Abstract
The heterogeneity in ACLF definitions proposed by different expert associations worldwide has led to variations in clinical phenotypes and prognosis. This study proposes a new approach for a unified definition based on the type of organ failure within the first week of disease onset.This prospective cohort study collected clinical data from hospitalized ACLF patients presenting with liver failure (total bilirubin ≥ 5 mg/dL, INR ≥ 1.5) across four liver centers in China. Patients with liver failure within the first week of onset were classified as Type I ACLF, while those with concurrent extrahepatic organ failure were categorized as Type II ACLF. We compared the clinical characteristics and prognosis of Type I and Type II ACLF patients, as well as the inclusivity of these classifications within existing mainstream ACLF definitions.A total of 846 patients were included in the analysis population. Type I ACLF occurred in 86.9% of cases, primarily in patients with non-cirrhotic chronic liver disease (34.3%) or compensated cirrhosis (48.6%), while Type II ACLF, observed in 13.1% of cases, predominantly developed in patients with cirrhosis (73%) (P < 0.001). Mortality rates at 28 and 90 days were significantly higher in Type II ACLF patients (43.2% and 52.3%) compared to those with Type I ACLF (15.8% and 24.9%) (P < 0.001). Patients with Type I ACLF met the APASL criteria more frequently (82.9%), whereas those with Type II ACLF more often met the EASL criteria (69.4%). For predicting the 90-day prognosis in Type I ACLF, the COSSH-ACLF II score achieved the highest AUROC at 0.788 (95% CI: 0.749–0.828). In Type II ACLF, the CLIF-C OF score provided the highest AUROC for predicting 90-day mortality, at 0.902 (95% CI: 0.840–0.964), outperforming the MELD, MELD-Na, and COSSH-ACLF II scores (P < 0.05) and showing no significant difference when compared to the CLIF-C ACLF score (0.872 [95% CI: 0.791–0.953], P = 0.426).This new clinical classification approach takes into account the characteristics defined by both Eastern and Western ACLF, and is expected to improve the comparability and interpretability of data in global studies, enhancing global understanding and management of ACLF.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-026-51936-7.
Keywords: Acute-on-chronic liver failure, Intrahepatic organ failure, Extrahepatic organ failure, New Clinical Classification
Subject terms: Diseases, Liver diseases
Introduction
Acute-on-chronic liver failure (ACLF) is a distinct clinical syndrome due to an acute insult in patients with chronic liver disease, characterized by an intense systemic inflammatory response, single or multiple organ failures, and high 28-day mortality1.
Different regions have varying definitions of ACLF, with heterogeneity mainly manifesting in the underlying chronic liver disease, the acute precipitating factors, and the types of organ failures.
The Asia-Pacific Association for the Study of the Liver (APASL) considers liver failure itself as the core criterion for diagnosing ACLF, without including extrahepatic organ failure2. The definition by the Chinese Medical Association (CMA) is similar to that of the APASL3. In contrast, the definitions of ACLF from Western countries, such as those by the European Association for the Study of the Liver (EASL)4,5 and the North American Consortium for the Study of End-Stage Liver Disease (NACSELD)6, describe it as multi-organ failure (liver, kidney, brain, coagulation, circulation, and respiratory systems) on the basis of cirrhosis. Additionally, Chinese scholars have established the COSSH criteria for hepatitis B-infected populations, which is similar to the EASL-ACLF criteria7.
A recent meta-analysis showed that different diagnostic criteria were used in different regions, with EASL(53.2%) and APASL(33.3%) being the most widely used criteria, and the diagnostic overlap between definitions ranged from 7.7% to 80.2%8. To reduce discrepancies in ACLF definitions, hepatology experts from different regions worldwide are working to develop standardized diagnostic criteria that integrate key elements from various existing definitions.
In 2022, the American College of Gastroenterology (ACG) proposed a more inclusive and comprehensive definition of ACLF, integrating the definitions from APASL, EASL, and NACSELD. According to this definition, ACLF can be identified by the presence of chronic liver disease (with or without cirrhosis) along with elevated TBIL and prolonged INR, with kidney, lung, circulatory, or brain failure supporting the diagnosis8. Recently, the American Association for the Study of Liver Diseases (AASLD) released practice guidelines for ACLF, which define it by the presence of liver failure indicated by elevated TBIL and INR in patients with chronic liver disease, with or without cirrhosis. Unlike the ACG definition, the AASLD definition also requires the presence of at least one extrahepatic organ failure9. Based on current consensus, elevated TBIL and INR are fundamental for diagnosing ACLF, with extrahepatic organ failure supporting this diagnosis.
The variability in ACLF definitions proposed by different associations, combined with regional preferences in their application, has led to inconsistencies in clinical presentation and prognosis, hindering accurate diagnosis of the disease. Establishing a unified diagnostic standard to address this issue is a key challenge10. While recent efforts by the ACG and AASLD have moved towards more inclusive definitions by requiring both liver failure and the presence of extrahepatic organ failure (AASLD) or using them as supportive criteria (ACG), these frameworks still present a binary categorization—ACLF present or absent—without accounting for the distinct clinical trajectories of patients who present with isolated liver failure versus those who already have multi-organ failure at onset. This binary approach may mask significant heterogeneity in disease progression, response to therapy, and optimal prognostic tools. The current study addresses this gap by proposing a novel, organ failure-based classification at the time of onset, which explicitly differentiates between Type I and Type II. This approach aims to provide a more nuanced and clinically actionable framework that reconciles the Eastern and Western perspectives, moving beyond a single diagnostic label to a staging system that can guide prognosis and management. Furthermore, since the current consensus is primarily based on expert opinion, we have used clinical data to propose a new, standardized definition of ACLF, aiming to enhance its applicability and foster a global consensus.
Methods
Study design
We conducted a prospective multicenter cohort study using data from liver units in four hospitals. Each liver unit included a regular ward, an intensive care unit, and a liver transplantation (LT) center. Patients were screened from January 2015 to December 2023 in Beijing You’an Hospital Affiliated with Capital Medical University, the First Affiliated Hospital of Zhejiang University School of Medicine, Tianjin Third Central Hospital, and the Fifth Medical Center of PLA General Hospital. The study protocol was approved by the Clinical Research Ethics Committee of Beijing You’an Hospital Affiliated with Capital Medical University (LL-2023-119-K). This study was registered on 18 June 2024 in the Chinese Clinical Trial Registry (ChiCTR2400085780). Informed consent was obtained from all participants. All methods were performed in accordance with the relevant guidelines and regulations.
