Early Prediction of Acute-on-Chronic Liver Failure Development in patients with diverse chronic liver diseases

Early Prediction of Acute-on-Chronic Liver Failure Development in patients with diverse chronic liver diseases | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Early Prediction of Acute-on-Chronic Liver Failure Development in patients with diverse chronic liver diseases Yuqiang Shen, Wan Xu, Yang Chen, Shengfen Wen, Qijiong Chen, Shanna Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4039311/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background & aims : Acute-on-chronic liver failure (ACLF) is a syndrome characterized by the acute decompensation of chronic liver disease, leading to organ failures and high short-term mortality. The course of ACLF is dynamic and reversible in a considerable proportion of patients during hospital admission. Early detection and accurate assessment of ACLF are crucial, yet ideal methods remain lacking. Therefore, this study is aimed to develop a new score for predicting the onset of ACLF in patients with diverse chronic liver diseases. Methods : A total of 6188 patients with diverse chronic liver diseases were included in the study. Clinical and laboratory data were collected, and the occurrence of ACLF within 28 days was recorded. Lasso-cox regression was utilized to establish prediction models for the development of ACLF at 7, 14, and 28 days. Findings : Among 5221 patients without ACLF, 477 patients progressed to ACLF within 28 days. Seven predictors were found to be significantly associated with the occurrence of ACLF at 7, 14, and 28 days. The new score had the best discrimination with the c-index of 0.958, 0.944, and 0.938 at 7, 14, and 28 days, respectively, outperforming those of four other scores(CLIF-C-ACLF-Ds, MELD, MELD-Na, and CLIF-C-ADs score, all P<0 .001). The new score also showed improvements in predictive accuracy, time-dependent receiver operating characteristics, probability density function evaluation, and calibration curves, making it highly predictive for the onset of ACLF at all time points. The optimal cut-off value (9.6) differentiated high and low-risk patients of ACLF onset. These findings were further validated in a separate group of patients. Conclusion : A new progressive score, based on seven predictors, has been developed to accurately predict the occurrence of ACLF within 7, 14, and 28 days in patients with diverse chronic liver diseases and might be used to identify high-risk patients, customize follow-up management, and guide escalation of care, prognostication, and transplant evaluation. Health sciences/Gastroenterology Health sciences/Risk factors ACLF Prediction model Diverse chronic liver diseases Progressive score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction ACLF is a common and severe clinical syndrome characterized by acute decompensation(AD) in patients with chronic liver diseases. The main manifestations include serious digestive tract symptoms, rapid aggravation of jaundice, bleeding tendency, and multiple organ failure, with a high short-term mortality of 50%-90%. 1,2 In recent years, various definitions and diagnostic criteria for the syndrome have been proposed by the major international scientific societies. 3 However, due to differences in the etiology, underlying liver diseases, and study population of ACLF in different countries and regions, the definition, diagnostic criteria, clinical classification, and prognosis assessment of ACLF are different or controversial, which brings certain confusion to the diagnosis and treatment of clinicians. The induced factors of ACLF are intricate and diverse, which can be categorized as intrahepatic and extrahepatic factors according to the site of occurrence. Currently, the most common intrahepatic factors include chronic HBV reactivation, acute HAV or HEV infection, alcoholism, hepatotoxic drugs, ischemic hepatitis, etc. The most prevalent extrahepatic factors consist of bacterial infection, upper gastrointestinal bleeding, and surgery. However, up to 40%-50% of ACLF cases still have no identifiable predisposing factors. The current management of ACLF is based on the supportive treatment of organ failures, mainly in an intensive care setting. 4 For selected patients, liver transplantation is the only effective treatment that offers a good long-term prognosis, but the high cost and the impact of post-transplant on patients' physiology, psychology, and life cannot be underestimated. 5,6 Generally, the course of ACLF is dynamic and reversible throughout hospital admission. Most of the patients will have a clear prognosis between days 3 and 7 of hospital admission. 7 Therefore, early identification and accurate assessment of disease conditions are crucial for the clinical decisions of ACLF patients. The present commonly utilized scoring systems include the CTP score, MELD, CLIF-C OFs, and CLIF-C ACLF score. All these scores have good performance in ACLF prognosis assessment and disease severity, but the effect of early warning and accurate assessment of ACLF for patients with various acute and chronic liver diseases was unsatisfactory. 8–12 In a retrospective study of 75,922 patients with compensatory cirrhosis, Karen Y. et al 13 used multivariate logistic regression to develop predictive models (called voice-Penn) for the occurrence of ACLF at 3, 6, and 12 months. They found that albumin(ALB), international normalized ratio(INR), total bilirubin(TBiL), and creatinine(Cr) were significant predictors at each time point. At 3 and 6 months time points, hemoglobin was an additional significant predictor, and age was a significant predictor in the 12-month model. However, the population in this study mainly included male veterans with cirrhosis and a low hepatitis B infection. In a prospective Chinese cohort of hospitalized patients with hepatitis B infection and AD, 33.7% of patients were diagnosed with ACLF. 14 Reactivation of chronic HBV infection has also been reported as an important and modifiable cause for ACLF. 4 , 15−18 Luo et al. 19 developed a new prognostic score based on four predictors(ALT, TB, INR, and ferritin) that could accurately predict the 7/14/28 day onset of ACLF, but only HBV-related patients were enrolled in this study, which might limit the generalization to other etiologies such as alcoholic liver disease. On the whole, the above study population lacks a certain representativeness. This study aimed to identify the clinical characteristics of patients with a high risk of ACLF onset and develop a novel risk prediction model for ACLF development using a large cohort of patients with diverse etiologies of liver disease. Further, we embed it into the electronic medical record system for real-time monitoring and early warning of ACLF in individuals with various acute and chronic liver diseases, thereby facilitating the development of innovative management strategies to prevent disease progression and improve patient prognosis. Patients and Methods Study Design To identify patients at a high risk of progression to ACLF, 6188 patients with various chronic liver diseases were recruited from the Fourth Affiliated Hospital of Zhejiang University School of Medicine between 2018 and 2023. Detailed clinical data and outcomes for all enrolled subjects were collected at admission and during the 28-day observation period from the electronic data capture system and case report forms. These clinical data were analyzed to determine the characteristics associated with ACLF progression. By employing the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression, a predictive score for the onset of ACLF was developed. Patients and Variable Collection We initially screened and enrolled patients aged 18–80 years with chronic liver disease through International Classification of Disease (ICD)-10 chronic liver disease codes and excluded patients who received liver transplantation at admission or did not have follow-up data during the 28-day observation period. We also excluded patients with baseline hepatocyte carcinoma or other malignancies, congestive heart failure, severe chronic kidney disease, pregnancy, receiving immunosuppressive drugs for other reasons, or HIV infection. Patients were divided into two groups: the ACLF group: patients diagnosed with ACLF at baseline; and the non-ACLF group: patients who did not fulfill the diagnostic criteria for ACLF at baseline. Patients with non-ACLF who progressed to ACLF during the 28-day observation period were further defined as the developed ACLF group, while patients who did not progress to ACLF during the 28-day observation period were defined as the non-developed ACLF group. During hospitalization, all patients received integrative treatment. ACLF was diagnosed based on the APASL ACLF criteria. We systematically collected clinical data for each patient, encompassing demographic information, comorbidities, complications, laboratory tests, and prognostic data. Detailed information about variables was provided in the Supplementary Methods section. Modeling Development and Validation Datasets . All data was randomly divided into 80% derivation/20% validation sets with no overlapping topics. This approach ensures the independence and integrity of the datasets for accurate and reliable analysis. Derivation datasets : 4716 subjects were assigned to the training and test datasets following a 9:1 ratio, including 3806 cases of non-developed ACLF and 370 cases of developed ACLF, further cross-validated 10 times to train model parameters. Validation datasets 1045 subjects including 938 non-developed ACLF cases and 107 developed ACLF cases were utilized to evaluate the performances of models. Modeling Approach and Evaluation The methods of identifying the clinical characteristics of enrolled patients and discovering predictors associated with the occurrence of ACLF are described in the Supplementary Methods section. 19 The predictors were used to develop the new score for predicting the occurrence of ACLF by multivariate Cox regression. 19 The performance of the new score was compared with four other established scoring systems (Chronic Liver Failure Consortium [CLIF-C] ACLF development score [CLIF-C ACLF-Ds], 20 Model for End-stage Liver Disease score [MELDs], 21 Model for End-stage Liver Disease-sodium score [MELD-Nas], 21 CLIF Consortium Acute Decompensation score [ CLIF-C ADs] 22 ) in predicting the onset of ACLF, including model discrimination, calibration, and overall performance. 23 Discrimination was assessed by the concordance index (C-index), time-dependent receiver operating characteristic (ROC), and probability density function (PDF); 24,25 calibration was assessed by calibration curves and goodness-of-fit with the Hosmer–Lemeshow statistic test; 23,24 and overall performance was tested using the R 2 and Brier scales. 26 Better performance is indicated by a higher R 2 and a lower Brier scale score. 19 Detailed methods of discrimination with the C-index, calibration and PDF are shown in the Supplementary Methods section. Statistical Analysis Continuous variables were expressed as medians and interquartile ranges, while categorical variables were summarized as counts and percentages. The cut-off value of the continuous variable was selected based on reference value ranges, ROC curves, and expert opinions. The analysis of continuous variables utilized the Two-Sample T-test and the Mann–Whitney U test, while the Chi-square test and the Fisher exact test were employed for categorical variables. A paired T-test and McNemar test were used to compare repeated measurements of continuous and categorical variables, respectively. The normality assumption was evaluated using the Kolmogorov–Smirnov test, and non-normal data was transformed using natural logarithms. All tests were two-sided with significance set at α less than 0·05. Statistical analysis was performed using IBM SPSS Ver.19.0. The Python programming language (Python Sofware Foundation, version 3.6.6, https://www.python.org/downloads/ ) and R V.4.3.2 ( https://www.r-project.org ) were employed for our models. Ethics statement The study protocol was approved by the Clinical Research Ethics Committee of the Fourth Affiliated Hospital of Zhejiang University School of Medicine. Written informed consent was obtained from patients or their legal surrogates before enrollment. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The data used in this study were anonymous before its use. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Result Study populations A total of 7689 patients with various chronic liver diseases were initially screened in this study, out of which 6118 patients meeting the inclusion and exclusion criteria were included for analysis. According to the APASL-ACLF criteria, 897 patients were diagnosed with ACLF, while 5221 patients were diagnosed with non-ACLF at enrollment(Fig. 1 ). The majority of the patients were male (3999, 65.4%), with a median age of 59 years (interquartile range: 49 to 69). The median days of death were found to be shorter in the ACLF group compared to the non-ACLF group (32 days vs.44 days). Short-term mortality rates at different time points (28/90/365 days) were significantly higher in the ACLF group compared to the non-ACLF group (18.8%/35.8%/45.9% vs 2.5%/5.7%/7.4%,p < 0 .001)(Supplementary Table 1). Among the initial cohort of non-ACLF patients (n = 5221), a total of 477 individuals progressed to ACLF within 28 days after enrollment. Patients who developed ACLF during follow-up had a significantly older age by a median difference of eight years when compared to those who did not develop it (66 years vs.58 years, P < 0 .001). Furthermore, there was also an observed increase in 28-day, 90-day, and 365-day mortality rates in the developed ACLF group compared to the non-developed ACLF group(18.4% vs 0.9%,36.6% vs 2.5%, and 41.7% vs3.9%, respectively; P < 0 .001)(Table 1 ). Table 1 The clinical characteristics of patients with non-ACLF including non-developed ACLF and developed ACLF at enrollment. Characteristic Non-ACLF (N = 5221) Non-developed ACLF (N = 4744) developed ACLF(N = 477) P值 Age (years), (median (IQR)) 59(49–69) 58(48–69) 66(54–77) 0.000 Gender, n (%) 0.001 Male 3372(64.6%) 3031(63.9%) 341(71.5%) Female 1849(35.4%) 1713(36.1%) 136(28.5%) Underlying diseases, n (%) Cirrhosis 1853(35.4%) 1561(32.9%) 292(61.2%) < 0.001 Atrial fibrillation 241(4.6%) 193(4.1%) 48(10.1%) 0.000 Pulmonary embolism 59(1.1%) 47(1.0%) 12(2.5%) 0.003 Hypertension 1656(31.7%) 1496(31.5%) 160(33.5%) 0.037 Diabetes 1128(21.6%) 1066(21.2%) 122(25.6) 0.027 Cerebrovascular accident 498(9.5%) 418(8.8%) 80(16.8%) 0.000 NAFLD 4(0.1%) 4(0.1%) 0(0.0%) 0.526 Alcoholic hepatitis 301(5.8%) 235(5.0%) 66(13.8%) 0.000 HAV 1(0.0%) 1(0.0%) 0(0.0%) 0.751 HBV 3506(67.2%) 3396(71.6%) 110(23.1%) 0.000 HCV 57(1.1%) 57(1.2%) 0(0.0%) 0.016 HDV 4(0.1%) 4(0.1%) 0(0.0%) 0.526 HEV 29(0.6%) 21(0.4%) 8(1.7%) 0.001 Drug-induced hepatitis 68(1.3%) 58(1.2%) 10(2.1%) 0.109 Toxic liver disease 0(0.0%) 0(0.0%) 0(0.0%) .. Complications, n (%) Gastrointestinal Hemorrhage 511(9.8%) 381(8.0%) 130(27.3%) 0.000 Bacterial infection 2072(39.7%) 1663(35.1%) 409(85.7%) 0.000 Ascites 674(12.9%) 554(11.7%) 120(25.2%) 0.000 Hepatic encephalopathy 338(6.5%) 242(5.1%) 96(20.1%) 0.000 Organ failure, n (%) Circulatory 419(8.0%) 223(4.7%) 196(41.1%) 0.000 Cerebral 0(0.0%) 0(0.0%) 0(0.0%) .. Kidney 282(5.4%) 231(4.9%) 51(10.7%) 0.000 Lung 759(14.5%) 505(10.6%) 254(53.2%) 0.000 Coagulation 40(0.8%) 31(0.7%) 9(1.9%) 0.003 Liver 135(2.6%) 58(1.2%) 77(16.1%) 0.000 Laboratory data Sodium, mmol/L 140.4(138.2-142.2) 140.4(138.4-142.1) 140.0(135.9–146.0) 0.000 Potassium, mmol/L 3.9(3.6–4.1) 3.9(3.6–4.1) 3.8(3.5–4.2) 0.990 Magnesium, mmol/l 0.8(0.8–0.9) 0.8(0.8–0.9) 0.8(0.7–0.9) 0.584 Calcium, mmol/L 2.2(2.1–2.3) 2.2(2.1–2.3) 2.1(2.0-2.2) 0.000 White blood cell count, 10 9 /L 5.9(4.2–8.1) 5.9(4.2-8.0) 6.4(4.0-9.6) 0.000 Platelet count, 10 9 /L 156.0(83.0-216.0) 164.00(97.0-220.0) 61.00(34.0-103.0) 0.000 Distribution, n (%) 0.000 < 100 1565(30.0%) 1216(25.6%) 349(73.2%) ≥ 100 3656(70.0%) 3528(74.4%) 128(26.8%) NEUT% 66.9(56.0-79.1) 65.8(55.4–77.4) 82.1(69.3–88.9) 0.000 Distribution, n (%) 0.000 < 75 3507(67.4%) 3352(70.9%) 155(32.6%) ≥ 75 1694(32.6%) 1374(29.1%) 320(67.4%) Neutrophil count, 10 9 /L 3.70(2.4–9.2) 3.60(2.4–5.7) 5.0(2.8–8.5) 0.000 Hemoglobin, g/L 116(89.0-134.0) 119.0(94.0-136.0) 78.0(67.0–93.0) 0.000 Distribution, n (%) 0.000 < 90 1339(25.6%) 1004(21.2%) 335(70.2%) ≥ 90 3882(74.4%) 3740(78.8%) 142(29.8%) Total protein, g/L 62.3(56.4–67.3) 62.8(57.3–67.6) 54.6(48.5–60.6) 0.000 Albumin, g/L 34.6(29.9–38.7) 35.2(30.6–39.0) 29.5(26.3–32.0) 0.000 ALT, U/L 22.0(15.0–38.0) 22.00(15.0–37.0) 34.0(19.0–71.0) 0.052 AST, U/L 29.0(21.0–58.0) 28.0(21.0–44.0) 53.0(31.0-130.0) 0.000 AST/ALT 1.2(0.9–1.7) 1.2(0.9–1.3) 1.6(1.0-2.1) 0.000 Alkaline phosphatase, U/L 85.0(68.0-113.0) 85.0(67.0-111.0) 106.0(84.0-144.0) 0.000 γ-Glutamyl transferase, U/L 28.0(17.0–58.0) 28.0(17.0–57.0) 43.0(15.0–78.0) 0.546 Total bilirubin, µmol/L 14.0(9.10–27.3) 13.1(8.8–22.7) 67.4(32.1-126.4) 0.000 Distribution, n (%) 0.000 < 35 4166(80.1%) 4041(85.5%) 125(26.5%) ≥ 35 1033(19.9%) 686(14.5%) 347(73.5%) Total bile acid, µmol/L 7.4(3.5–24.2) 7.00(3.4–19.4) 138.3(60.0-375.0) 0.000 Direct bilirubin, µmol/L 4.2(2.4–11.8) 3.8(2.3–8.7) 38.6(16.2–82.7) 0.000 Blood ammonia, g/L 57.0(37.0-83.8) 55.0(36.5–84.5) 60.0(40.0–81.0) 0.420 Creatinine, mmol/L 70(56–88) 69.0(56.0–86.0) 78.00(61.0-123.0) 0.000 C-reactive protein, mg/L 12.4(2.8–43.0) 9.6(2.3–34.4) 45.00(15.1-115.9) 0.000 Total cholesterol, mmol/L 3.9(3.1–4.7) 3.9(3.1–4.7) 2.3(1.4–2.9) 0.000 HDL-C, mmol/L 1.0(0.8–1.2) 1.0(0.8–1.2) 0.4(0.2–0.6) 0.000 Prothrombin time, S 12.4(11.4–14.5) 12.2(11.3–13.8) 15.8(13.9–18.2) 0.000 Prothrombin activity 1.4(0.9-2.0) 1.5(1.0–2.0) 0.8(0.6-1.0) 0.000 INR 1.1(1.0-1.3) 1.1(1.0-1.2) 1.4(1.2–1.7) 0.000 Glomerular filtration rate 98.0(80.0-118.0) 98.0(81.0-117.0) 96.5(71.0-133.0) 0.878 Urea nitrogen, mmol/L 5.3(4.0-7.2) 5.1(3.9–6.8) 9.8(5.7–16.9) 0.000 Mortality, n 44(26–187) 121(30–367) 29( 24 – 44 ) 0.000 28 days 132(2.5%) 44(0.9%) 88(18.4%) 0.000 90 days 295(5.7%) 120(2.5%) 175(36.6%) 0.000 365 days 385(7.4%) 186(3.9%) 199(41.7%) 0.000 Categoric variables are expressed as % (n); continuous variables are expressed as median [interquartile range]. P values are comparisons between non-developed ACLF and developed ACLF (Student t test, Mann–Whitney U test, chi-squared test, or Fisher exact test). n.,number; NAFLD,Non-alcoholic fatty liver disease; HAV,Hepatitis A virus; HBV,Hepatitis B virus; HCV,Hepatitis C virus; HDV,Hepatitis D virus;HEV,Hepatitis E virus; NEUT%, neutrophilic granulocyte percentage; ALT,Alanine aminotransferase; AST, Aspartate aminotransferase; HDL-C,high density lipoprotein cholesterol; INR,International normalized ratio. Clinical Characteristics of Patients at Admission Table 1 describes the clinical characteristics of the non-ACLF, developed ACLF, and non-developed ACLF groups at admission. 