Development and validation of a dynamic prognostic score for hepatitis B virus-related acute-on-chronic liver failure

Development and validation of a dynamic prognostic score for hepatitis B virus-related acute-on-chronic liver failure | 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 Research Article Development and validation of a dynamic prognostic score for hepatitis B virus-related acute-on-chronic liver failure Jiemenglu Li, Chunyan Jiang, Jian Yang, Qingting Zhao, Li Zhang, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6733150/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims Hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a rapidly progressive syndrome with high mortality. This study aimed to develop a dynamic prognostic score for precise outcome prediction in HBV-ACLF patients. Methods Data from a single-center prospective cohort were used to develop the dynamic prognostic score. The dynamic prognostic score was validated in an external multiple-center prospective cohort. Results A total of 124 patients with HBV-ACLF were enrolled in the derivation group. Chinese Severe Hepatitis B Research Group-ACLF II (COSSH-ACLF II) score outperformed CLIF Consortium acute-on-chronic liver failure (CLIF-C ACLF) score and Model for End-Stage Liver Disease (MELD) score in predicting 28/90-day mortality. The difference in COSSH-ACLF II score between day 7 and baseline (δCOSSH-ACLF II 7 − 0) had better performance than the baseline COSSH-ACLF II score. The multivariate COX regression found baseline total bilirubin (TB), baseline prothrombin time international normalized ratio (PT-INR) and δCOSSH-ACLF II 7 − 0 as independent predictors for 90-day survival. We proposed a dynamic prognostic score = 0.005 × TB + 0.609 × PT-INR + 1.234 × δCOSSH ACLF Ⅱ7 − 0. The AUC of the new score was 0.923 and 0.925 for 28- and 90-days mortality, surpassing the other three models. Calibration and decision curve analyses confirmed clinical utility, and a nomogram was developed for visualization. These findings were replicated in the external validation cohort. Conclusion A new prognostic score based on the dynamic clinical course can accurately predict short-term mortality in patients with HBV-ACLF. HBV-ACLF prognostic score dynamic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Acute-on-chronic liver failure (ACLF) is a complicated clinical syndrome characterized by acute decompensation of chronic liver disease, multiple organ failure, and a high mortality rate[ 1 ]. A variety of factors, such as alcoholic liver disease and hepatitis virus infection, can cause ACLF. In many Asian countries, over 70% of ACLF cases were related to hepatitis B virus (HBV) infection and in China, the number can rise to 80%[ 2 – 4 ]. Currently, liver transplantation was the most effective treatment for some ACLF cases[ 5 ]. However, the shortage of donor organ, high cost and lifelong administration of immunosuppressive drugs were still challenges of liver transplantation. Therefore, early diagnosis and prognosis assessment of ACLF are crucial in selecting suitable transplant candidates and reducing the high mortality rate. Several models were available to identify the severity of ACLF and predict the prognosis of ACLF patients. The model of end-stage liver disease (MELD) has been widely used to describe the severity of liver disease and to allocate and manage organs for liver transplantation in patients with liver failure[ 6 ]. A large prospective multicenter cohort of ACLF performed by the European Association for the Study of the Liver-chronic Liver Failure (EASL-CLIF) has proposed the chronic liver failure-sequential organ failure assessment score (CLIF-SOFA) score, which can more accurately assess outcomes. Then, they simplified it to the new chronic liver failure consortium acute on chronic liver failure score (CLIF-C-ACLF) score[ 7 , 8 ]. Another large prospective multicenter cohort Chinese Severe Hepatitis B Research Group (COSSH) studies have proposed new COSSH-ACLF scores and COSSH-ACLF II scores, especially for HBV-ACLF[ 9 ]. CLIF-C-ACLF and COSSH-ACLF II scores had predicting accuracy of around 80% for 28-and 90-day mortality[ 10 ]. Usually, the above scoring system models used clinical data collected on ACLF patient admission to assess their prognosis. However, the clinical course of ACLF is highly dynamic, besides the severity of the disease on admission, the response to treatment and the reversibility of the disease are also highly associated with the outcome[ 11 ]. The prognostic models based on the single initial time point may not be appropriate for rapidly progressing ACLF or those significantly improved after effective therapy. To provide a more accurate prognosis for 90-day mortality, we proposed a new prognostic model using the index on admission and the difference of COSSH-ACLF II scores at admission and 7 days. Then, we validated the new model using retrospective data from the COSSH prospective cohort. Materials and methods Study design First, clinical characteristics and prognostic indicators on admission, on day 3 and day 7, were collected to validate the model for end-stage liver disease (MELD), CLIF-C ACLF score and COSSH-ACLF II score and to develop the new prognostic score. Then, the new prognostic score was validated in the multiple-center prospective cohort. Detailed clinical data and outcomes for all enrolled patients were collected and recorded in case report forms (see supplementary materials) at admission, on day 3 and 7 and follow-up for 90 days. The study adhered to the Helsinki II Declaration and was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. All patients provided written informed consent. Patients ACLF was initially screened at the Department of Infectious Disease, the First Affiliated Hospital of Anhui Medical University from July 2021 to Jun 2022. The ACLF diagnosis was according to the COSSH-ACLF diagnostic criteria. The exclusion criteria are summarized in Figure 1. During hospitalization, all patients received the same treatment of the COSSH-ACLF study, including standard medical treatment (SMT; the detailed SMT protocols were described in supplementary materials), artificial liver support system (ALSS) treatment was used to treat patients according to the Chinese guideline for liver failure 2020. The patients in the validation group were enrolled under the same inclusion and exclusion criteria. They accepted the same treatment as patients in the derivation group from 5 liver units of different hospitals. Development and validation of an HBV-ACLF prognostic score We aimed to develop a new prognostic score for patients with HBV-ACLF for 90 days outcome. Four main steps were performed. First, we calculated the difference of COSSH-ACLF II for baseline and day 7 and used the delta value of the two scores as an index for the subsequent analysis. Second, the univariate and multivariate Cox regression analysis, including delta COSSH-ACLF II and other laboratory index as candidate risk factors, was conducted to select the factors most associated with 90-day mortality. The risk factors selected by multivariate Cox regression were used to propose the new prognostic score. Third, the performance of the new prognostic score was compared with the three prognostic score systems described above by receptor operation curve (ROC) analysis. The new prognostic score was estimated using the calibration curve and decision curve analysis, and finally, it was presented using a nomogram. Fourth, the new prognostic score was validated in an external validation group, including five center data. Statistical analysis The measurements were presented as median (IQR) or mean ± (SD) or numbers (%). Student’s t-test or the Mann-Whitney U test was used to compare continuous variables, the χ2 test was used to compare categorical variables. Significance was defined as P value <0.05. SPSS software V.25 (SPSS, Chicago, Illinois, USA) was used to compare baseline characteristics. GraphPad Prism 10.0 was used to perform the Kaplan-Meier curve. Other analyses were conducted using R software, version 4.40 (https://www.r-project.org). Results Patients and clinical characteristics Among 242 patients hospitalized for HBV-ACLF, 70 patients, including 46 patients with hepatocellular carcinoma or other tumours, 6 patients with severe extra-hepatic diseases, 6 patients receiving immunosuppression drugs for other reasons, 4 patients with uncontrolled mental illness, 2 patients older than 80 years, 2 pregnant patients and 4 patients with other reasons were excluded in this study. 