Patients
We screened patients who were hospitalized for at least 1 day with an acute hepatic insult occurring in patients with chronic liver disease of any etiology, manifested by jaundice (TBIL ≥ 85 µmol/L) and coagulation dysfunction (INR ≥ 1.5), and the chronic liver disease included non-cirrhotic chronic liver disease, compensated cirrhosis, and previous decompensated cirrhosis. These patients met the minimum diagnostic criteria for ACLF according to major current guidelines and consensus statements. Decompensated cirrhosis was defined by the acute development of large ascites, HE, gastrointestinal bleeding, bacterial infection, or any combination of these, occurring within 6 months prior to the current episode.
The exclusion criteria were as follows: patients with hepatocellular carcinoma or other tumors, pregnant women, patients with severe extrahepatic disease, and patients receiving immunosuppressive therapy for other reasons. All patient data were retrieved from electronic medical records.
We collected baseline clinical data, including demographic characteristics such as age and gender, etiology, laboratory indicators, complications, and organ failures within the first week. We followed up 90-day prognosis of ACLF patients. The Model for End-Stage Liver Disease (MELD), MELD-Na, COSSH-ACLF II, CLIF-C OF, and CLIF-C ACLF scores were calculated based on patients’ clinical indicators. Organ failure was defined as follows: liver failure, TBIL ≥ 12 mg/dL; coagulation failure, INR ≥ 2.5; cerebral failure, HE (West-Haven) grades III or IV; kidney failure, serum creatinine ≥ 2 mg/dL or renal replacement therapy; respiratory failure, PaO2/FiO2 ≤ 200 or SpO2/FiO2 ≤ 214, or the use of mechanical ventilation; circulation failure, use of vasopressors11.
Procedures
Based on the presentation of hepatic and extrahepatic organ failure at onset, patients are classified into Type I and Type II ACLF. Type I ACLF represents acute liver injury with a background of chronic liver disease, primarily presenting as liver failure characterized by jaundice and coagulopathy, with or without HE (grade 1–2). Type II ACLF is characterized by liver failure at the onset, accompanied by at least one extrahepatic organ failure—such as kidney, brain, respiratory, or circulatory system—within one week12. The clinical characteristics and outcomes of the two ACLF patient groups were compared, and the predictive utility of existing prognostic scoring systems for each ACLF type was assessed. Probabilities of meeting various diagnostic criteria (EASL, APASL, CMA, COSSH, and NACSELD) were analyzed for each group, along with the degree of overlap among these standards. Furthermore, preliminary prognostic models specific to each ACLF type were developed, and nomograms were constructed to illustrate predictive outcomes.
Statistical Analysis
All statistical analyses were performed with R × 64 4.0.3 (http://www.r-project.org/). Continuous variables were presented as Mean ± SD and median[interquartile range (IQR)], and categorical variables as n (%).Wilcoxon rank-sum and chi-squared tests were performed for continuous and categorical variables, respectively. Kaplan-Meier survival curves were plotted and compared with log-rank tests. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of different scores for the prognosis in ACLF patients and compared with Delong test. Multivariate Cox PH models were fitted with a stepwise method using significant baseline laboratory indicators that had been pre-filtered in univariate PH models (P < 0.05) to identify the independent risk factors of mortality in patients with Type I and Type II ACLF respectively. The selected variables were then incorporated into developing a nomogram model based on proportionally converting each multivariate regression coefficient to a scale from 0 to 100 points using the RMS package of R. A two-sided p < 0.05 was considered statistically significant.
Results
Clinical characteristics of ACLF patients
In this study, 846 hospitalized patients with ACLF were included, the majority of whom were male (83.1%), with a median age of 47.0 [39.0–55.0] years. The primary etiologies of ACLF were hepatitis B (68.6%) and alcoholic liver disease(16.1%). The proportions of non-cirrhotic chronic liver disease, compensated cirrhosis, and decompensated cirrhosis are 33.3%, 46.3%, and 20.3%, respectively. The 28-day and 90-day mortality rates of ACLF patients were 19.4% and 28.6%, respectively, and the liver transplantation (LT) rates were 15.5% and 20.3% (Table 1).
Table 1.
Baseline characteristics of ACLF patients.