35.4% of non-ACLF patients had a history of cirrhosis, with the proportion of the developed ACLF group significantly higher than that of the non-developed ACLF group(61.2% vs 32.9%, P < 0.001). In patients with chronic liver disease, HBV infection was the most frequent etiology, followed by alcoholic liver disease. Compared with the non-developed ACLF group, the developed ACLF group had a higher rate of alcoholic liver disease(13.8%% vs 5.0%, P < 0.001) and a lower rate of HBV infection(23.1%% vs 71.6%, P < 0.001). A similar trend was also observed between the ACLF group and the non-ACLF group (alcoholic liver disease:18.7% vs 5.8%, P < 0.001; HBV infection:32.0% vs 67.2%, P < 0.001) (Supplementary Table 1). The incidence of cerebrovascular accidents was higher in the developed ACLF group than in the non-developed ACLF group (16.8% vs 8.8%, P < 0.001). Furthermore, the incidence of complications differed significantly between the two groups. In the developed ACLF group, the main complications were bacterial infection (85.7%) and gastrointestinal hemorrhage(27.3%), followed by ascites (25.2%) and hepatic encephalopathy (20.1%). In the non-developed ACLF group, the main complications were bacterial infection (35.1%) and ascites (11.7%). Gastrointestinal hemorrhage and hepatic encephalopathy occurred in only 8.0% and 5.1% of patients, respectively. At baseline, the developed ACLF group exhibited significantly worse laboratory indicators compared with the non-developed ACLF group. Platelet count(PLT), hemoglobin, total protein(TP), ALB, and high-density lipoprotein(HDL-C) levels were all found to be significantly lower than the reference values and the non-developed ACLF group (P < 0.001). Conversely, neutrophil percentage(NUET%), TBiL, direct bilirubin(DBiL), total bile acid (TBA),c-reactive protein(CRP), prothrombin time (PT), AST/ALT, INR, and urea nitrogen(BUN) were markedly higher than those in non-developed ACLF (P < 0.001). For instance, 73.2% and 70.2% of developed ACLF patients presented thrombocytopenia (median:61.0; interquartile range:34.00-103.00) and moderate anemia (median:78.0; Interquartile range:67.00–93.00), respectively. CRP and NUET% were markedly elevated with a median of 45 mg/L and 82.1%, respectively, in the developed ACLF group, indicating higher grades of systemic inflammation. In comparison to the normal reference values, the liver function index of median TBiL increased by 3.9-fold (IQR: 32.1-126.4) and DBiL increased by 5.6-fold (IQR:16.2–82.7). Median plasma bile acids were continuously elevated, up to 13.8-fold (IQR: 60.0-375.0). This indicated significant impairment of liver function and potential cholestasis(Table 1 ). Besides, we contrasted the organ failures of various groups. In the ACLF group, the most prevalent failing organ systems were the liver (42.4%) and respiratory system (35.1%), with coagulation (22.4%) and circulatory system (19.4%) following closely behind(Supplementary Table 1). The most common organ failures among patients in the developed ACLF group were respiratory(53.2%) and circulatory (41.1%) failures, followed by liver(16.1%) and kidney(10.7%) failure. Except for respiratory failure accounting for 10.6%, there were far fewer cases of other organ failure in the non-developed ACLF group, compared with the first two groups(Table 1 ). Changes in clinical features during ACLF progression To observe the clinical changes that occurred in patients during ACLF progression, we compared the clinical characteristics of the developed group at enrollment and onset. Firstly, it took an average of 7 days (interquartile range: 3–15 days) for non-ACLF advancement to become ACLF. Secondly, deteriorating parameters of several intrahepatic and extrahepatic systems, such as liver damage, coagulation dysfunction, renal failure, and hematological disorders, subtly demonstrated the evolving disease course of ACLF development. Laboratory indicators such as AST, TBiL, DBiL, PT, NLR, and BUN all markedly increased and the proportion of PT less than 30 rose from 20.3–26.5% as disease progression. Thirdly, there was a statistically significant rise in the incidence of both liver failure and coagulation failure, which went from 16.1% and 1.9–29.8% and 9.0%, respectively(Supplementary Table 2). Risk factors for ACLF development Independent predictors associated with the development of ACLF were preliminary screened by a univariate Cox proportional hazard regression (p 1.0 or < 0.9; Supplementary Table 3). After removing similar indicators according to the advice of clinical specialists, 38 variables were selected for lasso regression screening (Supplementary Fig. 1). 11 factors ((HDL-C(< 0.5, mmol/L); NEUT(≥ 7,109 /L); TBiL (≥ 35, µmol/L); PT( 7, mmol/L); AST(> 40, U/L); ln (INR); ln(HGB); liver failure; bacterial infection; respiratory failure) were further introduced into the multivariate cox regression analysis, based on the results of lasso analysis and collinearity diagnosis(Supplementary Table 4 and Supplementary Table 5). The result revealed that except AST(> 40, U/L), the remaining 10 factors showed strong correlation with the progression of ACLF((HDL-C (< 0.5, mmol/L) (hazard ratio [HR] 2.82; 95% CI 2.3–3.45; p < 0.005), TBiL (≥ 35, µmol/L) (hazard ratio [HR] 2.48; 95% CI 2.08–2.96; p < 0.005),ln (INR)(hazard ratio [HR] 2.4; 95% CI 1.75–3.3; p < 0.005),NEUT(≥ 7,109 /L) (hazard ratio [HR] 1.63; 95% CI 1.37–1.94; p < 0.005),PLT (< 100, 109 /L) (hazard ratio [HR] 1.55; 95% CI 1.32–1.82; p 7, mmol/L) (hazard ratio [HR] 1.68; 95% CI 1.44–1.97; p < 0.005) and ln(HGB) (hazard ratio [HR] 0.41; 95% CI 0.31–0.54; p < 0.005) ;liver failure(hazard ratio [HR] 1.56; 95% CI 1.17–2.09; p < 0.005);bacterial infection(hazard ratio [HR] 1.59; 95% CI 1.36–1.87; p < 0.005);respiratory failure(hazard ratio [HR] 1.7; 95% CI 1.42–2.03; p < 0.005))(Table 2 ). Ultimately, we identified 7 indicators for modeling considering both early clinical easy acquisition and quantification((HDL-C (< 0.5, mmol/L), TBiL (≥ 35, µmol/L), ln(INR), NEUT(≥ 7,109/L), PLT( 7, mmol/L) and ln(HGB) )(Supplementary Fig. 2). Collinearity diagnosis analysis revealed that the effect of these 7 factors on ACLF development was independent of each other(Supplementary Table 6). Table 2 The multivariate regression for development of ACLF in patients in the derivation cohort. Characteristics HR (95% CI) P value Neutrophil count ≥ 7, 10 9 /L 1.63(1.37–1.94) < 0.005 Platelet count < 100, 10 9 /L 1.55(1.32–1.82) < 0.005 Total bilirubin ≥ 35, µmol/L 2.48(2.08–2.96) < 0.005 HDL-C < 0.5, mmol/L 2.82(2.30–3.45) < 0.005 Ln(HGB) 0.41(0.31–0.54) 7, mmol/L 1.68(1.44–1.97) < 0.005 Ln(INR) 2.40(1.75–3.30) < 0.005 Bacterial infection 1.59(1.36–1.87) < 0.005 Respiratory failure 1.70(1.42–2.03) < 0.005 Liver failure 1.56(1.17–2.09) < 0.005 HDL-C, high density lipoprotein cholesterol; HGB, hemoglobin; INR, International normalized ratio;HR, hazard ratio. Developing a model to Predict the Onset of ACLF Using a multivariate Cox regression approach, a model incorporating seven predictors((HDL-C (< 0.5, mmol/L), TBiL(≥ 35, µmol/L), ln(INR), NEUT(≥ 7,109 /L), PLT( 7, mmol/L) and ln(HGB) ) was established to predict the onset of ACLF at 7 /14/28 days. Their coefficients were used as a relative weight to calculate the corresponding score. The formula used for calculating the prediction score is as follows: score=[NEUT ≥ 7,109/L;1 or 0]×0.49 + [PLT = 35,µmol/L;1 or 0]×0.05 +[HDL-C 7,mmol/L;1 or 0]×0.51 + Ln[INR]×0.87 + 3.40. The probability of ACLF onset can be estimated by the equation P = 1- S(t) = 1 - exp(λ0(t) × exp(VE-ACLF-Dev)). λ0 was the cumulative baseline hazard and the score coefficient estimated by the model fitted for time t. λ0( 7 ) = 0.0373, λ0( 14 ) = 0.0817, λ0( 28 ) = 0.4070. Performance of the new prognostic score In comparison to CLIF-C-ACLF-Ds, MELD, MELD-Na, and CLIF-C-ADs scores, the VE-ACLF-Dev achieved high discriminative performance, as demonstrated by the C-index, ROC curve, and probability density function analysis. The C-indexes of VE-ACLF-Dev were the highest (0.958/0.944/0.938) for predicting the onset of ACLF at 7/14/28 days (CLIF-C ACLF-Ds, 0.870/0.855/0.850; MELDs, 0.884/0.866/0.859; MELD-Na,0.858/0.835/0.827; CLIF-C-ADs,0.627/0.623/0.622)(Supplementary Table 7). In addition, our new score's prediction error rates were far lower than those of the four other scores(CLIF-C ACLF-Ds, 67.7%/ 61.0%/58.5%; MELDs, 63.8%/57.9%/55.7%; MELD-Nas,70.5%/65.9%/63.9%; CLIF-C-ADs, 88.7%/85.1%/83.5%)(Supplementary Fig. 4A). The time-dependent ROC analysis also showed that the VE-ACLF-Dev had the largest area under the ROC curves (0.968/0.949/0.934) compared with the four other scores at 7/14/28 days ( Fig. 2 A, 2 C and Supplementary Table 8). The results of PDF analysis revealed that the number of patients with developed ACLF increased as scores rose, and an obvious distinction was observed between the peaks of patients with developed ACLF and those without. The overlapping coefficients of VE-ACLF-Dev (13.7%/ 23.7%/29.7%) significantly decreased compared to the four other scores (CLIF-C ACLF-Ds, 31.7%/37.9%/40.6%; MELDs, 32.8%/42.3%/47.4%; MELD-Nas, 35.6%/47.5%/55.1%; and CLIF-C-ADs, 74.6%/75.3%/74.1%, all P < 0.05)( Fig. 3 ). The overall performance and calibration of VE-ACLF-Dev were also excellent at each time point. The calibration plot demonstrated a strong agreement between the predicted and actual probability of the onset of ACLF at 7/14/28 days (Hosmer–Lemeshow X2 = 402.12/ 150.38/495.56, all P > 0 .05, Brier: 0.04/0.06/0.13)(Fig. 4 A). Risk Stratification of the New Score By the X-tile plot analysis of the VE-ACLF-Dev, the risk of ACLF onset at 7/14/28 days could be categorized into two strata with high risk (≥ 9.6) and low risk (< 9.6). The incidence of ACLF between the two groups on 7/14/28 days was significantly different (high-risk, 60.8%/68.4%/71.8%; low-risk group,1.9%/3.5%/5.9%, P < 0.001). We further calculated the hazard risk of ACLF development in both groups. Compared with the low-risk group, the hazard ratios in the high-risk group were 36.42/23.34/18.64 (P < 0.001)(Fig. 5 A). Validation of the VE-ACLF-Dev prediction model Out of 1045 patients without ACLF in the validation group, 107 developed ACLF during hospitalization. The derivation and validation groups exhibited similar rates and outcomes of ACLF, as well as comparable clinical characteristics(Supplementary Table 9).In the validation group, the c-indexes of the model for predicting the onset of ACLF at 7/14/28 days all exceeded 0.90 (0.948/0.922/0.908) and significantly outperformed four other generic scores (CLIF-C ACLF-Ds,0.833/0.818/0.807, P < 0.001; MELDs, 0.863/0.843/ 0.830, P < 0.001; MELD-Nas, 0.820/0.801/0.785, P < 0.001; and CLIF-C-ADs, 0.552/0.563/0.562, P < 0 .001)(Supplementary Table 7). Additionally, the prediction error rates of the new score for 7/14/28 day ACLF onset were considerably lower than those of the four other scores (CLIF-C ACLF-Ds, 69.2%/57.44%/52.16%; MELDs, 62.39%/50.53%/45.67%; MELD-Nas,71.32%/61.05%/57.01%; CLIF-C- ADs,88.5%/82.23%/78.96%)(Supplementary Fig. 4B). As demonstrated by the time-dependent ROC analysis, VE-ACLF-Dev had the highest AUC (0.961/0.939/0.912) at 7/14/28 days compared with the four other scores(Fig. 2 B, 2 D and Supplementary Table 8). The PDF analysis also revealed decreased overlapping coefficients of the new score between developed patients and non-developed patients in the validation group (VE-ACLF-Dev, 15.0%/27.4%/35.4%; CLIF-C ACLF-Ds, 36.4%/41.2%/46.8%; MELDs, 31.9%/39.8%/46.6%; MELD-Nas, 37.5%/44.0%/52.8%; CLIF-C-ADs, 85.2%/81.3%/78.5%, all P 0 .05, Brier: 0.04/0.07/0.15)(Fig. 4 B). The hazard ratios of ACLF onset at 7/14/28 days in the high-risk group (19.6/15.5/10.6, P < 0 .001) were also comparable to those in the derivation group compared with the low-risk group and demonstrated a similar separation efficiency in the validation group(Fig. 5 B). Collectively, these results indicated that the VE-ACLF-Dev prediction model to predict ACLF occurrence was proven to be statistically robust. Discussion Main findings In this study, we comprehensively compared the clinical characteristics of developed ACLF and non-developed ACLF groups and determined ten independent factors most relevant for ACLF progression, including bacterial infection, liver failure, respiratory failure, and clinical indicators such as HDL-c, TB, INR, NEUT, PLT, BUN, and HGB. Previous studies showed that bacterial infection was not only the most frequent extrahepatic precipitant for the development of ACLF 18,27,28 , but also the cause of short-term high mortality in ACLF, with remarkably higher rates compared to other causes of damage 27,29–31 . Our study further confirmed that bacterial infection was the only extrahepatic trigger associated with progression to ACLF, enforcing its central role of bacterial infection in ACLF extrahepatic precipitating events. In addition to precipitating factors, our study also identified the presence of systemic inflammation as a contributing factor for the progression of ACLF, with NEUT being an independent risk factor. Numerous studies have reported higher NEUT in ACLF patients compared to healthy controls, possibly due to increased levels of circulating granulocyte colony-stimulating factor (G-CSF) 32 . Moreover, neutrophil dysfunction is highly clinically relevant, as impaired respiratory burst and phagocytic activity correlate with a higher risk of infection, organ failure, and mortality 33,34 . Organ failure is a hallmark of ACLF and can include renal failure, now called Acute Kidney Injury(AKI), respiratory and circulatory failure 35 . A reversible acute increase in BUN was the classical biomarker of AKI which was considered a strong predictor of poor survival in the short and long term of ACLF 36–38 . According to our research, the BUN of the developed ACLF was markedly elevated compared with the non-developed ACLF, indicating that BUN could serve as an early warning indicator of ACLF. At the same time, our findings also suggested that the prevalence of liver failure and respiratory failure were higher in the developed ACLF group. Our study showed that platelet levels were lower in developed-ACLF patients, which was consistent with previous findings. Up to 75% of patients with advanced liver disease or cirrhosis experience thrombocytopenia, and its severity is usually directly correlated with the degree of liver failure. 39 Studies have shown that platelet levels is an independent prognostic factor in ACLF patients, and depletion or redistribution is responsible for thrombocytopenia. 40–42 Furthermore, we observed that patients in the developed ACLF group suffered from moderate anemia. Several studies have reported anemia as a common complication in ACLF patients with various contributing factors identified, such as autoimmune disorders, decrease in hematopoietic capability, and release of tumor necrosis factor, cytokine, and endotoxins. 