, ACLF is often diagnosed late, with patients transferred to specialized wards or ICU after deterioration. This can result in missing or delayed lab values (especially for acid-base and hemodynamic status) limiting the model’s prospective utility. The authors should acknowledge that while the model performs well in a retrospective ICU cohort, it may need validation for earlier or less data-complete clinical settings.\n4) The Supplementary Material states that clinical and laboratory variables were collected within the first 24 hours of ICU admission. This information should be included in the Methods section. Additionally, several variables used in the model are likely influenced by timing and prior interventions. For instance, the relatively high MAP across clusters (despite bleeding being a precipitating factor in 40% of patients) suggests many patients were already receiving vasopressors, fluids, or transfusions, which may confound interpretation of hemodynamic and metabolic status.\n5) The authors state that the majority of data points were distinctly grouped (Figure 3A), yet Supplementary Table 7 shows that 377 patients (30%) were classified as overlapping between clusters. This is a substantial proportion and suggests considerable uncertainty in cluster assignment. This finding should be explicitly acknowledged and discussed, as it impacts on the interpretability and applicability of the clusters.\n6) The internal validation method requires clarification. The manuscript reports that 1,334 patients with decompensated cirrhosis were considered for validation, including 782 with ACLF, overlapping substantially with the 1,256 ACLF patients used to derive the clusters. If these are not separate populations, the validation cannot be considered independent. The authors should clearly state whether the validation cohort is distinct from the derivation cohort and, if not, discuss the implications of this overlap.\n7) The eICU cohort is presented as an external validation dataset, but the manuscript does not clearly state whether the clustering model was applied to this cohort, and survival outcomes are not reported. To strengthen the external validity of the approach, the authors should report whether similar prognostic differences between clusters were observed in the external cohort as well. This is particularly important given that the eICU cohort differs from the derivation cohort in the rates of brain and respiratory failure.\n8) The number of organ failures was significantly higher in cluster 1 which in turn was associated with higher mortality. Was there consideration for not including number of organ failures in the model, to avoid this potential confounding? The advantage of unsupervised clustering approach is that it allows to understand the different phenotypes, recognizing that not all organ failures impact outcomes equally. It would be helpful to include a KM/survival analysis comparing the 2 clusters based on number of organ failures (ie. Splitting the 2 clusters in figure 3c).\nMinor comments:\n1) The mortality observed in the validation cohort of patients with decompensated cirrhosis was higher than in the original ACLF cohort (31.7% vs. 26.1% in Cluster 2, and 73.4% vs. 70.4% in Cluster 1). It would be helpful if the authors could comment on possible reasons for these differences.\n2) The use of MIMIC-IV data up to 2019 may limit the generalizability of the findings, as ICU care and early recognition of ACLF may have improved in recent years, including greater access to liver transplantation. This should be acknowledged in the discussion.\n3) Line 109: please specify “in real-world ICU settings”.\n4) Figure 2B: The font size of the variables is small. Please consider increasing it for better readability if possible.\n5) Figure 2C is unclear. The axis labels and legend need clarification.\n6) Line 150: Please clarify what is meant by “traditional classification method in mortality risk prediction”.\n7) Please report the number of events and at-risk subjects in figure 3B and 3C.", "reviewer_2": "Prognostication in ICU is difficult, especially in patients with cirrhosis. The authors have performed ML and other algorithmic approaches in stratifying mortality risk in ACLF patients based on the NACSELD criteria. They validated this in an external cohort and found clusters that associated with different mortality risks. The data used were also studied in a small group of decompensated patients.\nGeneral comments:\na. Restricting the cohort to only NACSELD-ACLF patients selects for a very sick group already in the ICU. The authors should expand this to all patients with cirrhosis in the hospital as well to better predict outcomes.\nb. A prediction model with a better AUC is needed to aid clinicians; RFA was performed with a poor AUC..can authors clarify?\nc. Were the data used only at the time of admission or throughout the inpatient course?\nd. We need a section discussing how this model can be applied to clinical practice. If a patient is predicted to be in the lower-risk cluster, there is still a moderately high mortality rate of nearly 30%. Can we further stratify risk? Additionally, some of the important variables had high missingness rates (i.e. lactate with 14.65% missingness). Would clinicians be expected to impute these variables themselves, or is there a way to still use the model with incomplete data? Finally, adding on to this point, would clinicians be expected to enter all 50 variables manually in order to generate results? I worry that this might be too time-consuming, and a streamlined model, perhaps including the top-10 or top-15 variables, could improve clinical utility. Ideally one needs a model like this to be available publicly with as little manual input needed for busy clinicians.\ne. The authors should reference recent studies that also assess mortality in cirrhosis and discuss their impact vis-à-vis the current experience. https://www.sciencedirect.com/science/article/pii/S0016508525057701\nhttps://jamanetwork.com/journals/jamanetworkopen/fullarticle/2772522\nSpecific Comments:\nLines 384-386: “For patients with multiple episodes of ACLF, only the most recent episode was included to avoid potential biases related to repeated measures.” It is possible that prior hospitalization / prior ACLF history may affect mortality, compared to a patient who is experiencing their first ACLF episode. Is it possible to quantify this and add it to the model as another predictor?\nLines 410-411: Is there justification for the use of k=5 in KNN imputation, and exclusion of pts. with >20 missing variables?\nLines 415-416: Is there justification for transforming all continuous variables into categorial variables? Typically, keeping continuous variables in their original form will preserve information.\nLines 509-520: If I understand this correctly, the authors expanded the criteria for inclusion into the cohort to just be decompensated cirrhosis, rather than ACLF, and then re-applied their model. For this validation, they included 1,334 pts. (vs. 1,256 w/ACLF used for model training). Does this mean that a large majority of the patients w/decompensated cirrhosis also had ACLF, seeing as only 78 additional patients were included? If this is the case, then the authors effectively validated their model on the same cohort that they originally derived the clusters from.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "This study examined unsupervised clustering of 1,256 NACSELD-defined ACLF patients using MIMIC-IV, a publicly available dataset. Acid-base balance variables were identified as major predictors of clustering. External validation was done using the eICU database, another publicly available dataset. Here are my comments.