| Characteristic | Total(n = 846) | Type I (n = 735) | Type II (n = 111) | P-value |
|---|---|---|---|---|
| Gender (male,%) | 707 (83.6%) | 611 (83.1%) | 96 (86.5%) | 0.452 |
| Age, years | 47.0 (39.0–55.0) | 47.0 (39.0–55.0) | 46.0 (40.0–54.0) | 0.937 |
| Underlying liver disease (%) | < 0.001 | |||
| A | 282 (33.3%) | 252 (34.3%) | 30 (27.0%) | |
| B | 392 (46.3%) | 357 (48.6%) | 35 (31.5%) | |
| C | 172 (20.3%) | 126 (17.1%) | 46 (41.5%) | |
| Etiology (%) | 0.087 | |||
| HBV | 580 (68.6%) | 515 (70.1%) | 65 (58.6%) | |
| Alcohol | 136 (16.1%) | 112 (15.2%) | 24 (21.6%) | |
| HBV+Alcohol | 62 (7.33%) | 53 (7.21%) | 9 (8.11%) | |
| Others | 68 (8.04%) | 55 (7.48%) | 13 (11.7%) | |
| Complications | ||||
| Bacteria infection (%) | 239 (60.5%) | 215 (59.7%) | 24 (68.6%) | 0.400 |
| GI bleeding (%) | 32 (5.91%) | 25 (5.18%) | 7 (12.1%) | 0.068 |
| HE | < 0.001 | |||
| 0 | 612 (72.3%) | 584 (79.5%) | 28 (25.2%) | |
| 1–2 | 169 (19.9%) | 151 (20.5%) | 18 (16.2%) | |
| 3–4 | 65 (7.6%) | 0 (0.0%) | 65 (58.6%) | |
| Lab Tests | ||||
| ALT (IU/L) | 144 (55.0–489.0) | 158 (59.0–519.0) | 105 (44.5–318) | 0.022 |
| AST (IU/L) | 153 (88.0–350.0) | 160 (88.0–373.0) | 130 (88.0–286.0) | 0.179 |
| Total protein (g/L) | 61.1 (55.0–67.5.0.5) | 61.2 (55.1–67.7) | 60.6 (54.7–66.2) | 0.181 |
| Albumin (g/L) | 30.0 (26.8–33.1) | 30.2 (27.1–33.2) | 29.0 (24.5–31.9) | 0.008 |
| TBIL (umol/L) | 352 (239–462) | 348 (237–458) | 400 (280–508) | 0.015 |
| PTA (%) | 33.0 (24.0–40.0) | 33.0 (25.0–41.0) | 27.0 (20.0–35.0) | < 0.001 |
| INR | 2.21 (1.86–2.85) | 2.17 (1.84–2.77) | 2.60 (2.13–3.58) | < 0.001 |
| Serum urea (mmol/L) | 4.58 (3.32–6.37) | 4.42 (3.27–6.04) | 7.07 (3.99–13.8) | < 0.001 |
| Creatinine (umol/L) | 62.0 (51.0–77.0) | 61.0 (50.0–74.0) | 75.0 (56.9–182) | < 0.001 |
| Na(mmol/L) | 136 (133–139) | 137 (134–139) | 135 (130–138) | < 0.001 |
| K(mmol/L) | 3.93 (3.52–4.36) | 3.94 (3.54–4.34) | 3.90 (3.46–4.39) | 0.677 |
| Hemoglobin(g/L) | 122 (103–136) | 123 (104–136) | 115 (90.5–133) | 0.003 |
| WBC(*109/mm3) | 6.94 (5.02–9.70) | 6.80 (4.94–9.33) | 8.68 (5.68–12.1) | < 0.001 |
| Neutrophil (*109/mm3) | 4.80 (3.17–7.21) | 4.64 (3.02–6.90) | 6.50 (4.20–9.76) | < 0.001 |
| PLT(*109/mm3) | 96.0 (61.0–137.0) | 98.0 (63.0–140.0) | 81.0 (53.0–128.0) | 0.015 |
| Scores | ||||
| MELD | 22.5 (18.8–26.4) | 22.0 (18.6–25.6) | 27.8 (21.4–34.4) | < 0.001 |
| MELD-Na | 23.7 (20.0–29.2.0.2) | 23.2 (19.8–27.8) | 31.0 (24.6–41.4) | < 0.001 |
| CLIF-C ACLF | 41.8 (36.9–46.7) | 40.8 (36.3–45.5) | 49.8 (44.1–54.5) | < 0.001 |
| COSSH-ACLF IIs | 7.40 (6.80–8.10) | 7.20 (6.70–7.90) | 8.30 (7.60–9.05) | < 0.001 |
| CLIF-C OF | 9.00 (8.00–10.0) | 9.00 (8.00–10.0) | 11.0 (10.0–13.0) | < 0.001 |
| Extrahepatic organ failureƚ | ||||
| Kidney (%) | 40 (4.73%) | 0 (0.00%) | 40 (36.0%) | < 0.001 |
| Brain (%) | 65 (7.68%) | 0 (0.00%) | 65 (58.6%) | < 0.001 |
| Respiration (%) | 27 (3.19%) | 0 (0.00%) | 27 (24.3%) | < 0.001 |
| Circulation (%) | 16 (1.89%) | 0 (0.00%) | 16 (14.4%) | < 0.001 |
| Definitions | ||||
| APASL (%) | 674 (79.7%) | 609 (82.9%) | 65 (58.6%) | < 0.001 |
| CMA (%) | 752 (88.9%) | 653 (88.8%) | 99 (89.2%) | 1.000 |
| EASL (%) | 276 (32.6%) | 199 (27.1%) | 77 (69.4%) | < 0.001 |
| COSSH (%) | 556 (65.7%) | 484 (65.9%) | 72 (64.9%) | 0.923 |
| NACSELD (%) | 21 (2.48%) | 0 (0.00%) | 21 (18.9%) | < 0.001 |
| Prognosis | ||||
| 28-day | < 0.001 | |||
| survival | 551 (65.1%) | 506 (68.8%) | 45 (40.5%) | |
| death | 164 (19.4%) | 116 (15.8%) | 48 (43.2%) | |
| LT | 131 (15.5%) | 113 (15.4%) | 18 (16.2%) | |
| 90-day | < 0.001 | |||
| survival | 428 (51.1%) | 399 (55.0%) | 29 (26.1%) | |
| death | 239 (28.6%) | 181 (24.9%) | 58 (52.3%) | |
| LT | 170 (20.3%) | 146 (20.1%) | 24 (21.6%) |
A, Non-cirrhotic chronic liver disease; B, compensated cirrhosis; C, decompensated cirrhosis; HBV, hepatitis B virus; GI, gastrointestinal; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TBIL, Total Bilirubin; PTA, prothrombin activity; INR, international normalized ratio; WBC, white blood cells; PLT, platelet count; MELD, Model for End-stage Liver Disease; CLIF-C ACLF, Chronic Liver Failure Consortium Acute-on-chronic liver failure; COSSH-ACLF IIs, Chinese Group on the Study of Severe Hepatitis B-ACLF IIs; CLIF-C OF, Chronic Liver Failure Consortium Organ Failure; APASL, Asian Pacific Association for the Study of the Liver; CMA, Chinese Medical Association; EASL, European Association for the Study of the Liver; NACSELD, North American Consortium for the Study of End-stage Liver Disease.
ƚ Extrahepatic organ failure is based on the CLIF-C OF criteria.
Comparison of clinical characteristics and prognosis of patients with Type I and Type II ACLF
Patients were classified based on the occurrence of organ failure within the first week of onset:735 patients (86.9%) were categorized as having Type I ACLF, and 111 patients (13.1%) as having Type II ACLF. At onset, Type I ACLF patients did not have extrahepatic organ failure, with jaundice and coagulation dysfunction being the main manifestations, and they could present with grade 1–2 HE (20.5%). In contrast, most Type II ACLF patients presented with concomitant brain failure (58.6%) or kidney failure (36.0%) at onset, followed by respiratory failure (24.3%), and fewer cases of circulation failure (14.4%). The 28-day and 90-day mortality rates for Type II ACLF patients were 43.2% and 52.3%, respectively, which were higher than those for Type I ACLF patients (15.8% and 24.9%) (P < 0.001) (Table 1; Fig. 1).