48 patients who accepted liver transplantation or dropped out during the 90-day follow-up were excluded from calculating 90-day mortality (Figure.1). Among the 124 patients analyzed in the study, 27 patients died in 7 days, with a mortality rate of 21.8%. There were 71 patients who died in 90 days, with a mortality rate of 57.3%. The K-M curve showed the survival risk of patients with HBV-ACLF in different ACLF grades for 7, 28 and 90 days (Figure.2). The clinical characteristics of all enrolled patients are summarized in Table 1 . Most HBV-ACLF patients in both the deceased and survived group were male. The mean age was 50 ± 12 years old, which was insignificant compared to that of the survived group (48 ± 12 years old). Laboratory indicators, including ALB, TB, γ-GT, serum creatinine, serum urea, TG, Tch, HDL-C, LDL-C, serum sodium, White blood cell count, neutrophils, INR and prothrombin time were significantly worse in the deceased group than those in the survival group. The values of the COSSH-ACLF II, CLIF-C ACLF and MELD scores for patients with HBV-ACLF in deceased group were 7.5 (7.1–8.3), 43.7 (38.5–47.0), 25.4 (22.3–30.3) respectively, and were significantly higher than those for patients in survived group [CLIF-C ACLF: 39.3 (34.2–43.5), COSSH-ACLF II: 6.8(6.4–7.3), MELD: 21.1(18.5–23.8)]. Table 1 The clinical characteristics of patients in deceased and survival groups in derivation cohort. Characteristic Deceased (n = 71) Survived (n = 53) P Gender (male) 53 (74.6%) 46 (86.7%) 0.095 Age (years) 50 ± 12 48 ± 12 0.462 Laboratory data TP (g/L) 57.9 (52.5–66.6) 61.2 (53.8–65.3) 0.238 ALB (g/L) 29.9 (27.2–33.3) 31.6 (27.7–34.4) 0.034 GLO (g/L) 27.7 (22.6–34.9) 28.4 (24.0–34.0) 0.921 ALT (U/L) 232.0 (97.0-715.0) 245.5 (115.0-644.5) 0.577 AST (U/L) 238.0 (118.0-547.0) 175.5 (99.3-400.2) 0.263 ALP (U/L) 145.0 (122.0-181.0) 130.5 (116.5—177.8a) 0.464 TBA (µmol/L) 202.8 (88.5-242.4) 167.5 (102.3–188.0) 0.611 TBIL (µmol/L) 354.9 (279.5-435.8) 304.3 (237.6-384.1) 0.012 γ-GT (U/L) 76.0 (46.0-108.0) 108.5 (65.8-180.5) 0.002 Serum creatinine (µmol/L) 65.1 (52.0-99.1) 60.4 (52.8–69.6) 0.038 Serum urea (µmol/L) 4.7 (3.1-8.0) 3.7 (2.7–4.5) 0.002 TG (mmol/L) 1.0 (0.9–1.2) 1.9 (1.4–2.1) 0.002 Tch (mmol/L) 1.5 (1.3–1.8) 2.8 (2.2-3.0) 0.006 HDL-C (mmol/L) 0.2 (0.1–0.2) 0.2 (0.1–0.2) 0.042 LDL-C (mmol/L) 1.0 (0.7–1.2) 1.9 (1.5–2.1) 0.006 K + (mmol/L) 3.9 (3.5–4.4) 3.9 (3.5–4.2) 0.47 Na+ (mmol/L) 134.5 (128.2–138.0) 136.3 (133.8-138.6) 0.042 Glu (mmol/L) 4.8 (4.0-6.2) 4.9 (4.1–5.8) 0.854 WBC (×10 9 /L) 7.9 (5.8–10.4) 6.4 (5.0-9.1) 0.017 Neutrophil (×10 9 /L) 5.8 (3.9–8.5) 4.3 (2.8–6.8) 0.013 Hs-CRP (mg/L) 9.0 (5.8–27.0) 8.3 (4.4–17.5) 0.075 HGB (g/L) 122.0 (107.0-135.0) 126.5 (110.5–136.0) 0.17 HCT (%) 34.4 (30.1–38.2) 36.1 (30.7–39.1) 0.118 PLT (×10 9 /L) 94.0 (53.0-154.0) 113.0 (72.8-135.5) 0.388 PT-INR 2.5 (2.0-2.9) 1.9 (1.7–2.3) < 0.001 Fib (g/L) 1.5 (1.2–1.9) 1.8 (1.4–2.3) 0.002 PT (s) 26.9 (22.7–31.9) 21.6 (19.8–25.5) < 0.001 DD (µg/L) 2.6 (1.6-4.0) 2.3 (1.2–3.4) 0.177 Ferritin (ng/ml) 1433.4 (632.6–2000.0) 1161.3 (384.2–1957.0) 0.269 HBV-DNA (IU/ml) 3.0×10 5 (3.2×10 4 -3.4×10 6 ) 3.8×10 4 (9.9×10 3 -7.2×10 5 ) 0.116 CLIF-C ACLF 43.7(38.5–47.0) 39.3(34.2–43.5) < 0.001 COSSH-ACLF П 7.5(7.1–8.3) 6.8(6.4–7.3) < 0.001 MELD 25.4(22.3–30.3) 21.1(18.5–23.8) < 0.001 Survival time 11(5–27) NA Note: The data are expressed as medians (IQR), mean ± (SD) or number of patients (%).TP, total protein; ALB, albumin; GLO, globulin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; TBA, total bile acid; TB, total bilirubin; γ-GT ,glutamyl transpeptidase; TG, triglycerides; Tch, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; K, serum potassium; Na, serum sodium; Glu, fasting blood glucose; WBC, white blood cell count; HGB hemoglobin; PLT, platelet count; PT-INR, prothrombin time-international normalized ratio; Fib, fibrinogen; PT, prothrombin time; DD, D dimer; BI, bacterial infection; GIH, gastrointestinal haemorrhage; HE, hepatic encephalopathy; MAP, mean arterial pressure..CLIF-C ACLFs, Chronic Liver Failure Consortium ACLF score; COSSH-ACLF П, Chinese Group on the Study of Severe Hepatitis B-ACLF score II; MELDs, model for end-stage liver disease score. The ROC was used to validate the predictive ability of the model to compare the prognostic value of the above-mentioned prognostic scores. The AUC of MELD score was 0.801, 0.621 and 0.673 for 7 days, 28 days and 90 days, respectively. The AUC of CLIF-C ACLF score was 0.705, 0.696, and 0.640 for 7 days, 28 days and 90 days, respectively. The AUC of COSSH-ACLF II score was 0.790, 0.774 and 0.733 for 7 days, 28 days and 90 days, respectively. Moreover, the COSSH-ACLF II score best predicted 7-day, 28-day, 90-day outcomes in this study. Here, we found that the 90-day predictive ability of all three models decreased compared to those for 7 days (Figure.3A), which indicated the baseline disease severity was not the only factor associated with the 3-month outcome. Then, we calculated the difference between the three scores on day 3 and day 7 compared to baseline. The ROC showed that δMELD 7 − 0 was better than δMELD 3 − 0 (AUC 0.872 VS. 0.741, P = 0.0518) and δCLIF-C ACLF 7 − 0, δCOSSH-ACLF II 7 − 0 were significantly better than δCLIF-C ACLF3-0 and δCOSSH-ACLF II 3 − 0 (AUC 0.879 VS. 0.712, P = 0.0062, 0.913 VS. 0.800, P = 0.0277, respectively) in predicting 28-day mortality(Figure.3B). In predicting 90-day mortality, δMELD 7 − 0, δCLIF-C ACLF 7 − 0, δCOSSH-ACLF II 7 − 0 (AUC: 0.887, 0.848, 0.905, respectively) also showed significantly better performance than those of δMELD3-0, δCLIF-C ACLF3-0 and δCOSSH-ACLF II 3 − 0 (0.686, 0.681, 0.737, P = 0.0001, P = 0.0012, P = 0.0001 respectively)(Figure.3C). Taken together, we choose the δCOSSH-ACLF II7-0 as a factor in the following analysis. Development of a prognostic score The univariate Cox regression analysis found that TB, ALT, serum sodium, serum creatinine, serum urea, PT-INR, FIB and COSSH-ACLF Ⅱ7 − 0 were the factors associated with 90-day mortality (Supplementary table 1 ). The further multivariate Cox analysis showed that TB, INR and COSSH ACLF Ⅱ7 − 0 were the independent factors of 90-day mortality (Supplementary table 2 ). Then we established the prognostic score using the following formula: 7-day dynamic score model (DSM) = 0.005 × TB + 0.609 × INR + 1.234 × COSSH ACLF Ⅱ7 − 0. Compared with the MELD, CLIF-C ACLF and COSSH-ACLF II scores, the new score yielded a significantly more accurate prognosis, with the highest AUC for predicting the 90-days mortality of patients with HBV-ACLF (28-and 90-days mortality: 0.923 and 0.925, P < 0.05 compared other 3 models,Figure.4). Estimation, validation and visual of the new score To validate the performance of the 7-day DSM, a 5-center external validation group of 72 patients were enrolled. The clinical characteristics were summarized in Table 2 , and the main index, including severity score and mortality, had no significant difference compared to the derivation group. The calibration performance showed that the 7-day DSM had good overall performance and exhibited promising predictive accuracy for death at 90 days both in the derivation and validation group (Figure.5A, B). The Decision curve analysis (DCA) also showed the good net benefit of 7-day DSM in both derivation and validation groups, which indicated the higher value of 7-day DSM for clinical application (Fig. 5 .C, D). The ROC analysis showed that the 7-day DSM score was more accurate than COSSH-ACLF II in predicting 28-day and 90-day mortality in the validation group (28-day AUC:0.8424 vs 0.7998, 90-day AUC:0.8798 vs 0.8186 Fig. 5 .E, F). Finally, for the visual of the 7-day DSM, we established a nomogram to predict the risk of 90-day survival (Figure. 6). Table 2 The clinical characteristics of patients in derivation and validation groups. Characteristic derivation group validation group P Gender (male) 99 (79.8%) 65 (90.2%) 0.057 Age (years) 49 ± 12 50 ± 12 0.460 Laboratory data TP (g/L) 60.5 (53.2–65.7) 57.2 (53.1–61.3) 0.086 ALB (g/L) 30.7 (27.5–34.2) 31.8 (28.8–33.7) 0.473 GLO (g/L) 27.7 (23.2–34.8) 25.5 (21.2–30.4) 0.032 ALT (U/L) 259.5 (109.0-714.5) 336.0 (79.0-785.0) 0.544 AST (U/L) 210.0 (110.3-486.5) 182.0 (94.0-470.0) 0.480 ALP (U/L) 142.0 (120.3-179.3) 142.0 (116.0-159.0) 0.504 TBA (µmol/L) 178.2 (120.3-231.8) 165.6 (114.3.6-230.8) 0.562 TB (µmol/L) 321.6 (263.0-400.8) 297.7 (197.0-366.5) 0.132 γ-GT (U/L) 80.0 (50.5-143.5) 86.5 (50.0-125.5) 0.942 Serum creatinine (µmol/L) 62.4 (51.9–71.8) 61.5 (52.0-69.5) 0.996 Serum urea (µmol/L) 4.1 (3.2-6.0) 4.6 (3.5–39.8) 0.226 TG (mmol/L) 1.2 (1.0-1.6) 1.6 (1.3–2.4) 0.108 Tch (mmol/L) 2.0 (1.6–2.5) 2.3 (1.6–2.9) 0.377 HDL-C (mmol/L) 0.2 (0.1–0.2) 0.2 (0.2–0.2) 0.018 LDL-C (mmol/L) 1.3 (1.0-1.7) 0.4 (0.3-1.0) < 0.001 K+ (mmol/L) 4.0 (3.5–4.3) 4.1 (3.7-5.0) 0.536 Na+ (mmol/L) 134.8 (130.6–138.0) 136.0 (132.0-138.6) 0.963 Glu (mmol/L) 5.0 (4.1–6.2) 5.9 (4.2–7.6) 0.023 WBC (×10 9 /L) 7.4 (5.3–10.1) 6.9 (4.7–13.0) 0.439 Neutrophil (×10 9 /L) 5.7 (3.7–7.9) 4.9(2.8–9.3) 0.967 Hs-CRP (mg/L) 11.0 (6.4–18.7) 8.8 (4.5–14.6) 0.303 HGB (g/L) 125.0 (109.0-136.0) 126.0 (104.0-136.0) 0.174 HCT (%) 35.4 (30.6–38.7) 34.5 (30.0-39.7) 0.229 PLT (×10 9 /L) 102.5(62.3–140.0) 89.0 (66.0-120.0) 0.595 INR 2.2 (1.8–2.7) 2.2 (1.6–2.9) 0.814 Fib (g/L) 1.6 (1.3-2.0) 1.4 (1.0-1.9) 0.096 PT (s) 24.6 (21.1–29.0) 24.9 (18.8–30.9) 0.521 DD (µg/L) 2.4 (1.5–3.9) 1.4 (0.8–2.1) < 0.001 Ferritin (ng/ml) 1905.0 (596.9–2000.0) 3309.5 (2322.0-4297.0) 0.013 HBV-DNA (IU/ml) 8.45E + 04 (5.55E + 03-2.34E + 06) 1.11E + 05 (2.42E + 03-3.54E + 06) 0.935 COSSH-ACLF П 7.2(6.6–7.8) 7.1(6.4–8.1) 0.548 Survival time 42(9–91) 35.5 (13.3–91) 0.739 28 mortality rate 45.2% 41.7% 0.635 90 mortality rate 57.3% 58.3% 0.883 Note: The data are expressed as medians (IQR), mean ± (SD) or number of patients (%).TP, total protein; ALB, albumin; GLO, globulin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; TBA, total bile acid; TB, total bilirubin; γ-GT ,glutamyl transpeptidase; TG, triglycerides; Tch, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; K+, serum potassium; Na+, serum sodium; Glu, fasting blood glucose; WBC, white blood cell count; HGB hemoglobin; PLT, platelet count; INR, international normalized ratio; Fib, fibrinogen; PT, prothrombin time; DD, D dimer; BI, bacterial infection; GIH, gastrointestinal haemorrhage;MAP, mean arterial pressure;COSSH-ACLF П, Chinese Group on the Study of Severe Hepatitis B-ACLF score. Discussion Because of the complexity