Prognostic markers, detection methods and systems for hepatic encephalopathy

Abstract

Overt hepatic encephalopathy is a serious complication of cirrhosis and affects lipid and lipoprotein metabolism. The present invention relates to a biomarker for the prognosis of hepatic encephalopathy. The biomarker includes one or more of high-density lipoprotein cholesterol, model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol, serum creatinine, neutrophil-lymphocyte ratio, prothrombin time, and prothrombin activity level. Compared with patients with alcoholic hepatitis, liver disease, liver failure, etc. proposed in the prior art, due to the specificity of the target population, the biomarker proposed in this application can significantly improve the accuracy of the assessment of the prognosis of overt hepatic encephalopathy.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and particularly to prognostic markers, detection methods and systems for hepatic encephalopathy. Background Art

[0002] Hepatic Encephalopathy (HE) is a clinical syndrome characterized by metabolic disorders and central nervous system dysfunction caused by chronic liver failure and portal shunt. Hepatic encephalopathy can be divided into Overt Hepatic Encephalopathy (OHE) and Covert Hepatic Encephalopathy (CHE). Among them, the symptoms of patients with overt hepatic encephalopathy are more obvious and the disease progresses faster. Therefore, the risk assessment of the prognosis of patients with overt hepatic encephalopathy is particularly important.

[0003] The clinical features of overt hepatic encephalopathy include sleep disorders, personality changes, cognitive impairment, coma, etc. Research shows that the incidence of overt hepatic encephalopathy in patients with cirrhosis is 30-45%, and the mortality rate is extremely high. The 1-year survival rate is <50%, and the 3-year survival rate is <25%. In addition, overt hepatic encephalopathy is one of the main factors causing patients to be frequently admitted to the hospital, and it is also one of the main factors causing extrahepatic failure. There are few prediction methods for hepatic encephalopathy or the prognosis risk of hepatic encephalopathy disclosed in the prior art, and some of the methods used have relatively complicated steps and cannot be used frequently. For example, the method for predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt disclosed in CN117011242A includes the following steps: obtaining a three-dimensional model of the liver region and a three-dimensional model of the spleen; respectively performing morphological analysis on the three-dimensional model of the liver region and the three-dimensional model of the spleen to obtain the first liver morphological characteristics and the first spleen morphological characteristics; standardizing the original liver volume in the first liver morphological characteristics through the geometric characteristics of the portal vein to obtain the second liver morphological characteristics; standardizing the original spleen volume in the first spleen morphological characteristics through the geometric characteristics of the splenic vein to obtain the second spleen morphological characteristics; obtaining the liver and spleen morphological characteristics according to the second liver morphological characteristics and the second spleen morphological characteristics; performing high-dimensional imaging omics feature analysis on the three-dimensional scan model of the liver and spleen regions to obtain the liver and spleen high-dimensional imaging omics features; performing classification prediction according to the liver and spleen morphological characteristics and the liver and spleen high-dimensional imaging omics features to obtain the probability of hepatic encephalopathy occurring after transjugular intrahepatic portosystemic shunt. Due to the few prediction methods for hepatic encephalopathy or the prognosis risk of hepatic encephalopathy, some current patients will be recommended to use the prediction methods for diseases with clinical symptoms similar to theirs. For example, the product for evaluating the prognosis of hepatitis B-related acute-on-chronic liver failure disclosed in CN117890605A contains the biomarker myeloperoxidase-DNA complex; or the method for predicting the mortality of liver diseases using lipoprotein LP-Z disclosed in CN113614540A, which includes the following steps: determining the Z-index score based on the lipoprotein component LP-Z in plasma and serum to determine the mortality of patients with alcoholic hepatitis.

[0004] In the prior art, the Model for End-Stage Liver Disease (MELD) scoring system has been widely used to evaluate the priority of liver transplantation, and its original design and clinical application focus on evaluating the severity of liver failure. The MELD score is based on biochemical indicators such as serum creatinine, bilirubin, and international normalized ratio, and does not involve specific biochemical or metabolic abnormalities related to hepatic encephalopathy. Therefore, although the current MELD scoring system can reflect the decline of liver function, there are no relevant reports or studies on specifically predicting the risk of death in the prognosis of hepatic encephalopathy.

[0005] Currently, there is a wide variety of markers or models directly used for liver function assessment or prediction, and there are also a large number of markers or models related to encephalopathy. Existing markers such as transaminases (ALT, AST) and bilirubin are mainly used to evaluate liver function impairment, rather than directly assessing central nervous system damage, which also results in insufficient marker specificity. This lack of specificity leads to the situation that even if liver function shows abnormalities, it may not necessarily accurately reflect the prognosis of hepatic encephalopathy.

[0006] Hepatic encephalopathy is a complex brain dysfunction caused by liver failure. Its pathogenesis involves multiple biochemical and metabolic factors, such as amino acid metabolism imbalance, cerebral blood flow changes, and the role of inflammatory mediators. At the same time, the prognostic risk of hepatic encephalopathy is not only about the protection of liver function, but also includes the monitoring of related factors such as in vivo metabolism and cerebral blood flow. The condition of patients with hepatic encephalopathy fluctuates greatly, and the risk of death not only depends on the degree of liver failure, but is also closely related to the degree of brain function impairment and its recovery.

[0007] Due to the complex mechanism of hepatic encephalopathy, and there are significant differences in the responses of different patients to liver injury and ammonia levels, and this individual difference has not been fully considered in the assessment of existing markers. It is difficult to obtain indicators that can effectively evaluate hepatic encephalopathy through limited experimental means. It can be said that there are significant physiological differences between the pathogenesis and prognostic risk factors of hepatic encephalopathy and those of liver function decline, which also makes those skilled in the art never consider using the Model for End-Stage Liver Disease (MELD) score system to evaluate the prognostic risk of hepatic encephalopathy.

[0008] Although many markers are used in the assessment of liver diseases, there is currently a lack of systematic research on the direct correlation between these markers and the prognostic risk of hepatic encephalopathy. This lack will make it impossible for clinicians to clarify the selection direction of prognostic risk markers when using these markers to evaluate the prognostic risk of hepatic encephalopathy, thus restricting their clinical application and practical implications.

[0009] Since it is not a product specifically for the prognostic assessment of hepatic encephalopathy (especially overt hepatic encephalopathy), the prediction results will have large errors due to individual differences and differences in disease mechanisms.

[0010] Therefore, low-cost and easy-to-use markers that can predict the prognostic risk of patients with hepatic encephalopathy (especially overt hepatic encephalopathy) are urgently needed by current patients with hepatic encephalopathy (especially overt hepatic encephalopathy).

[0011] In addition, on the one hand, there are differences in the understanding among those skilled in the art; on the other hand, when the applicant made this invention, a large number of documents and patents were studied, but due to space limitations, not all details and content were listed in detail. However, this by no means indicates that this invention does not possess the features of these prior arts. On the contrary, this invention already possesses all the features of the prior arts, and the applicant reserves the right to add relevant prior arts in the background art. Summary of the Invention

[0012] One object of the present application is to provide a method for diagnosing and monitoring hepatic encephalopathy.

[0013] One object of the present application is to provide a method for evaluating the condition of patients with hepatic encephalopathy.

[0014] One object of the present application is to provide a composition for diagnosing and monitoring hepatic encephalopathy.

[0015] One object of the present application is to provide a composition for evaluating the condition of patients with hepatic encephalopathy.

[0016] One object of the present application is to provide a system for evaluating the condition of patients with hepatic encephalopathy.

[0017] One object of the present application is to provide a system for diagnosing and monitoring hepatic encephalopathy.

[0018] One object of the present application is to provide a method for predicting the prognostic mortality of patients with hepatic encephalopathy.

[0019] One object of the present application is to provide a composition for predicting the prognostic mortality of patients with hepatic encephalopathy.