35,43 Another study by Cheng et al. also found that accelerated suicidal cell death or apoptosis could contribute to anemia in patients with HBV-ACLF. 44 Besides, patients with liver failure would experience liver synthesis decreased and metabolic dysfunction, as well as coagulation dysfunction, resulting in malnutrition and chronic occult bleeding. Due to the patient being in a severe state of anemia, it may lead to a decrease in hemoglobin and platelets. In ACLF patients, bilirubin was reported to trigger anemia by inducing erythrocyte death 45,46 . Moreover, it has been found that hemoglobin could not only identify individuals at high risk of developing ACLF but also serve as a strong predictor in the survival rate of patients with cirrhosis, 47 making it a potential target for ACLF prevention. Other predictive variables, such as TB and INR, which are well-established indicators of liver and coagulation dysfunction/failure, have been extensively utilized in various diagnostic criteria and prognostic scores for patients with ACLF. During the development and progression of ACLF, hepatocytes undergo varying degrees of degeneration and necrosis, leading to the disorder of coagulation factor synthesis. This is manifested as prolonged coagulation time and elevated bilirubin levels, both of which are important indicators of liver reserve function. Low levels of HDL-C are commonly observed in patients with chronic liver disease and are inversely correlated with disease severity. 48 Previous studies have demonstrated that besides predicting poor outcomes in HBV-ACLF patients, low high-density lipoprotein cholesterol levels also played an important role in the pathophysiology of systemic inflammation driving the onset of ACLF. 49,50 Consistent with these findings, our study also found that HDL-C < 0.5 mmol/L was a robust predictor for ACLF progression. Comparison with previous scores ACLF is a life-threatening clinical syndrome with a high short-term mortality. 7 Despite liver transplantation being considered to be the most effective treatment for ACLF, its limited availability due to organ shortage and the high cost of the procedure hinders its widespread application. 51,52 Furthermore, it is widely acknowledged that ACLF is a dynamic syndrome that can be reversible in a considerable proportion of patients. 4 This feature is closely related to the improvement of prognosis, highlighting the importance of dynamic assessment. A recent PREDICT study identified that pre-ACLF patients could develop ACLF within 3-month, with a mortality rate of 53.7% at three months and 67.4% at one year. 20 Under such circumstances, both early diagnosis of ACLF and accurate prediction of prognosis are critical for distinguishing patients who require transplantation from those who can survive with intensive medical care alone. In this study, we observed that approximately 9.13%(477/5221) of non-ACLF patients developed ACLF during hospitalization within 28 days, among which 51.8% (247/477) progressed within 7 days and 72.7% (347/477) progressed within 14 days based on APASL-ACLF criteria. These findings further underscore the significance of predicting the onset of ACLF at different periods after admission and may facilitate timely interventions to prevent progression. Thus, utilizing the framework of trigger factors-systemic inflammation-organ dysfunction/failure, we have developed a concise and accurate prognostic score to predict the progression of ACLF. In comparison with the other four scores(MELD, MELD-Na, CLIF-C-ADs, and CLIF-C-ACLF-Ds), our model demonstrated superior discriminability and overall performance. While MELD, MELD-Na, and CLIF-C-ADs have been extensively used to predict the mortality and prognosis in patients with various severe liver diseases with strong predictive value, their sensitivity and accuracy in predicting ACLF development are comparatively lower. 8,10 Although the CLIF-C ACLF-Ds was specifically designed to forecast ACLF occurrence in the AD population within 3 months, based on the PREDICT study, it did not exhibit greater accuracy than traditional clinical scores. 20 Our score could effectively screen high-risk patients prone to developing ACLF, thereby providing valuable clinical guidance for managing and treating patients with chronic liver disease. Meanwhile, significant differences in the definition of ACLF between Eastern and Western countries have led to variations in the potential etiology, precipitating events, and prognosis of ACLF across different regions. 53 Consequently, recent years have witnessed a surge in research efforts devoted to developing diagnostic criteria and prognostic models specifically for single-etiology ACLF. For instance, the COSSH study conducted in China has provided a valuable exploration of the diagnostic criteria and prognosis of HBV-ACLF. 24 However, there remains a clinical need for a progression model capable of identifying high-risk ACLF populations among patients with different etiologies and states of chronic liver diseases. In this study, both the various etiologies and the state of chronic liver disease were included in the new model, which significantly expanded the clinical applicability of the model. Limitations Nevertheless, our study has several limitations. Firstly, this was a retrospective study and there might be selective bias. However, its large sample size, strict inclusion and exclusion criteria, and low data loss helped to mitigate the likelihood of bias. Secondly, previous study has reported that HBV reactivation and superimposed infection on HBV were the main causes of ACLF. 18,54 However, due to the lack of dynamic HBV-DNA and HBV coinfection data, the impact of viral response and multiple infections of HBV on ACLF development could not be investigated in this study. Thirdly, our data mainly came from the Ningbo area, so future studies should aim to externally validate our models in other cohorts from different regions. Conclusion In summary, early detection of patients at high risk of progression to ACLF is essential to lowering the prevalence and fatality rate of ACLF. Our newly created predictive model was not only applicable to diverse patients with chronic liver disease but also outperformed MELD, MELD-Na, CLIF-C-ADs, and CLIF-C-ACLF-Ds scores. This model can help to mitigate risk factors, identify high-risk patients, and customize follow-up management, which is especially crucial for improving patient outcomes and reducing the burden of ACLF. Declarations Acknowledgments We would like to thank the medical staffs in the Department of Hepatology for their support in providing information about patients. We also thank the patients for their willingness to participate. This research was supported by the Spring City Plan: the High-level Talent Promotion and Training Project of Kunming (Grant No.2022SCP002), Key R&D Program of Zhejiang (2023C03101). Author Contributors Study concept and design: Bin Ju, Wan Xu; Literature search: Wan Xu, Bin Ju; Funding acquisition: Yuqiang Shen, Li Li; Acquisition of data and technique support: Yuqiang Shen, Shengfen Wen, Yang Chen; Data analysis and interpretation: Wan Xu, Shengfen Wen; Figures drawing: Shengfen Wen, Wan Xu; Drafting manuscript: Wan Xu; Critical revision of the manuscript: Wan Xu, Yuqiang Shen, Bin Ju. Access and verification of the underlying data: Yuqiang Shen, Shengfen Wen, Yang Chen. All authors read and approved the final manuscript. Data sharing statement We are unable to provide access to our datasets for privacy reasons. The protocol and statistical analysis methods used in the study can be requested directly from the corresponding author after approval. Competing interests All authors have no conflict of interest related to this publication. References Wu, T. et al. Development of diagnostic criteria and a prognostic score for hepatitis B virus-related acute-on-chronic liver failure. Gut 67, 2181–2191, doi: 10.1136/gutjnl-2017-314641 (2018). Ngu, N. L. Y., Flanagan, E., Bell, S. & Le, S. T. Acute-on-chronic liver failure: Controversies and consensus. World J Gastroenterol 29, 232–240, doi: 10.3748/wjg.v29.i2.232 (2023). Sarin, S. K. et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update. Hepatol Int 13, 353–390, doi: 10.1007/s12072-019-09946-3 (2019). Kumar, R., Mehta, G. & Jalan, R. Acute-on-chronic liver failure. Clin Med (Lond) 20, 501–504, doi: 10.7861/clinmed.2020-0631 (2020). Li, X., Zhang, L., Pu, C. & Tang, S. Liver transplantation in Acute-on-Chronic liver failure: Timing of transplantation and selection of patient population. Front Med (Lausanne) 9, 1030336, doi: 10.3389/fmed.2022.1030336 (2022). Schulz, M. S., Gu, W., Schnitzbauer, A. A. & Trebicka, J. Liver Transplantation as a Cornerstone Treatment for Acute-On-Chronic Liver Failure. Transpl Int 35, 10108, doi: 10.3389/ti.2022.10108 (2022). Hernaez, R., Sola, E., Moreau, R. & Gines, P. Acute-on-chronic liver failure: an update. Gut 66, 541–553, doi: 10.1136/gutjnl-2016-312670 (2017). Blasco-Algora, S., Masegosa-Ataz, J., Gutierrez-Garcia, M. L., Alonso-Lopez, S. & Fernandez-Rodriguez, C. M. Acute-on-chronic liver failure: Pathogenesis, prognostic factors and management. World J Gastroenterol 21, 12125–12140, doi: 10.3748/wjg.v21.i42.12125 (2015). Rashed, E. & Soldera, J. CLIF-SOFA and CLIF-C scores for the prognostication of acute-on-chronic liver failure and acute decompensation of cirrhosis: A systematic review. World J Hepatol 14, 2025–2043, doi: 10.4254/wjh.v14.i12.2025 (2022). Zhang, Y., Nie, Y., Liu, L. & Zhu, X. Assessing the prognostic scores for the prediction of the mortality of patients with acute-on-chronic liver failure: a retrospective study. PeerJ 8, e9857, doi: 10.7717/peerj.9857 (2020). Chen, C. et al. A new dyslipidemia-based scoring model to predict transplant-free survival in patients with hepatitis E-triggered acute-on-chronic liver failure. Lipids Health Dis 22, 80, doi: 10.1186/s12944-023-01826-y (2023). Li, Q. et al. Noninvasive models for predicting poor prognosis of chronic HBV infection patients precipitating acute HEV infection. Sci Rep 10, 2753, doi: 10.1038/s41598-020-59670-4 (2020). Xiao, K. Y. et al. Models for acute on chronic liver failure development and mortality in a veterans affairs cohort. Hepatol Int 14, 587–596, doi: 10.1007/s12072-020-10060-y (2020). Li, H. et al. Characteristics, Diagnosis and Prognosis of Acute-on-Chronic Liver Failure in Cirrhosis Associated to Hepatitis B. Sci Rep 6, 25487, doi: 10.1038/srep25487 (2016). Zhu, Y. et al. Hepatitis B Virus Reactivation Increased the Risk of Developing Hepatic Failure and Mortality in Cirrhosis With Acute Exacerbation. Front Microbiol 13, 910549, doi: 10.3389/fmicb.2022.910549 (2022). Lei, J. H., Peng, F., Chen, Z. & Xiao, X. Q. Is HBV viral load at admission associated with development of acute-on-chronic liver failure in patients with acute decompensation of chronic hepatitis B related cirrhosis. BMC Infect Dis 19, 363, doi: 10.1186/s12879-019-3988-1 (2019). Yao, N., He, Y., Wu, Y., Wang, F. & Tian, Z. Prognostic value of plasma level of superoxide dismutase in HBV-related acute-on-chronic liver failure. BMC Gastroenterol 22, 312, doi: 10.1186/s12876-022-02371-1 (2022). Wang, T. et al. Role of precipitants in transition of acute decompensation to acute-on-chronic liver failure in patients with HBV-related cirrhosis. JHEP Rep 4, 100529, doi: 10.1016/j.jhepr.2022.100529 (2022). Luo, J. et al. Predicting the Onset of Hepatitis B Virus-Related Acute-on-Chronic Liver Failure. Clin Gastroenterol Hepatol 21, 681–693, doi: 10.1016/j.cgh.2022.03.016 (2023). Trebicka, J. et al. The PREDICT study uncovers three clinical courses of acutely decompensated cirrhosis that have distinct pathophysiology. J Hepatol 73, 842–854, doi: 10.1016/j.jhep.2020.06.013 (2020). Mahmud, N., Hubbard, R. A., Kaplan, D. E., Taddei, T. H. & Goldberg, D. S. Risk prediction scores for acute on chronic liver failure development and mortality. Liver Int 40, 1159–1167, doi: 10.1111/liv.14328 (2020). Jalan, R. et al. The CLIF Consortium Acute Decompensation score (CLIF-C ADs) for prognosis of hospitalised cirrhotic patients without acute-on-chronic liver failure. J Hepatol 62, 831–840, doi: 10.1016/j.jhep.2014.11.012 (2015). Alba, A. C. et al. Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature. JAMA 318, 1377–1384, doi: 10.1001/jama.2017.12126 (2017). Li, J. et al. Development and validation of a new prognostic score for hepatitis B virus-related acute-on-chronic liver failure. J Hepatol 75, 1104–1115, doi: 10.1016/j.jhep.2021.05.026 (2021). Kang, L., Chen, W., Petrick, N. A. & Gallas, B. D. Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med 34, 685–703, doi: 10.1002/sim.6370 (2015). Steyerberg, E. W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128–138, doi: 10.1097/EDE.0b013e3181c30fb2 (2010). Fernandez, J. et al. Bacterial and fungal infections in acute-on-chronic liver failure: prevalence, characteristics and impact on prognosis. Gut 67, 1870–1880, doi: 10.1136/gutjnl-2017-314240 (2018). Wong, F. et al. Clinical features and evolution of bacterial infection-related acute-on-chronic liver failure. J Hepatol 74, 330–339, doi: 10.1016/j.jhep.2020.07.046 (2021). Van der Merwe, S., Chokshi, S., Bernsmeier, C. & Albillos, A. The multifactorial mechanisms of bacterial infection in decompensated cirrhosis. J Hepatol 75 Suppl 1, S82-S100, doi: 10.1016/j.jhep.2020.11.029 (2021). Zanetto, A. et al. Severity of systemic inflammation is the main predictor of ACLF and bleeding in individuals with acutely decompensated cirrhosis. J Hepatol 78, 301–311, doi: 10.1016/j.jhep.2022.09.005 (2023). Mucke, M. M. et al. Bacterial infection-triggered acute-on-chronic liver failure is associated with increased mortality. Liver Int 38, 645–653, doi: 10.1111/liv.13568 (2018). Casulleras, M., Zhang, I. W., Lopez-Vicario, C. & Claria, J. Leukocytes, Systemic Inflammation and Immunopathology in Acute-on-Chronic Liver Failure. Cells 9, doi: 10.3390/cells9122632 (2020). Mookerjee, R. P. et al. Neutrophil dysfunction in alcoholic hepatitis superimposed on cirrhosis is reversible and predicts the outcome. Hepatology 46, 831–840, doi: 10.1002/hep.21737 (2007). Taylor, N. J. et al. The severity of circulating neutrophil dysfunction in patients with cirrhosis is associated with 90-day and 1-year mortality. Aliment Pharmacol Ther 40, 705–715, doi: 10.1111/apt.12886 (2014). Piano, S. et al. Incidence, predictors and outcomes of acute-on-chronic liver failure in outpatients with cirrhosis. J Hepatol 67, 1177–1184, doi: 10.1016/j.jhep.2017.07.008 (2017). Weil, D. et al. Prognosis of cirrhotic patients admitted to intensive care unit: a meta-analysis. Ann Intensive Care 7, 33, doi: 10.1186/s13613-017-0249-6 (2017). Belcher, J. M. et al. Association of AKI with mortality and complications in hospitalized patients with cirrhosis. Hepatology 57, 753–762, doi: 10.1002/hep.25735 (2013). Wong, F. et al. New consensus definition of acute kidney injury accurately predicts 30-day mortality in patients with cirrhosis and infection. Gastroenterology 145, 1280–1288 e1281, doi: 10.1053/j.gastro.2013.08.051 (2013). Scharf, R. E. Thrombocytopenia and Hemostatic Changes in Acute and Chronic Liver Disease: Pathophysiology, Clinical and Laboratory Features, and Management. J Clin Med 10, doi: 10.3390/jcm10071530 (2021). Peng, H. et al. A prognostic model of acute-on-chronic liver failure based on sarcopenia. Hepatol Int 16, 964–972, doi: 10.1007/s12072-022-10363-2 (2022). Xia, Q. et al. A modified MELD model for Chinese pre-ACLF and ACLF patients and it reveals poor prognosis in pre-ACLF patients. PLoS One 8, e64379, doi: 10.1371/journal.pone.0064379 (2013). Ye, Q. X. et al. Short-term prognostic factors for hepatitis B virus-related acute-on-chronic liver failure. World J Clin Cases 10, 8186–8195, doi: 10.12998/wjcc.v10.i23.8186 (2022). Scheiner, B. et al. Prevalence of and risk factors for anaemia in patients with advanced chronic liver disease. Liver Int 40, 194–204, doi: 10.1111/liv.14229 (2020). Mei, C. et al. Increased suicidal erythrocyte death in patients with hepatitis B-related acute-on-chronic liver failure. Am J Physiol Gastrointest Liver Physiol 323, G9-G20, doi: 10.1152/ajpgi.00050.2020 (2022). Khan, N. M. & Poduval, T. B. Bilirubin augments radiation injury and leads to increased infection and mortality in mice: molecular mechanisms. Free Radic Biol Med 53, 1152–1169, doi: 10.1016/j.freeradbiomed.2012.07.007 (2012). Lang, E. et al. Conjugated bilirubin triggers anemia by inducing erythrocyte death. Hepatology 61, 275–284, doi: 10.1002/hep.27338 (2015). Zaccherini, G. et al. Prediction of nosocomial acute-on-chronic liver failure in patients with cirrhosis admitted to hospital with acute decompensation. JHEP Rep 1, 270–277, doi: 10.1016/j.jhepr.2019.07.005 (2019). Trieb, M. et al. HDL-related biomarkers are robust predictors of survival in patients with chronic liver failure. J Hepatol 73, 113–120, doi: 10.1016/j.jhep.2020.01.026 (2020). Claria, J. et al. Systemic inflammation in decompensated cirrhosis: Characterization and role in acute-on-chronic liver failure. Hepatology 64, 1249–1264, doi: 10.1002/hep.28740 (2016). Zhang, Y., Chen, P., Zhang, Y., Nie, Y. & Zhu, X. Low high-density lipoprotein cholesterol levels predicting poor outcomes in patients with hepatitis B virus-related acute-on-chronic liver failure. Front Med (Lausanne) 9, 1001411, doi: 10.3389/fmed.2022.1001411 (2022). Rahimi, R. S. & Rockey, D. C. Acute on chronic liver failure: definitions, treatments and outcomes. Curr Opin Gastroenterol 32, 172–181, doi: 10.1097/MOG.0000000000000265 (2016). Luo, J. et al. Acute-on-chronic liver failure: far to go-a review. Crit Care 27, 259, doi: 10.1186/s13054-023-04540-4 (2023). Verdelho, M. et al. Acute-on-Chronic Liver Failure: A Portuguese Single-Center Reference Review. GE Port J Gastroenterol 25, 18–23, doi: 10.1159/000478988 (2018). Hu, C. et al. Serum ammonia is a strong prognostic factor for patients with acute-on-chronic liver failure. Sci Rep 10, 16970, doi: 10.1038/s41598-020-73603-1 (2020). Additional Declarations No competing interests reported. Supplementary Files SupplementalData.docx Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Oct, 2024 Reviews received at journal 11 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers agreed at journal 21 May, 2024 Reviews received at journal 28 Apr, 2024 Reviewers agreed at journal 17 Apr, 2024 Reviewers invited by journal 24 Mar, 2024 Editor assigned by journal 24 Mar, 2024 Editor invited by journal 21 Mar, 2024 Submission checks completed at journal 21 Mar, 2024 First submitted to journal 08 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4039311","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283253064,"identity":"508d9db4-cbcf-434f-8345-61f317637b94","order_by":0,"name":"Yuqiang Shen","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuqiang","middleName":"","lastName":"Shen","suffix":""},{"id":283253067,"identity":"f15b47e8-6648-4042-a4ce-e939cf432ffa","order_by":1,"name":"Wan Xu","email":"","orcid":"","institution":"Hangzhou Xiaoshan District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Wan","middleName":"","lastName":"Xu","suffix":""},{"id":283253069,"identity":"649e22ee-9253-482d-a9d5-e0d5bad676fd","order_by":2,"name":"Yang Chen","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Chen","suffix":""},{"id":283253071,"identity":"d48eb6f7-f823-4cf7-b404-20db05f61ad7","order_by":3,"name":"Shengfen Wen","email":"","orcid":"","institution":"SanOmics AI Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Shengfen","middleName":"","lastName":"Wen","suffix":""},{"id":283253072,"identity":"9c089dc6-89da-440c-8bdd-45d0f84ac249","order_by":4,"name":"Qijiong Chen","email":"","orcid":"","institution":"Hangzhou Xiaoshan District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qijiong","middleName":"","lastName":"Chen","suffix":""},{"id":283253073,"identity":"cc03e1ad-5c3d-4267-b387-685ed0b4000f","order_by":5,"name":"Shanna Liu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shanna","middleName":"","lastName":"Liu","suffix":""},{"id":283253074,"identity":"4d95c49a-d1b5-44c3-af1e-929c03a3dee8","order_by":6,"name":"Xinjian Zhu","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinjian","middleName":"","lastName":"Zhu","suffix":""},{"id":283253075,"identity":"8375ff62-c4e5-4210-a156-e3b1e10c2d90","order_by":7,"name":"Li Li","email":"","orcid":"","institution":"The First People's Hospital of Kunming","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""},{"id":283253076,"identity":"09927e52-29a0-465b-8a73-90c9ac1b548b","order_by":8,"name":"Bin Ju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACxgYehgMMQMTPzHz4AWlaJNvZ0gyItIcHRBxgMDjPoyBBlAbmGbkHDxcw3LE3PszDYMBQYxNN2GEz8hIOz2B4xmx2mPfAA4ZjabkNhLXkGADNP8xmdpgvwYCx4TDxWniMm3kMJEjSImHATLSWnncJIC0GEoeBgZxAjF8M23MPfwZqsefvP3z4wYcaGyK0gFQw/oPyEggpBwF5YhSNglEwCkbBCAcAwik9XMSop9YAAAAASUVORK5CYII=","orcid":"","institution":"SanOmics AI Co., Ltd","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Ju","suffix":""}],"badges":[],"createdAt":"2024-03-08 07:56:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4039311/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4039311/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-79486-w","type":"published","date":"2024-11-15T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53491296,"identity":"c535abac-8647-48f7-9ed8-a773cb47e140","added_by":"auto","created_at":"2024-03-26 15:46:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2599764,"visible":true,"origin":"","legend":"\u003cp\u003eScreening, enrollment and classification flow chart of patients. ACLF, acute-on-chronic liver failure; HIV, human immunodeficiency virus.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/5d830c9d76e36de44c27ddd4.png"},{"id":53491297,"identity":"297e9a75-73f0-45ef-91ef-b1403c91682e","added_by":"auto","created_at":"2024-03-26 15:46:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5437815,"visible":true,"origin":"","legend":"\u003cp\u003eTime-dependent ROC curves of the new score and the five other scores in the derivation group and validation group.(A) Receiver operating characteristic (ROC) curves for predicting development of ACLF at 7 day,14 day and 28 day in derivation cohort;(B) Receiver operating characteristic (ROC) curves for predicting development of ACLF at 7 day , 14 day and 28 day in validation cohort;(C)time-dependent ROC curves of 28 day since admission in the derivation group; (D)time-dependent ROC curves of 28 day since admission in the validation group;ROC,receiver operating characteristics; AUROC, area under the ROC curve;ACLF, acute-on-chronic liver \u0026nbsp;failure;AE-ACLF-Dev,Various Etiologies ACLF Development Prediction; CLIF-C ACLF-Ds, Chronic Liver Failure Consortium acute-on-chronic liver failure development score; MELDs, Model for End-Stage Liver Disease score; MELD-Nas, Model for End-Stage Liver Disease-sodium score; CLIF-C-ADs, CLIF Consortium Acute Decompensation score.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/dc72359e73c00dcfca5a6ea9.png"},{"id":53491279,"identity":"5df3a1aa-a979-4b0b-8e63-261888eb52c2","added_by":"auto","created_at":"2024-03-26 15:46:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1352676,"visible":true,"origin":"","legend":"\u003cp\u003eProbability density function of AE-ACLF-Dev for predicting 7-/14-/28-day ACLF onset in the derivation group. *P \u0026lt; 0.05 (Student t test) for comparisons of the overlapping coefficient between the AE-ACLF-Dev and the other scores.\u003csup\u003e#\u003c/sup\u003eP \u0026lt; 0 .001 (Mann–Whitney U test) for comparisons of scores between developed ACLF and non-developed ACLF patients. ACLF, acute-on-chronic liver failure;\u003c/p\u003e\n\u003cp\u003eAE-ACLF-Dev,Various Etiologies ACLF Development Prediction; CLIF-C ACLF-Ds, Chronic Liver Failure Consortium acute-on-chronic liver failure development score; MELDs, Model for End-Stage Liver Disease score; MELD-Nas, Model for End-Stage Liver Disease-sodium score; CLIF-C-ADs, CLIF Consortium Acute Decompensation score.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/de2024adfdb8badcf14527f4.png"},{"id":53491295,"identity":"f30a2809-d1ac-4c17-aee5-6c3b4d80a655","added_by":"auto","created_at":"2024-03-26 15:46:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1989992,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot of the Various Etiologies ACLF Development Prediction (AE-ACLF-Dev). (A) The derivation group. (B) The validation group. R\u003csup\u003e2\u003c/sup\u003e and the Brier score: a higher R\u003csup\u003e2 \u003c/sup\u003evalue and a lower Brier score indicated better performance.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/7bced17e10fac93ed0cc37df.png"},{"id":53491298,"identity":"357edb7a-4664-4661-bcf0-e652db40b607","added_by":"auto","created_at":"2024-03-26 15:46:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1141986,"visible":true,"origin":"","legend":"\u003cp\u003eRisk stratification of Various Etiologies ACLF Development Prediction (AE-ACLF-Dev).\u003c/p\u003e\n\u003cp\u003e(A) Cumulative incidence of progression to ACLF at 7/14/28 days stratified according to the AE-ACLF-Dev classification rule (high/low risk: AE-ACLF-Dev 9.6/\u0026lt;9.6) in the derivation group. P \u0026lt;0 .001 (log-rank test) for comparison of the cumulative incidence. (B) Cumulative incidence of progression in the validation group. HR, hazard ratio.\u003c/p\u003e","description":"","filename":"figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/9f466602c339a4f5e355b445.png"},{"id":53491380,"identity":"1f21a430-2d0b-4ddf-afdc-b449032576ce","added_by":"auto","created_at":"2024-03-26 15:46:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2617127,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/341f9b6e-cf96-4c0d-8170-0a5ca719c2d4.pdf"},{"id":53491302,"identity":"d3248725-f968-424a-9666-1c8be0bd1416","added_by":"auto","created_at":"2024-03-26 15:46:17","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":654285,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalData.docx","url":"https://assets-eu.researchsquare.com/files/rs-4039311/v1/ac2f433d9b027a3a263e8305.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Prediction of Acute-on-Chronic Liver Failure Development in patients with diverse chronic liver diseases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eACLF is a common and severe clinical syndrome characterized by acute decompensation(AD) in patients with chronic liver diseases. The main manifestations include serious digestive tract symptoms, rapid aggravation of jaundice, bleeding tendency, and multiple organ failure, with a high short-term mortality of 50%-90%.\u003csup\u003e1,2\u003c/sup\u003e In recent years, various definitions and diagnostic criteria for the syndrome have been proposed by the major international scientific societies. \u003csup\u003e3\u003c/sup\u003e However, due to differences in the etiology, underlying liver diseases, and study population of ACLF in different countries and regions, the definition, diagnostic criteria, clinical classification, and prognosis assessment of ACLF are different or controversial, which brings certain confusion to the diagnosis and treatment of clinicians.\u003c/p\u003e \u003cp\u003eThe induced factors of ACLF are intricate and diverse, which can be categorized as intrahepatic and extrahepatic factors according to the site of occurrence. Currently, the most common intrahepatic factors include chronic HBV reactivation, acute HAV or HEV infection, alcoholism, hepatotoxic drugs, ischemic hepatitis, etc. The most prevalent extrahepatic factors consist of bacterial infection, upper gastrointestinal bleeding, and surgery. However, up to 40%-50% of ACLF cases still have no identifiable predisposing factors.\u003c/p\u003e \u003cp\u003eThe current management of ACLF is based on the supportive treatment of organ failures, mainly in an intensive care setting.\u003csup\u003e4\u003c/sup\u003e For selected patients, liver transplantation is the only effective treatment that offers a good long-term prognosis, but the high cost and the impact of post-transplant on patients' physiology, psychology, and life cannot be underestimated.\u003csup\u003e5,6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGenerally, the course of ACLF is dynamic and reversible throughout hospital admission. Most of the patients will have a clear prognosis between days 3 and 7 of hospital admission.\u003csup\u003e7\u003c/sup\u003e Therefore, early identification and accurate assessment of disease conditions are crucial for the clinical decisions of ACLF patients. The present commonly utilized scoring systems include the CTP score, MELD, CLIF-C OFs, and CLIF-C ACLF score. All these scores have good performance in ACLF prognosis assessment and disease severity, but the effect of early warning and accurate assessment of ACLF for patients with various acute and chronic liver diseases was unsatisfactory.\u003csup\u003e8\u0026ndash;12\u003c/sup\u003e In a retrospective study of 75,922 patients with compensatory cirrhosis, Karen Y. et al\u003csup\u003e13\u003c/sup\u003e used multivariate logistic regression to develop predictive models (called voice-Penn) for the occurrence of ACLF at 3, 6, and 12 months. They found that albumin(ALB), international normalized ratio(INR), total bilirubin(TBiL), and creatinine(Cr) were significant predictors at each time point. At 3 and 6 months time points, hemoglobin was an additional significant predictor, and age was a significant predictor in the 12-month model. However, the population in this study mainly included male veterans with cirrhosis and a low hepatitis B infection. In a prospective Chinese cohort of hospitalized patients with hepatitis B infection and AD, 33.7% of patients were diagnosed with ACLF.\u003csup\u003e14\u003c/sup\u003e Reactivation of chronic HBV infection has also been reported as an important and modifiable cause for ACLF.\u003csup\u003e4\u003c/sup\u003e,\u003csup\u003e15\u0026minus;18\u003c/sup\u003e Luo et al.\u003csup\u003e19\u003c/sup\u003e developed a new prognostic score based on four predictors(ALT, TB, INR, and ferritin) that could accurately predict the 7/14/28 day onset of ACLF, but only HBV-related patients were enrolled in this study, which might limit the generalization to other etiologies such as alcoholic liver disease. On the whole, the above study population lacks a certain representativeness.\u003c/p\u003e \u003cp\u003eThis study aimed to identify the clinical characteristics of patients with a high risk of ACLF onset and develop a novel risk prediction model for ACLF development using a large cohort of patients with diverse etiologies of liver disease. Further, we embed it into the electronic medical record system for real-time monitoring and early warning of ACLF in individuals with various acute and chronic liver diseases, thereby facilitating the development of innovative management strategies to prevent disease progression and improve patient prognosis.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eTo identify patients at a high risk of progression to ACLF, 6188 patients with various chronic liver diseases were recruited from the Fourth Affiliated Hospital of Zhejiang University School of Medicine between 2018 and 2023. Detailed clinical data and outcomes for all enrolled subjects were collected at admission and during the 28-day observation period from the electronic data capture system and case report forms. These clinical data were analyzed to determine the characteristics associated with ACLF progression. By employing the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression, a predictive score for the onset of ACLF was developed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients and Variable Collection\u003c/h2\u003e \u003cp\u003eWe initially screened and enrolled patients aged 18\u0026ndash;80 years with chronic liver disease through International Classification of Disease (ICD)-10 chronic liver disease codes and excluded patients who received liver transplantation at admission or did not have follow-up data during the 28-day observation period. We also excluded patients with baseline hepatocyte carcinoma or other malignancies, congestive heart failure, severe chronic kidney disease, pregnancy, receiving immunosuppressive drugs for other reasons, or HIV infection. Patients were divided into two groups: the ACLF group: patients diagnosed with ACLF at baseline; and the non-ACLF group: patients who did not fulfill the diagnostic criteria for ACLF at baseline. Patients with non-ACLF who progressed to ACLF during the 28-day observation period were further defined as the developed ACLF group, while patients who did not progress to ACLF during the 28-day observation period were defined as the non-developed ACLF group. During hospitalization, all patients received integrative treatment. ACLF was diagnosed based on the APASL ACLF criteria. We systematically collected clinical data for each patient, encompassing demographic information, comorbidities, complications, laboratory tests, and prognostic data. Detailed information about variables was provided in the Supplementary Methods section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eModeling Development and Validation\u003c/h2\u003e \u003cp\u003e \u003cb\u003eDatasets\u003c/b\u003e. All data was randomly divided into 80% derivation/20% validation sets with no overlapping topics. This approach ensures the independence and integrity of the datasets for accurate and reliable analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDerivation datasets\u003c/b\u003e: 4716 subjects were assigned to the training and test datasets following a 9:1 ratio, including 3806 cases of non-developed ACLF and 370 cases of developed ACLF, further cross-validated 10 times to train model parameters.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eValidation datasets\u003c/strong\u003e \u003cp\u003e1045 subjects including 938 non-developed ACLF cases and 107 developed ACLF cases were utilized to evaluate the performances of models.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eModeling Approach and Evaluation\u003c/h2\u003e \u003cp\u003eThe methods of identifying the clinical characteristics of enrolled patients and discovering predictors associated with the occurrence of ACLF are described in the Supplementary Methods section.\u003csup\u003e19\u003c/sup\u003e The predictors were used to develop the new score for predicting the occurrence of ACLF by multivariate Cox regression.\u003csup\u003e19\u003c/sup\u003e The performance of the new score was compared with four other established scoring systems (Chronic Liver Failure Consortium [CLIF-C] ACLF development score [CLIF-C ACLF-Ds], \u003csup\u003e20\u003c/sup\u003e Model for End-stage Liver Disease score [MELDs],\u003csup\u003e21\u003c/sup\u003e Model for End-stage Liver Disease-sodium score [MELD-Nas],\u003csup\u003e21\u003c/sup\u003e CLIF Consortium Acute Decompensation score [ CLIF-C ADs]\u003csup\u003e22\u003c/sup\u003e) in predicting the onset of ACLF, including model discrimination, calibration, and overall performance.