and high mortality, the early prognosis was a pivotal topic in ACLF research. The classic MELD score proposed for liver transplantation assessment was often used in predicting the outcome of patients with ACLF[ 12 ]. An ACLF specific prognostic model CLIF-C ACLF score showed liable predicting ability in all cause ACLF[ 13 ]. Compared to other models, a recently proposed COSSH-ACLF II score was demonstrated to be superior in patients with HBV-ACLF[ 10 ].In this study, we found that the current prognostic models had a high predicting ability for the prognosis of ACLF patients, especially for those who died in a very short time. Nevertheless, for those patients who survived for a comparable long time, the predicting ability of the prognostic models using the baseline index would decrease, which indicated that the severity of ACLF in admission is not the only determining factor of prognosis. Based on the findings, we provide a hypothesis that not only the severity of the disease but also the reversibility and the response to treatment would lead to different outcomes. Around 60% of ACLF patients had identified precipitating events[ 14 ]. Either hepatic or non-hepatic precipitating events can induce systemic inflammatory response syndrome (SIRS) by damage-associated molecular patterns (DAMPs) or pathogen-associated molecular patterns (PAMPs) and finally result in multiple organ failure[ 15 ]. Studies have described that the SIRS period lasted about one week and was called the “golden window”[ 16 ]. A survey of ACLF found that the cumulative incidence of new SIRS was 29% by days 4 but abruptly increased to 92.8% by days 7, and the absence of SIRS in the first week was associated with a reduced incidence of organ failure[ 17 ]. Another study found that the lactate clearance rate measured over the course of 1-week post-admission was significantly higher in the survival group than in the death group[ 18 ].In our study, we compared the scores on day 3 and day 7 and also found that the dynamic difference of scores on day 7 compared to baseline was more accurate than day 3 versus baseline in predicting 90-day outcomes. We thought that the effective therapy in the initial 1-week from the symptom onset was most important in the whole clinical course and strongly associated with outcomes. Reversibility is a typical characteristic of ACLF that is distinct from end-stage liver disease. The CANONIC study found that the ACLF resolved or improved in 49.2% of cases during the clinical course[ 11 ]. A Study of HBV-ACLF found that patients with prior decompensation history diagnosed by the APASL-ACLF Research Consortium (AARC) criteria showed favourable reversibility and maintained a stable status after receiving nucleoside analogues[ 19 ]. Since the reversibility of ACLF disease, studies paid more attention to the dynamic assessments of the severity of ACLF in recent years. A previously mentioned study evaluated the clinical course by comparing the CLIF-C ACLF scores on day 3 and day 7 and found that the dynamic change of CLIF-C ACLF scores were strongly associated with the prognosis[ 11 ]. A recent multiple-centre study of HBV-ACLF established a new multi-state model by accessing ACLF grade at different time points, which performed better than traditional prognostic scores[ 20 ]. Another study found that age, World Gastroenterology Organization (WGO) type, basic aetiology, total bilirubin, creatinine, prothrombin activity, and hepatic encephalopathy stage were all independent prognostic factors in ACLF. It proposed a DP-ACLF score based on the dynamic trending of those indicators[ 21 ]. However, the current study of dynamic scores is based on retrospective data, and most scores were complicated for clinical use. In this study, we validated the mainstream score in a prospective cohort and, based on the cohort, we proposed a new dynamic score based on baseline total bilirubin, PT-INR and dynamic δCOSSH-ACLF II score on day 7 and baseline. We then validated the score in a multiple-centre prospective cohort and further estimated the score by discrimination and calibration curve. In summary, we established a new dynamic prognostic for predicting HBV-ACLF prognosis, which was more accurate than the current scoring systems. However, although this study used a multicenter cohort for external validation, due to the limited sample size, the validity of the validation depends on the size and diversity of the external cohort. Therefore, a larger and more diverse prospective cohort is needed to verify the new scores. Abbreviations ACLF Acute-on-chronic liver failure ALSS artificial liver support system SIRS systemic inflammatory response syndrome COSSH-ACLF II Chinese Severe Hepatitis B Research Group-ACLF II CLIF-C ACLF CLIF Consortium acute-on-chronic liver failure CLIF-SOFA chronic liver failure-sequential organ failure assessment score DAMPs damage-associated molecular patterns EASL-CLIF European Association for the Study of the Liver-chronic Liver Failure HBV-ACLF hepatitis virus B related acute-on-chronic liver failure HBV hepatitis B virus MELD model for end-stage liver disease PAMPs pathogen-associated molecular patterns PT-INR prothrombin time international normalized ratio SMT standard medical treatment TB bilirubin Declarations Funding The study was supported by national natural science foundation of China Youth Program (Funding number: 82102293) Author contributions Jiang Li, Jiemenglu Li, Chunyan Jiang and Jian Yang contributed equally. The study was designed by Jiang Li and supervised by Jiemenglu Li, Chunyan Jiang,Jian Yang and Jiang Li. The manuscript was written by Jiemenglu Li, Chunyan Jiang and Jian Yang. The data collection, analysis and interpretation were performed by Jiang Li, Jiemenglu Li, Chunyan Jiang and Jian Yang, Qingting Zhao, Li Zhang, Wenyuan Li, Daxian Wu, Qian Zhou, Xifei Hong, Tianzhou Wu, Wenting Li, Jun Cheng, Nan Xu, Yufeng Gao and Jiang Li . All authors were involved in the critical revision of the manuscript. Conflict of interest The authors have no conflict to report. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was performed according to the Helsinki II Declaration and was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all patients or their legal surrogates before enrolment. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Bernal W, Jalan R, Quaglia A, Simpson K, Wendon J, Burroughs A. Acute-on-chronic liver failure. Lancet. 2015;386:1576–87. Zheng MH, Shi KQ, Fan YC, Li H, Ye C, Chen QQ, et al. A model to determine 3-month mortality risk in patients with acute-on-chronic hepatitis B liver failure. Clin Gastroenterol hepatology: official Clin Pract J Am Gastroenterological Association. 2011;9:351–6. e353. Sarin SK, Kumar A, Almeida JA, Chawla YK, Fan ST, Garg H, et al. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific Association for the study of the liver (APASL). Hep Intl. 2009;3:269–82. Wan Z, Wu Y, Yi J, You S, Liu H, Sun Z, et al. Combining serum cystatin C with total bilirubin improves short-term mortality prediction in patients with HBV-related acute-on-chronic liver failure. PLoS ONE. 2015;10:e0116968. European Association for the Study of the Liver. Electronic address eee, European Association for the Study of the L. EASL Clinical Practice Guidelines on acute-on-chronic liver failure. J Hepatol. 2023;79:461–91. Kamath PS, Kim WR. Advanced Liver Disease Study G. The model for end-stage liver disease (MELD). Hepatology. 2007;45:797–805. Moreau R, Jalan R, Gines P, Pavesi M, Angeli P, Cordoba J, et al. Acute-on-chronic liver failure is a distinct syndrome that develops in patients with acute decompensation of cirrhosis. Gastroenterology. 2013;144:1426–37. 1437 e1421-1429. Jalan R, Saliba F, Pavesi M, Amoros A, Moreau R, Gines P, et al. Development and validation of a prognostic score to predict mortality in patients with acute-on-chronic liver failure. J Hepatol. 2014;61:1038–47. Wu T, Li J, Shao L, Xin J, Jiang L, Zhou Q, et al. Development of diagnostic criteria and a prognostic score for hepatitis B virus-related acute-on-chronic liver failure. Gut. 2018;67:2181–91. Li J, Liang X, You S, Feng T, Zhou X, Zhu B, et al. Development and validation of a new prognostic score for hepatitis B virus-related acute-on-chronic liver failure. J Hepatol. 2021;75:1104–15. Gustot T, Fernandez J, Garcia E, Morando F, Caraceni P, Alessandria C, et al. Clinical Course of acute-on-chronic liver failure syndrome and effects on prognosis. Hepatology. 2015;62:243–52. Qi T, Zhu C, Wang J, Li B, Huang Z, Zhu Z, et al. MELD score < 18 rule out 28-day ACLF development among inpatients with hepatitis B-related previous compensated liver disease. J Viral Hepatitis. 2022;29:1089–98. Barosa R, Roque Ramos L, Patita M, Nunes G, Fonseca J. CLIF-C ACLF score is a better mortality predictor than MELD, MELD-Na and CTP in patients with Acute on chronic liver failure admitted to the ward. Rev Esp Enferm Dig. 2017;109:399–405. Arroyo V, Moreau R, Kamath PS, Jalan R, Gines P, Nevens F, et al. Acute-on-chronic liver failure in cirrhosis. Nat reviews Disease primers. 2016;2:16041. Moreau R. The Pathogenesis of ACLF: The Inflammatory Response and Immune Function. Semin Liver Dis. 2016;36:133–40. Br VK, Sarin SK. Acute-on-chronic liver failure: Terminology, mechanisms and management. Clin Mol Hepatol. 2023;29:670–89. Choudhury A, Kumar M, Sharma BC, Maiwall R, Pamecha V, Moreau R, et al. Systemic inflammatory response syndrome in acute-on-chronic liver failure: Relevance of 'golden window': A prospective study. J Gastroenterol Hepatol. 