\nMajor\n1. The discrepancy in exclusion criteria for missing values between the training and external validation sets needs clarification. While patients with 20 or fewer missing values were allowed in the MIMIC-IV database, the criterion was reduced to 5 or fewer missing values for patients in the eICU database. Was that a typo?\n2. Consequently, it was noted that around 75% of patients in the external validation set were excluded due to missing data, which may lead to selection bias. Missing data imputation using K-neighbor algorithm was only performed in the remaining 25% of the patients.\n3. How was k selected for the kNN algorithm?\n4. It would be good to compare the clinical characteristics of patients who were excluded due to having missing data in more than 20 variables to those who remained in the analysis.\n5. [Figure 3D] What does that mean by an importance of 1? As shown in Figure 3C, it appeared that the number of organ failures (ACLF-1, -2, -3) was an important predictor of mortality. Yet, it was not an important factor for clustering.\n6. A total of 50 variables are needed for identifying the clustering, and the HR was 3.6 comparing cluster 1 to cluster 2. If the study aims to identify patients at risk of short-term mortality as mentioned in the Introduction, would that be possible to perform a conventional survival analysis, e.g., Cox regression analysis, to identify important risk factors associated with mortality in ACLF? While acid-base balance variables are useful in separating the patients into 2 clusters, they may not necessarily be the most important in predicting short-term mortality. Consequently, maybe considering the number of organ failures plus a few important factors (maybe acid-base balance variables) would already be able to separate the patients into two groups with HR of 3.6. A clustering that requires 50 variables may not be applicable to many patients, as shown in the substantial exclusion in the eICU database.\nMinor\n1. [Figure 1] k neighbors algorithm should be k-nearest neighbors algorithm.\n2. It would be good to justify the choices of the number of categories chosen for the study variables. It was noticed that some variables were categorized into four groups, while some were in three groups or two groups.\n3. Please provide the number at risk for Kaplan-Meier analysis."}, "author_response": "Response: We sincerely appreciate the reviewer's positive comments on our work.\nPlease find detailed comments below.\nMajor comments:\n1) The clustering model is based on NACSELD-defined ACLF, but it is unclear whether it would be\napplicable to patients classified using the EASL-CLIF criteria, which are more widely adopted\ninternationally including also liver and coagulation failures (bilirubin and INR). NACSELD criteria,\noriginally developed in the context of infection-driven ACLF, tend to capture patients with more\nadvanced extrahepatic organ failure, whereas EASL-CLIF identifies earlier organ dysfunction,\nwhich still carries prognostic value. Incorporating a broader definition of ACLF could improve the\nmodel’s generalizability. Notably, the RF analysis (Suppl. Fig. 2) highlights liver and coagulation\nparameters among the most influential variables, further supporting their relevance. This limitation\nshould be more clearly acknowledged in the Discussion as it may not yet represent a truly ‘holistic\napproach’.\nResponse: Thank you for your insightful recommendation. We agreed that it is important to\nvalidate the widespread applicability of the Lee-2 model in the EASL-CLIF-defined ACLF\npatient cohort. We performed the analyses as requested. The results showed that the 30-day\nmortality rates of Cluster 1 and Cluster 2 in the EASL-CLIF cohort were 69.0% and 27.0%,\nrespectively (Supplemental Figure 3), which is highly consistent with the results derived from\nour main model. In addition, the distribution of the top 10 clustering factors in ACLF patients\ndefined by EASL-CLIF was similar to the distribution observed in ACLF cohorts defined by\nNACSELD (Supplemental Figure 4 and Supplemental Table 7) (Lines 199-215).\n2) The manuscript does not account for liver transplantation, a key competing event in the natural\ncourse of ACLF. Excluding transplant data may bias survival analysis, especially if transplant\neligibility differed between clusters (e.g., patients with respiratory failure may be less likely to\nundergo transplantation). The authors should clarify whether transplant information was available\nand, if not, address this as a limitation.\nResponse: Thank you for the suggestion. The core concept of this study is to determine the\noptimal number of clusters of ACLF using a hypothesis-free method, and to investigate the\ndriving factors of the clustering. With this non-biased method, we obtained factors that were\nnot commonly emphasized. We excluded patients who had a liver transplant before the\noccurrence of ACLF, because the pathophysiology of ACLF between those with a native liver\nand a liver graft is substantially different. By excluding these patients, we were able to ensure\nthat the clustering of ACLF was not affected by this confounding event (Figure 1 and\nSupplemental Table 11). In terms of the outcome analysis, transplant is not a competing event\nbecause mortality is the outcome of interest.\n3) In real-world settings, ACLF is often diagnosed late, with patients transferred to specialized\nwards or ICU after deterioration. This can result in missing or delayed lab values (especially for\nacid-base and hemodynamic status) limiting the model’s prospective utility. The authors should\nacknowledge that while the model performs well in a retrospective ICU cohort, it may need\nvalidation for earlier or less data-complete clinical settings.\nResponse: Thank you for bringing this to our attention. In the study, we validated on a larger\ncohort of decompensated cirrhosis patients, and the results were consistent with the ACLF\npatient cohort. The cohort of decompensated cirrhosis patients can represent the early\ncondition of ACLF patients.\nWith due respect, we do not recommend expanding to stages earlier than decompensated\ncirrhosis, as the pathophysiological background in these patients can be substantially different\nfrom acute decompensation or ACLF. Nevertheless, we appreciate your suggestion, as many\npatients with decompensation or organ failure are staying in the ward and have not yet been\nadmitted to the ICU. Therefore, we suggest implementing clustering models in other non-ICU\ncohorts with either acute decompensation or ACLF in the future. We have brought up this\nlimitation in the discussion section (Lines 381-386).\n4) The Supplementary Material states that clinical and laboratory variables were collected within\nthe first 24 hours of ICU admission. This information should be included in the Methods section.\nAdditionally, several variables used in the model are likely influenced by timing and prior\ninterventions. For instance, the relatively high MAP across clusters (despite bleeding being a\nprecipitating factor in 40% of patients) suggests many patients were already receiving vasopressors,\nfluids, or transfusions, which may confound interpretation of hemodynamic and metabolic status.\nResponse: Thank you for bringing this to our attention. The clinical and laboratory variables\nused in the study were collected within 24 hours prior to the diagnosis of ACLF in patients,\nand we have added this information to the methods section (Lines 445-447). We have\nmentioned in the limitations section that ACLF is a dynamic state where disease trajectories\ncan rapidly evolve. This study did not consider the dynamic impact of treatment on ACLF\npatients (Lines 401-405).