\u003csup\u003e23\u003c/sup\u003e Discrimination was assessed by the concordance index (C-index), time-dependent receiver operating characteristic (ROC), and probability density function (PDF);\u003csup\u003e24,25\u003c/sup\u003e calibration was assessed by calibration curves and goodness-of-fit with the Hosmer\u0026ndash;Lemeshow statistic test;\u003csup\u003e23,24\u003c/sup\u003e and overall performance was tested using the R\u003csup\u003e2\u003c/sup\u003e and Brier scales.\u003csup\u003e26\u003c/sup\u003e Better performance is indicated by a higher R\u003csup\u003e2\u003c/sup\u003e and a lower Brier scale score.\u003csup\u003e19\u003c/sup\u003e Detailed methods of discrimination with the C-index, calibration and PDF are shown in the Supplementary Methods section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were expressed as medians and interquartile ranges, while categorical variables were summarized as counts and percentages. The cut-off value of the continuous variable was selected based on reference value ranges, ROC curves, and expert opinions. The analysis of continuous variables utilized the Two-Sample T-test and the Mann\u0026ndash;Whitney U test, while the Chi-square test and the Fisher exact test were employed for categorical variables. A paired T-test and McNemar test were used to compare repeated measurements of continuous and categorical variables, respectively. The normality assumption was evaluated using the Kolmogorov\u0026ndash;Smirnov test, and non-normal data was transformed using natural logarithms. All tests were two-sided with significance set at α less than 0\u0026middot;05. Statistical analysis was performed using IBM SPSS Ver.19.0. The Python programming language (Python Sofware Foundation, version 3.6.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org/downloads/\u003c/span\u003e\u003cspan address=\"https://www.python.org/downloads/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and R V.4.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were employed for our models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the Clinical Research Ethics Committee of the Fourth Affiliated Hospital of Zhejiang University School of Medicine. Written informed consent was obtained from patients or their legal surrogates before enrollment. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The data used in this study were anonymous before its use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eRole of the funding source\u003c/h2\u003e \u003cp\u003eThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy populations\u003c/h2\u003e \u003cp\u003eA total of 7689 patients with various chronic liver diseases were initially screened in this study, out of which 6118 patients meeting the inclusion and exclusion criteria were included for analysis. According to the APASL-ACLF criteria, 897 patients were diagnosed with ACLF, while 5221 patients were diagnosed with non-ACLF at enrollment(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of the patients were male (3999, 65.4%), with a median age of 59 years (interquartile range: 49 to 69). The median days of death were found to be shorter in the ACLF group compared to the non-ACLF group (32 days vs.44 days). Short-term mortality rates at different time points (28/90/365 days) were significantly higher in the ACLF group compared to the non-ACLF group (18.8%/35.8%/45.9% vs 2.5%/5.7%/7.4%,p\u0026thinsp;\u0026lt;\u0026thinsp;0 .001)(Supplementary Table\u0026nbsp;1). Among the initial cohort of non-ACLF patients (n\u0026thinsp;=\u0026thinsp;5221), a total of 477 individuals progressed to ACLF within 28 days after enrollment. Patients who developed ACLF during follow-up had a significantly older age by a median difference of eight years when compared to those who did not develop it (66 years vs.58 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0 .001). Furthermore, there was also an observed increase in 28-day, 90-day, and 365-day mortality rates in the developed ACLF group compared to the non-developed ACLF group(18.4% vs 0.9%,36.6% vs 2.5%, and 41.7% vs3.9%, respectively; P\u0026thinsp;\u0026lt;\u0026thinsp;0 .001)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe clinical characteristics of patients with non-ACLF including non-developed ACLF and developed ACLF at enrollment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-ACLF\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-developed ACLF\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4744)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edeveloped ACLF(N\u0026thinsp;=\u0026thinsp;477)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP值\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(49\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(48\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(54\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3372(64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3031(63.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e341(71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1849(35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1713(36.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136(28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderlying diseases, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1853(35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1561(32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292(61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtrial fibrillation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e241(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193(4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary embolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1656(31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1496(31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160(33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1128(21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1066(21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e498(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418(8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80(16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNAFLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcoholic hepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e301(5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235(5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66(13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3506(67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3396(71.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110(23.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug-induced hepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplications, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal Hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e511(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2072(39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1663(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e409(85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e674(12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e554(11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120(25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic encephalopathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338(6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e242(5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96(20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrgan failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirculatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e419(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223(4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196(41.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e231(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51(10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759(14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e505(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e254(53.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoagulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77(16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140.4(138.2-142.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.4(138.4-142.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e140.0(135.9\u0026ndash;146.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9(3.6\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9(3.6\u0026ndash;4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8(3.5\u0026ndash;4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium, mmol/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8(0.8\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8(0.8\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8(0.7\u0026ndash;0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2(2.1\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2(2.1\u0026ndash;2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1(2.0-2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell count, 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9(4.2\u0026ndash;8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9(4.2-8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.4(4.0-9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count, 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.0(83.0-216.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164.00(97.0-220.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.00(34.0-103.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1565(30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1216(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e349(73.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3656(70.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3528(74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128(26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.9(56.0-79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.8(55.4\u0026ndash;77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.1(69.3\u0026ndash;88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3507(67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3352(70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155(32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1694(32.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1374(29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320(67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count, 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70(2.4\u0026ndash;9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.60(2.4\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0(2.8\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116(89.0-134.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.0(94.0-136.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.0(67.0\u0026ndash;93.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1339(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1004(21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e335(70.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3882(74.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3740(78.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142(29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.3(56.4\u0026ndash;67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.8(57.3\u0026ndash;67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.6(48.5\u0026ndash;60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6(29.9\u0026ndash;38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.2(30.6\u0026ndash;39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.5(26.3\u0026ndash;32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.0(15.0\u0026ndash;38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.00(15.0\u0026ndash;37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.0(19.0\u0026ndash;71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.0(21.0\u0026ndash;58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0(21.0\u0026ndash;44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.0(31.0-130.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2(0.9\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2(0.9\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6(1.0-2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.0(68.0-113.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.0(67.0-111.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.0(84.0-144.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-Glutamyl transferase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.0(17.0\u0026ndash;58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0(17.0\u0026ndash;57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.0(15.0\u0026ndash;78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0(9.10\u0026ndash;27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1(8.8\u0026ndash;22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.4(32.1-126.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4166(80.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4041(85.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125(26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1033(19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e686(14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e347(73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bile acid, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.4(3.5\u0026ndash;24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00(3.4\u0026ndash;19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138.3(60.0-375.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2(2.4\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8(2.3\u0026ndash;8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6(16.2\u0026ndash;82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood ammonia, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.0(37.0-83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0(36.5\u0026ndash;84.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.0(40.0\u0026ndash;81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(56\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0(56.0\u0026ndash;86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.00(61.0-123.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4(2.8\u0026ndash;43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.6(2.3\u0026ndash;34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.00(15.1-115.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9(3.1\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9(3.1\u0026ndash;4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3(1.4\u0026ndash;2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0(0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0(0.8\u0026ndash;1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4(0.2\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin time, S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.4(11.4\u0026ndash;14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.2(11.3\u0026ndash;13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.8(13.9\u0026ndash;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProthrombin activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(0.9-2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5(1.0\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8(0.6-1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1(1.0-1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(1.0-1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4(1.2\u0026ndash;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlomerular filtration rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.0(80.0-118.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.0(81.0-117.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.5(71.0-133.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3(4.0-7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1(3.9\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.8(5.7\u0026ndash;16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(26\u0026ndash;187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121(30\u0026ndash;367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88(18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e175(36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e365 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e385(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186(3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e199(41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCategoric variables are expressed as % (n); continuous variables are expressed as median [interquartile range]. P values are comparisons between non-developed ACLF and developed ACLF (Student t test, Mann\u0026ndash;Whitney U test, chi-squared test, or Fisher exact test).