[0020] One object of the present application is to provide a kit for predicting the prognostic mortality of patients with hepatic encephalopathy.

[0021] One object of the present application is to provide the use of a composition for predicting the prognostic mortality of patients with hepatic encephalopathy or a kit containing the composition in formulating an individualized treatment plan for patients with hepatic encephalopathy.

[0022] One object of the present application is to provide a treatment plan for hepatic encephalopathy.

[0023] One object of the present application is to provide a system for predicting the prognostic mortality of patients with hepatic encephalopathy.

[0024] Hepatic encephalopathy is a common complication and cause of death in severe liver diseases. Due to the unclear pathogenesis, combined treatment is still the main approach at present. Finding simple, inexpensive, and rapid biomarkers related to the prognosis of hepatic encephalopathy can reduce mortality and improve the prognosis.

[0025] One aspect of the present application relates to a biomarker for assessing the prognostic risk of hepatic encephalopathy. The biomarker includes one or more of high-density lipoprotein cholesterol (HDL-C), model for end-stage liver disease (MELD) score, age, gender, gastrointestinal bleeding (GIB), spontaneous bacterial peritonitis (SBP), classification of overt hepatic encephalopathy (OHE), ascites, aspartate aminotransferase (AST), alanine aminotransferase (ALT), total bilirubin (TBIL), serum albumin (ALB), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), serum creatinine (Cr), neutrophil-lymphocyte ratio (NLR), prothrombin time (PT), and prothrombin activity (PTA) level. Preferably, the biomarker includes multiple ones of the MELD score, age, gender, GIB, SBP, OHE classification, ascites, AST, ALT, TBIL, ALB, TC, LDL-C, Cr, NLR, PT, and PTA level. Preferably, the biomarker includes multiple ones of HDL-C, age, gender, GIB, SBP, OHE classification, ascites, AST, ALT, TBIL, ALB, TC, LDL-C, Cr, NLR, PT, and PTA level. Preferably, the biomarker includes age, ascites, MELD score, HDL-C, and NLR. Preferably, the biomarker includes HDL-C. The biomarker includes the MELD score. The biomarker includes the NLR.More preferably, the biomarker includes high-density lipoprotein cholesterol and the Model for End-Stage Liver Disease score.

[0026] On the other hand, the present application relates to the use of a biomarker in the assessment of the prognostic risk of hepatic encephalopathy.

[0027] According to a preferred embodiment, the use includes assessing the prognostic risk of hepatic encephalopathy complicated with ascites using the biomarker.

[0028] According to a preferred embodiment, assessing the prognostic risk of hepatic encephalopathy includes predicting the likelihood of patient death within 1 year. Preferably, assessing the prognostic risk of hepatic encephalopathy includes predicting the likelihood of patient death within 1 month. Preferably, assessing the prognostic risk of hepatic encephalopathy includes predicting the likelihood of patient death within 3 months.

[0029] On the other hand, the present application relates to a kit for predicting the transplant-free (TF) prognostic risk of hepatic encephalopathy. The kit includes reagents for detecting biomarkers used to assess the prognostic risk of hepatic encephalopathy. Preferably, the biomarker includes high-density lipoprotein cholesterol. Preferably, the biomarker includes the Model for End-Stage Liver Disease. Preferably, the biomarker includes high-density lipoprotein cholesterol and the Model for End-Stage Liver Disease of the patient. The kit includes reagents for detecting high-density lipoprotein cholesterol. The kit also includes a score sheet for the Model for End-Stage Liver Disease of the patient.

[0030] On the other hand, the present application relates to the use of a kit for predicting the transplant-free mortality of hepatic encephalopathy, which includes the steps of:

[0031] a) providing a body fluid sample from a subject;

[0032] b) determining the absolute or relative quantitative values of one or more of high-density lipoprotein cholesterol, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol, serum creatinine, prothrombin time, and prothrombin activity level in the biomarker in the body fluid sample from step a), and / or determining one or more indicators of the Model for End-Stage Liver Disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, grade of overt hepatic encephalopathy, ascites, and neutrophil-lymphocyte ratio of the subject.

[0033] On the other hand, the present application relates to the use of a reagent for determining the abundance of high-density lipoprotein cholesterol in the preparation of a kit for evaluating the prognosis risk of hepatic encephalopathy. The evaluation includes: 1) collecting a blood sample from a patient; 2) detecting the abundance of high-density lipoprotein cholesterol in the blood sample. Among them, when the high-density lipoprotein cholesterol is less than 0.5 mmol / L, the patient belongs to the high-risk group of the non-transplant mortality of the prognosis of the first type of hepatic encephalopathy; when the high-density lipoprotein cholesterol is not less than 0.5 mmol / L, the patient belongs to the low-risk group of the non-transplant mortality of the prognosis of the first type of hepatic encephalopathy.

[0034] On the other hand, the present application relates to a system for evaluating the prognosis risk of hepatic encephalopathy. The system is used to collect the model for end-stage liver disease score of a patient. Among them, when the model for end-stage liver disease score is not less than 17, the patient belongs to the high-risk group of the non-transplant mortality of the prognosis of the first type of hepatic encephalopathy; when the model for end-stage liver disease score is less than 17, the patient belongs to the low-risk group of the non-transplant mortality of the prognosis of the first type of hepatic encephalopathy.

[0035] On the other hand, the present application relates to a method for evaluating the prognosis risk of hepatic encephalopathy in an object. The method includes: determining the level of one or more of high-density lipoprotein cholesterol, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol, serum creatinine, neutrophils, lymphocytes, prothrombin time, and prothrombin activity level in the body fluid obtained from the object and / or determining one or more indicators of the model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, grade of overt hepatic encephalopathy, and ascites of the object; correlating the prognosis risk of hepatic encephalopathy with the level of one or more components of high-density lipoprotein cholesterol, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol, serum creatinine, prothrombin time, and prothrombin activity obtained from the body fluid and / or correlating the prognosis risk of hepatic encephalopathy with one or more indicator levels of the model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, grade of overt hepatic encephalopathy, ascites, and neutrophil-lymphocyte ratio of the object. Preferably, the method includes: determining the high-density lipoprotein cholesterol in the body fluid obtained from the object and / or determining the model for end-stage liver disease score of the object; correlating the prognosis risk of hepatic encephalopathy with the high-density lipoprotein cholesterol obtained from the body fluid and / or correlating the prognosis risk of hepatic encephalopathy with the model for end-stage liver disease score of the object.

[0036] On the other hand, the present application relates to a system for evaluating the prognostic risk of hepatic encephalopathy. The system includes a data acquisition module that collects one or more of high-density lipoprotein cholesterol, model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol, serum creatinine, neutrophil-lymphocyte ratio, prothrombin time, and prothrombin activity level; and a data processing module for correlating the prognostic mortality of hepatic encephalopathy with the levels in the obtained body fluid components and / or correlating the prognostic mortality without transplantation of hepatic encephalopathy with one or more of the indicators of model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, and neutrophil-lymphocyte ratio.

[0037] On the other hand, the present application relates to a system for evaluating the prognostic risk of hepatic encephalopathy. The system includes a data acquisition module that collects one or more of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, model for end-stage liver disease, international normalized ratio (INR), neutrophil-lymphocyte ratio, and prothrombin time levels; and a data processing module for correlating the prognostic mortality of hepatic encephalopathy with one or more of the indicators of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, model for end-stage liver disease, international normalized ratio, neutrophil-lymphocyte ratio, and prothrombin time obtained, where the levels of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, model for end-stage liver disease, international normalized ratio, neutrophil-lymphocyte ratio, and / or prothrombin time measured and / or calculated at a time point of the patient's prognosis are used as a control with a healthy group as a control. When the levels of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, model for end-stage liver disease, international normalized ratio, neutrophil-lymphocyte ratio, and / or prothrombin time corresponding to the patient collected by the data acquisition module increase, the data processing module generates an indication that the risk of the patient's hepatic encephalopathy mortality increases.