Fig. 1.
The survival curve of patients with Type I and Type II ACLF. (A) 28-day survival curve of patients with ACLF. (B) 90-day survival curve of patients with ACLF.
To assess whether the prognostic value of the classification differs by etiology, we compared outcomes between Type I and Type II ACLF separately in HBV and non-HBV subgroups. In the HBV subgroup (n = 580), Type II ACLF had significantly higher 28-day and 90-day mortality than Type I (50.8% vs. 17.9%, P < 0.001; 56.9% vs. 26.7%, P < 0.001, respectively). Similarly, in the non-HBV subgroup (n = 266), Type II ACLF consistently showed significantly higher mortality than Type I at both 28 days (32.6% vs. 10.9%, P < 0.001) and 90 days (45.7% vs. 20.7%, P < 0.001).
There is no difference in etiology between the two types. Regarding chronic liver disease status, Type I ACLF was mainly associated with non-cirrhotic chronic liver disease (34.3%) and compensated cirrhosis (48.6%), while Type II ACLF was primarily associated with compensated (31.5%) and decompensated cirrhosis (41.5%), showing a statistically significant difference between the two groups (P < 0.001). Scores reflecting the severity of ACLF, such as MELD (27.8 vs. 22.0), MELD-Na (31.0 vs. 23.2), COSSH-ACLF II (8.3 vs. 7.2), CLIF-C OF (11.0vs. 9.0), and CLIF-C ACLF (49.8 vs. 40.8), were also significantly higher in Type II ACLF patients compared to Type I (P < 0.001).
The association of underlying state of chronic liver disease with prognosis
Our results showed that the 28-day mortality for non-cirrhotic(A), compensated cirrhotic(B), and previously decompensated cirrhotic(C) patients were 18.7%, 19.1%, and 20.9%, respectively; the 90-day mortality rates were 25.5%, 29.3%, and 30.2%, respectively. Among Type I ACLF patients, the 28-day mortality for the three chronic liver diseases were 14.2%, 16.8%, and 15.8%, and the 90-day mortality were 20.6%, 26.6%, and 26.9%, respectively. In Type II ACLF patients, the 28-day mortality were 56.6%, 42.8%, and 34.7%, and the 90-day mortality were 66.6%, 57.1%, and 39.1% respectively (Fig. 2).
Fig. 2.
Prognosis of different underlying chronic liver disease. In the horizontal axis, A represents non-cirrhotic chronic liver disease, B represents compensated cirrhosis and C represents decompensated cirrhosis. (A) 28-day mortality of patients with ACLF. (B) 28-day mortality of patients with Type I and Type II ACLF. (C) 90-day mortality of patients with ACLF. (D) 90-day mortality of patients with Type I and Type II ACLF.
One of the main differences in widely used diagnostic criteria lies in the underlying status of liver, the APASL definition includes A and B, the EASL definition includes B and C, while the CMA and AASLD definitions apply to both non-cirrhotic and cirrhotic conditions. This study further analyzed the prognosis of Type I and Type II ACLF patients with different combinations of liver disease. The 28-day mortality for A + B and B + C are 18.9% and 19.6%, respectively, while the 90-day mortality are 27.7% and 29.6%, respectively. In type I ACLF patients, the 28-day mortality for A + B and B + C are 15.7% and 16.5%, respectively, and the 90-day mortality are 24.1% and 26.7%, respectively. Among type II ACLF patients, the 28-day mortality for A + B and B + C are 49.2% and 38.2%, respectively, and the 90-day mortality rates are 61.5% and 46.9%, respectively(Fig. 2).
Survival analysis showed that there was no statistically significant difference in transplant-free survival rates between the three chronic liver diseases in Type I ACLF patients (P > 0.05), while in Type II ACLF patients, the 90-day transplant-free survival rate was highest for those with previously decompensated cirrhosis (45.5%), followed by those with compensated cirrhosis (31.0%), and lowest for those with non-cirrhotic chronic liver disease (16.7%) (P = 0.048) (Fig. 3).
Fig. 3.
The survival curve of ACLF patients with different underlying chronic liver disease. (A) 28-day survival curve of patients with ACLF. (B) 28-day survival curve of patients with Type I ACLF. (C) 28-day survival curve of patients with Type II ACLF. (D) 90-day survival curve of patients with ACLF. (E) 90-day survival curve of patients with Type I ACLF. (F) 90-day survival curve of patients with Type II ACLF.
Probabilities of meeting various diagnostic criteria in each type and the diagnostic criteria overlap of ACLF
As shown in Table 1; Fig. 4A and B, the proportion of patients with Type I ACLF and Type II ACLF meeting the CMA diagnostic criteria was 88.8% and 89.2%, respectively (P = 1.000), and the proportion meeting the COSSH criteria was 65.9% and 64.9%, respectively (P = 0.923), these differences were not statistically significant. However, a significantly higher proportion of Type I ACLF patients met the APASL criteria compared to Type II ACLF patients (82.9% vs. 58.6%, P < 0.001). On the other hand, Type II ACLF patients were significantly more likely to meet the EASL (69.4% vs. 27.1%, P < 0.001) and NACSELD (18.9% vs. 0%, P < 0.001) diagnostic criteria compared to Type I ACLF patients. Figure 4C illustrates the proportion of patients who meet both diagnostic criteria within the overall population. There is a relatively high overlap among the APASL, CMA, and COSSH definitions, with 71.9% of patients meeting both the APASL and CMA criteria, 64.9% meeting both the COSSH and CMA criteria, and 53.9% meeting both the APASL and COSSH criteria. The overlap between one of these three definitions and the EASL definition is moderate, with 21.6% for APASL, 31.4% for CMA, and 23.3% for COSSH. In contrast, the overlap between the APASL, CMA, COSSH, and EASL definitions with the NACSELD definition is minimal, at just 0.8%, 2.1%, 1.5%, and 2.5%, respectively.
Fig. 4.