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003en.,number; NAFLD,Non-alcoholic fatty liver disease; HAV,Hepatitis A virus; HBV,Hepatitis B virus; HCV,Hepatitis C virus; HDV,Hepatitis D virus;HEV,Hepatitis E virus; NEUT%, neutrophilic granulocyte percentage; ALT,Alanine aminotransferase; AST, Aspartate aminotransferase; HDL-C,high density lipoprotein cholesterol; INR,International normalized ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics of Patients at Admission\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the clinical characteristics of the non-ACLF, developed ACLF, and non-developed ACLF groups at admission. 35.4% of non-ACLF patients had a history of cirrhosis, with the proportion of the developed ACLF group significantly higher than that of the non-developed ACLF group(61.2% vs 32.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In patients with chronic liver disease, HBV infection was the most frequent etiology, followed by alcoholic liver disease. Compared with the non-developed ACLF group, the developed ACLF group had a higher rate of alcoholic liver disease(13.8%% vs 5.0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a lower rate of HBV infection(23.1%% vs 71.6%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A similar trend was also observed between the ACLF group and the non-ACLF group (alcoholic liver disease:18.7% vs 5.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; HBV infection:32.0% vs 67.2%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary Table\u0026nbsp;1). The incidence of cerebrovascular accidents was higher in the developed ACLF group than in the non-developed ACLF group (16.8% vs 8.8%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, the incidence of complications differed significantly between the two groups. In the developed ACLF group, the main complications were bacterial infection (85.7%) and gastrointestinal hemorrhage(27.3%), followed by ascites (25.2%) and hepatic encephalopathy (20.1%). In the non-developed ACLF group, the main complications were bacterial infection (35.1%) and ascites (11.7%). Gastrointestinal hemorrhage and hepatic encephalopathy occurred in only 8.0% and 5.1% of patients, respectively.\u003c/p\u003e \u003cp\u003eAt baseline, the developed ACLF group exhibited significantly worse laboratory indicators compared with the non-developed ACLF group. Platelet count(PLT), hemoglobin, total protein(TP), ALB, and high-density lipoprotein(HDL-C) levels were all found to be significantly lower than the reference values and the non-developed ACLF group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, neutrophil percentage(NUET%), TBiL, direct bilirubin(DBiL), total bile acid (TBA),c-reactive protein(CRP), prothrombin time (PT), AST/ALT, INR, and urea nitrogen(BUN) were markedly higher than those in non-developed ACLF (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For instance, 73.2% and 70.2% of developed ACLF patients presented thrombocytopenia (median:61.0; interquartile range:34.00-103.00) and moderate anemia (median:78.0; Interquartile range:67.00\u0026ndash;93.00), respectively. CRP and NUET% were markedly elevated with a median of 45 mg/L and 82.1%, respectively, in the developed ACLF group, indicating higher grades of systemic inflammation. In comparison to the normal reference values, the liver function index of median TBiL increased by 3.9-fold (IQR: 32.1-126.4) and DBiL increased by 5.6-fold (IQR:16.2\u0026ndash;82.7). Median plasma bile acids were continuously elevated, up to 13.8-fold (IQR: 60.0-375.0). This indicated significant impairment of liver function and potential cholestasis(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides, we contrasted the organ failures of various groups. In the ACLF group, the most prevalent failing organ systems were the liver (42.4%) and respiratory system (35.1%), with coagulation (22.4%) and circulatory system (19.4%) following closely behind(Supplementary Table\u0026nbsp;1). The most common organ failures among patients in the developed ACLF group were respiratory(53.2%) and circulatory (41.1%) failures, followed by liver(16.1%) and kidney(10.7%) failure. Except for respiratory failure accounting for 10.6%, there were far fewer cases of other organ failure in the non-developed ACLF group, compared with the first two groups(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eChanges in clinical features during ACLF progression\u003c/h2\u003e \u003cp\u003eTo observe the clinical changes that occurred in patients during ACLF progression, we compared the clinical characteristics of the developed group at enrollment and onset. Firstly, it took an average of 7 days (interquartile range: 3\u0026ndash;15 days) for non-ACLF advancement to become ACLF. Secondly, deteriorating parameters of several intrahepatic and extrahepatic systems, such as liver damage, coagulation dysfunction, renal failure, and hematological disorders, subtly demonstrated the evolving disease course of ACLF development. Laboratory indicators such as AST, TBiL, DBiL, PT, NLR, and BUN all markedly increased and the proportion of PT less than 30 rose from 20.3\u0026ndash;26.5% as disease progression. Thirdly, there was a statistically significant rise in the incidence of both liver failure and coagulation failure, which went from 16.1% and 1.9\u0026ndash;29.8% and 9.0%, respectively(Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for ACLF development\u003c/h2\u003e \u003cp\u003eIndependent predictors associated with the development of ACLF were preliminary screened by a univariate Cox proportional hazard regression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, HR\u0026thinsp;\u0026gt;\u0026thinsp;1.0 or \u0026lt;\u0026thinsp;0.9; Supplementary Table\u0026nbsp;3). After removing similar indicators according to the advice of clinical specialists, 38 variables were selected for lasso regression screening (Supplementary Fig.\u0026nbsp;1). 11 factors ((HDL-C(\u0026lt;\u0026thinsp;0.5, mmol/L); NEUT(\u0026ge;\u0026thinsp;7,109 /L); TBiL (\u0026ge;\u0026thinsp;35, \u0026micro;mol/L); PT(\u0026lt;\u0026thinsp;100, 109 /L); BUN (\u0026gt;\u0026thinsp;7, mmol/L); AST(\u0026gt;\u0026thinsp;40, U/L); ln (INR); ln(HGB); liver failure; bacterial infection; respiratory failure) were further introduced into the multivariate cox regression analysis, based on the results of lasso analysis and collinearity diagnosis(Supplementary Table\u0026nbsp;4 and Supplementary Table\u0026nbsp;5). The result revealed that except AST(\u0026gt;\u0026thinsp;40, U/L), the remaining 10 factors showed strong correlation with the progression of ACLF((HDL-C (\u0026lt;\u0026thinsp;0.5, mmol/L) (hazard ratio [HR] 2.82; 95% CI 2.3\u0026ndash;3.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005), TBiL (\u0026ge;\u0026thinsp;35, \u0026micro;mol/L) (hazard ratio [HR] 2.48; 95% CI 2.08\u0026ndash;2.96; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005),ln (INR)(hazard ratio [HR] 2.4; 95% CI 1.75\u0026ndash;3.3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005),NEUT(\u0026ge;\u0026thinsp;7,109 /L) (hazard ratio [HR] 1.63; 95% CI 1.37\u0026ndash;1.94; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005),PLT (\u0026lt;\u0026thinsp;100, 109 /L) (hazard ratio [HR] 1.55; 95% CI 1.32\u0026ndash;1.82; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005),BUN (\u0026gt;\u0026thinsp;7, mmol/L) (hazard ratio [HR] 1.68; 95% CI 1.44\u0026ndash;1.97; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) and ln(HGB) (hazard ratio [HR] 0.41; 95% CI 0.31\u0026ndash;0.54; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) ;liver failure(hazard ratio [HR] 1.56; 95% CI 1.17\u0026ndash;2.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005);bacterial infection(hazard ratio [HR] 1.59; 95% CI 1.36\u0026ndash;1.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005);respiratory failure(hazard ratio [HR] 1.7; 95% CI 1.42\u0026ndash;2.03; p\u0026thinsp;\u0026lt;\u0026thinsp;0.005))(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Ultimately, we identified 7 indicators for modeling considering both early clinical easy acquisition and quantification((HDL-C (\u0026lt;\u0026thinsp;0.5, mmol/L), TBiL (\u0026ge;\u0026thinsp;35, \u0026micro;mol/L), ln(INR), NEUT(\u0026ge;\u0026thinsp;7,109/L), PLT(\u0026lt;\u0026thinsp;100, 109/L), BUN (\u0026gt;\u0026thinsp;7, mmol/L) and ln(HGB) )(Supplementary Fig.\u0026nbsp;2). Collinearity diagnosis analysis revealed that the effect of these 7 factors on ACLF development was independent of each other(Supplementary Table\u0026nbsp;6).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe multivariate regression for development of ACLF in patients in the derivation cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil count\u0026thinsp;\u0026ge;\u0026thinsp;7, 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.63(1.37\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count\u0026thinsp;\u0026lt;\u0026thinsp;100, 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55(1.32\u0026ndash;1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin\u0026thinsp;\u0026ge;\u0026thinsp;35, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.48(2.08\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C\u0026thinsp;\u0026lt;\u0026thinsp;0.5, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.82(2.30\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn(HGB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41(0.31\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea nitrogen\u0026thinsp;\u0026gt;\u0026thinsp;7, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.68(1.44\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLn(INR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.40(1.75\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacterial infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59(1.36\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70(1.42\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56(1.17\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eHDL-C, high density lipoprotein cholesterol; HGB, hemoglobin; INR, International normalized ratio;HR, hazard ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDeveloping a model to Predict the Onset of ACLF\u003c/h2\u003e \u003cp\u003eUsing a multivariate Cox regression approach, a model incorporating seven predictors((HDL-C (\u0026lt;\u0026thinsp;0.5, mmol/L), TBiL(\u0026ge;\u0026thinsp;35, \u0026micro;mol/L), ln(INR), NEUT(\u0026ge;\u0026thinsp;7,109 /L), PLT(\u0026lt;\u0026thinsp;100, 109 /L), BUN (\u0026gt;\u0026thinsp;7, mmol/L) and ln(HGB) ) was established to predict the onset of ACLF at 7 /14/28 days. Their coefficients were used as a relative weight to calculate the corresponding score. The formula used for calculating the prediction score is as follows: score=[NEUT\u0026thinsp;\u0026ge;\u0026thinsp;7,109/L;1 or 0]\u0026times;0.49 + [PLT\u0026thinsp;\u0026lt;\u0026thinsp;100,109/L;1 or 0]\u0026times;0.44 + [TBIL\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;35,\u0026micro;mol/L;1 or 0]\u0026times;0.05 +[HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;0.5,mmol/L;1 or 0]\u0026times;1.04 - Ln[Hb,g/L]\u0026times;0.89 + [BUN\u0026thinsp;\u0026gt;\u0026thinsp;7,mmol/L;1 or 0]\u0026times;0.51\u0026thinsp;+\u0026thinsp;Ln[INR]\u0026times;0.87\u0026thinsp;+\u0026thinsp;3.40. The probability of ACLF onset can be estimated by the equation P\u0026thinsp;=\u0026thinsp;1- S(t)\u0026thinsp;=\u0026thinsp;1 - exp(λ0(t) \u0026times; exp(VE-ACLF-Dev)). λ0 was the cumulative baseline hazard and the score coefficient estimated by the model fitted for time t. λ0(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;0.0373, λ0(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;0.0817, λ0(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;0.4070.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePerformance of the new prognostic score\u003c/h2\u003e \u003cp\u003eIn comparison to CLIF-C-ACLF-Ds, MELD, MELD-Na, and CLIF-C-ADs scores, the VE-ACLF-Dev achieved high discriminative performance, as demonstrated by the C-index, ROC curve, and probability density function analysis. The C-indexes of VE-ACLF-Dev were the highest (0.958/0.944/0.938) for predicting the onset of ACLF at 7/14/28 days (CLIF-C ACLF-Ds, 0.870/0.855/0.850; MELDs, 0.884/0.866/0.859; MELD-Na,0.858/0.835/0.827; CLIF-C-ADs,0.627/0.623/0.622)(Supplementary Table\u0026nbsp;7). In addition, our new score's prediction error rates were far lower than those of the four other scores(CLIF-C ACLF-Ds, 67.7%/ 61.0%/58.5%; MELDs, 63.8%/57.9%/55.7%; MELD-Nas,70.5%/65.9%/63.9%; CLIF-C-ADs, 88.7%/85.1%/83.5%)(Supplementary Fig.\u0026nbsp;4A). The time-dependent ROC analysis also showed that the VE-ACLF-Dev had the largest area under the ROC curves (0.968/0.949/0.934) compared with the four other scores at 7/14/28 days ( Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Supplementary Table\u0026nbsp;8). The results of PDF analysis revealed that the number of patients with developed ACLF increased as scores rose, and an obvious distinction was observed between the peaks of patients with developed ACLF and those without. The overlapping coefficients of VE-ACLF-Dev (13.7%/ 23.7%/29.7%) significantly decreased compared to the four other scores (CLIF-C ACLF-Ds, 31.7%/37.9%/40.6%; MELDs, 32.8%/42.3%/47.4%; MELD-Nas, 35.6%/47.5%/55.1%; and CLIF-C-ADs, 74.6%/75.3%/74.1%, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)( Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overall performance and calibration of VE-ACLF-Dev were also excellent at each time point. The calibration plot demonstrated a strong agreement between the predicted and actual probability of the onset of ACLF at 7/14/28 days (Hosmer\u0026ndash;Lemeshow X2\u0026thinsp;=\u0026thinsp;402.12/ 150.38/495.56, all P\u0026thinsp;\u0026gt;\u0026thinsp;0 .05, Brier: 0.04/0.06/0.13)(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRisk Stratification of the New Score\u003c/h2\u003e \u003cp\u003eBy the X-tile plot analysis of the VE-ACLF-Dev, the risk of ACLF onset at 7/14/28 days could be categorized into two strata with high risk (\u0026ge;\u0026thinsp;9.6) and low risk (\u0026lt;\u0026thinsp;9.6). The incidence of ACLF between the two groups on 7/14/28 days was significantly different (high-risk, 60.8%/68.4%/71.8%; low-risk group,1.9%/3.5%/5.9%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). We further calculated the hazard risk of ACLF development in both groups. Compared with the low-risk group, the hazard ratios in the high-risk group were 36.42/23.34/18.64 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the VE-ACLF-Dev prediction model\u003c/h2\u003e \u003cp\u003eOut of 1045 patients without ACLF in the validation group, 107 developed ACLF during hospitalization. The derivation and validation groups exhibited similar rates and outcomes of ACLF, as well as comparable clinical characteristics(Supplementary Table\u0026nbsp;9).In the validation group, the c-indexes of the model for predicting the onset of ACLF at 7/14/28 days all exceeded 0.90 (0.948/0.922/0.908) and significantly outperformed four other generic scores (CLIF-C ACLF-Ds,0.833/0.818/0.807, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; MELDs, 0.863/0.843/ 0.830, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; MELD-Nas, 0.820/0.801/0.785, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and CLIF-C-ADs, 0.552/0.563/0.562, P\u0026thinsp;\u0026lt;\u0026thinsp;0 .001)(Supplementary Table\u0026nbsp;7). Additionally, the prediction error rates of the new score for 7/14/28 day ACLF onset were considerably lower than those of the four other scores (CLIF-C ACLF-Ds, 69.2%/57.44%/52.16%; MELDs, 62.39%/50.53%/45.67%; MELD-Nas,71.32%/61.05%/57.01%; CLIF-C- ADs,88.5%/82.23%/78.96%)(Supplementary Fig.\u0026nbsp;4B). As demonstrated by the time-dependent ROC analysis, VE-ACLF-Dev had the highest AUC (0.961/0.939/0.912) at 7/14/28 days compared with the four other scores(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD and Supplementary Table\u0026nbsp;8). The PDF analysis also revealed decreased overlapping coefficients of the new score between developed patients and non-developed patients in the validation group (VE-ACLF-Dev, 15.0%/27.4%/35.4%; CLIF-C ACLF-Ds, 36.4%/41.2%/46.8%; MELDs, 31.9%/39.8%/46.6%; MELD-Nas, 37.5%/44.0%/52.8%; CLIF-C-ADs, 85.2%/81.3%/78.5%, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)(Supplementary Fig.\u0026nbsp;3). The calibration curve analysis showed a good agreement between predicted and actual probabilities in the validation(Hosmer\u0026ndash;Lemeshow X2\u0026thinsp;=\u0026thinsp;119.73/ 24.25/109.82, all P\u0026thinsp;\u0026gt;\u0026thinsp;0 .05, Brier: 0.04/0.07/0.15)(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The hazard ratios of ACLF onset at 7/14/28 days in the high-risk group (19.6/15.5/10.6, P\u0026thinsp;\u0026lt;\u0026thinsp;0 .001) were also comparable to those in the derivation group compared with the low-risk group and demonstrated a similar separation efficiency in the validation group(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Collectively, these results indicated that the VE-ACLF-Dev prediction model to predict ACLF occurrence was proven to be statistically robust.