2017;32:1989–97. Chen W, You J, Chen J, Zhu Y. Combining the serum lactic acid level and the lactate clearance rate into the CLIF-SOFA score for evaluating the short-term prognosis of HBV-related ACLF patients. Expert Rev Gastroenterol Hepatol. 2020;14:483–9. Wang H, Tong J, Xu X, Chen J, Mu X, Zhai X, et al. Reversibility of acute-on-chronic liver failure syndrome in hepatitis B virus-infected patients with and without prior decompensation. J Viral Hepatitis. 2022;29:890–8. Yu X, Liu X, Tan W, Wang X, Zheng X, Huang Y et al. The clinical courses of HBV-related acute-on-chronic liver failure and a multi-state model to predict disease evolution. Hepatol Commun 2024;8. Yu Z, Zhang Y, Cao Y, Xu M, You S, Chen Y, et al. A dynamic prediction model for prognosis of acute-on-chronic liver failure based on the trend of clinical indicators. Sci Rep. 2021;11:1810. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6733150","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475569687,"identity":"bd41ed90-62a9-4ed8-9490-bf53e0e83268","order_by":0,"name":"Jiemenglu Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiemenglu","middleName":"","lastName":"Li","suffix":""},{"id":475569688,"identity":"63c5c2ee-62fd-4c32-b9f8-a7f1e9b9c084","order_by":1,"name":"Chunyan Jiang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Jiang","suffix":""},{"id":475569689,"identity":"91f4f007-32a9-4ed2-b862-e7f2e86a4112","order_by":2,"name":"Jian Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yang","suffix":""},{"id":475569690,"identity":"64667c75-92ec-49a2-8cc8-7f7e81f0afbe","order_by":3,"name":"Qingting Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingting","middleName":"","lastName":"Zhao","suffix":""},{"id":475569691,"identity":"da1a8c5b-555f-445c-a15a-3d093c046e02","order_by":4,"name":"Li Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Anhui Medical 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13:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6733150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6733150/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85617136,"identity":"2a9de416-2950-47d5-a5f5-601426ebb140","added_by":"auto","created_at":"2025-06-29 14:44:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":428526,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the study shows the screening, enrolment, and exclusion of patients according to the COSSH-ACLF criteria. ACLF, acute-on-chronic liver failure; COSSH, Chinese Group on the Study of Severe Hepatitis B; HBV-ACLF, HBV-related ACLF; LT, liver transplantation.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/912c9cd47a735baa49835651.png"},{"id":85617141,"identity":"66a63091-a743-4a3f-b980-9fa9a21f86b0","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177631,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan Meier curve of ACLF grade 1, ACLF grade 2, and ACLF grade 3 patients for 7, 28 and 90 days.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/a1fc8f2e36a8812c840fd6d5.png"},{"id":85617157,"identity":"83de7f68-d562-40e8-b383-71cdb3de681e","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":563400,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC of prognostic scores. A: The ROC of MELD, CLIF-C-ACLF and COSSH-ACLF II for 7 days, 28 days and 90 days. B: The ROC of δMELD, δCLIF-C-ACLF and δCOSSH-ACLF II between day 7, day 3 and baseline for 28 days. C: The ROC of δMELD, δCLIF-C-ACLF and δCOSSH-ACLF II between day 7, day 3 and baseline for 90 days.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/5f83bcd796af9cf4434d1d1e.png"},{"id":85617147,"identity":"cffef218-43e3-420b-b070-72d276f0a7c0","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178870,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC of 7-day dynamic scoring model. A: The ROC of 7-day dynamic scoring model, MELD, CLIF-C ACLF and COSSH-ACLF II scores for 28 days. B: The ROC of 7-day dynamic scoring model, MELD, CLIF-C ACLF and COSSH-ACLF II scores for 90 days.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/70970d49e49ea6ba66b1a801.png"},{"id":85617144,"identity":"adf4d95e-c8c5-47dd-9bcb-4adc98bcf07d","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":516821,"visible":true,"origin":"","legend":"\u003cp\u003eEstimation and validation of the 7-day dynamic scoring model. A, B: The estimation curve of the 7-day DSM in the derivation and validation groups. C, D: The DCA of the 7-day DSM in the derivation and validation group. E, F: The ROC of 7-day DSM and COSSH-ACLF II in predicting 28- and 90-day mortality in the validation group.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/527e9d3cae8cdae23297c0b6.png"},{"id":85617155,"identity":"88ba0bac-cc74-406d-ba7e-e3b62f4eefb5","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":126194,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram of 7-day dynamic scoring model for predicting 90-day outcome.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/b4508367df8a6242d3dd5c4a.png"},{"id":89035669,"identity":"911ea35f-c0c3-4dd1-80fe-7245bc91686f","added_by":"auto","created_at":"2025-08-14 03:53:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1385646,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/647246c8-7ade-48d8-8574-8a0b7685b058.pdf"},{"id":85618800,"identity":"6a798623-2d3f-4ca8-aea9-e4fc1f036028","added_by":"auto","created_at":"2025-06-29 14:52:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18583,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/ce9b45c194979ad84ddb5bc3.docx"},{"id":85617151,"identity":"c3a2ede4-03d4-41b8-aa2c-036cf00ae588","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":15790,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/67e2331fda1c03e7ff8aefe8.docx"},{"id":85617143,"identity":"d56a44b1-bef9-4673-a2b2-c96958be0582","added_by":"auto","created_at":"2025-06-29 14:44:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15614,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6733150/v1/81ab32bc799ca282f1bf25b9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a dynamic prognostic score for hepatitis B virus-related acute-on-chronic liver failure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute-on-chronic liver failure (ACLF) is a complicated clinical syndrome characterized by acute decompensation of chronic liver disease, multiple organ failure, and a high mortality rate[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A variety of factors, such as alcoholic liver disease and hepatitis virus infection, can cause ACLF. In many Asian countries, over 70% of ACLF cases were related to hepatitis B virus (HBV) infection and in China, the number can rise to 80%[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, liver transplantation was the most effective treatment for some ACLF cases[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the shortage of donor organ, high cost and lifelong administration of immunosuppressive drugs were still challenges of liver transplantation. Therefore, early diagnosis and prognosis assessment of ACLF are crucial in selecting suitable transplant candidates and reducing the high mortality rate.\u003c/p\u003e \u003cp\u003eSeveral models were available to identify the severity of ACLF and predict the prognosis of ACLF patients. The model of end-stage liver disease (MELD) has been widely used to describe the severity of liver disease and to allocate and manage organs for liver transplantation in patients with liver failure[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A large prospective multicenter cohort of ACLF performed by the European Association for the Study of the Liver-chronic Liver Failure (EASL-CLIF) has proposed the chronic liver failure-sequential organ failure assessment score (CLIF-SOFA) score, which can more accurately assess outcomes. Then, they simplified it to the new chronic liver failure consortium acute on chronic liver failure score (CLIF-C-ACLF) score[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Another large prospective multicenter cohort Chinese Severe Hepatitis B Research Group (COSSH) studies have proposed new COSSH-ACLF scores and COSSH-ACLF II scores, especially for HBV-ACLF[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. CLIF-C-ACLF and COSSH-ACLF II scores had predicting accuracy of around 80% for 28-and 90-day mortality[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUsually, the above scoring system models used clinical data collected on ACLF patient admission to assess their prognosis. However, the clinical course of ACLF is highly dynamic, besides the severity of the disease on admission, the response to treatment and the reversibility of the disease are also highly associated with the outcome[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The prognostic models based on the single initial time point may not be appropriate for rapidly progressing ACLF or those significantly improved after effective therapy. To provide a more accurate prognosis for 90-day mortality, we proposed a new prognostic model using the index on admission and the difference of COSSH-ACLF II scores at admission and 7 days. Then, we validated the new model using retrospective data from the COSSH prospective cohort.