Differences in diagnostic criteria between two types and the diagnostic criteria overlap of ACLF. (A) Differences in diagnostic criteria between Type I and Type II ACLF. (B) Sankey diagram showing the proportions of different definitions in patients with Type I and Type II ACLF. (C) The diagnostic criteria overlap of patients with ACLF. The number of lines between two points represents the proportion that meets both diagnostic criteria.
The predictive value of different prognostic scores for prognosis of patients with Type I and Type II ACLF
For the overall population, the COSSH-ACLF II score demonstrated the highest AUROC for predicting 28-day and 90-day prognosis, with values of 0.813 (0.772–0.844) and 0.807 (0.773–0.842), respectively. These results were superior to the MELD and MELD-Na scores (P < 0.05), but there was no statistically significant difference compared to the CLIF-C OF and CLIF-C ACLF scores (P > 0.05). For patients with type I ACLF, the COSSH-ACLF II score also had the highest AUROC for predicting 28-day and 90-day outcomes, with values of 0.800 (0.757–0.844) and 0.788 (0.749–0.828), respectively, with no statistically significant difference compared to the CLIF-C OF score (P > 0.05) but superior to other scores. For patients with type II ACLF, the CLIF-C OF score had the highest AUROC for predicting 90-day mortality, at 0.902 (0.840–0.964), which was superior to the MELD, MELD-Na, and COSSH-ACLF II scores (P < 0.05), with no statistically significant difference compared to the CLIF-C ACLF score (0.872[0.791–0.953], P = 0.426). The predictive values of the various scores are detailed in Table 2; Fig. 5. Further, the study performed univariate and multivariate Cox regression analyses, which showed that the MELD, COSSH-ACLF II, and CLIF-C OF scores were independent risk factors for 90-day mortality in Type I ACLF patients, while the CLIF-C OF and CLIF-C ACLF scores were independent risk factors for 90-day mortality in Type II ACLF patients, as detailed in Table 3.
Table 2.
Performance of scoring models in predicting 28-day and 90-day mortality in patients with ACLF.
| Model | AUC (95%CI) | P | Cut-off | Sensitivity | Specificity |
|---|---|---|---|---|---|
| 28-day | |||||
| All | |||||
| MELD | 0.762(0.717–0.806) | 0.016 | 25.2 | 64.4 | 79.0 |
| MELD-Na | 0.757(0.711–0.798) | 0.002 | 26.7 | 66.3 | 78.6 |
| COSSH-ACLF IIs | 0.813(0.772–0.844) | reference | 7.35 | 84.0 | 65.8 |
| CLIF-C ACLF | 0.798(0.753–0.831) | 0.101 | 44.8 | 68.1 | 77.6 |
| CLIF-C OF | 0.810(0.772–0.844) | 0.791 | 9.5 | 77.3 | 72.3 |
| Type I | |||||
| MELD | 0.746(0.693–0.799) | 0.028 | 24.7 | 60.0 | 79.2 |
| MELD-Na | 0.739(0.684–0.788) | 0.007 | 26.7 | 60.9 | 81.2 |
| COSSH-ACLF IIs | 0.800(0.757–0.844) | reference | 7.35 | 80 | 68.9 |
| CLIF-C ACLF | 0.768(0.713–0.809) | 0.013 | 44.8 | 60.0 | 79.9 |
| CLIF-C OF | 0.780 (0.736–0.820) | 0.307 | 9.5 | 69.6 | 74.8 |
| Type II | |||||
| MELD | 0.714(0.605–0.822) | 0.146 | 32.8 | 45.8 | 89.5 |
| MELD-Na | 0.670(0.553–0.786) | 0.045 | 27.4 | 79.2 | 52.6 |
| COSSH-ACLF IIs | 0.747(0.644–0.850) | 0.326 | 8.85 | 50.0 | 92.1 |
| CLIF-C ACLF | 0.767(0.644–0.850) | 0.460 | 43.7 | 93.8 | 50.0 |
| CLIF-C OF | 0.797(0.701–0.893) | reference | 10.5 | 81.3 | 65.8 |
| 90-day | |||||
| All | |||||
| MELD | 0.758(0.719–0.798) | 0.012 | 24.6 | 62.2 | 81.1 |
| MELD-Na | 0.755(0.714–0.792) | 0.003 | 25.1 | 68.5 | 74.4 |
| COSSH-ACLF IIs | 0.807(0.773–0.842) | reference | 7.35 | 78.6 | 71.7 |
| CLIF-C ACLF | 0.791(0.749–0.822) | 0.055 | 44.6 | 63.0 | 81.6 |
| CLIF-C OF | 0.792(0.755–0.825) | 0.320 | 9.5 | 69.3 | 76.9 |
| Type I | |||||
| MELD | 0.743(0.698–0.788) | 0.048 | 24.6 | 56.1 | 82.8 |
| MELD-Na | 0.738(0.694–0.782) | 0.022 | 24.1 | 68.9 | 71.0 |
| COSSH-ACLF IIs | 0.788(0.749–0.828) | reference | 7.40 | 73.3 | 74.1 |
| CLIF-C ACLF | 0.748(0.705–0.791) | 0.003 | 43.3 | 61.1 | 76.0 |
| CLIF-C OF | 0.750(0.709–0.791) | 0.072 | 9.5 | 60.6 | 78.4 |
| Type II | |||||
| MELD | 0.740(0.625–0.855) | 0.001 | 25.9 | 75.9 | 67.9 |
| MELD-Na | 0.700(0.576–0.824) | < 0.001 | 27.4 | 77.6 | 60.7 |
| COSSH-ACLF IIs | 0.807(0.712–0.902) | 0.039 | 7.95 | 77.6 | 71.4 |
| CLIF-C ACLF | 0.872(0.791–0.953) | 0.426 | 43.7 | 94.8 | 67.9 |
| CLIF-C OF | 0.902(0.840–0.964) | reference | 10.5 | 81.0 | 82.1 |
Fig. 5.
Receiver operating curves (ROC) for the abilities of prognostic models to predict the 28-day and 90-day mortality of patients with ACLF. (A–C) ROC curves for the abilities of the prognostic models to predict the 28-day mortality of all ACLF patients (A), Type I ACLF patients (B) and Type II ACLF patients (C). (D–F) ROC curves for the abilities of the prognostic models to predict the 90-day mortality of all ACLF patients (D), Type I ACLF patients (E) and Type II ACLF patients (F).