\n5) The authors state that the majority of data points were distinctly grouped (Figure 3A), yet\nSupplementary Table 7 shows that 377 patients (30%) were classified as overlapping between\nclusters. This is a substantial proportion and suggests considerable uncertainty in cluster\nassignment. This finding should be explicitly acknowledged and discussed, as it impacts on the\ninterpretability and applicability of the clusters.\nResponse: Thank you for bringing this to our attention. The partial overlap between the two\nclusters observed in the t-SNE visualization (Figure 3A) did not indicate instability of the\nclustering results. This was because clustering was performed in the original high-dimensional\nspace, and its validity was supported by statistical metrics. As a dimensionality reduction tool\nfor visualization, t-SNE aimed to preserve local structure rather than strict class boundaries\n— overlap was therefore a common artifact of projection compression.\nMoreover, Supplemental Table 2 showed that patients within the overlapping region exhibited\nclinical variables between Cluster 1 and Cluster 2, suggesting that this area may have\ncorresponded to a “transitional phenotype,” which reflected the inherent heterogeneity and\ndynamic nature of the disease. Furthermore, the consensus matrix shown in Figure 2C\ndisplayed clear and non-overlapping boundaries, confirming the robustness and stability of\nthe clustering model.\n6) The internal validation method requires clarification. The manuscript reports that 1,334 patients\nwith decompensated cirrhosis were considered for validation, including 782 with ACLF,\noverlapping substantially with the 1,256 ACLF patients used to derive the clusters. If these are not\nseparate populations, the validation cannot be considered independent. The authors should clearly\nstate whether the validation cohort is distinct from the derivation cohort and, if not, discuss the\nimplications of this overlap.\nResponse: Thank you for your comment. In the study, the decompensated cirrhosis cohort\nincluded 1,334 patients, among whom 782 were ACLF patients. However, the timing of data\ncollection differed between groups: for decompensated cirrhosis patients, data were collected\non the day of ICU admission, whereas for the ACLF cohort used in model development, data\nwere collected on the day of ACLF diagnosis after ICU admission. Validating the clustering\nmodel in a cohort of patients with decompensated cirrhosis is not internal validation; rather,\nit is primarily intended to demonstrate that the model performs well in a broader conceptual\ncohort, thereby proving its broad applicability.\nFurthermore, we also performed an external, independent validation using the ACLF cohort\nfrom the eICU database, demonstrating the generalizability of the clustering model in a cohort\nof ACLF patients defined by the same criteria.\n7) The eICU cohort is presented as an external validation dataset, but the manuscript does not\nclearly state whether the clustering model was applied to this cohort, and survival outcomes are not\nreported. To strengthen the external validity of the approach, the authors should report whether\nsimilar prognostic differences between clusters were observed in the external cohort as well. This\nis particularly important given that the eICU cohort differs from the derivation cohort in the rates of\nbrain and respiratory failure.\nResponse: Thank you for your comment. We validated our unsupervised clustering results\nthrough two distinct approaches.\nFirst, we applied the same NMF clustering approaches used in the training cohort to the\nexternal validation cohort. This allowed for a comparison of the optimal clustering model and\nthe key factors influencing clustering between the two cohorts. Second, we applied the optimal\nclustering model derived from the training cohort to the external cohorts to assess whether\nthe resulting subtypes and their characteristics were consistent with those from the original\nanalysis (Lines 584-591). The analytical results for the external validation cohort are presented\nin Supplemental Figure 7. The first validation method confirmed that the optimal number of\nclusters for NMF clustering in the external validation cohort is also two. Additionally, 7 of the\ntop 15 important variables in this cohort aligned with the top 10 important variables identified\nin the original cohort.\nThe second validation method demonstrated that the characteristics of the two clusters,\nobtained by directly applying the Lee-2 model to the external validation cohort, were\nconsistent with those identified in the original cohort. These findings suggest that the Lee-2\nmodel developed from the original cohort is stable and reliable (Lines 234-247). Due to the\nlack of follow-up data on patient discharge mortality in the eICU database, we were only able\nto compare clinical and laboratory characteristics between clusters during validation, without\nperforming survival outcome analysis. This represents a limitation of the external validation,\nwhich we have explicitly acknowledged in the external validation section of the Methods (Lines\n582-584).\n8) The number of organ failures was significantly higher in cluster 1 which in turn was associated\nwith higher mortality. Was there consideration for not including number of organ failures in the\nmodel, to avoid this potential confounding? The advantage of unsupervised clustering approach is\nthat it allows to understand the different phenotypes, recognizing that not all organ failures impact\noutcomes equally. It would be helpful to include a KM/survival analysis comparing the 2 clusters\nbased on number of organ failures (ie. Splitting the 2 clusters in figure 3c).\nResponse: Thank you for your comment. During the training of the clustering model, we\nused 50 variables covering demographic characteristics, vital signs, laboratory results,\nmicrobial infections, and medication use.\nWith due respect, we would like to point out that we did not use the number of organ failures\nas a feature (Lines 425-445). Only after clustering was completed, we analyzed the\ndistribution of organ failure status of patients in different clusters (Table 4). We found that\nCluster 1, which exhibited higher mortality, contained a significantly greater proportion of\npatients with three or more organ failures, while Cluster 2, associated with lower mortality,\nwas predominantly composed of patients with two organ failures. This precisely indicates that\nunsupervised clustering models can accurately classify patients without knowing the number\nof organ failures. It also proves that the number of organ failures has a significant impact on\npatient mortality.\nMinor comments:\n1) The mortality observed in the validation cohort of patients with decompensated cirrhosis was\nhigher than in the original ACLF cohort (31.7% vs. 26.1% in Cluster 2, and 73.4% vs. 70.4% in\nCluster 1). It would be helpful if the authors could comment on possible reasons for these differences.\nResponse: Thank you for bringing this to our attention. We analyzed mortality outcomes in\nboth the decompensated cirrhosis cohort and the original ACLF cohort. The original ACLF\ncohort included 1,256 patients, of whom 632 died within 30 days, yielding a 30-day mortality\nrate of 50.32%. The decompensated cirrhosis cohort comprised 1,334 patients, with 732 deaths\nwithin 30 days, resulting in a 30-day mortality rate of 54.87%.