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eStudy design\u003c/p\u003e\n\u003cp\u003eFirst, clinical characteristics and prognostic indicators on admission, on day 3 and day 7, were collected to validate\u0026nbsp;the model for end-stage liver disease (MELD), CLIF-C ACLF score and COSSH-ACLF II score and to develop the new prognostic score. Then, the new prognostic score was validated in the multiple-center prospective cohort. Detailed clinical\u0026nbsp;data and outcomes for all enrolled patients were collected and\u0026nbsp;recorded in case report forms (see supplementary materials) at admission, on day 3 and 7 and follow-up for 90 days. The study adhered to the Helsinki II Declaration and was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. All patients provided written informed consent.\u003c/p\u003e\n\u003cp\u003ePatients\u003c/p\u003e\n\u003cp\u003eACLF was initially screened at the Department of Infectious Disease, the First Affiliated Hospital of Anhui Medical University from July 2021 to Jun 2022. The ACLF diagnosis was according to the COSSH-ACLF diagnostic\u0026nbsp;criteria. The exclusion criteria are summarized in Figure 1. During hospitalization, all patients received the same treatment of the COSSH-ACLF study, including standard medical treatment\u0026nbsp;(SMT; the detailed SMT protocols were described in supplementary materials), artificial liver support system (ALSS) treatment was used to treat patients according to the Chinese guideline for liver failure 2020. The patients in the validation group were enrolled under the same inclusion and exclusion criteria. They accepted the same treatment as patients in the derivation group from 5 liver units of different\u0026nbsp;hospitals.\u003c/p\u003e\n\u003cp\u003eDevelopment and validation of an HBV-ACLF prognostic score\u003c/p\u003e\n\u003cp\u003eWe aimed to develop a new prognostic score for patients with HBV-ACLF for 90 days outcome. Four main steps were performed. First, we calculated the difference of COSSH-ACLF II for baseline and day 7 and used the delta value of the two scores as an index for the subsequent analysis. Second, the univariate and multivariate Cox regression analysis, including delta COSSH-ACLF II and other laboratory index as candidate risk factors, was conducted to select the factors most associated with 90-day mortality. The risk factors selected by multivariate Cox regression were used to propose the new prognostic score. Third, the performance of the new prognostic score was compared with the three prognostic score systems described above by receptor operation curve (ROC) analysis. The new prognostic score was estimated using the calibration curve and decision curve analysis, and finally, it was presented using a nomogram. Fourth, the new prognostic score was validated in an external validation group, including five center data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eThe measurements were presented as median (IQR) or mean \u0026plusmn; (SD) or numbers (%). Student\u0026rsquo;s t-test or the Mann-Whitney U test was used to compare continuous variables, \u0026nbsp;the \u0026chi;2 test was used to compare categorical variables. Significance was defined as P value \u0026lt;0.05. SPSS software V.25 (SPSS, Chicago, Illinois, USA) was used to compare baseline characteristics. GraphPad Prism 10.0 was used to perform the Kaplan-Meier curve. Other analyses were conducted using R software, version 4.40 (https://www.r-project.org).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatients and clinical characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 242 patients hospitalized for HBV-ACLF, 70 patients, including 46 patients with hepatocellular carcinoma or other tumours, 6 patients with severe extra-hepatic diseases, 6 patients receiving immunosuppression drugs for other reasons, 4 patients with uncontrolled mental illness, 2 patients older than 80 years, 2 pregnant patients and 4 patients with other reasons were excluded in this study. 48 patients who accepted liver transplantation or dropped out during the 90-day follow-up were excluded from calculating 90-day mortality (Figure.1). Among the 124 patients analyzed in the study, 27 patients died in 7 days, with a mortality rate of 21.8%. There were 71 patients who died in 90 days, with a mortality rate of 57.3%. The K-M curve showed the survival risk of patients with HBV-ACLF in different ACLF grades for 7, 28 and 90 days (Figure.2). The clinical characteristics of all enrolled patients are summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Most HBV-ACLF patients in both the deceased and survived group were male. The mean age was 50\u0026thinsp;\u0026plusmn;\u0026thinsp;12 years old, which was insignificant compared to that of the survived group (48\u0026thinsp;\u0026plusmn;\u0026thinsp;12 years old). Laboratory indicators, including ALB, TB, \u0026gamma;-GT, serum creatinine, serum urea, TG, Tch, HDL-C, LDL-C, serum sodium, White blood cell count, neutrophils, INR and prothrombin time were significantly worse in the deceased group than those in the survival group. The values of the COSSH-ACLF II, CLIF-C ACLF and MELD scores for patients with HBV-ACLF in deceased group were 7.5 (7.1\u0026ndash;8.3), 43.7 (38.5\u0026ndash;47.0), 25.4 (22.3\u0026ndash;30.3) respectively, and were significantly higher than those for patients in survived group [CLIF-C ACLF: 39.3 (34.2\u0026ndash;43.5), COSSH-ACLF II: 6.8(6.4\u0026ndash;7.3), MELD: 21.1(18.5\u0026ndash;23.8)].\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe clinical characteristics of patients in deceased and survival groups in derivation cohort.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDeceased (n\u0026thinsp;=\u0026thinsp;71)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSurvived (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (74.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLaboratory data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.9 (52.5\u0026ndash;66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.2 (53.8\u0026ndash;65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.9 (27.2\u0026ndash;33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.6 (27.7\u0026ndash;34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLO (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.7 (22.6\u0026ndash;34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.4 (24.0\u0026ndash;34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232.0 (97.0-715.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245.5 (115.0-644.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238.0 (118.0-547.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175.5 (99.3-400.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.0 (122.0-181.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.5 (116.5\u0026mdash;177.8a)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBA (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202.8 (88.5-242.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e167.5 (102.3\u0026ndash;188.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354.9 (279.5-435.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e304.3 (237.6-384.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.0 (46.0-108.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108.5 (65.8-180.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum creatinine (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.1 (52.0-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.4 (52.8\u0026ndash;69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum urea (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.7 (3.1-8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.7 (2.7\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (0.9\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.4\u0026ndash;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTch (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.3\u0026ndash;1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.8 (2.2-3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.1\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.1\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0 (0.7\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.5\u0026ndash;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9 (3.5\u0026ndash;4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9 (3.5\u0026ndash;4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa+ (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.5 (128.2\u0026ndash;138.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.3 (133.8-138.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlu (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8 (4.0-6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9 (4.1\u0026ndash;5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9 (5.8\u0026ndash;10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.4 (5.0-9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.8 (3.9\u0026ndash;8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3 (2.8\u0026ndash;6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHs-CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.0 (5.8\u0026ndash;27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.3 (4.4\u0026ndash;17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.0 (107.0-135.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.5 (110.5\u0026ndash;136.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.4 (30.1\u0026ndash;38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.1 (30.7\u0026ndash;39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.0 (53.0-154.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.0 (72.8-135.