Table 3.
Univariate and multivariate Cox regression analysis of 5 scores for predicting 28-day and 90-day mortality in patients with ACLF.
| Univariable | Multivariable | |||
|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | |
| 28-day | ||||
| All | ||||
| MELD | 1.13(1.11–1.16) | < 0.001 | 1.05(1.01–1.10) | 0.019 |
| MELD-Na | 1.07(1.06–1.08) | < 0.001 | 0.98(0.96–1.01) | 0.212 |
| COSSH-ACLF IIs | 2.88(2.48–3.35) | < 0.001 | 1.56(1.12–2.17) | 0.008 |
| CLIF-C ACLF | 1.15(1.12–1.17) | < 0.001 | 1.03(0.98–1.07) | 0.251 |
| CLIF-C OF | 1.95(1.78–2.13) | < 0.001 | 1.39(1.20–1.61) | < 0.001 |
| Type I | ||||
| MELD | 1.16(1.13–1.20) | < 0.001 | 1.04(1.00–1.08.00.08) | 0.055 |
| MELD-Na | 1.09(1.07–1.11) | < 0.001 | 1.00(0.96–1.04) | 0.924 |
| COSSH-ACLF IIs | 2.97(2.42–3.63) | < 0.001 | 1.87(1.42–2.46) | < 0.001 |
| CLIF-C ACLF | 1.14(1.11–1.17) | < 0.001 | 1.03(0.98–1.09) | 0.231 |
| CLIF-C OF | 2.40(2.01–2.86) | < 0.001 | 1.53(1.21–1.94) | < 0.001 |
| Type II | ||||
| MELD | 1.06(1.03–1.10) | < 0.001 | 1.05(0.98–1.13) | 0.159 |
| MELD-Na | 1.03(1.01–1.04) | 0.010 | 0.98(0.94–1.02) | 0.241 |
| COSSH-ACLF IIs | 1.97(1.48–2.61) | < 0.001 | 1.21(0.71–2.06) | 0.494 |
| CLIF-C ACLF | 1.11(1.07–1.15) | < 0.001 | 1.06(1.00–1.24.00.24) | 0.039 |
| CLIF-C OF | 1.56(1.32–1.86) | < 0.001 | 1.31(1.03–1.65) | 0.025 |
| 90-day | ||||
| All | ||||
| MELD | 1.12(1.10–1.14) | < 0.001 | 1.05(1.01–1.09) | 0.007 |
| MELD-Na | 1.06(1.05–1.07) | < 0.001 | 0.98(0.96–1.01) | 0.120 |
| COSSH-ACLF IIs | 2.58(2.28–2.92) | < 0.001 | 1.47(1.12–1.94) | 0.006 |
| CLIF-C ACLF | 1.13(1.11–1.15) | < 0.001 | 1.03(0.99–1.06) | 0.173 |
| CLIF-C OF | 1.88(1.74–2.03) | < 0.001 | 1.36(1.20–1.54) | < 0.001 |
| Type I | ||||
| MELD | 1.16(1.12–1.19) | < 0.001 | 1.05(1.01–1.08) | 0.007 |
| MELD-Na | 1.08(1.07–1.10) | < 0.001 | 1.00(0.97–1.03) | 0.883 |
| COSSH-ACLF IIs | 2.63(2.25–3.09) | < 0.001 | 1.80(1.45–2.23) | < 0.001 |
| CLIF-C ACLF | 1.12(1.10–1.15) | < 0.001 | 1.03(0.98–1.07) | 0.246 |
| CLIF-C OF | 2.06(1.80–2.36) | < 0.001 | 1.32(1.10–1.58) | 0.003 |
| Type II | ||||
| MELD | 1.06(1.03–1.09) | < 0.001 | 1.05(0.98–1.12) | 0.149 |
| MELD-Na | 1.02(1.01–1.04) | 0.005 | 0.97(0.94–1.01) | 0.182 |
| COSSH-ACLF IIs | 2.01(1.55–2.59) | < 0.001 | 1.11(0.68–1.81) | 0.685 |
| CLIF-C ACLF | 1.12(1.08–1.16) | < 0.001 | 1.06(1.00–1.12.00.12) | 0.040 |
| CLIF-C OF | 1.67(1.43–1.95) | < 0.001 | 1.38(1.11–1.73) | 0.005 |
Development of new prognostic model for patients with type I and type II ACLF
Supplementary Tables 1 and Supplementary Table 2 compare the clinical characteristics of survivors and non-survivors in Type I ACLF and Type II ACLF, respectively. Multivariate COX regression analysis identified age (HR [95% CI], 1.03 [1.02–1.05]), total protein (HR [95% CI], 0.97 [0.95–0.98]), TBIL (HR [95% CI], 1.003 [1.002–1.003]), INR (HR [95% CI], 1.98 [1.71–2.30]), and HE grade 1–2 (HR [95% CI], 1.46 [1.04–2.05]) as independent risk factors for 90-day mortality in patients with type I ACLF (Table 4). Using these variables, we developed a nomogram prediction model (Fig. 6A) with an AUROC of 0.810 (95% CI, 0.774–0.846), which outperformed other scores (Type I nomogram vs. COSSH, P = 0.185; vs. the other four scores, P < 0.01) (Fig. 6B). For patients with type II ACLF, total protein (HR [95% CI], 0.95 [0.92–0.99]), INR (HR [95% CI], 1.30 [1.13–1.49]), HE grade 1–2 (HR [95% CI], 1.90 [1.01–3.55]), HE grade 3–4 (HR [95% CI], 2.57 [1.29–5.13]), and the use of vasopressors (HR [95% CI], 2.58 [1.35–4.92]) were identified as independent risk factors for 90-day mortality (Table 4). Based on these indicators, we developed a nomogram model (Fig. 6C) that predicted the prognosis of type II ACLF patients with an AUROC of 0.804 (0.709–0.900.709.900). This performance was lower than the CLIF-C OF (P = 0.03) while was not significantly different compared to the CLIF-C ACLF, MELD, MELD-Na, and COSSH-ACLF II scores (P > 0.05) (Fig. 6D).