\nWe further stratified the decompensated cirrhosis cohort into ACLF and non-ACLF\nsubgroups. Among the 782 ACLF patients, the 30-day mortality rate was 64.32%, whereas\namong the 552 non-ACLF patients, it was 41.49%.\nThese findings indicate that the higher 30-day mortality rate observed in the\ndecompensated cirrhosis cohort compared to the original ACLF cohort is primarily driven by\nthe elevated mortality among ACLF patients within the decompensated cirrhosis cohort.\nTo further investigate this, we examined the prevalence of organ failure among ACLF\npatients in the decompensated cirrhosis cohort. Among the 732 deceased patients, 261 (33.37%)\nhad failure of three or more organs, a proportion higher than that in the original ACLF cohort\n(28.42%). This higher burden of multi-organ failure contributes to the increased mortality in\nthe decompensated cirrhosis cohort. Detailed results are presented in the table below.\nDecompensated Cirrhosis cohort Original ACLF cohort\nnon-ACLF ACLF\ntotal number of patients 552 782 1256\n30 day death toll 229 503 632\n30 day mortality rate 41.49% 64.32% 50.32%\nNumber of organ failures 2 / 521 (66.62%) 899 (71.58%)\n3 / 229 (29.28%) 315 (25.08%)\n4 / 32 (4.09%) 42 (3.34%)\n2) The use of MIMIC-IV data up to 2019 may limit the generalizability of the findings, as ICU care\nand early recognition of ACLF may have improved in recent years, including greater access to liver\ntransplantation. This should be acknowledged in the discussion.\nResponse: Thank you for bringing this to our attention. We have further discussed adding\nthis limitation to the discussion (Lines 392-394).\n3) Line 109: please specify “in real-world ICU settings”.\nResponse: Thank you for bringing this to our attention. We have made modifications to\nindicate that the study patients are in the ICU settings.\n4) Figure 2B: The font size of the variables is small. Please consider increasing it for better\nreadability if possible.\nResponse: Thank you for bringing this to our attention. We have made modifications to\nFigure 2B.\n5) Figure 2C is unclear. The axis labels and legend need clarification.\nResponse: Thank you for bringing this to our attention. Figure 2C presents the consistency\nmatrix, a key diagnostic tool for evaluating the stability of NMF clustering results. In the\nsymmetric matrix, both rows and columns represent individual patients. Each cell (i,j)\nindicates the proportion of times patients i and j were assigned to the same cluster across\nmultiple NMF runs. The clear block-diagonal structure with two distinct red blocks along the\ndiagonal demonstrates robust separation between the two identified subtypes, confirming the\nreliability of the clustering solution. The dendrogram at the top further supports this finding\nby revealing consistent hierarchical grouping. We have added explanations for the axis labels\nand legend in Figure 2c in the Results section (Lines 139-144).\n6) Line 150: Please clarify what is meant by “traditional classification method in mortality risk\nprediction”.\nResponse: Thank you for bringing this to our attention. The phrase “traditional classification\nmethod in mortality risk prediction” referred to classifying patients based on the number of\norgan failures. We have clarified in the paper. Supplemental Figure 1 showed the\neffectiveness of two methods, Lee-2 clustering model and traditional organ failure quantity\nclassification, in predicting patient mortality.\n7) Please report the number of events and at-risk subjects in figure 3B and 3C.\nResponse: Thank you for bringing this to our attention. We have added the number of events\nand at-risk subjects to Figures 3B and 3C.\nReviewer #2 (Remarks to the Author):\nPrognostication in ICU is difficult, especially in patients with cirrhosis. The authors have performed\nML and other algorithmic approaches in stratifying mortality risk in ACLF patients based on the\nNACSELD criteria. They validated this in an external cohort and found clusters that associated\nwith different mortality risks. The data used were also studied in a small group of decompensated\npatients.\nResponse: We sincerely appreciate the reviewer's positive comments on our work.\nGeneral comments:\na. Restricting the cohort to only NACSELD-ACLF patients selects for a very sick group already in\nthe ICU. The authors should expand this to all patients with cirrhosis in the hospital as well to better\npredict outcomes.\nResponse: Thank you for your comment. The MIMIC database used in the study provided\ncritical care data for patients admitted to intensive care units at the Beth Israel Deaconess\nMedical Center (BIDMC) (Lines 416-423). We have already explained in the limitations\nsection that the cohort in the study only includes patients in the ICU environment (Lines 381-\n386). The decompensated cirrhosis cohort in the ICU was already the largest queue that could\nbe collected in the study.\nWith due respect, we do not suggest expanding the cohort to include all patients with cirrhosis\n(both compensated and decompensated cirrhosis). The occurrence of hepatic decompensation\nreflects a tremendous dysregulation in systemic immunomodulation and complementary\npathways among organs. Therefore, we only included patients with hepatic decompensation\nin the validation cohort. However, we acknowledge that our cohort did not include non-ICU\npatients. Future studies that include non-ICU patients’ data are needed (Lines 385-386).\nb. A prediction model with a better AUC is needed to aid clinicians; RFA was performed with a poor\nAUC can authors clarify?\nResponse: We thank the reviewer for the comment. Our study did not mention the AUC value\nof the random forest model, and the purpose of establishing the random forest model in the\nstudy was to identify important factors influencing mortality risk, while comparing the\ndifferences in mortality importance factors between the two subtypes derived from the\nclustering model (Lines 189-197).\nc. Were the data used only at the time of admission or throughout the inpatient course?\nResponse: Thank you for your comment. The data used in the study were collected within 24\nhours prior to the diagnosis of ACLF in patients; we have described these in the Methods\nsection (Lines 445-447).\nd. We need a section discussing how this model can be applied to clinical practice. If a patient is\npredicted to be in the lower-risk cluster, there is still a moderately high mortality rate of nearly 30%.\nCan we further stratify risk? Additionally, some of the important variables had high missingness\nrates (i.e. lactate with 14.65% missingness). Would clinicians be expected to impute these variables\nthemselves, or is there a way to still use the model with incomplete data? Finally, adding on to this\npoint, would clinicians be expected to enter all 50 variables manually in order to generate results?\nI worry that this might be too time-consuming, and a streamlined model, perhaps including the top-\n10 or top-15 variables, could improve clinical utility. Ideally one needs a model like this to be\navailable publicly with as little manual input needed for busy clinicians.\nResponse: Thank you for bringing this to our attention. We would like to clarify that the\nprimary aim of this study is not immediate clinical application. Instead, our focus is on\napplying unsupervised clustering to patient data without any pre-specified hypotheses to\nidentify key features that drive patient stratification and potentially uncover novel, clinically\nrelevant factors that may have been overlooked in current practice.