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT-INR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5 (2.0-2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.9 (1.7\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFib (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5 (1.2\u0026ndash;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8 (1.4\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.9 (22.7\u0026ndash;31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.6 (19.8\u0026ndash;25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDD (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6 (1.6-4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (1.2\u0026ndash;3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFerritin (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1433.4 (632.6\u0026ndash;2000.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1161.3 (384.2\u0026ndash;1957.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBV-DNA (IU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u0026times;10\u003csup\u003e5\u003c/sup\u003e (3.2\u0026times;10\u003csup\u003e4\u003c/sup\u003e -3.4\u0026times;10\u003csup\u003e6\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u0026times;10\u003csup\u003e4\u003c/sup\u003e (9.9\u0026times;10\u003csup\u003e3\u003c/sup\u003e -7.2\u0026times;10\u003csup\u003e5\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLIF-C ACLF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.7(38.5\u0026ndash;47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.3(34.2\u0026ndash;43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOSSH-ACLF П\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5(7.1\u0026ndash;8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.8(6.4\u0026ndash;7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMELD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.4(22.3\u0026ndash;30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1(18.5\u0026ndash;23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(5\u0026ndash;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: The data are expressed as medians (IQR), mean \u0026plusmn; (SD) or number of patients (%).TP, total protein; ALB, albumin; GLO, globulin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; TBA, total bile acid; TB, total bilirubin; \u0026gamma;-GT ,glutamyl transpeptidase; TG, triglycerides; Tch, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; K, serum potassium; Na, serum sodium; Glu, fasting blood glucose; WBC, white blood cell count; HGB hemoglobin; PLT, platelet count; PT-INR, prothrombin time-international normalized ratio; Fib, fibrinogen; PT, prothrombin time; DD, D dimer; BI, bacterial infection; GIH, gastrointestinal haemorrhage; HE, hepatic encephalopathy; MAP, mean arterial pressure..CLIF-C ACLFs, Chronic Liver Failure Consortium ACLF score; COSSH-ACLF П, Chinese Group on the Study of Severe Hepatitis B-ACLF score II; MELDs, model for end-stage liver disease score.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe ROC was used to validate the predictive ability of the model to compare the prognostic value of the above-mentioned prognostic scores. The AUC of MELD score was 0.801, 0.621 and 0.673 for 7 days, 28 days and 90 days, respectively. The AUC of CLIF-C ACLF score was 0.705, 0.696, and 0.640 for 7 days, 28 days and 90 days, respectively. The AUC of COSSH-ACLF II score was 0.790, 0.774 and 0.733 for 7 days, 28 days and 90 days, respectively. Moreover, the COSSH-ACLF II score best predicted 7-day, 28-day, 90-day outcomes in this study. Here, we found that the 90-day predictive ability of all three models decreased compared to those for 7 days (Figure.3A), which indicated the baseline disease severity was not the only factor associated with the 3-month outcome. Then, we calculated the difference between the three scores on day 3 and day 7 compared to baseline.\u003c/p\u003e\n\u003cp\u003eThe ROC showed that \u0026delta;MELD 7\u0026thinsp;\u0026minus;\u0026thinsp;0 was better than \u0026delta;MELD 3\u0026thinsp;\u0026minus;\u0026thinsp;0 (AUC 0.872 VS. 0.741, P\u0026thinsp;=\u0026thinsp;0.0518) and \u0026delta;CLIF-C ACLF 7\u0026thinsp;\u0026minus;\u0026thinsp;0, \u0026delta;COSSH-ACLF II 7\u0026thinsp;\u0026minus;\u0026thinsp;0 were significantly better than \u0026delta;CLIF-C ACLF3-0 and \u0026delta;COSSH-ACLF II 3\u0026thinsp;\u0026minus;\u0026thinsp;0 (AUC 0.879 VS. 0.712, P\u0026thinsp;=\u0026thinsp;0.0062, 0.913 VS. 0.800, P\u0026thinsp;=\u0026thinsp;0.0277, respectively) in predicting 28-day mortality(Figure.3B). In predicting 90-day mortality, \u0026delta;MELD 7\u0026thinsp;\u0026minus;\u0026thinsp;0, \u0026delta;CLIF-C ACLF 7\u0026thinsp;\u0026minus;\u0026thinsp;0, \u0026delta;COSSH-ACLF II 7\u0026thinsp;\u0026minus;\u0026thinsp;0 (AUC: 0.887, 0.848, 0.905, respectively) also showed significantly better performance than those of \u0026delta;MELD3-0, \u0026delta;CLIF-C ACLF3-0 and \u0026delta;COSSH-ACLF II 3\u0026thinsp;\u0026minus;\u0026thinsp;0 (0.686, 0.681, 0.737, P\u0026thinsp;=\u0026thinsp;0.0001, P\u0026thinsp;=\u0026thinsp;0.0012, P\u0026thinsp;=\u0026thinsp;0.0001 respectively)(Figure.3C). Taken together, we choose the \u0026delta;COSSH-ACLF II7-0 as a factor in the following analysis.\u003c/p\u003e\n\u003cp\u003eDevelopment of a prognostic score\u003c/p\u003e\n\u003cp\u003eThe univariate Cox regression analysis found that TB, ALT, serum sodium, serum creatinine, serum urea, PT-INR, FIB and COSSH-ACLF Ⅱ7\u0026thinsp;\u0026minus;\u0026thinsp;0 were the factors associated with 90-day mortality (Supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The further multivariate Cox analysis showed that TB, INR and COSSH ACLF Ⅱ7\u0026thinsp;\u0026minus;\u0026thinsp;0 were the independent factors of 90-day mortality (Supplementary table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Then we established the prognostic score using the following formula: 7-day dynamic score model (DSM)\u0026thinsp;=\u0026thinsp;0.005 \u0026times; TB\u0026thinsp;+\u0026thinsp;0.609 \u0026times; INR\u0026thinsp;+\u0026thinsp;1.234 \u0026times; COSSH ACLF Ⅱ7\u0026thinsp;\u0026minus;\u0026thinsp;0. Compared with the MELD, CLIF-C ACLF and COSSH-ACLF II scores, the new score yielded a significantly more accurate prognosis, with the highest AUC for predicting the 90-days mortality of patients with HBV-ACLF (28-and 90-days mortality: 0.923 and 0.925, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 compared other 3 models,Figure.4).\u003c/p\u003e\n\u003cp\u003eEstimation, validation and visual of the new score\u003c/p\u003e\n\u003cp\u003eTo validate the performance of the 7-day DSM, a 5-center external validation group of 72 patients were enrolled. The clinical characteristics were summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and the main index, including severity score and mortality, had no significant difference compared to the derivation group. The calibration performance showed that the 7-day DSM had good overall performance and exhibited promising predictive accuracy for death at 90 days both in the derivation and validation group (Figure.5A, B). The Decision curve analysis (DCA) also showed the good net benefit of 7-day DSM in both derivation and validation groups, which indicated the higher value of 7-day DSM for clinical application (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.C, D). The ROC analysis showed that the 7-day DSM score was more accurate than COSSH-ACLF II in predicting 28-day and 90-day mortality in the validation group (28-day AUC:0.8424 vs 0.7998, 90-day AUC:0.8798 vs 0.8186 Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e.E, F). Finally, for the visual of the 7-day DSM, we established a nomogram to predict the risk of 90-day survival (Figure. 6).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe clinical characteristics of patients in derivation and validation groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ederivation group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003evalidation group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (79.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (90.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLaboratory data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTP (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5 (53.2\u0026ndash;65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.2 (53.1\u0026ndash;61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.7 (27.5\u0026ndash;34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.8 (28.8\u0026ndash;33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLO (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.7 (23.2\u0026ndash;34.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.5 (21.2\u0026ndash;30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259.5 (109.0-714.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336.0 (79.0-785.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210.0 (110.3-486.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.0 (94.0-470.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.0 (120.3-179.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.0 (116.0-159.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBA (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178.2 (120.3-231.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.6 (114.3.6-230.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTB (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321.6 (263.0-400.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297.7 (197.0-366.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.0 (50.5-143.