[0038] On the other hand, the present application relates to a system for evaluating the prognostic risk of hepatic encephalopathy. The system includes a data acquisition module for collecting data on one or more of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity, and serum albumin level; and a data processing module for correlating the prognostic mortality of hepatic encephalopathy with one or more indicators of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity, and serum albumin obtained. Wherein, taking the healthy group as a control or taking the levels of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity, and / or serum albumin measured at a time point of the patient's prognosis as a control, when the levels of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity, and / or serum albumin of the corresponding patient collected by the data acquisition module decrease, the data processing module generates an indication of an increased risk of non-transplant mortality of hepatic encephalopathy in the patient.

[0039] On the other hand, the present application relates to a computing device, which includes: a data storage module for storing data; and a control unit for performing computing operations, wherein the control unit includes a data processing module for evaluating the prognostic risk of hepatic encephalopathy based on the evaluation model involved in the present application.

[0040] According to a preferred embodiment, taking the healthy group as a control or taking the high-density lipoprotein cholesterol level measured at a time point of the patient's prognosis as a control, a decrease in high-density lipoprotein cholesterol represents an increased risk of non-transplant mortality in patients with hepatic encephalopathy. Taking the healthy group as a control or taking the high-density lipoprotein cholesterol level measured at a time point of the patient's prognosis as a control, an increase in high-density lipoprotein cholesterol represents a decreased risk of non-transplant mortality in patients with hepatic encephalopathy.

[0041] According to a preferred embodiment, taking the healthy group as a control or taking the Model for End-Stage Liver Disease (MELD) score calculated at a time point of the patient's prognosis as a control, a decrease in the MELD score represents a decreased risk of non-transplant mortality in patients with hepatic encephalopathy. Taking the healthy group as a control or taking the MELD score calculated at a time point of the patient's prognosis as a control, an increase in the MELD score represents an increased risk of non-transplant mortality in patients with hepatic encephalopathy.

[0042] According to a preferred embodiment, taking the healthy group as a control or taking the high-density lipoprotein cholesterol level measured at a time point of the patient's prognosis and the calculated Model for End-Stage Liver Disease (MELD) score as controls, a decrease in the MELD score and an increase in high-density lipoprotein cholesterol indicate a reduced risk of non-transplant mortality in patients with hepatic encephalopathy. Taking the healthy group as a control or taking the high-density lipoprotein cholesterol level measured at a time point of the patient's prognosis and the calculated MELD score as controls, an increase in the MELD score and a decrease in high-density lipoprotein cholesterol indicate an increased risk of non-transplant mortality in patients with hepatic encephalopathy.

[0043] According to a preferred embodiment, taking the healthy group as a control or taking the neutrophil-to-lymphocyte ratio level calculated at a time point of the patient's prognosis as a control, a higher neutrophil-to-lymphocyte ratio indicates an increased risk of non-transplant mortality in patients with hepatic encephalopathy.

[0044] On the other hand, the present application relates to an assessment model for the prognosis risk of hepatic encephalopathy. The assessment criteria in the assessment model include: for patients with low risk of non-transplant mortality in the prognosis of type II hepatic encephalopathy, the high-density lipoprotein cholesterol is not less than 0.5 mmol / L and the MELD score is less than 17; for patients with medium risk of non-transplant mortality in the prognosis of type II hepatic encephalopathy, the high-density lipoprotein cholesterol is less than 0.5 mmol / L or the MELD score is not less than 17; and / or for patients with high risk of non-transplant mortality in the prognosis of type II hepatic encephalopathy, the high-density lipoprotein cholesterol is less than 0.5 mmol / L and the MELD score is not less than 17.

[0045] On the other hand, the present application relates to an assessment model for the prognosis risk of hepatic encephalopathy. The assessment criteria in the assessment model include: for patients with high risk of non-transplant mortality in the prognosis of type I hepatic encephalopathy, the high-density lipoprotein cholesterol is less than 0.5 mmol / L or the MELD score is not less than 17; and / or for patients with low risk of non-transplant mortality in the prognosis of type I hepatic encephalopathy, the high-density lipoprotein cholesterol is not less than 0.5 mmol / L or the MELD score is less than 17.

[0046] Preferably, when the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5 mmol / L, the data processing module generates an indication of high risk of non-transplant mortality for the patient with hepatic encephalopathy. When the high-density lipoprotein cholesterol collected by the data acquisition module is not less than 0.5 mmol / L, the data processing module generates an indication of low risk of non-transplant mortality for the patient with hepatic encephalopathy. When the MELD score collected by the data acquisition module is less than 17, the data processing module generates an indication of low risk of non-transplant mortality for the patient with hepatic encephalopathy. When the MELD score collected by the data acquisition module is not less than 17, the data processing module generates an indication of high risk of non-transplant mortality for the patient with hepatic encephalopathy.

[0047] On the other hand, the present application relates to a system for evaluating the prognostic risk of hepatic encephalopathy. The system includes a data acquisition module for collecting data on high-density lipoprotein cholesterol and the Model for End-Stage Liver Disease (MELD) score; and a data processing module for correlating the prognostic mortality of hepatic encephalopathy with the obtained high-density lipoprotein cholesterol and the indicators of the Model for End-Stage Liver Disease. Among them, the data processing module is configured to: when the high-density lipoprotein cholesterol collected by the data acquisition module is not less than 0.5 mmol / L and the Model for End-Stage Liver Disease score is less than 17, the data processing module generates an indication of low risk of non-transplant mortality for patients with type II hepatic encephalopathy. When the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5 mmol / L or the Model for End-Stage Liver Disease score is not less than 17, the data processing module generates an indication of medium risk of non-transplant mortality for patients with type II hepatic encephalopathy. When the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5 mmol / L and the Model for End-Stage Liver Disease score is not less than 17, the data processing module generates an indication of high risk of non-transplant mortality for patients with type II hepatic encephalopathy. Preferably, the data acquisition module includes a blood lipid detection unit and a Model for End-Stage Liver Disease score unit. More preferably, the blood lipid detection module collects the level of high-density lipoprotein cholesterol. The Model for End-Stage Liver Disease score unit collects the Model for End-Stage Liver Disease score. Preferably, the hepatic encephalopathy includes overt hepatic encephalopathy.

[0048] On the other hand, the present application relates to a method for evaluating the prognostic risk of hepatic encephalopathy. The method for evaluating the prognostic risk of hepatic encephalopathy comprises the following steps: determining the level of high-density lipoprotein cholesterol in a body fluid obtained from a subject and / or obtaining the Model for End-Stage Liver Disease (MELD) score of the subject; and correlating the prognostic mortality of hepatic encephalopathy with the level in the obtained body fluid component and / or correlating the prognostic mortality of hepatic encephalopathy with the model score level, and evaluating the risk of the prognostic mortality of hepatic encephalopathy based on a first type of evaluation method or a second type of evaluation method; wherein, the first type of evaluation method: when the high-density lipoprotein cholesterol is not less than 0.5 mmol / L and the MELD score is less than 17, predicting that the prognostic risk of hepatic encephalopathy belongs to the low risk of non-transplant mortality of the second type of hepatic encephalopathy prognosis; when the high-density lipoprotein cholesterol is less than 0.5 mmol / L or the MELD score is not less than 17, predicting that the prognostic risk of hepatic encephalopathy belongs to the medium risk of non-transplant mortality of the second type of hepatic encephalopathy prognosis; when the high-density lipoprotein cholesterol is less than 0.5 mmol / L and the MELD score is not less than 17, predicting that the prognostic risk of hepatic encephalopathy belongs to the high risk of non-transplant mortality of the second type of hepatic encephalopathy prognosis; or the second type of evaluation method: when the high-density lipoprotein cholesterol is not less than 0.5 mmol / L or the MELD score is less than 17, predicting that the prognostic risk of hepatic encephalopathy belongs to the low risk of non-transplant mortality of the first type of hepatic encephalopathy prognosis; when the high-density lipoprotein cholesterol is less than 0.5 mmol / L or the MELD score is not less than 17, predicting that the prognostic risk of hepatic encephalopathy belongs to the high risk of non-transplant mortality of the first type of hepatic encephalopathy prognosis.