\nIndeed, ACLF patients generally exhibit high short-term mortality, and the fact that the low-\nrisk cluster shows a mortality rate of approximately 30% already suggests a meaningfully\nlower risk profile. In future work, the newly identified important features found in the study\ncould be translated into clinical applications—for instance, for individualized prognosis or\nprecise prediction of mortality risk for each patient.\nIn real-world clinical settings, clinicians could use patients’ historical data to fill some missing\nvariables, as data completeness is crucial for ensuring the accuracy of model-based\nclassification.\nDuring model development, we also explored reducing the number of features to simplify the\nmodel. However, this led to noticeably worse classification performance, indicating that the\ncurrent feature set is necessary to maintain model efficacy.\ne. The authors should reference recent studies that also assess mortality in cirrhosis and discuss\nt\nhhttps://www.sciencedirect.com/science/article/pii/S0016508525057701\nehttps://jamanetwork.com/journals/jamanetworkopen/fullarticle/2772522\niResponse: Thank you for your comment. We have cited in the discussion section that machine\nrlearning methods can be used in future research to establish ACLF mortality prediction\nmodels similar to liver cirrhosis prediction1,2 (Lines 398-401).\ni\nm1. Kanwal, F. et al. Development, validation, and evaluation of a simple machine learning model to\nppredict cirrhosis mortality. JAMA Netw. Open 3, e2023780 (2020).\na2. Silvey, S. et al. Enhancement of inpatient mortality prognostication with machine learning in a\ncprospective global cohort of patients with cirrhosis with external validation. Gastroenterology 2025,\ntS0016-5085(25)05770-1 (2025).\nvSpecific Comments:\niLines 384-386: “For patients with multiple episodes of ACLF, only the most recent episode was\nsincluded to avoid potential biases related to repeated measures.” It is possible that prior\n-hospitalization / prior ACLF history may affect mortality, compared to a patient who is experiencing\n-thviesi rt hfier sctu ArrCeLnFt e exppiesroideen.c Ies. it possible to quantify this and add it to the model as another predictor?\nResponse: Thank you for your attention. In the study, for patients with multiple\nepisodes of ACLF, only the most recent episode was included to avoid potential biases related\nto repeated. The previous hospitalization and ACLF history did indeed affect the mortality of\npatients. We have explained this limitation in the Discussion section, that the study did not\nconsider the dynamic changes of the ACLF patients and the impact of the history of ACLF\n(Lines 401-405).\nLines 410-411: Is there justification for the use of k=5 in KNN imputation, and exclusion of pts.\nwith >20 missing variables?\nResponse: Thank you for your comment. We selected k = 5 for KNN imputation based on\nempirical testing and prior literature suggesting that small-to-moderate k values in clinical\ndatasets often achieve an optimal balance between bias and variance while preserving local\ndata structure3. We further validated this choice by comparing the stability of imputation\nacross k = 3, 5, and 7. The preliminary analyses consistently showed that k = 5 yielded the\nlowest imputation error and the most stable clustering results. Regarding the exclusion of\npatients with more than 20 missing variables (a total of 50 features), this threshold was set to\nensure data quality and model reliability. We considered patients with >40% missing data as\nhaving insufficient information to support meaningful imputation or clustering.\n3.Waljee AK, Mukherjee A, Singal AG, Zhang Y, Warren J, Balis U, Marrero J, Zhu J, Higgins PD.\nComparison of imputation methods for missing laboratory data in medicine. BMJ Open. 2013 Aug\n1;3(8):e002847.\nLines 415-416: Is there justification for transforming all continuous variables into categorial\nvariables? Typically, keeping continuous variables in their original form will preserve information.\nResponse: The reasons for converting continuous variables into categorical variables in the\nstudy were as follows:\n1. Many clinical scoring systems (e.g., SOFA, MELD, CLIF-C ACLF) and guidelines rely on\ncategorized or threshold-based variables (e.g., lactate >2 mmol/L, creatinine >1.5 mg/dL)\nto define organ failure or risk strata.\n2. Some continuous variables exhibited highly skewed distributions with extreme outliers.\nCategorization based on established clinical cut-offs or quantiles reduced the undue\ninfluence of extreme values.\n3. The non-negative matrix factorization (NMF) algorithm we employed performs optimally\nwhen the input data are non-negative and typically discrete (e.g., count data or categorical\nvariables). Although NMF can mathematically accommodate continuous variables, its\nconvergence stability and robustness can be compromised when the data exhibit large-\nscale variations, skewed distributions, or noise, common characteristics of raw clinical\ncontinuous measurements. To enhance model robustness and interpretability, we\ndiscretized the continuous variables during preprocessing (e.g., using clinically established\nthresholds or quantile-based binning) and converted them into ordinal categorical\nfeatures. This approach aligns better with NMF’s inherent assumption of additive, non-\nnegative combinations and helps mitigate the influence of outliers while improving feature\ncomparability. More importantly, such discretization significantly improves the\nconvergence stability of NMF, leading to more consistent and reliable clustering results.\nLines 509-520: If I understand this correctly, the authors expanded the criteria for inclusion into\nthe cohort to just be decompensated cirrhosis, rather than ACLF, and then re-applied their model.\nFor this validation, they included 1,334 pts. (vs. 1,256 w/ACLF used for model training). Does this\nmean that a large majority of the patients w/decompensated cirrhosis also had ACLF, seeing as only\n78 additional patients were included? If this is the case, then the authors effectively validated their\nmodel on the same cohort that they originally derived the clusters from.\nResponse: Thank you for your comment. The decompensated cirrhosis cohort used in this\nstudy included 1334 patients, of which only 782 were ACLF patients (Figure 1). Although there\nare duplicates in the patients, the time points for collecting patient data were different. For\ndecompensated cirrhosis patients, data were collected on the day of ICU admission, whereas\nfor the ACLF cohort used in model development, data were collected on the day of ACLF\ndiagnosis after ICU admission. The validation of the clustering model in the decompensated\ncirrhosis cohort is mainly to demonstrate that the model can also perform well in the larger\npopulation of decompensated cirrhosis, demonstrating its broad applicability.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We sincerely thank the reviewers for thoughtful and constructive feedback.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We sincerely thank the reviewers for thoughtful and constructive feedback.\nReviewer #5 (Remarks to the Author):\nThis study examined unsupervised clustering of 1,256 NACSELD-defined ACLF patients using\nMIMIC-IV, a publicly available dataset. Acid-base balance variables were identified as major\npredictors of clustering. External validation was done using the eICU database, another publicly\navailable dataset. Here are my comments.\nResponse: We sincerely appreciate the reviewer's comments on our work.\nMajor\n1. The discrepancy in exclusion criteria for missing values between the training and external\nvalidation sets needs clarification. While patients with 20 or fewer missing values were allowed in\nthe MIMIC-IV database, the criterion was reduced to 5 or fewer missing values for patients in the\neICU database. Was that a typo?