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.5 (50.0-125.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum creatinine (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.4 (51.9\u0026ndash;71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.5 (52.0-69.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum urea (\u0026micro;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1 (3.2-6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6 (3.5\u0026ndash;39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (1.0-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (1.3\u0026ndash;2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTch (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0 (1.6\u0026ndash;2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3 (1.6\u0026ndash;2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.1\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.2\u0026ndash;0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (1.0-1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4 (0.3-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK+ (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0 (3.5\u0026ndash;4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1 (3.7-5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa+ (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.8 (130.6\u0026ndash;138.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.0 (132.0-138.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlu (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0 (4.1\u0026ndash;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.9 (4.2\u0026ndash;7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4 (5.3\u0026ndash;10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.9 (4.7\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7 (3.7\u0026ndash;7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9(2.8\u0026ndash;9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHs-CRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0 (6.4\u0026ndash;18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8 (4.5\u0026ndash;14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125.0 (109.0-136.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.0 (104.0-136.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.4 (30.6\u0026ndash;38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5 (30.0-39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.5(62.3\u0026ndash;140.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.0 (66.0-120.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2 (1.8\u0026ndash;2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2 (1.6\u0026ndash;2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFib (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6 (1.3-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.0-1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.6 (21.1\u0026ndash;29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.9 (18.8\u0026ndash;30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDD (\u0026micro;g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4 (1.5\u0026ndash;3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (0.8\u0026ndash;2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFerritin (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1905.0 (596.9\u0026ndash;2000.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3309.5 (2322.0-4297.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHBV-DNA (IU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.45E\u0026thinsp;+\u0026thinsp;04 (5.55E\u0026thinsp;+\u0026thinsp;03-2.34E\u0026thinsp;+\u0026thinsp;06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11E\u0026thinsp;+\u0026thinsp;05 (2.42E\u0026thinsp;+\u0026thinsp;03-3.54E\u0026thinsp;+\u0026thinsp;06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOSSH-ACLF П\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2(6.6\u0026ndash;7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1(6.4\u0026ndash;8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurvival time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(9\u0026ndash;91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.5 (13.3\u0026ndash;91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 mortality rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 mortality rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: The data are expressed as medians (IQR), mean \u0026plusmn; (SD) or number of patients (%).TP, total protein; ALB, albumin; GLO, globulin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; ALP, alkaline phosphatase; TBA, total bile acid; TB, total bilirubin; \u0026gamma;-GT ,glutamyl transpeptidase; TG, triglycerides; Tch, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; K+, serum potassium; Na+, serum sodium; Glu, fasting blood glucose; WBC, white blood cell count; HGB hemoglobin; PLT, platelet count; INR, international normalized ratio; Fib, fibrinogen; PT, prothrombin time; DD, D dimer; BI, bacterial infection; GIH, gastrointestinal haemorrhage;MAP, mean arterial pressure;COSSH-ACLF П, Chinese Group on the Study of Severe Hepatitis B-ACLF score.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBecause of the complexity and high mortality, the early prognosis was a pivotal topic in ACLF research. The classic MELD score proposed for liver transplantation assessment was often used in predicting the outcome of patients with ACLF[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. An ACLF specific prognostic model CLIF-C ACLF score showed liable predicting ability in all cause ACLF[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Compared to other models, a recently proposed COSSH-ACLF II score was demonstrated to be superior in patients with HBV-ACLF[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].In this study, we found that the current prognostic models had a high predicting ability for the prognosis of ACLF patients, especially for those who died in a very short time. Nevertheless, for those patients who survived for a comparable long time, the predicting ability of the prognostic models using the baseline index would decrease, which indicated that the severity of ACLF in admission is not the only determining factor of prognosis. Based on the findings, we provide a hypothesis that not only the severity of the disease but also the reversibility and the response to treatment would lead to different outcomes.\u003c/p\u003e \u003cp\u003eAround 60% of ACLF patients had identified precipitating events[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Either hepatic or non-hepatic precipitating events can induce systemic inflammatory response syndrome (SIRS) by damage-associated molecular patterns (DAMPs) or pathogen-associated molecular patterns (PAMPs) and finally result in multiple organ failure[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Studies have described that the SIRS period lasted about one week and was called the \u0026ldquo;golden window\u0026rdquo;[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A survey of ACLF found that the cumulative incidence of new SIRS was 29% by days 4 but abruptly increased to 92.8% by days 7, and the absence of SIRS in the first week was associated with a reduced incidence of organ failure[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Another study found that the lactate clearance rate measured over the course of 1-week post-admission was significantly higher in the survival group than in the death group[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].In our study, we compared the scores on day 3 and day 7 and also found that the dynamic difference of scores on day 7 compared to baseline was more accurate than day 3 versus baseline in predicting 90-day outcomes. We thought that the effective therapy in the initial 1-week from the symptom onset was most important in the whole clinical course and strongly associated with outcomes.\u003c/p\u003e \u003cp\u003eReversibility is a typical characteristic of ACLF that is distinct from end-stage liver disease.\u003c/p\u003e \u003cp\u003eThe CANONIC study found that the ACLF resolved or improved in 49.2% of cases during the clinical course[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A Study of HBV-ACLF found that patients with prior decompensation history diagnosed by the APASL-ACLF Research Consortium (AARC) criteria showed favourable reversibility and maintained a stable status after receiving nucleoside analogues[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Since the reversibility of ACLF disease, studies paid more attention to the dynamic assessments of the severity of ACLF in recent years. A previously mentioned study evaluated the clinical course by comparing the CLIF-C ACLF scores on day 3 and day 7 and found that the dynamic change of CLIF-C ACLF scores were strongly associated with the prognosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A recent multiple-centre study of HBV-ACLF established a new multi-state model by accessing ACLF grade at different time points, which performed better than traditional prognostic scores[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Another study found that age, World Gastroenterology Organization (WGO) type, basic aetiology, total bilirubin, creatinine, prothrombin activity, and hepatic encephalopathy stage were all independent prognostic factors in ACLF. It proposed a DP-ACLF score based on the dynamic trending of those indicators[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the current study of dynamic scores is based on retrospective data, and most scores were complicated for clinical use. In this study, we validated the mainstream score in a prospective cohort and, based on the cohort, we proposed a new dynamic score based on baseline total bilirubin, PT-INR and dynamic δCOSSH-ACLF II score on day 7 and baseline. We then validated the score in a multiple-centre prospective cohort and further estimated the score by discrimination and calibration curve.