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eIn this study, we comprehensively compared the clinical characteristics of developed ACLF and non-developed ACLF groups and determined ten independent factors most relevant for ACLF progression, including bacterial infection, liver failure, respiratory failure, and clinical indicators such as HDL-c, TB, INR, NEUT, PLT, BUN, and HGB. Previous studies showed that bacterial infection was not only the most frequent extrahepatic precipitant for the development of ACLF\u003csup\u003e18,27,28\u003c/sup\u003e, but also the cause of short-term high mortality in ACLF, with remarkably higher rates compared to other causes of damage\u003csup\u003e27,29\u0026ndash;31\u003c/sup\u003e. Our study further confirmed that bacterial infection was the only extrahepatic trigger associated with progression to ACLF, enforcing its central role of bacterial infection in ACLF extrahepatic precipitating events. In addition to precipitating factors, our study also identified the presence of systemic inflammation as a contributing factor for the progression of ACLF, with NEUT being an independent risk factor. Numerous studies have reported higher NEUT in ACLF patients compared to healthy controls, possibly due to increased levels of circulating granulocyte colony-stimulating factor (G-CSF)\u003csup\u003e32\u003c/sup\u003e. Moreover, neutrophil dysfunction is highly clinically relevant, as impaired respiratory burst and phagocytic activity correlate with a higher risk of infection, organ failure, and mortality\u003csup\u003e33,34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOrgan failure is a hallmark of ACLF and can include renal failure, now called Acute Kidney Injury(AKI), respiratory and circulatory failure\u003csup\u003e35\u003c/sup\u003e. A reversible acute increase in BUN was the classical biomarker of AKI which was considered a strong predictor of poor survival in the short and long term of ACLF\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e. According to our research, the BUN of the developed ACLF was markedly elevated compared with the non-developed ACLF, indicating that BUN could serve as an early warning indicator of ACLF. At the same time, our findings also suggested that the prevalence of liver failure and respiratory failure were higher in the developed ACLF group.\u003c/p\u003e \u003cp\u003eOur study showed that platelet levels were lower in developed-ACLF patients, which was consistent with previous findings. Up to 75% of patients with advanced liver disease or cirrhosis experience thrombocytopenia, and its severity is usually directly correlated with the degree of liver failure.\u003csup\u003e39\u003c/sup\u003e Studies have shown that platelet levels is an independent prognostic factor in ACLF patients, and depletion or redistribution is responsible for thrombocytopenia.\u003csup\u003e40\u0026ndash;42\u003c/sup\u003e Furthermore, we observed that patients in the developed ACLF group suffered from moderate anemia. Several studies have reported anemia as a common complication in ACLF patients with various contributing factors identified, such as autoimmune disorders, decrease in hematopoietic capability, and release of tumor necrosis factor, cytokine, and endotoxins.\u003csup\u003e35,43\u003c/sup\u003e Another study by Cheng et al. also found that accelerated suicidal cell death or apoptosis could contribute to anemia in patients with HBV-ACLF.\u003csup\u003e44\u003c/sup\u003e Besides, patients with liver failure would experience liver synthesis decreased and metabolic dysfunction, as well as coagulation dysfunction, resulting in malnutrition and chronic occult bleeding. Due to the patient being in a severe state of anemia, it may lead to a decrease in hemoglobin and platelets. In ACLF patients, bilirubin was reported to trigger anemia by inducing erythrocyte death \u003csup\u003e45,46\u003c/sup\u003e. Moreover, it has been found that hemoglobin could not only identify individuals at high risk of developing ACLF but also serve as a strong predictor in the survival rate of patients with cirrhosis,\u003csup\u003e47\u003c/sup\u003e making it a potential target for ACLF prevention. Other predictive variables, such as TB and INR, which are well-established indicators of liver and coagulation dysfunction/failure, have been extensively utilized in various diagnostic criteria and prognostic scores for patients with ACLF. During the development and progression of ACLF, hepatocytes undergo varying degrees of degeneration and necrosis, leading to the disorder of coagulation factor synthesis. This is manifested as prolonged coagulation time and elevated bilirubin levels, both of which are important indicators of liver reserve function. Low levels of HDL-C are commonly observed in patients with chronic liver disease and are inversely correlated with disease severity.\u003csup\u003e48\u003c/sup\u003e Previous studies have demonstrated that besides predicting poor outcomes in HBV-ACLF patients, low high-density lipoprotein cholesterol levels also played an important role in the pathophysiology of systemic inflammation driving the onset of ACLF.\u003csup\u003e49,50\u003c/sup\u003e Consistent with these findings, our study also found that HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;0.5 mmol/L was a robust predictor for ACLF progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eComparison with previous scores\u003c/h2\u003e \u003cp\u003eACLF is a life-threatening clinical syndrome with a high short-term mortality.\u003csup\u003e7\u003c/sup\u003e Despite liver transplantation being considered to be the most effective treatment for ACLF, its limited availability due to organ shortage and the high cost of the procedure hinders its widespread application.\u003csup\u003e51,52\u003c/sup\u003e Furthermore, it is widely acknowledged that ACLF is a dynamic syndrome that can be reversible in a considerable proportion of patients.\u003csup\u003e4\u003c/sup\u003e This feature is closely related to the improvement of prognosis, highlighting the importance of dynamic assessment. A recent PREDICT study identified that pre-ACLF patients could develop ACLF within 3-month, with a mortality rate of 53.7% at three months and 67.4% at one year.\u003csup\u003e20\u003c/sup\u003e Under such circumstances, both early diagnosis of ACLF and accurate prediction of prognosis are critical for distinguishing patients who require transplantation from those who can survive with intensive medical care alone. In this study, we observed that approximately 9.13%(477/5221) of non-ACLF patients developed ACLF during hospitalization within 28 days, among which 51.8% (247/477) progressed within 7 days and 72.7% (347/477) progressed within 14 days based on APASL-ACLF criteria. These findings further underscore the significance of predicting the onset of ACLF at different periods after admission and may facilitate timely interventions to prevent progression.\u003c/p\u003e \u003cp\u003eThus, utilizing the framework of trigger factors-systemic inflammation-organ dysfunction/failure, we have developed a concise and accurate prognostic score to predict the progression of ACLF. In comparison with the other four scores(MELD, MELD-Na, CLIF-C-ADs, and CLIF-C-ACLF-Ds), our model demonstrated superior discriminability and overall performance. While MELD, MELD-Na, and CLIF-C-ADs have been extensively used to predict the mortality and prognosis in patients with various severe liver diseases with strong predictive value, their sensitivity and accuracy in predicting ACLF development are comparatively lower.\u003csup\u003e8,10\u003c/sup\u003e Although the CLIF-C ACLF-Ds was specifically designed to forecast ACLF occurrence in the AD population within 3 months, based on the PREDICT study, it did not exhibit greater accuracy than traditional clinical scores.\u003csup\u003e20\u003c/sup\u003e Our score could effectively screen high-risk patients prone to developing ACLF, thereby providing valuable clinical guidance for managing and treating patients with chronic liver disease.\u003c/p\u003e \u003cp\u003eMeanwhile, significant differences in the definition of ACLF between Eastern and Western countries have led to variations in the potential etiology, precipitating events, and prognosis of ACLF across different regions.\u003csup\u003e53\u003c/sup\u003e Consequently, recent years have witnessed a surge in research efforts devoted to developing diagnostic criteria and prognostic models specifically for single-etiology ACLF. For instance, the COSSH study conducted in China has provided a valuable exploration of the diagnostic criteria and prognosis of HBV-ACLF.\u003csup\u003e24\u003c/sup\u003e However, there remains a clinical need for a progression model capable of identifying high-risk ACLF populations among patients with different etiologies and states of chronic liver diseases. In this study, both the various etiologies and the state of chronic liver disease were included in the new model, which significantly expanded the clinical applicability of the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eNevertheless, our study has several limitations. Firstly, this was a retrospective study and there might be selective bias. However, its large sample size, strict inclusion and exclusion criteria, and low data loss helped to mitigate the likelihood of bias. Secondly, previous study has reported that HBV reactivation and superimposed infection on HBV were the main causes of ACLF.\u003csup\u003e18,54\u003c/sup\u003e However, due to the lack of dynamic HBV-DNA and HBV coinfection data, the impact of viral response and multiple infections of HBV on ACLF development could not be investigated in this study. Thirdly, our data mainly came from the Ningbo area, so future studies should aim to externally validate our models in other cohorts from different regions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, early detection of patients at high risk of progression to ACLF is essential to lowering the prevalence and fatality rate of ACLF. Our newly created predictive model was not only applicable to diverse patients with chronic liver disease but also outperformed MELD, MELD-Na, CLIF-C-ADs, and CLIF-C-ACLF-Ds scores. This model can help to mitigate risk factors, identify high-risk patients, and customize follow-up management, which is especially crucial for improving patient outcomes and reducing the burden of ACLF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the medical staffs in the Department of Hepatology for their support in providing information about patients. We also thank the patients for their willingness to participate.\u0026nbsp;This research was supported by the Spring City Plan: the High-level Talent Promotion and Training Project of Kunming (Grant No.2022SCP002), Key R\u0026amp;D Program of Zhejiang (2023C03101).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Bin Ju, Wan Xu; Literature search: Wan Xu, Bin Ju; Funding acquisition: Yuqiang Shen, Li Li; Acquisition of data and technique support: Yuqiang Shen, Shengfen Wen, Yang Chen; Data analysis and interpretation: Wan Xu, Shengfen Wen; Figures drawing: Shengfen Wen, Wan Xu; Drafting manuscript: Wan Xu; Critical revision of the manuscript: Wan Xu, Yuqiang Shen, Bin Ju. Access and verification of the underlying data: Yuqiang Shen, Shengfen Wen, Yang Chen. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are unable to provide access to our datasets for privacy reasons. The protocol and statistical analysis methods used in the study can be requested directly from the corresponding author after approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflict of interest related to this publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu, T. et al. Development of diagnostic criteria and a prognostic score for hepatitis B virus-related acute-on-chronic liver failure. Gut 67, 2181\u0026ndash;2191, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2017-314641\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2017-314641\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNgu, N. L. Y., Flanagan, E., Bell, S. \u0026amp; Le, S. T. Acute-on-chronic liver failure: Controversies and consensus. World J Gastroenterol 29, 232\u0026ndash;240, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v29.i2.232\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v29.i2.232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSarin, S. K. et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update. Hepatol Int 13, 353\u0026ndash;390, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12072-019-09946-3\u003c/span\u003e\u003cspan address=\"10.1007/s12072-019-09946-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, R., Mehta, G. \u0026amp; Jalan, R. Acute-on-chronic liver failure. Clin Med (Lond) 20, 501\u0026ndash;504, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7861/clinmed.2020-0631\u003c/span\u003e\u003cspan address=\"10.7861/clinmed.2020-0631\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, X., Zhang, L., Pu, C. \u0026amp; Tang, S. Liver transplantation in Acute-on-Chronic liver failure: Timing of transplantation and selection of patient population. Front Med (Lausanne) 9, 1030336, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2022.1030336\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2022.1030336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulz, M. S., Gu, W., Schnitzbauer, A. A. \u0026amp; Trebicka, J. Liver Transplantation as a Cornerstone Treatment for Acute-On-Chronic Liver Failure. Transpl Int 35, 10108, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/ti.2022.10108\u003c/span\u003e\u003cspan address=\"10.3389/ti.2022.10108\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHernaez, R., Sola, E., Moreau, R. \u0026amp; Gines, P. Acute-on-chronic liver failure: an update. Gut 66, 541\u0026ndash;553, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2016-312670\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2016-312670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlasco-Algora, S., Masegosa-Ataz, J., Gutierrez-Garcia, M. L., Alonso-Lopez, S. \u0026amp; Fernandez-Rodriguez, C. M. Acute-on-chronic liver failure: Pathogenesis, prognostic factors and management. World J Gastroenterol 21, 12125\u0026ndash;12140, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3748/wjg.v21.i42.12125\u003c/span\u003e\u003cspan address=\"10.3748/wjg.v21.i42.12125\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRashed, E. \u0026amp; Soldera, J. CLIF-SOFA and CLIF-C scores for the prognostication of acute-on-chronic liver failure and acute decompensation of cirrhosis: A systematic review. World J Hepatol 14, 2025\u0026ndash;2043, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4254/wjh.v14.i12.2025\u003c/span\u003e\u003cspan address=\"10.4254/wjh.v14.i12.2025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y., Nie, Y., Liu, L. \u0026amp; Zhu, X. Assessing the prognostic scores for the prediction of the mortality of patients with acute-on-chronic liver failure: a retrospective study. PeerJ 8, e9857, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.9857\u003c/span\u003e\u003cspan address=\"10.7717/peerj.9857\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, C. et al. A new dyslipidemia-based scoring model to predict transplant-free survival in patients with hepatitis E-triggered acute-on-chronic liver failure. Lipids Health Dis 22, 80, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-023-01826-y\u003c/span\u003e\u003cspan address=\"10.1186/s12944-023-01826-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Q. et al. Noninvasive models for predicting poor prognosis of chronic HBV infection patients precipitating acute HEV infection. Sci Rep 10, 2753, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-59670-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-59670-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao, K. Y. et al. Models for acute on chronic liver failure development and mortality in a veterans affairs cohort. Hepatol Int 14, 587\u0026ndash;596, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12072-020-10060-y\u003c/span\u003e\u003cspan address=\"10.1007/s12072-020-10060-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. et al. Characteristics, Diagnosis and Prognosis of Acute-on-Chronic Liver Failure in Cirrhosis Associated to Hepatitis B. Sci Rep 6, 25487, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep25487\u003c/span\u003e\u003cspan address=\"10.1038/srep25487\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, Y. et al. Hepatitis B Virus Reactivation Increased the Risk of Developing Hepatic Failure and Mortality in Cirrhosis With Acute Exacerbation. Front Microbiol 13, 910549, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2022.910549\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2022.910549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei, J. H., Peng, F., Chen, Z. \u0026amp; Xiao, X. Q. Is HBV viral load at admission associated with development of acute-on-chronic liver failure in patients with acute decompensation of chronic hepatitis B related cirrhosis. BMC Infect Dis 19, 363, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-019-3988-1\u003c/span\u003e\u003cspan address=\"10.1186/s12879-019-3988-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao, N., He, Y., Wu, Y., Wang, F. \u0026amp; Tian, Z. Prognostic value of plasma level of superoxide dismutase in HBV-related acute-on-chronic liver failure. BMC Gastroenterol 22, 312, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-022-02371-1\u003c/span\u003e\u003cspan address=\"10.1186/s12876-022-02371-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, T. et al. Role of precipitants in transition of acute decompensation to acute-on-chronic liver failure in patients with HBV-related cirrhosis. JHEP Rep 4, 100529, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhepr.2022.100529\u003c/span\u003e\u003cspan address=\"10.1016/j.jhepr.2022.100529\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, J. et al. Predicting the Onset of Hepatitis B Virus-Related Acute-on-Chronic Liver Failure. Clin Gastroenterol Hepatol 21, 681\u0026ndash;693, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cgh.2022.03.016\u003c/span\u003e\u003cspan address=\"10.1016/j.cgh.2022.03.016\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrebicka, J. et al. The PREDICT study uncovers three clinical courses of acutely decompensated cirrhosis that have distinct pathophysiology. J Hepatol 73, 842\u0026ndash;854, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2020.06.