[0049] On the other hand, the present invention relates to a method for constructing a scoring model for predicting the non-transplant mortality of hepatic encephalopathy. The construction method comprises the following steps:

[0050] (1) Obtaining the level of one or more of high-density lipoprotein cholesterol, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol, serum creatinine, neutrophils, lymphocytes, prothrombin time, and prothrombin activity level in a body fluid of a subject, and / or obtaining one or more indicators of the Model for End-Stage Liver Disease (MELD) score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, grade of overt hepatic encephalopathy, and ascites of the subject;

[0051] (2) Based on the results of step (1), screening independent risk factors with prognostic indicative value through Cox proportional hazards regression analysis, and constructing a scoring model for predicting the non-transplant mortality of hepatic encephalopathy.

[0052] The beneficial effects of the present technical solution:

[0053] There are significant differences in the epidemiological characteristics among different types of liver diseases. Hepatic encephalopathy is closely associated with clinical symptoms such as liver dysfunction, ammonia metabolism disorders, and nervous system damage. It involves complex neurophysiological changes. While the direct influencing factors of alcoholic hepatitis, liver disease, and liver failure are hepatocyte damage and inflammation. There are significant differences in their pathological mechanisms. At the same time, the prognosis of hepatic encephalopathy is affected by multiple factors, including ammonia level, liver function, kidney function, and nerve repair. While alcoholic hepatitis, liver disease, and liver failure mainly use liver function indicators as the evaluation parameters for the prognosis level. Therefore, a prognosis model for hepatic encephalopathy needs to be established in a specific patient population. And the model established based on the population of alcoholic hepatitis, liver disease, and liver failure may have large prediction errors when predicting hepatic encephalopathy.

[0054] This application proposes a biomarker and an evaluation model based on the prognosis risk of patients with overt hepatic encephalopathy. Compared with the patients with alcoholic hepatitis, liver disease, liver failure, etc. proposed in the prior art, due to the specificity of the targeted population, the relevant test results have significant evaluation accuracy. In this study, the patients were divided into a training group and a validation group, aiming to verify the prognostic value and risk stratification evaluation of HDL-C level and MELD score, making the results more persuasive and credible.

[0055] By comparing the baseline characteristics of surviving and deceased patients in the training cohort, this application found that age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, model for end-stage liver disease score, aspartate aminotransferase, alanine aminotransferase, total bilirubin, serum albumin, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, serum creatinine, neutrophil-lymphocyte ratio, prothrombin time, and prothrombin activity level were related to the disease development of patients with overt hepatic encephalopathy. The levels of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, and serum albumin in deceased patients were lower than those in surviving patients (all p < 0.001). The levels of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, model for end-stage liver disease score, international normalized ratio, neutrophil-lymphocyte ratio, and prothrombin time in deceased patients were higher than those in surviving patients. This reveals that the level changes of the above characteristics or the level changes compared with the healthy group can indicate the death risk of patients with hepatic encephalopathy.

[0056] Independent prognostic factors associated with mortality without transplantation were explored through multivariable Cox analysis, and the prognostic value of these factors was evaluated using the area under the receiver operating characteristic curve (AUC). Finally, the good prognostic value of high-density lipoprotein cholesterol was found. Then, more in-depth analysis was conducted, including subgroup analysis and survival analysis, and the indicators were further verified through a validation cohort to determine the credibility and applicability of high-density lipoprotein cholesterol as an indicator.

[0057] Hepatic encephalopathy is a metabolic disorder syndrome caused by severe liver disease or portosystemic shunting, manifested as dysfunction of the central nervous system. Its pathogenesis involves multiple factors such as ammonia intoxication and neurotransmitter changes. Limited by traditional cognition, research design, interdisciplinary knowledge barriers, the complexity of physiological mechanisms, as well as data and sample size, the existing technology has not mainly focused on the interdisciplinary combination of HDL-C in the cardiovascular field with hepatology and neurology. This application gradually discovered the potential impact of HDL-C on hepatic encephalopathy through clinical data calculation and analysis. The process is as follows: This study included multiple demographic characteristics and commonly used clinical laboratory indicators. Through univariate and multivariable risk regression analysis, three laboratory indicators were finally found to be independent predictors of 1-year mortality without transplantation in patients. The three laboratory indicators are the MELD score, HDL-C, and NLR levels. Compared with the MELD score, it was found that HDL-C and the MELD score had higher prognostic values at 1, 3, and 12 months, and the AUC (an indicator evaluating prognostic value) values of the two were similar, without significant statistical differences, indicating that HDL-C has strong prognostic value and significance. In addition, compared with NLR, the performance of HDL-C at 1, 3, and 12 months was significantly higher than that of NLR (with significant statistical differences, p < 0.05). Thus, we determined that HDL-C is an important risk factor affecting hepatic encephalopathy. Therefore, the potential impact of HDL-C on hepatic encephalopathy is a new and unexpected discovery in the research.

[0058] Furthermore, the present application further verified the prognostic value of high-density lipoprotein cholesterol level and Model for End-Stage Liver Disease (MELD) score through the Receiver Operator Characteristic (ROC) curve, and gave the further evaluation boundaries of high-density lipoprotein cholesterol level and MELD score. The present application found that the optimal cut-off value of high-density lipoprotein cholesterol was 0.5 mmol / L. Based on the high-density lipoprotein cholesterol index alone, it was found that the 1-year mortality without transplantation in patients with high-density lipoprotein cholesterol < 0.5 mmol / L was significantly higher than that in patients with high-density lipoprotein cholesterol ≥ 0.5 mmol / L (38.7% vs. 10.3%, p < 0.0001; Figure 3 B). We further combined the MELD score and divided the patients into three groups: low (HDL-C ≥ 0.5 mmol / L and MELD < 17), medium (HDL-C < 0.5 mmol / L or MELD ≥ 17), and high risk (HDL-C < 0.5 mmol / L and MELD ≥ 17). The 1-year mortality without transplantation in the low, medium, and high-risk subgroups was 7.5%, 20.2%, and 51.5% respectively. The combined use of the two indicators could further clearly distinguish low-risk and high-risk patients.

[0059] By combining these two most important prognostic scores and performing risk grouping on patients, the patients could be further divided into three risk levels: low, medium, and high. When these two risk factors were not included, the patient mortality rate was approximately 10%. When these two risk factors (HDL-C < 0.5 mmol / L and MELD ≥ 17) were present simultaneously, the patient mortality rate was ≥ 50%. The implication of this result for clinical practice was that targeted treatment could be carried out for patients in the medium- to high-risk population to reduce the mortality rate.

[0060] Meanwhile, from an economic perspective (or in terms of clinical application value and transformation), the data acquisition method of the biomarker proposed in the present application was simple. Both the high-density lipoprotein cholesterol level and the MELD score were clinical indicators, which were easy to obtain and the acquisition process was simple and rapid. The biomarker proposed in the present application could simply and rapidly calculate the patient's death risk, guide doctors in the next step of treatment, and this biomarker combination could be widely applied to primary hospitals in the actual application process. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is the research flow chart of the present application;

[0062] Figure 2The predictive value of independent risk factors in the training cohort and the relationship between high-density lipoprotein cholesterol and prognosis. Among them, (A), (B), and (C) are the ROC curves of high-density lipoprotein cholesterol, neutrophil-lymphocyte ratio, and Model for End-Stage Liver Disease score predicting mortality without transplantation at 1 month, 3 months, and 12 months after prognosis, respectively; (D), (E), and (F) are the relationships between high-density lipoprotein cholesterol and mortality without transplantation in patients at 1 month, 3 months, and 12 months after prognosis, respectively. The red line represents the hazard ratio (HR) reference, and the red area represents the 95% CI.