\u003c/p\u003e \u003cp\u003eIn summary, we established a new dynamic prognostic for predicting HBV-ACLF prognosis, which was more accurate than the current scoring systems. However, although this study used a multicenter cohort for external validation, due to the limited sample size, the validity of the validation depends on the size and diversity of the external cohort. Therefore, a larger and more diverse prospective cohort is needed to verify the new scores.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACLF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute-on-chronic liver failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eartificial liver support system\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic inflammatory response syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOSSH-ACLF II\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChinese Severe Hepatitis B Research Group-ACLF II\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLIF-C ACLF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCLIF Consortium acute-on-chronic liver failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCLIF-SOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic liver failure-sequential organ failure assessment score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAMPs damage-associated molecular patterns\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEASL-CLIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEuropean Association for the Study of the Liver-chronic Liver Failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBV-ACLF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatitis virus B related acute-on-chronic liver failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehepatitis B virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMELD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodel for end-stage liver disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePAMPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epathogen-associated molecular patterns\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePT-INR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprothrombin time international normalized ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard medical treatment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebilirubin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e The study was supported by national natural science foundation of China Youth Program (Funding number: 82102293)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eJiang Li, Jiemenglu Li, Chunyan Jiang and Jian Yang contributed equally. The study was designed by Jiang Li and supervised by Jiemenglu Li, Chunyan Jiang,Jian Yang and Jiang Li. The manuscript was written by Jiemenglu Li, Chunyan Jiang and Jian Yang. The data collection, analysis and interpretation were performed by Jiang Li, Jiemenglu Li, Chunyan Jiang and Jian Yang, Qingting Zhao, Li Zhang, Wenyuan Li, Daxian Wu, Qian Zhou, \u0026nbsp; Xifei Hong, Tianzhou Wu, Wenting Li, Jun Cheng, Nan Xu, Yufeng Gao and Jiang Li . All authors were involved in the critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors have no conflict to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u0026nbsp; \u0026nbsp;The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed according to the Helsinki II Declaration and was approved by the Clinical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all\u0026nbsp;patients or their legal surrogates before enrolment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBernal W, Jalan R, Quaglia A, Simpson K, Wendon J, Burroughs A. Acute-on-chronic liver failure. Lancet. 2015;386:1576\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng MH, Shi KQ, Fan YC, Li H, Ye C, Chen QQ, et al. A model to determine 3-month mortality risk in patients with acute-on-chronic hepatitis B liver failure. 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J Viral Hepatitis. 2022;29:1089\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarosa R, Roque Ramos L, Patita M, Nunes G, Fonseca J. CLIF-C ACLF score is a better mortality predictor than MELD, MELD-Na and CTP in patients with Acute on chronic liver failure admitted to the ward. Rev Esp Enferm Dig. 2017;109:399\u0026ndash;405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArroyo V, Moreau R, Kamath PS, Jalan R, Gines P, Nevens F, et al. Acute-on-chronic liver failure in cirrhosis. Nat reviews Disease primers. 2016;2:16041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreau R. The Pathogenesis of ACLF: The Inflammatory Response and Immune Function. Semin Liver Dis. 2016;36:133\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBr VK, Sarin SK. Acute-on-chronic liver failure: Terminology, mechanisms and management. Clin Mol Hepatol. 2023;29:670\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury A, Kumar M, Sharma BC, Maiwall R, Pamecha V, Moreau R, et al. Systemic inflammatory response syndrome in acute-on-chronic liver failure: Relevance of 'golden window': A prospective study. J Gastroenterol Hepatol. 2017;32:1989\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, You J, Chen J, Zhu Y. Combining the serum lactic acid level and the lactate clearance rate into the CLIF-SOFA score for evaluating the short-term prognosis of HBV-related ACLF patients. Expert Rev Gastroenterol Hepatol. 2020;14:483\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Tong J, Xu X, Chen J, Mu X, Zhai X, et al. Reversibility of acute-on-chronic liver failure syndrome in hepatitis B virus-infected patients with and without prior decompensation. J Viral Hepatitis. 2022;29:890\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu X, Liu X, Tan W, Wang X, Zheng X, Huang Y et al. The clinical courses of HBV-related acute-on-chronic liver failure and a multi-state model to predict disease evolution. Hepatol Commun 2024;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Z, Zhang Y, Cao Y, Xu M, You S, Chen Y, et al. A dynamic prediction model for prognosis of acute-on-chronic liver failure based on the trend of clinical indicators. Sci Rep. 2021;11:1810.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HBV-ACLF, prognostic score, dynamic","lastPublishedDoi":"10.21203/rs.3.rs-6733150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6733150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e \u003cp\u003eHepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is a rapidly progressive syndrome with high mortality. This study aimed to develop a dynamic prognostic score for precise outcome prediction in HBV-ACLF patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from a single-center prospective cohort were used to develop the dynamic prognostic score. The dynamic prognostic score was validated in an external multiple-center prospective cohort.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 124 patients with HBV-ACLF were enrolled in the derivation group. Chinese Severe Hepatitis B Research Group-ACLF II (COSSH-ACLF II) score outperformed CLIF Consortium acute-on-chronic liver failure (CLIF-C ACLF) score and Model for End-Stage Liver Disease (MELD) score in predicting 28/90-day mortality. The difference in COSSH-ACLF II score between day 7 and baseline (δCOSSH-ACLF II 7\u0026thinsp;\u0026minus;\u0026thinsp;0) had better performance than the baseline COSSH-ACLF II score. The multivariate COX regression found baseline total bilirubin (TB), baseline prothrombin time international normalized ratio (PT-INR) and δCOSSH-ACLF II 7\u0026thinsp;\u0026minus;\u0026thinsp;0 as independent predictors for 90-day survival. We proposed a dynamic prognostic score\u0026thinsp;=\u0026thinsp;0.005 \u0026times; TB\u0026thinsp;+\u0026thinsp;0.609 \u0026times; PT-INR\u0026thinsp;+\u0026thinsp;1.234\u0026thinsp;\u0026times;\u0026thinsp;δCOSSH ACLF Ⅱ7\u0026thinsp;\u0026minus;\u0026thinsp;0. The AUC of the new score was 0.923 and 0.925 for 28- and 90-days mortality, surpassing the other three models. Calibration and decision curve analyses confirmed clinical utility, and a nomogram was developed for visualization. These findings were replicated in the external validation cohort.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA new prognostic score based on the dynamic clinical course can accurately predict short-term mortality in patients with HBV-ACLF.\u003c/p\u003e","manuscriptTitle":"Development and validation of a dynamic prognostic score for hepatitis B virus-related acute-on-chronic liver failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-29 14:44:04","doi":"10.21203/rs.3.rs-6733150/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b0c428b-93fc-4be2-bc0b-30441a92d864","owner":[],"postedDate":"June 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-14T03:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-29 14:44:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6733150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6733150","identity":"rs-6733150","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}