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2020.06.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahmud, N., Hubbard, R. A., Kaplan, D. E., Taddei, T. H. \u0026amp; Goldberg, D. S. Risk prediction scores for acute on chronic liver failure development and mortality. Liver Int 40, 1159\u0026ndash;1167, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/liv.14328\u003c/span\u003e\u003cspan address=\"10.1111/liv.14328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJalan, R. et al. The CLIF Consortium Acute Decompensation score (CLIF-C ADs) for prognosis of hospitalised cirrhotic patients without acute-on-chronic liver failure. J Hepatol 62, 831\u0026ndash;840, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2014.11.012\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2014.11.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlba, A. C. et al. Discrimination and Calibration of Clinical Prediction Models: Users' Guides to the Medical Literature. JAMA 318, 1377\u0026ndash;1384, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2017.12126\u003c/span\u003e\u003cspan address=\"10.1001/jama.2017.12126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J. et al. Development and validation of a new prognostic score for hepatitis B virus-related acute-on-chronic liver failure. J Hepatol 75, 1104\u0026ndash;1115, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2021.05.026\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2021.05.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang, L., Chen, W., Petrick, N. A. \u0026amp; Gallas, B. D. Comparing two correlated C indices with right-censored survival outcome: a one-shot nonparametric approach. Stat Med 34, 685\u0026ndash;703, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sim.6370\u003c/span\u003e\u003cspan address=\"10.1002/sim.6370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteyerberg, E. W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128\u0026ndash;138, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/EDE.0b013e3181c30fb2\u003c/span\u003e\u003cspan address=\"10.1097/EDE.0b013e3181c30fb2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez, J. et al. Bacterial and fungal infections in acute-on-chronic liver failure: prevalence, characteristics and impact on prognosis. Gut 67, 1870\u0026ndash;1880, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/gutjnl-2017-314240\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2017-314240\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, F. et al. Clinical features and evolution of bacterial infection-related acute-on-chronic liver failure. J Hepatol 74, 330\u0026ndash;339, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2020.07.046\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2020.07.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan der Merwe, S., Chokshi, S., Bernsmeier, C. \u0026amp; Albillos, A. The multifactorial mechanisms of bacterial infection in decompensated cirrhosis. J Hepatol 75 Suppl 1, S82-S100, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2020.11.029\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2020.11.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanetto, A. et al. Severity of systemic inflammation is the main predictor of ACLF and bleeding in individuals with acutely decompensated cirrhosis. J Hepatol 78, 301\u0026ndash;311, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2022.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2022.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMucke, M. M. et al. Bacterial infection-triggered acute-on-chronic liver failure is associated with increased mortality. Liver Int 38, 645\u0026ndash;653, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/liv.13568\u003c/span\u003e\u003cspan address=\"10.1111/liv.13568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasulleras, M., Zhang, I. W., Lopez-Vicario, C. \u0026amp; Claria, J. Leukocytes, Systemic Inflammation and Immunopathology in Acute-on-Chronic Liver Failure. Cells 9, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells9122632\u003c/span\u003e\u003cspan address=\"10.3390/cells9122632\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMookerjee, R. P. et al. Neutrophil dysfunction in alcoholic hepatitis superimposed on cirrhosis is reversible and predicts the outcome. Hepatology 46, 831\u0026ndash;840, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.21737\u003c/span\u003e\u003cspan address=\"10.1002/hep.21737\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor, N. J. et al. The severity of circulating neutrophil dysfunction in patients with cirrhosis is associated with 90-day and 1-year mortality. Aliment Pharmacol Ther 40, 705\u0026ndash;715, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/apt.12886\u003c/span\u003e\u003cspan address=\"10.1111/apt.12886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiano, S. et al. Incidence, predictors and outcomes of acute-on-chronic liver failure in outpatients with cirrhosis. J Hepatol 67, 1177\u0026ndash;1184, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2017.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2017.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeil, D. et al. Prognosis of cirrhotic patients admitted to intensive care unit: a meta-analysis. Ann Intensive Care 7, 33, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13613-017-0249-6\u003c/span\u003e\u003cspan address=\"10.1186/s13613-017-0249-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelcher, J. M. et al. Association of AKI with mortality and complications in hospitalized patients with cirrhosis. Hepatology 57, 753\u0026ndash;762, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.25735\u003c/span\u003e\u003cspan address=\"10.1002/hep.25735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, F. et al. New consensus definition of acute kidney injury accurately predicts 30-day mortality in patients with cirrhosis and infection. Gastroenterology 145, 1280\u0026ndash;1288 e1281, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1053/j.gastro.2013.08.051\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2013.08.051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScharf, R. E. Thrombocytopenia and Hemostatic Changes in Acute and Chronic Liver Disease: Pathophysiology, Clinical and Laboratory Features, and Management. J Clin Med 10, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm10071530\u003c/span\u003e\u003cspan address=\"10.3390/jcm10071530\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, H. et al. A prognostic model of acute-on-chronic liver failure based on sarcopenia. Hepatol Int 16, 964\u0026ndash;972, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12072-022-10363-2\u003c/span\u003e\u003cspan address=\"10.1007/s12072-022-10363-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, Q. et al. A modified MELD model for Chinese pre-ACLF and ACLF patients and it reveals poor prognosis in pre-ACLF patients. PLoS One 8, e64379, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0064379\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0064379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe, Q. X. et al. Short-term prognostic factors for hepatitis B virus-related acute-on-chronic liver failure. World J Clin Cases 10, 8186\u0026ndash;8195, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12998/wjcc.v10.i23.8186\u003c/span\u003e\u003cspan address=\"10.12998/wjcc.v10.i23.8186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheiner, B. et al. Prevalence of and risk factors for anaemia in patients with advanced chronic liver disease. Liver Int 40, 194\u0026ndash;204, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/liv.14229\u003c/span\u003e\u003cspan address=\"10.1111/liv.14229\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMei, C. et al. Increased suicidal erythrocyte death in patients with hepatitis B-related acute-on-chronic liver failure. Am J Physiol Gastrointest Liver Physiol 323, G9-G20, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1152/ajpgi.00050.2020\u003c/span\u003e\u003cspan address=\"10.1152/ajpgi.00050.2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, N. M. \u0026amp; Poduval, T. B. Bilirubin augments radiation injury and leads to increased infection and mortality in mice: molecular mechanisms. Free Radic Biol Med 53, 1152\u0026ndash;1169, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.freeradbiomed.2012.07.007\u003c/span\u003e\u003cspan address=\"10.1016/j.freeradbiomed.2012.07.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLang, E. et al. Conjugated bilirubin triggers anemia by inducing erythrocyte death. Hepatology 61, 275\u0026ndash;284, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.27338\u003c/span\u003e\u003cspan address=\"10.1002/hep.27338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaccherini, G. et al. Prediction of nosocomial acute-on-chronic liver failure in patients with cirrhosis admitted to hospital with acute decompensation. JHEP Rep 1, 270\u0026ndash;277, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhepr.2019.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jhepr.2019.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrieb, M. et al. HDL-related biomarkers are robust predictors of survival in patients with chronic liver failure. J Hepatol 73, 113\u0026ndash;120, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jhep.2020.01.026\u003c/span\u003e\u003cspan address=\"10.1016/j.jhep.2020.01.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClaria, J. et al. Systemic inflammation in decompensated cirrhosis: Characterization and role in acute-on-chronic liver failure. Hepatology 64, 1249\u0026ndash;1264, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hep.28740\u003c/span\u003e\u003cspan address=\"10.1002/hep.28740\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y., Chen, P., Zhang, Y., Nie, Y. \u0026amp; Zhu, X. Low high-density lipoprotein cholesterol levels predicting poor outcomes in patients with hepatitis B virus-related acute-on-chronic liver failure. Front Med (Lausanne) 9, 1001411, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2022.1001411\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2022.1001411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahimi, R. S. \u0026amp; Rockey, D. C. Acute on chronic liver failure: definitions, treatments and outcomes. Curr Opin Gastroenterol 32, 172\u0026ndash;181, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MOG.0000000000000265\u003c/span\u003e\u003cspan address=\"10.1097/MOG.0000000000000265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, J. et al. Acute-on-chronic liver failure: far to go-a review. Crit Care 27, 259, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13054-023-04540-4\u003c/span\u003e\u003cspan address=\"10.1186/s13054-023-04540-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerdelho, M. et al. Acute-on-Chronic Liver Failure: A Portuguese Single-Center Reference Review. GE Port J Gastroenterol 25, 18\u0026ndash;23, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000478988\u003c/span\u003e\u003cspan address=\"10.1159/000478988\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, C. et al. Serum ammonia is a strong prognostic factor for patients with acute-on-chronic liver failure. Sci Rep 10, 16970, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-020-73603-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-020-73603-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ACLF, Prediction model, Diverse chronic liver diseases, Progressive score","lastPublishedDoi":"10.21203/rs.3.rs-4039311/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4039311/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u0026amp; aims\u003c/strong\u003e: Acute-on-chronic liver failure (ACLF) is a syndrome characterized by the acute decompensation of chronic liver disease, leading to organ failures and high short-term mortality. The course of ACLF is dynamic and reversible in a considerable proportion of patients during hospital admission. Early detection and accurate assessment of ACLF are crucial, yet ideal methods remain lacking. Therefore, this study is aimed to develop a new score for predicting the onset of ACLF in patients with diverse chronic liver diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 6188 patients with diverse chronic liver diseases were included in the study. Clinical and laboratory data were collected, and the occurrence of ACLF within 28 days was recorded. Lasso-cox regression was utilized to establish prediction models for the development of ACLF at 7, 14, and 28 days.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings\u003c/strong\u003e: Among 5221 patients without ACLF, 477 patients progressed to ACLF within 28 days. Seven predictors were found to be significantly associated with the occurrence of ACLF at 7, 14, and 28 days. The new score had the best discrimination with the c-index of 0.958, 0.944, and 0.938 at 7, 14, and 28 days, respectively, outperforming those of four other scores(CLIF-C-ACLF-Ds, MELD, MELD-Na, and CLIF-C-ADs score, all P\u0026lt;0 .001). The new score also showed improvements in predictive accuracy, time-dependent receiver operating characteristics, probability density function evaluation, and calibration curves, making it highly predictive for the onset of ACLF at all time points. The optimal cut-off value (9.6) differentiated high and low-risk patients of ACLF onset. These findings were further validated in a separate group of patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: A new progressive score, based on seven predictors, has been developed to accurately predict the occurrence of ACLF within 7, 14, and 28 days in patients with diverse chronic liver diseases and might be used to identify high-risk patients, customize follow-up management, and guide escalation of care, prognostication, and transplant evaluation.\u003c/p\u003e","manuscriptTitle":"Early Prediction of Acute-on-Chronic Liver Failure Development in patients with diverse chronic liver diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 15:45:58","doi":"10.21203/rs.3.rs-4039311/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-14T03:42:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-11T11:56:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69599065721465384036461411628569727935","date":"2024-10-01T08:07:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280580426471123559637441708021013127729","date":"2024-05-21T14:38:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-28T11:01:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a8bd52dd-970f-4ed0-8bfe-1d6039c3f4a4","date":"2024-04-17T17:19:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-24T14:06:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-24T13:59:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-21T10:32:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-21T10:31:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-08T07:47:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38081eb8-62e8-46cb-ba60-ab60f60b2f4f","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29820541,"name":"Health sciences/Gastroenterology"},{"id":29820542,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-11-18T19:33:10+00:00","versionOfRecord":{"articleIdentity":"rs-4039311","link":"https://doi.org/10.1038/s41598-024-79486-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-11-15 15:56:57","publishedOnDateReadable":"November 15th, 2024"},"versionCreatedAt":"2024-03-26 15:45:58","video":"","vorDoi":"10.1038/s41598-024-79486-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-79486-w","workflowStages":[]},"version":"v1","identity":"rs-4039311","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4039311","identity":"rs-4039311","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}