[0063] Figure 3 Scatter plots and risk stratification of patients with overt hepatic encephalopathy in the training cohort. (A) Distribution of patients with overt hepatic encephalopathy who died and survived; (B) 1-year survival rates of patients with HDL-C < 0.5 mmol / L and ≥ 0.5 mmol / L; (C) 1-year survival rates of patients with MELD < 17 and ≥ 17; (D) Survival probabilities of patients in the low, medium, and high-risk groups; (E) Predictive ability of different indicators for 1-year mortality without transplantation in patients with hepatic encephalopathy complicated with ascites; (F) Survival probabilities of patients with hepatic encephalopathy complicated with ascites in the low, medium, and high-risk groups.

[0064] Figure 4 Cox proportional hazards analysis was used to evaluate the prognostic significance of high-density lipoprotein cholesterol levels in different subgroups.

[0065] Figure 5 Predictive values of different risk factors and risk stratifications in the validation cohort. (A), (B), and (C) are the ROC curves of high-density lipoprotein cholesterol, neutrophil-lymphocyte ratio, and Model for End-Stage Liver Disease score predicting mortality without transplantation at 1 month, 3 months, and 12 months after prognosis, respectively; (D) Survival rates of patients with HDL-C < 0.5 mmol / L and ≥ 0.5 mmol / L; (E) Survival probabilities of patients with MELD < 17 and ≥ 17; (F) Survival probabilities of patients in the low, medium, and high-risk groups. Detailed implementation manners

[0066] The following is a detailed description with reference to the accompanying drawings.

[0067] For those of ordinary skill in the art, the specific meanings of the terms in the present invention can be understood according to specific circumstances. The experimental procedures described in the following examples are all conventional procedures unless otherwise specified. The reagents and the like used in the following examples can be obtained through commercial channels unless otherwise specified.

[0068] High-Density Lipoprotein (HDL) is a lipoprotein in the blood, characterized by a relatively high density and a relatively high protein content. It helps collect excess cholesterol from tissues and cells and transport it back to the liver for metabolism and excretion. HDL has functions such as reverse cholesterol transport, antioxidant, anti-inflammatory, antithrombotic, and immunomodulatory effects. High-density lipoprotein is a major lipid component of serum and can bind lipid components such as cholesterol and phospholipids. Since the liver plays a crucial role in lipid synthesis, transport, and metabolism, lipid level abnormalities often occur in patients with liver cirrhosis. HDL dysfunction may lead to the occurrence of inflammatory diseases such as cardiovascular diseases, diabetes, and kidney diseases. Advanced liver cirrhosis patients have problems with lipid metabolism disorders caused by HDL dysfunction, such as abnormal HDL-C levels. Previous studies have shown that HDL-C is associated with an increased mortality rate in patients with alcoholic hepatitis and non-cholestatic liver cirrhosis, and lipid levels are related to nutritional status, such as hypoproteinemia and sarcopenia, which are common in patients with overt hepatic encephalopathy. In addition, many lipid molecules can act as signaling molecules and are closely related to inflammation. The inflammatory response caused by infection is a common and susceptible risk factor for the development of overt hepatic encephalopathy. HDL-C plays an important role in inhibiting endogenous inflammation, and a decrease in HDL-C levels is related to the systemic inflammatory response. HDL-C exerts its anti-inflammatory effect by attaching to and dissolving lipopolysaccharide and weakening the expression of adhesion molecules.

[0069] In the prior art, there is no relevant research to confirm the prognostic value of HDL-C and non-transplant mortality in patients with overt hepatic encephalopathy. The method for determining patients with high mortality based on HDL-C levels is unclear.

[0070] Specifically, 821 patients with overt hepatic encephalopathy who visited Beijing Ditan Hospital from January 2010 to August 2016 were selected. Multivariate Cox analysis was used to explore independent prognostic factors related to the non-transplant mortality of the patients, and the prognostic value of these factors was evaluated using the area under the receiver operating characteristic curve (AUC). The log-rank test and Kaplan-Meier curve were used to analyze the 1-year non-transplant mortality. All results were confirmed in an internal cohort from September 2016 to December 2020 (n = 480).

[0071] The results showed that the HDL-C level in patients with overt hepatic encephalopathy who died was lower than that in surviving patients. The prognostic value of HDL-C was good (AUC at 1 year: 0.745), comparable to the MELD score (AUC at 1 year: 0.788). In the validation cohort, the AUC value of HDL-C at 1 year was similar to the MELD score (0.724 vs. 0.724). The optimal cut-off values of HDL-C and MELD were 0.5 mmol / L and 17, respectively. The 1-year transplant-free mortality rates in the low-risk group (HDL-C ≥ 0.5 mmol / L and MELD < 17) and high-risk group (HDL-C < 0.5 mmol / L and MELD ≥ 17) were 7.5% and 51.5% in the training cohort, and 10.1% and 51.2% in the validation cohort, respectively.

[0072] Preferably, the relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the first type of hepatic encephalopathy ranges from 50% to 100%. More preferably, the relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the first type of hepatic encephalopathy is greater than 50%, 60%, 70%, 80% or 90%. Preferably, the relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the first type of hepatic encephalopathy is less than 50%. More preferably, the relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the first type of hepatic encephalopathy is less than 10%, 20%, 30% or 40%.

[0073] Preferably, the relative incidence of death in patients at low risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy is not less than 10%. The relative incidence of death in patients at medium risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy ranges from 10% to 50%. The relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy is greater than 50%. More preferably, the relative incidence of death in patients at medium risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy ranges from 10% to 20%, 20% to 30%, 30% to 40%, 40% to 50%. The relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy is greater than 60%, 70%, 80%, 90% or other values within the range of 50% to 100%. Particularly preferably, the relative incidence of death in patients at medium risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy is 20%, 30%, 40%, 50% or other values within the range of 10% to 50%. The relative incidence of death in patients at high risk of transplant-free mortality in the prognosis of the second type of hepatic encephalopathy is 60%, 70%, 80%, 90% or other values within the range of 50% to 100%.

[0074] Based on the above results, it can be seen that HDL-C is closely related to the 1-year transplant-free mortality of patients with overt hepatic encephalopathy. Generally speaking, HDL-C < 0.5 mmol / L and MELD ≥ 17 are helpful in identifying high-risk patients and providing timely treatment and care. At the same time, based on the risk prediction results, such as Figure 3 E, HDL-C, MELD score or NLR can also be used as markers to predict the 1-year transplant-free mortality of patients with hepatic encephalopathy complicated with ascites.

[0075] It should be noted that the ROC curve drawn and the calculated area under the ROC curve (AUC) can be used to determine the test quality. It is currently recognized that the ROC curve is a curve that allows the prediction of test quality, and the ROC curve with an AUC value greater than 0.7 is a good prediction curve.

[0076] The 95% confidence interval (95% Confidence Interval, 95% CI) indicates that in repeated experiments, there is a 95% probability that this interval contains the true parameter value.

[0077] Restricted Cubic Splines (RCS) are used to represent the non-linear relationship between the independent variable x and the dependent variable y.

[0078] The Kaplan-Meier curve is a data analysis method used to estimate survival rates or time to events. The Kaplan-Meier curve can show the differences in survival rates / mortality rates between control group patients and experimental group patients.