Table 4.
Univariate and multivariate Cox regression analysis for predicting 90-day mortality in patients with ACLF.
| Univariable | Multivariable | |||
|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | |
| Type I | ||||
| Age | 1.03(1.02–1.04) | < 0.001 | 1.03(1.02–1.05) | < 0.001 |
| Total protein (g/L) | 0.97(0.95–0.98) | < 0.001 | 0.97(0.95–0.98) | < 0.001 |
| TBIL (umol/L) | 1.003(1.002–1.004) | < 0.001 | 1.003(1.002–1.003) | < 0.001 |
| INR | 1.97(1.73–2.25) | < 0.001 | 1.98(1.71–2.30) | < 0.001 |
| Serum urea (mmol/L) | 1.07(1.04–1.10) | < 0.001 | ||
| Creatinine (umol/L) | 1.01(1.00–1.01.00.01) | 0.014 | ||
| Na(mmol/L) | 0.96(0.93–0.99) | 0.006 | ||
| WBC(×109/mm3) | 1.02(1.00–1.03.00.03) | 0.034 | ||
| Neutrophil (×109/mm3) | 1.02(1.00–1.03.00.03) | 0.019 | ||
| Hepatic encephalopathy 1–2 | 2.36(1.72–3.23) | < 0.001 | 1.46(1.04–2.05) | 0.028 |
| Type II | ||||
| Total protein (g/L) | 0.95(0.92–0.98) | 0.002 | 0.95(0.92–0.99) | 0.010 |
| INR | 1.39(1.23–1.58) | < 0.001 | 1.30(1.13–1.49) | < 0.001 |
| Hepatic encephalopathy | ||||
| 1–2 | 1.84(0.99–3.42) | 0.055 | 1.90(1.01–3.55) | 0.045 |
| 3–4 | 2.62(1.37–5.01) | 0.004 | 2.57(1.29–5.13) | 0.007 |
| Vasopressors | 3.95(2.22–7.04) | < 0.001 | 2.58(1.35–4.92) | 0.004 |
Fig. 6.
Nomogram model for predicting 28-day and 90-day survival in patients with Type I and Type II ACLF. (A) Nomogram model for patients with Type I ACLF. (B) ROC curves for the abilities of the prognostic models to predict the 90-day mortality in patients with Type I ACLF. (C) Nomogram model for patients with Type II ACLF. (D)ROC curves for the abilities of the prognostic models to predict the 90-day mortality in patients with Type II ACLF.
Discussion
The study classified ACLF into two types based on the presentation of hepatic and extrahepatic organ failure at onset. The results indicate that Type I ACLF and Type II ACLF differ in their underlying chronic liver disease, clinical characteristics, and prognosis, with each type requiring different prognostic scoring systems. This study proposes a new approach that integrates the Eastern and Western characteristics of ACLF to achieve a unified definition. The aim of this study is to narrow the differences between various definitions of ACLF, suggesting that regardless of the diagnostic criteria, they all represent different manifestations of the same disease.
ACLF can occur in non-cirrhotic, compensated cirrhosis, decompensated cirrhosis three underlying chronic liver disease, a view that has gained consensus in most countries and regions, with research supporting this perspective. For the APASL definition, studies have shown that the 28-day, 90-day, 180-day, 360-day, 540-day, and 720-day mortality rates for ACLF patients with prior decompensated cirrhosis are 24.8%, 42.9%, 48.7%, 57.3%, 63.4%, and 68.1%, respectively, which are significantly higher than those for ACLF patients with non-cirrhotic and compensated cirrhosis. Therefore, ACLF that occurs on previously decompensated cirrhosis should be included in the diagnostic criteria for APASL-ACLF and treated according to ACLF management protocols13. When patients are diagnosed with ACLF based on the EASL criteria, the definition of chronic liver disease should include non-cirrhotic, compensated cirrhosis, and decompensated cirrhosis, which has been confirmed in HBV-related ACLF patients7. Our study shows that regardless of whether cirrhosis is present or whether there is a history of decompensation, the 28-day mortality rate exceeds the 15% threshold previously used to establish ACLF criteria11, and there is no statistically significant difference in the 28-day mortality rate between the three underlying liver disease (P = 0.3). Therefore, non-cirrhotic chronic liver disease, compensated cirrhosis, and decompensated cirrhosis should all be included in the definition of ACLF.
Interestingly, for Type I ACLF, the underlying liver disease has no statistically significant impact on the 28-day and 90-day mortality rates. However, for Type II ACLF patients, those with a history of decompensated cirrhosis have a higher 90-day survival rate compared to patients with non-cirrhotic chronic liver disease and compensated cirrhosis (P = 0.048). In a multicenter, large-sample study using the EASL diagnostic criteria, it was found that ACLF patients without a history of acute decompensation had a significantly higher risk of death compared to those with prior acute decompensation, at the same white blood cell count level11. ACLF patients without a history of acute decompensation may have organs that are more sensitive to the host’s inflammatory response or have a lower tolerance when faced with the dual challenges of acute hepatic decompensation and extrahepatic organ failure, leading to a higher mortality.
Our study found that among patients in Asia, most ACLF patients met the diagnostic criteria of CMA, APASL, or COSSH, with a high degree of overlap among these standards. Relatively fewer patients met the EASL diagnostic criteria, while the NACSELD definition, which requires at least two extrahepatic organ failures, was only met by 18.9% of Type II ACLF patients. The proportion of patients meeting the CMA and COSSH diagnostic criteria was very similar between Type I and Type II ACLF patients, with no statistically significant difference, indicating that these two diagnostic criteria have strong inclusivity. However, the proportion of Type I ACLF patients meeting the APASL criteria was significantly higher than that of Type II, whereas the proportion of Type II ACLF patients meeting the EASL and NACSELD criteria was significantly higher than that of Type I, which aligns with the characteristics of each type. Type I ACLF is primarily associated with non-cirrhotic chronic liver disease (34.3%) and compensated cirrhosis (48.6%), with liver failure being the initial manifestation, and extrahepatic organ failure may occur as the disease progresses. The 28-day and 90-day mortality rates for Type I ACLF are 15.8% and 24.9%, respectively. Type II ACLF mainly occurs on cirrhosis (73%) with acute liver decompensation, accompanied by extrahepatic organ failure, and the 28-day and 90-day mortality rates are 43.2% and 52.3%, respectively. Our subgroup analysis by etiology further supports the generalizability of this classification. The prognostic distinction between Type I and Type II ACLF was consistently observed in both HBV and non-HBV patient populations, with Type II ACLF carrying a significantly worse prognosis regardless of underlying etiology. These findings suggest that the proposed classification is robust across different etiologies and may be applicable to non-HBV-dominant populations, although larger multi-etiology cohorts are needed for definitive validation. A meta-analysis of 30 cohort studies worldwide, involving 184,041 patients with acutely decompensated cirrhosis, using the EASL diagnostic criteria for the first time, showed that the 28-day and 90-day mortality rates for ACLF patients were 45% and 58%, respectively14. Therefore, in terms of the underlying liver disease, types of organ failure, and mortality rates, Type I ACLF aligns more closely with the APASL definition, while Type II ACLF aligns more closely with the EASL definition.