\nResponse: Thank you for your comment. In the training cohort, we excluded patients with\nmore than 20 missing variables. This threshold was chosen to balance sample size retention\nwith data completeness, thereby supporting robust clustering. In the eICU external validation\ncohort, we applied a stricter criterion (≤5 missing variables). To ensure comparability and\nrobustness of the validation results, we prioritized data completeness over sample size in the\nexternal cohort, considering only patients with ≤5 missing variables as reducing the number\nof missing data can improve data accuracy and authenticity.\n2. Consequently, it was noted that around 75% of patients in the external validation set were\nexcluded due to missing data, which may lead to selection bias. Missing data imputation using K-\nneighbor algorithm was only performed in the remaining 25% of the patients.\nResponse: Thank you for bringing this to our attention. Due to the stricter selection criteria\napplied to the external validation cohort in this study, approximately 75% of patients in the\neICU cohort were excluded due to excessive missing data, which may introduce selection bias.\nWe have explicitly acknowledged this limitation in the Discussion section (Lines 394-398).\nHowever, this rigorous data filtering improved data completeness, thereby enhancing the\ncredibility and reliability of the validation results. Moreover, in Supplemental Table 10, we\nhave provided descriptive analyses comparing baseline characteristics before and after cohort\nselection in the eICU validation set. The results show no statistically significant differences in\nthe demographic and clinical variables used in our classification model between the pre- and\npost-filtering populations, indicating that the final selected cohort remains representative of\nthe original eICU cohort.\n3. How was k selected for the kNN algorithm?\nResponse: Thank you for your comment. We selected k = 5 for KNN imputation based on\nempirical testing and prior literature suggesting that small-to-moderate k values in clinical\ndatasets often achieve an optimal balance between bias and variance while preserving local\ndata structure3. We further validated this choice by comparing the stability of imputation\nacross k = 3, 5, and 7. The preliminary analyses consistently showed that k = 5 yielded the\nlowest imputation error and the most stable clustering results.\n3. Waljee AK, Mukherjee A, Singal AG, Zhang Y, Warren J, Balis U, Marrero J, Zhu J, Higgins PD.\nComparison of imputation methods for missing laboratory data in medicine. BMJ Open. 2013 Aug\n1;3(8):e002847.\n4. It would be good to compare the clinical characteristics of patients who were excluded due to\nhaving missing data in more than 20 variables to those who remained in the analysis.\nResponse: Thank you for your comment. We have compared the clinical characteristics of\npatients who were excluded due to having missing data in more than 20 variables to those who\nremained in the analysis, as shown in Supplemental Table 16. The results indicate that the\nmajority of clinical variables did not differ significantly between the two groups, with only a\nfew clinical parameters showing statistically significant differences. Specifically, compared to\npatients with substantial missingness (>20 missing variables), the modeling cohort (≤20\nmissing variables) exhibited more severe organ dysfunction and metabolic derangements,\nwhile showing no significant bias in demographic characteristics. This suggests that patients\nwith more complete data were generally sicker and received more intensive monitoring, likely\nleading to more comprehensive documentation due to higher clinical attention.\nAlthough the inclusion of a higher proportion of critically ill patients in the modeling\ncohort may lead the model to learn \"high-risk\" features biased toward extreme values,\npotentially, resulting in more conservative (i.e., overestimated) risk predictions for real-world\npatients with milder disease. The Discussion section of our manuscript have described that\nour cohort was derived from an ICU setting, where the majority of patients had organ failure\nand severe illness (Lines 381-383). Consequently, the model remains robust and clinically\nvaluable in its intended application setting (the ICU). Moreover, the data completeness\ncriterion enhances the quality and reliability of the model’s inputs.\n5. [Figure 3D] What does that mean by an importance of 1? As shown in Figure 3C, it appeared\nthat the number of organ failures (ACLF-1, -2, -3) was an important predictor of mortality. Yet, it\nwas not an important factor for clustering.\nResponse: Thank you for your comment. We used the Kim method to quantify the\ncontribution of each of the 50 variables to the different clusters in the model. Figure 3D\ndisplayed the top 10 contributing features for both clusters. In NMF-based clustering, the\nKim method identified key drivers of clustering by calculating the relative weight of each original\nvariable within each NMF basis vector. The larger the contribution value, the more important\nthe feature was. In the study, the contribution values were normalized, with a value of 1\nindicating that the feature was the most important for clustering. As shown in Figure 3C, the\nnumber of organ failures was indeed a key indicator for predicting mortality. Moreover, the\nstudy did not use the feature of the number of organ failures in patients during the\nestablishment of the clustering model. We drew Figure 3C to compare the discriminative\npower of using the number of organ failures classification method with the Lee-2 classification\nmodel for patient mortality (Figure 3B).\nMeanwhile, after clustering was completed using the Lee-2 classification model, we\nanalyzed the distribution of organ failure status of patients in different clusters (Table 4). We\nfound that Cluster 1, which exhibited higher mortality, contained a significantly greater\nproportion of patients with three or more organ failures. While Cluster 2 was associated with\nlower mortality, it was predominantly composed of patients with two organ failures. This\nprecisely indicates that unsupervised clustering models can accurately classify patients\nwithout knowing the number of organ failures. It also proves that the number of organ failures\nhas a significant impact on patient mortality.\n6. A total of 50 variables are needed for identifying the clustering, and the HR was 3.6 comparing\ncluster 1 to cluster 2. If the study aims to identify patients at risk of short-term mortality as\nmentioned in the Introduction, would that be possible to perform a conventional survival analysis,\ne.g., Cox regression analysis, to identify important risk factors associated with mortality in ACLF?\nWhile acid-base balance variables are useful in separating the patients into 2 clusters, they may not\nnecessarily be the most important in predicting short-term mortality. Consequently, maybe\nconsidering the number of organ failures plus a few important factors (maybe acid-base balance\nvariables) would already be able to separate the patients into two groups with HR of 3.6. A\nclustering that requires 50 variables may not be applicable to many patients, as shown in the\nsubstantial exclusion in the eICU database.