[0079] In the present invention, the prognostic risk is, for example, the mortality risk or the survival risk. Generally speaking, the sum of the mortality rate and the survival rate is 1. The mortality rate is associated with the survival rate. A high mortality risk means a low survival rate, and a low mortality risk means a high survival rate. Therefore, the kit for predicting the mortality of hepatic encephalopathy proposed in this application is equivalent to the kit for predicting the survival rate of hepatic encephalopathy. The biomarker for predicting the mortality of hepatic encephalopathy proposed in this application is equivalent to the biomarker for predicting the survival rate of hepatic encephalopathy. The evaluation model for predicting the mortality of hepatic encephalopathy proposed in this application is equivalent to the evaluation model for predicting the survival rate of hepatic encephalopathy. The system for predicting the mortality of hepatic encephalopathy proposed in this application is equivalent to the device for predicting the survival rate of hepatic encephalopathy. The system for predicting the mortality of hepatic encephalopathy proposed in this application is equivalent to the system for predicting the survival rate of hepatic encephalopathy. The method for predicting the mortality of hepatic encephalopathy proposed in this application is equivalent to the method for predicting the survival rate of hepatic encephalopathy.

[0080] Preferably, the computing device involved in the present application may include a high-speed computing device using circuits, such as a personal computer, a workstation, and a supercomputer. In addition to fixed devices such as computers, workstations, and supercomputers, the computing device may also include a mobile device having a central processing unit and performing computing processing, such as a smart phone, a PDA, or a portable computer. The data storage module stores programs for operating the data processing module and temporarily stores input and output data. In addition, the data storage module may also store transmitted or received data. The data storage module may include at least one of the following storage media: flash memory, hard disk, multimedia card micro memory, card-type memory (e.g., SD or XD memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disc, etc.

[0081] Example 1

[0082] 1. Materials and Methods.

[0083] (1) Study Population.

[0084] Patients with overt hepatic encephalopathy (n = 1640) who visited Beijing Ditan Hospital from January 2010 to August 2016 were included for analysis. The exclusion criteria were as follows: 1) age < 18 years or > 80 years; 2) patients with malignant tumors or liver transplantation; 3) patients infected with human immunodeficiency virus; 4) patients with severe mental illness or using psychotropic drugs; 5) patients with neurological diseases, using psychotropic drugs, or long-term alcoholics; 6) patients with covert hepatic encephalopathy. Finally, 821 patients with overt hepatic encephalopathy were included in the training cohort. Using the same inclusion and exclusion criteria, 480 patients from September 2016 to December 2020 were included as the validation cohort ( Figure 1 ).

[0085] (2) Data Collection and Clinical Definition.

[0086] We collected baseline demographic and laboratory data from the electronic medical record database, including age, gender, complications, ALT, AST, ALB, TBIL, triglyceride (TG), TC, Cr, HDL-C, and LDL-C levels. In addition, we also collected data on PT, platelet count (PLT), PTA, and INR. NLR was calculated as the absolute neutrophil count divided by the absolute lymphocyte count. Laboratory test results within 48 hours after the patients were admitted were collected. The endpoint event was defined as death without transplantation within 1 year or the end of 1-year follow-up. The MELD score was used to determine the severity of liver disease. The formula for calculating the MELD score: MELD score = 3.78 × ln[total bilirubin (μmol / L) ÷ 17.1] + 11.2 × ln[international normalized ratio] + 9.57 × ln[creatinine (μmol / L) ÷ 88.4] + 6.433. Hepatic encephalopathy was scored according to the West-Haven criteria.

[0087] (3) Statistical analysis.

[0088] All statistical analyses were performed using IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) and R version 4.2.3 (The R Foundation, Nashville, TN, USA). Continuous variables were expressed as mean ± standard deviation or median (interquartile range), and categorical variables were expressed as frequency or percentage. Cox proportional hazards regression analysis was used to identify independent prognostic biomarkers. In addition, the receiver operating characteristic AUC was used to describe the predictive value of independent risk indicators, and the DeLong test was used to determine whether there were statistical differences between various prognostic indicators. Restricted cubic spline (RCS) curves were used to visualize whether there was a non-linear correlation between HDL-C and mortality without transplantation at 1 month, 3 months, and 12 months. The Kaplan-Meier method was used to evaluate 1-year survival rate, and the log-rank test was used. Forest plots were used to determine the relationship between HDL-C and prognosis in different subgroups. All p values < 0.05 were considered statistically significant.

[0089] 2. Results.

[0090] (1) Baseline characteristics of patients with overt hepatic encephalopathy

[0091] The training cohort and validation cohort included 821 and 480 patients, respectively. Table 1 shows the baseline characteristics of the two groups of patients with overt hepatic encephalopathy. The median age of all patients was 55 years (range 47 - 63 years), with 962 males (73.9%) and 339 females (26.1%). In the training cohort, 679 (82.7%) were diagnosed with grade II overt hepatic encephalopathy and 142 (17.3%) were diagnosed with grades III - IV overt hepatic encephalopathy. During the 1-year follow-up period, 200 (24.4%) patients died in the training cohort and 130 (27.0%) patients died in the validation cohort. There were no significant differences in the baseline characteristics between the two subgroups.

[0092] In addition, we analyzed the characteristics of the surviving and deceased patients in the training cohort. As shown in Table 2, compared with the surviving patients (median age 54.0 years), the deceased patients were older (median age 56.5 years); compared with the surviving patients (median ALT 28.2 IU / L), the deceased patients had higher ALT (median 51.8 IU / L); compared with the surviving patients (median TBIL 44.8 μmol / L), the deceased patients had higher TBIL (median 174.1 μmol / L); compared with the surviving patients (median AST 43.9 IU / L), the deceased patients had higher AST (median 97.4 IU / L); compared with the surviving patients (median Cr 67.4 μmol / L), the deceased patients had higher Cr (median 88.9 μmol / L); compared with the surviving patients (median MELD score 13.0), the deceased patients had a higher MELD score (median 23.2); compared with the surviving patients (median PTA 53.8%), the deceased patients had lower PTA, indicating a worse prognosis (median 34.8%); compared with the surviving patients (median INR 1.5), the deceased patients had higher INR (median 1.9); compared with the surviving patients (median NLR 3.7), the deceased patients had higher NLR (median 6.5); compared with the surviving patients (median PT 16.9 s), the deceased patients had higher PT (median 22.4 s). The p-values for all of the above pairs of data were less than 0.001. Additionally, the likelihood of GIB, SBP, and ascites was increased in the deceased patients. Notably, the levels of LDL-C, TC, HDL-C, and ALB were lower in the deceased patients than in the surviving patients (all p < 0.001).

[0093] The above results showed that age, gender, GIB, SBP, grade of overt hepatic encephalopathy, ascites, MELD score, AST, ALT, TBIL, ALB, TC, HDL-C, LDL-C, Cr, NLR, PT, and PTA levels were associated with the disease progression of patients with overt hepatic encephalopathy.

[0094] (2) Independent risk factors for the prognosis of patients with overt hepatic encephalopathy

[0095] In the training cohort, univariate COX analysis showed that age, gender, GIB, SBP, grade of overt hepatic encephalopathy, ascites, MELD score, AST, ALT, TBIL, ALB, TC, HDL-C, LDL-C, Cr, NLR, PT, and PTA levels were potential influencing factors for 1-year transplant-free mortality in patients with overt hepatic encephalopathy (all p < 0.05). These significant factors identified in the univariate analysis were included in the multivariate Cox regression analysis. As shown in Table 3, age [adjusted hazard ratio (aHR), 1.030; 95% confidence interval (CI): 1.016 - 1.044, p < 0.001], ascites (aHR, 1.974; 95% CI: 1.452 - 2.683, p < 0.001), MELD score (aHR, 1.107; 95% CI: 1.066 - 1.150, p < 0.001), HDL-C (aHR, 0.393; 95% CI: 0.218 - 0.711, p = 0.002), and NLR (aHR, 1.003; 95% CI: 1.014 - 1.053, p = 0.001) were independent prognostic influencing factors for patients with overt hepatic encephalopathy.