ROC analysis revealed that for Type I ACLF patients, the COSSH-ACLF II score has the highest accuracy in predicting prognosis, while for Type II ACLF patients, the CLIF-C OF and CLIF-C ACLF scores are more suitable for prognostic prediction. Therefore, if patients with Type I ACLF develop extrahepatic organ failure during disease progression, the CLIF-C OF or CLIF-C ACLF scores should also be used for evaluation. The COSSH-IIs score includes age, INR, TBIL, serum urea, hepatic encephalopathy, and neutrophil count, reflecting the patient’s overall physiological state, liver function, coagulation function, renal function, brain function, and inflammation level, making it particularly suitable for Type I ACLF patients15. The CLIF-C OF score, established in cirrhosis patients, comprehensively assesses the severity of diseases in six systems: liver, kidney, brain, coagulation, respiratory, and circulatory. The CLIF-C ACLF score builds on the CLIF-C OF score by incorporating age and white blood cell count. Both scores consider patients with characteristics of both ‘critical illness’ and ‘liver disease,’ making them more suitable for patients with extrahepatic organ failure4,16. On the other hand, the MELD or MELD-Na scores do not account for the function of extrahepatic organs like the brain, circulatory, and respiratory systems, or the impact of systemic inflammation on the prognosis of ACLF patients, which may lead to an underestimation of disease severity, especially in patients with extrahepatic multiple organ failures17–20. Based on clinical indicators, we developed nomogram models to predict the 90-day prognosis for Type I and Type II ACLF patients respectively. The results indicate that for Type I ACLF patients, the nomogram model or COSSH-ACLF II score can be used for prognostic prediction, while for Type II ACLF, it is recommended to continue using the CLIF-C OF or CLIF-C ACLF scores for predicting prognosis. The nomogram for Type II ACLF, while robust, demonstrated a lower AUROC than the CLIF-C OF score, likely because the CLIF-C OF score is a validated composite tool that systematically quantifies the severity of multiple organ systems, whereas the nomogram was constrained by the independent risk factors identified in this specific cohort.
This novel classification has direct implications for clinical management. The identification of Type II ACLF, characterized by a high risk of 28-day mortality (43.2%), should prompt clinicians to consider early escalation of care, including transfer to an intensive care unit (ICU) or a specialized liver failure unit. The high predictive accuracy of the CLIF-C OF score in Type II ACLF means that a rising CLIF-C OF score could serve as a quantitative trigger for such decisions, as well as for initiating discussions regarding LT candidacy. For Type I ACLF, where mortality is lower and the COSSH-ACLF II score performs best, management may initially focus on intensive medical therapy for liver failure and close monitoring for the development of extrahepatic organ failure. If extrahepatic organ failure occurs, necessitating a shift to the CLIF-C OF-based monitoring and a reassessment of prognosis and treatment intensity.
The strength of this study lies in its prospective, multicenter cohort, and its innovative approach to classifying ACLF based on the presentation of hepatic and extrahepatic organ failure at onset, integrating both Eastern and Western diagnostic criteria, which helps to facilitate the fusion and unification of these definitions. However, the study’s limitation is the relatively small sample size of Type II ACLF patients, further prospective, multicenter studies with larger sample sizes are needed to validate these findings.
Conclusion
In conclusion, this new clinical classification approach takes into account the characteristics defined by both Eastern and Western ACLF, and is expected to improve the comparability and interpretability of data in global studies, enhancing global understanding and management of ACLF.
Supplementary Information
Below is the link to the electronic supplementary material.
Abbreviations
- ACLF
Acute-on-chronic liver failure
- APASL
Asia-Pacific Association for the Study of the Liver
- TBIL
Total bilirubin
- INR
International Normalized Ratio
- CMA
Chinese Medical Association
- EASL
European Association for the Study of the Liver
- CLIF-C OF
Chronic Liver Failure-Consortium Organ Failure
- COSSH
Chinese Group on the Study of Severe Hepatitis B
- NACSELD
North American Consortium for the Study of End-Stage Liver Disease
- HE
Hepatic encephalopathy
- ACG
American College of Gastroenterology
- AASLD
American Association for the Study of Liver Diseases
- MELD
Model for End-Stage Liver Disease
- ROC
Receiver operating characteristic curve
Author contributions
Y.C. and X.X conceived and designed the project. Y.C. obtained funding. M.X., Y.W., and W.W. contributed to data collection. M.X. and Y.W. operated the software. W.W.,Y.Y.,H.Z.,M.K.,Z.D.,S.X.,S.Y.,J.L. and T.H. interpreted the data. M.X.,Y.W. and W.W. drafted the manuscript. X.X. and Y.C. revised the manuscript. All the authors approved the final version of the manuscript.
Funding
This study was supported by the Capital’s Funds for the Health Improvement and Research (2024-1-2181).
Data availability
Our data is available upon request. Please contact the correspondence author for access to the data.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Manman Xu, Yu Wu and Wenling Wang contributed equally to this work.
Contributor Information
Xiaogang Xiang, Email: shine-xxg@163.com.
Yu Chen, Email: chybeyond1071@ccmu.edu.cn.
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Associated Data
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Supplementary Materials
Data Availability Statement
Our data is available upon request. Please contact the correspondence author for access to the data.