\nResponse: Thank you for your comment. The primary aim of the study was to classify\npatients using unsupervised clustering without any preconceived hypotheses, with the goal of\nidentifying novel and important features that drive patient stratification. To preserve this\nassumption-free framework, we did not apply any clinical judgment to pre-select or exclude\nvariables prior to modeling. Accordingly, we included all 50 clinical features with relatively\nlow missingness as input variables for the clustering model without further filtering.\nThe analysis of feature importance with respect to mortality was a secondary objective\nof the study. The study employed the random forest algorithm to identify key factors\ninfluencing short-term mortality risk in patients (Supplemental Figure 2 and Supplemental\nTable 5). The results showed that the importance features associated with mortality risk\ndiffered between Cluster 1 and Cluster 2, further underscoring the necessity of stratifying\nACLF patients into subgroups.\nMinor\n1. [Figure 1] k neighbors algorithm should be k-nearest neighbors algorithm.\nResponse: Thank you for bringing this to our attention. We have made modifications to Figure\n1.\n2. It would be good to justify the choices of the number of categories chosen for the study variables.\nIt was noticed that some variables were categorized into four groups, while some were in three\ngroups or two groups.\nResponse: Thank you for your comment. In the study, continuous variables were converted\ninto categorical variables based on a combination of clinical expertise and quantile-based cut-\noffs. The specific transformation rules for each variable are detailed in Supplemental Table\n13.\n3. Please provide the number at risk for Kaplan-Meier analysis.\nResponse: Thank you for bringing this to our attention. We have added the number at risk\nfor Kaplan-Meier analysis to Figures 3B and 3C.\nReviewer #5 (Remarks on code availability):\nPlease consider putting the code on a public platform, e.g., GitHub.\nResponse: Thank you for your comment. In accordance with open science principles, we will\ndeposit all analysis code used in this study in a public repository (GitHub) upon acceptance\nand publication of the manuscript, to ensure reproducibility and facilitate further research by\nthe community."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript by Zhang and colleagues shows some improvements, and the clarifications provided by the authors are appreciated. However, several important issues (data selection, cohort definition, and the validation analyses) remain unresolved, and new areas have emerged that may impact the clinical applicability of the proposed ML model.\n1) As the authors pointed out, exclusion of prior LT is essential, however, LT post-ACLF diagnosis is important to consider. In a time-to-event analysis, LT constitutes an important competing risk that precludes the occurrence of the primary endpoint (death).\n2) The description of when variables were collected remains confusing, both in the manuscript and in the authors’ responses. The authors stated that, in the ACLF cohort, variables were collected within 24 hours prior to ACLF diagnosis, meaning the data may not truly represent patients at the time of ACLF onset. It is also unclear how all 50 variables would be available for patients not yet critically ill. Moreover, in the validation cohort, data were collected at the time of ACLF diagnosis after ICU admission, which introduces an inconsistency with the derivation cohort.\n3) In the “decompensated cirrhosis cohort,” more than half of the patients had ACLF, which creates confusion regarding the terminology of this cohort. It also remains unclear whether individual patients could have been included in both analyses: first as decompensated cirrhosis patients and later (when organ failures developed) as ACLF patients, introducing possible duplication.\n4) The absence of mortality data in the eICU validation cohort (as acknowledged in the new version) substantially limits the capacity to validate the prognostic impact of the proposed clusters. Moreover, the exclusion of approximately 75% of the eICU patients due to missing data further undermines the robustness of the findings.\n5) The Methods should clearly specify how cirrhosis was defined and identified in both the MIMIC and eICU databases.", "reviewer_2": "Authors have responded adequately to all my concerns", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "Thank you for addressing the comments. Here ia a remaining minor comment for your consideration.\n1. Please describe the justification of the discrepancy in exclusion criteria for missing values between the training and external validation sets (5 vs 20) in the Discussion section.\n2. It would be good to include the GitHub page link of the source codes into the manuscript."}, "author_response": "Response: We sincerely appreciate the reviewer's comments on our work.\n1) As the authors pointed out, exclusion of prior LT is essential, however, LT post-ACLF diagnosis is important to consider. In a time-to-event analysis, LT constitutes an important competing risk that precludes the occurrence of the primary endpoint (death).\nResponse: Thank you for your comment. In the cohort of 5,994 patients with cirrhosis, only\n254 patients (4.2%) underwent transplantation. Given the limited number of patients\nundergoing transplantation in the database, those who received transplants were excluded\nfrom the study. Therefore, the liver transplantation constitutes an important competing risk\nwas not considered in the analysis of the study. We have added the limitation to the discussion\nsection (Lines 411-415).\n2) The description of when variables were collected remains confusing, both in the manuscript and\nin the authors’ responses. The authors stated that, in the ACLF cohort, variables were collected\nwithin 24 hours prior to ACLF diagnosis, meaning the data may not truly represent patients at the\ntime of ACLF onset. It is also unclear how all 50 variables would be available for patients not yet\ncritically ill. Moreover, in the validation cohort, data were collected at the time of ACLF diagnosis\nafter ICU admission, which introduces an inconsistency with the derivation cohort.\nResponse: We sincerely thank the reviewer for this important and thoughtful comment\nregarding the timing of variable collection. We acknowledge that our initial description was\nnot clear enough, and in order to clarify the timing of cross queue variable collection, we\nprovide a more accurate and consistent description:\n In the training ACLF cohort: Variables were collected from the 24-hour window prior to\nthe time of ACLF diagnosis.\n In the external validation ACLF cohort: Variables were similarly collected from the 24-\nhour window prior to ACLF diagnosis, ensuring methodological consistency with the\ntraining cohort.\n In the decompensated cirrhosis cohort: Variables were collected within 24 hours after\nICU admission.\nThis latter approach was not an inconsistency, but a deliberate design choice to evaluate\nwhether our clustering model can generalize to a broader, more clinically heterogeneous\npopulation of patients with decompensated cirrhosis who may not yet meet formal ACLF\ncriteria at the time of ICU admission.\nThe rationale for using the first 24 hours after ICU admission as the reference timepoint\nin this cohort is grounded in real-world clinical practice: in the absence of a confirmed ACLF\ndiagnosis at admission, clinicians rely on the earliest available physiological and laboratory\ndata to assess disease severity and risk stratify patients. The first 24 hours of ICU care\nrepresent the most standardized, comprehensive, and clinically actionable snapshot available\nfor all patients, regardless of whether they later progress to ACLF.