[0096] (3) Prognostic value of HDL-C level in patients with overt hepatic encephalopathy

[0097] The ROC curve is used to plot the relationship between the true positive rate and the false positive rate. The AUC represents the area under the ROC curve, which is a value between 0 and 1 and is used to measure the quality of the classification model's prediction ability. The closer the AUC value is to 1, the better the model performance.

[0098] The ROC curve was used to evaluate the prognostic value of HDL-C at 1, 3, and 12 months. As Figure 2 shown in A - C, in the training cohort, the AUCs of HDL-C at 1 month, 3 months, and 12 months were 0.792 (95% CI: 0.755 - 0.829), 0.777 (95% CI: 0.735 - 0.810), and 0.745 (95% CI: 0.707 - 0.784), respectively.

[0099] Figure 2 The results shown in A - C indicate that the AUCs of HDL-C at 1 month, 3 months, and 12 months were all greater than 0.7, suggesting that the HDL-C level has good prognostic value (transplant-free mortality) for predicting the prognosis of patients with overt hepatic encephalopathy in the short and long term, especially at 1 month (0.792 > 0.777 > 0.745).

[0100] As Figure 2As shown in A–C, the MELD score showed similar predictive ability (AUC at 1, 3, and 12 months was 0.821, 0.812, and 0.788, respectively).

[0101] As Figure 2 shown in A–C, the performance of HDL-C at 1, 3, and 12 months (AUC at 1, 3, and 12 months was 0.792, 0.777, and 0.745, respectively) was significantly higher than that of NLR (AUC at 1, 3, and 12 months was 0.701, 0.701, and 0.684, respectively) (both p < 0.05), indicating that HDL-C was more accurate than NLR in predicting the mortality without transplantation in patients with overt hepatic encephalopathy. This result suggested that HDL-C had an advantage over a single inflammatory index (such as NLR) in reflecting the prognosis development of hepatic encephalopathy (or the mortality of hepatic encephalopathy).

[0102] As Figure 2 shown in D–F, the RCS curve ( Figure 2 the ordinate of D–F represents the HR of mortality without transplantation) showed an “L-shaped” relationship between HDL-C levels and the mortality without transplantation at 1 month, 3 months, and 12 months, indicating that the relationship between HDL-C levels and the risk of death was non-linear (p < 0.001): when the HDL-C level was low (<0.05 mmol / L), the risk of death increased significantly, while when the HDL-C level was high (≥0.05 mmol / L), the risk of death decreased significantly.

[0103] As Figure 2 shown in D–F, the RCS curves at 1, 3, and 12 months showed a consistent L-shaped trend, indicating that the HDL-C level was non-linearly correlated with the mortality without transplantation at different time points.

[0104] The above findings suggested that the HDL-C level was an important indicator for evaluating the prognosis of overt hepatic encephalopathy. The above findings also suggested that the HDL-C level was non-linearly correlated with the risk of mortality without transplantation in hepatic encephalopathy, that is, when the HDL-C level was low (<0.05 mmol / L), the risk of death increased significantly, while when the HDL-C level was high (≥0.05 mmol / L), the risk of death decreased significantly.

[0105] (4) Optimal thresholds of HDL-C and MELD score

[0106] Based on Figure 2 the ROC curve at 12 months shown in C, the cut-off values of HDL-C and MELD score were determined, and then the optimal thresholds of HDL-C and MELD score were determined.

[0107] Figure 3The scatter plot shown in A shows the relationship between HDL-C, MELD score, and 12-month mortality without transplantation. Among them, red represents the death group (poor prognosis); blue represents the survival group (good prognosis).

[0108] As Figure 3 shown in A, in the training cohort, as the MELD score increased, the number of the death group (red) increased relatively; as the HDL-C level decreased, the number of the death group (red) increased relatively. According to the distribution results of survival and death patients, it shows that most of the death groups (red) correspond to high MELD scores and low HDL-C levels. Most of the survival group patients correspond to low MELD scores and high HDL-C levels.

[0109] The above findings further prove that patients with HDL-C < 0.5 mmol / L and MELD score ≥ 17 have a poor prognosis.

[0110] Comparing the characteristics of patients with HDL-C < 0.5 mmol / L and those with HDL-C ≥ 0.5 mmol / L, as shown in Table 4, it can be known that: compared with patients with HDL-C ≥ 0.5 mmol / L (median 11.7), the MELD score of patients with HDL-C < 0.5 mmol / L increased (median 19.7);

[0111] compared with patients with HDL-C ≥ 0.5 mmol / L (percentage 2.7%), the incidence of SBP in patients with HDL-C < 0.5 mmol / L increased (percentage 9.6%);

[0112] compared with patients with HDL-C ≥ 0.5 mmol / L (percentage 63%), the incidence of ascites in patients with HDL-C < 0.5 mmol / L increased (percentage 80.1%);

[0113] compared with patients with HDL-C ≥ 0.5 mmol / L (median 25.8 IU / L), the ALT of patients with HDL-C < 0.5 mmol / L increased (median 42.3 IU / L);

[0114] compared with patients with HDL-C ≥ 0.5 mmol / L (median 38.5 IU / L), the AST of patients with HDL-C < 0.5 mmol / L increased (median 81.2 IU / L);

[0115] compared with patients with HDL-C ≥ 0.5 mmol / L (median 34.2 μmol / L), the TBIL of patients with HDL-C < 0.5 mmol / L increased (median 124.1 μmol / L);

[0116] Compared with patients with HDL-C ≥ 0.5 mmol / L (median 66.7 μmol / L), patients with HDL-C < 0.5 mmol / L had elevated Cr (median 76.1 μmol / L);

[0117] Compared with patients with HDL-C ≥ 0.5 mmol / L (median 3.5), patients with HDL-C < 0.5 mmol / L had elevated NLR (median 5.1);

[0118] Compared with patients with HDL-C ≥ 0.5 mmol / L (median 15.8 s), patients with HDL-C < 0.5 mmol / L had elevated PT (median 20.9 s);

[0119] Compared with patients with HDL-C ≥ 0.5 mmol / L (median 1.3), patients with HDL-C < 0.5 mmol / L had elevated INR (median 1.8).

[0120] The above results showed that compared with patients with HDL-C ≥ 0.5 mmol / L, patients with HDL-C < 0.5 mmol / L had elevated MELD scores, increased incidence of SBP, increased incidence of ascites, elevated ALT, elevated AST, elevated TBIL, elevated Cr, elevated NLR, elevated PT, and elevated INR.

[0121] Biochemical indicators or indicators calculated based on biochemical levels (MELD score, ALT, AST, TBIL, Cr, NLR, PT, INR) are associated with the liver function, inflammatory response, and coagulation function of patients. The elevation of these indicators reflects liver insufficiency and its systemic effects, and is closely related to the clinical manifestations and severity of hepatic encephalopathy. For example: when liver cells are damaged or inflamed, ALT present in liver cells will be released into the blood, so the elevation of ALT can reflect liver cell damage or inflammation, and hepatic encephalopathy is often accompanied by the progressive deterioration of liver function. It can be said that the elevation of ALT is closely related to the prognosis of hepatic encephalopathy.

[0122] The incidence of ascites and the incidence of SBP are also clinical features closely related to hepatic encephalopathy. Ascites itself does not directly cause hepatic encephalopathy, but ascites can indicate significant liver function impairment, which is a potential risk factor affecting the prognosis risk of hepatic encephalopathy. The systemic inflammatory response caused by SBP can promote the development of hepatic encephalopathy, which is also a potential risk factor affecting the prognosis risk of hepatic encephalopathy.