\nWe further note that it is clinically implausible for all 50 variables to be measured\nsimultaneously. In real-world clinical practice, laboratory tests and physiological\nmeasurements are ordered incrementally based on clinical need and workflow. To\napproximate the patient’s status at the moment of ACLF diagnosis while maximizing data\ncompleteness, we adopted the following pragmatic and clinically grounded approach:\nFor each variable, we selected the most recent measurement within the 24-hour window\npreceding ACLF diagnosis. If multiple measurements were available, we used the value closest\nin time to the diagnostic timestamp. This ensures that the model reflects the best available\nclinical snapshot at the time of diagnosis, rather than an artificial or averaged state.\n3) In the “decompensated cirrhosis cohort,” more than half of the patients had ACLF, which creates\nconfusion regarding the terminology of this cohort. It also remains unclear whether individual\npatients could have been included in both analyses: first as decompensated cirrhosis patients and\nlater (when organ failures developed) as ACLF patients, introducing possible duplication.\nResponse: Thank you for your comment. Of the 1,334 patients initially classified as having\ndecompensated cirrhosis at ICU admission, 782 later developed ACLF during their\nhospitalization. However, it is critical to emphasize that this cohort was not intended as a\nconventional control group, but rather as a real-world pragmatic cohort representing the\nbroader population of patients with decompensated cirrhosis—many of whom are at risk of\nprogressing to ACLF.\nRegarding potential duplication:\nYes, some patients who eventually met ACLF criteria were included in both the ACLF\ncohort and the decompensated cirrhosis cohort. However, the timepoints and clinical contexts\nof data collection differ fundamentally between the two analyses:\n In the ACLF cohort, variables were extracted from the 24-hour window immediately\nprior to formal ACLF diagnosis.\n In the decompensated cirrhosis cohort, variables were collected within 24 hours after\nICU admission.\nThus, even for the same patient, the two analyses evaluate distinct physiological states at\ndifferent timepoints along the disease trajectory. There is no overlap in the data used for\nmodeling or evaluation.\nThe purpose of including this cohort was not to serve as a strict comparator to ACLF\npatients, but to assess whether our model could also generalize to a heterogeneous ICU\npopulation with decompensated cirrhosis, including those who would later deteriorate to\nACLF. This reflects a real-world clinical scenario where clinicians must risk-stratify\ndecompensated cirrhosis patients at ICU admission, before organ failures manifest.\n4) The absence of mortality data in the eICU validation cohort (as acknowledged in the new version)\nsubstantially limits the capacity to validate the prognostic impact of the proposed clusters. Moreover,\nthe exclusion of approximately 75% of the eICU patients due to missing data further undermines\nthe robustness of the findings.\nResponse: Thank you for your comment. We fully acknowledge that the absence of mortality\ndata in eICU precludes direct validation of the clusters’ prognostic performance in this\nexternal dataset. However, we would like to clarify the primary objective of our study:\nThis work is not primarily a prognostic modeling study, but rather an application of\nunsupervised clustering to patient data in the absence of pre-specified hypotheses, with the\ngoal of identifying key features that drive patient stratification and potentially uncovering\nnovel, clinically relevant factors that may have been overlooked in current practice.\nIn this context, the core contribution lies in:\n Discovering clusters driven by distinct physiological and biochemical patterns;\n Highlighting potentially underappreciated clinical features that differentiate patient\nsubgroups;\n Demonstrating that these clustering patterns are not dataset-specific, but generalize\nacross independent cohorts.\nTo that end, the eICU cohort serves a different validation purpose:\nIt confirms the consistency of the optimal number of clusters, the robustness of the\nclustering algorithm, and the stability of the relative importance of key variables across\ndifferent cohorts.\nThe ability of cluster prognostic impact has been validated in both the decompensated\ncirrhosis cohort and EASL-CLIF definition ACLF cohort.\nRegarding the high exclusion rate (75%) due to missing data:\nDue to the stricter selection criteria applied to the external validation cohort in this study,\napproximately 75% of patients in the eICU cohort were excluded due to excessive missing\ndata, which may introduce selection bias. We have explicitly acknowledged this limitation in\nthe Discussion section (Lines 402-405). However, this rigorous data filtering improved data\ncompleteness, thereby enhancing the credibility and reliability of the validation results.\nMoreover, in Supplemental Table 10, we have provided descriptive analyses comparing\nbaseline characteristics before and after cohort selection in the eICU validation set. The results\nshow no statistically significant differences in the demographic and clinical variables used in\nour classification model between the pre- and post-filtering populations, indicating that the\nfinal selected cohort remains representative of the original eICU cohort.\n5) The Methods should clearly specify how cirrhosis was defined and identified in both the MIMIC\nand eICU databases.\nResponse: Thank you for your comment. We have mentioned in the Methods section that we\nused ICD-9 and ICD-10 codes to identify cirrhosis patients in the MIMIC and eICU databases,\nas detailed in Supplementary Table 11.\nReviewer #2 (Remarks to the Author):\nAuthors have responded adequately to all my concerns\nResponse: We sincerely appreciate the reviewer's comments on our work.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We sincerely thank the reviewers for thoughtful and constructive feedback.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part\nof the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: We sincerely thank the reviewers for thoughtful and constructive feedback.\nReviewer #5 (Remarks to the Author):\nThank you for addressing the comments. Here ia a remaining minor comment for your consideration.\n1. Please describe the justification of the discrepancy in exclusion criteria for missing values\nbetween the training and external validation sets (5 vs 20) in the Discussion section.\nResponse: Thank you for your comment. We have added the reasons for excluding standard\ndifferences in missing values between the training group and the external validation set (5 vs\n20) to the discussion section (Lines 394-401).\n2. It would be good to include the GitHub page link of the source codes into the manuscript.\nResponse: Thank you for your comment. We have added the GitHub page link of the source\ncode into the manuscript (Lines 622-624)."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have adequately addressed the questions raised and the manuscript has improved with greater clarity in the methodology and results. Lastly, since they provided the ICD codes for cirrhosis diagnosis, the etiology of liver disease should also be reported in Table 1, as it is important for ACLF characterization and for future comparisons.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We sincerely appreciate the reviewer's comments on our work. We have added the\npatient's etiology in Table 1, including ALD, viral hepatitis, MASLD, and others."}]

Decision letter