[0123] The above results suggest that the optimal threshold of HDL-C at 0.5 mmol / L can significantly distinguish the prognosis status of hepatic encephalopathy, and it can distinguish the changing trends of, but not limited to, biochemical indicators or indicators calculated based on biochemical levels (MELD score, ALT, AST, TBIL, Cr, NLR, PT, INR), the changing trend of the risk of ascites occurrence, and the changing trend of the risk of GIB occurrence. For example: There is a risk of increase in one or more parameters among ALT, AST, TBIL, Cr, NLR, PT, and INR in the prognosis of hepatic encephalopathy patients with HDL-C < 0.5 mmol / L. The risk of ascites occurrence in the prognosis of hepatic encephalopathy patients with HDL-C < 0.5 mmol / L is also significantly increased. The risk of SBP occurrence in the prognosis of hepatic encephalopathy patients with HDL-C < 0.5 mmol / L is also significantly increased. Based on the above indications, HDL-C in this application can be used for the risk of occurrence of complications in the prognosis of hepatic encephalopathy patients. HDL-C in this application can also be used to assist medical staff in evaluating the treatment effect of hepatic encephalopathy patients in the prognosis. HDL-C in this application can also be used to evaluate the improvement / deterioration degree of liver function in the prognosis of hepatic encephalopathy patients.

[0124] (5) Risk stratification of patients with overt hepatic encephalopathy

[0125] According to the optimal cut-off values of HDL-C and MELD score (as Figure 3 shown in A), Kaplan-Meier curves were plotted to show the 1-year transplant-free mortality rate of patients in the training cohort.

[0126] As Figure 3 shown in B, the 1-year transplant-free mortality rate of patients with HDL-C < 0.5 mmol / L was significantly higher than that of patients with HDL-C ≥ 0.5 mmol / L [38.7% (HDL-C < 0.5 mmol / L) vs. 10.3% (HDL-C ≥ 0.5 mmol / L), p < 0.0001].

[0127] As Figure 3 shown in C, the 1-year transplant-free mortality rate of patients in the MELD score ≥ 17 group was 47%, and the mortality rate of patients in the MELD score < 17 group was 10.7% (p < 0.0001).

[0128] Patients were further divided into three groups: low (HDL-C ≥ 0.5 mmol / L and MELD < 17), medium (HDL-C < 0.5 mmol / L or MELD ≥ 17), and high-risk (HDL-C < 0.5 mmol / L and MELD ≥ 17). As Figure 3As shown in Figure D, the 1-year transplantation-free mortality rates of patients in the low-, medium-, and high-risk subgroups were 7.5% (HDL-C ≥ 0.5 mmol / L and MELD < 17), 20.2% (HDL-C < 0.5 mmol / L or MELD ≥ 17), and 51.5% (HDL-C < 0.5 mmol / L and MELD ≥ 17), respectively, with p < 0.0001.

[0129] In this study, ascites was an independent poor prognostic factor for patients with overt hepatic encephalopathy. We further evaluated the prognostic value of HDL-C levels in patients with hepatic encephalopathy complicated by ascites and assessed the risk stratification of these patients. As Figure 3 shown in Figure E, the AUCs of the HDL-C, MELD score, and NLR curves were 0.744 (95% CI: 0.701 - 0.787), 0.781 (95% CI: 0.738 - 0.824), and 0.678 (95% CI: 0.629 - 0.726), respectively. These results showed that HDL-C, MELD score, and NLR could predict the 1-year mortality of patients with hepatic encephalopathy complicated by ascites. Among them, the prognostic value of HDL-C was significantly higher than that of NLR (p < 0.05), and was comparable to that of the MELD score (p > 0.05).

[0130] As Figure 3 shown in Figure F, the 1-year transplantation-free mortality rates of patients with low-, medium-, and high-risk hepatic encephalopathy complicated by ascites were 9.1%, 22.5%, and 51.8% (p < 0.0001), respectively. These results showed that the 1-year transplantation-free mortality rate of patients with low-risk hepatic encephalopathy complicated by ascites was within 10%; the 1-year transplantation-free mortality rate of patients with medium-risk hepatic encephalopathy complicated by ascites was between 10% and 50%; and the 1-year transplantation-free mortality rate of patients with high-risk hepatic encephalopathy complicated by ascites exceeded 50%.

[0131] (6) Subgroup analysis

[0132] Figure 4 is the forest plot obtained by Cox proportional hazards model analysis. We performed a comprehensive multi-faceted stratified analysis to show the relationship between HDL-C levels and 1-year transplantation-free mortality in different subgroups, including demographic factors (age and gender), complications (ascites), liver function parameters (AST and ALB), inflammatory markers (NLR), lipid indices (TC), renal function (Cr), and MELD score. Figure 4It shows the results of subgroup analysis of HDL-C in subgroups with age (≤55 or >55 years), gender (male or female), ascites (present or absent), AST (≤50 or >50 U / L), ALB (≤45 or >45 g / L), TC (≤50 or >50 mmol / L), NLR (≤4 or >4), Cr (≤60 or >60 μmol / L), and MELD score (≤17 or >17) as key outcome factors. The above continuous variables were classified according to the cutoff values.

[0133] The aHR of each subgroup was less than 1.0 and greater than 0.0, and the 95% CI (horizontal line) of aHR was to the left of the null line (vertical line), indicating that in the above subgroups, the relationship between HDL-C and 1-year mortality without transplantation was negative, that is, a decrease in HDL-C or a low HDL-C level had a poor prognosis in each subgroup.

[0134] Figure 4 It shows that in each subgroup [age (with a cutoff of 55 years), gender, ascites (present or absent), AST (with a cutoff of 50 U / L), ALB (with a cutoff of 45 g / L), TC (with a cutoff of 50 mmol / L), MELD score (with a cutoff of 17)], the HDL-C level was a protective factor for the prognosis of patients in each subgroup. (Note: When aHR < 1, this factor is a protective factor; when aHR > 1, it indicates that this factor is a risk factor.)

[0135] (7) Verify the prognostic value of HDL-C level and MELD score

[0136] As Figure 5 Shown in A - C, the AUCs of HDL-C at 1, 3, and 12 months in the validation cohort were 0.771 (95% CI: 0.724 - 0.818), 0.757 (95% CI: 0.710 - 0.806), and 0.724 (95% CI: 0.675 - 0.773), respectively. In addition, the prognostic abilities of HDL-C level and MELD score (AUC at 12 months: 0.724; 95% CI: 0.671 - 0.787) were similar (p > 0.05), but significantly higher than the AUCs of NLR at 1, 3, and 12 months (0.645, 0.65, 0.636), with p < 0.05 for all.

[0137] As Figure 5 Shown in D, the 1-year mortality without transplantation of patients with HDL-C < 0.5 mmol / L was significantly higher than that of patients with HDL-C ≥ 0.5 mmol / L (39.6% vs 10%, p < 0.0001).

[0138] As Figure 5As shown in Figure E, the 1-year mortality without transplantation in patients with a MELD score ≥ 17 was significantly higher than that in patients with a MELD score < 17 (44.2% vs. 14.3%, p < 0.0001).

[0139] As Figure 5 As shown in Figure F, the 1-year mortality without transplantation in the low-risk group (HDL-C ≥ 0.5 mmol / L and MELD < 17), medium group (HDL-C < 0.5 mmol / L or MELD ≥ 17), and high-risk group (HDL-C < 0.5 mmol / L and MELD ≥ 17) was 10.1%, 20.8%, and 51.2% respectively (p < 0.0001).

[0140] The research results indicate that a low HDL-C level can be a reliable indicator for predicting the 1-year mortality without transplantation in patients with overt hepatic encephalopathy. The prognosis of patients with HDL-C < 0.5 mmol / L and MELD ≥ 17 is significantly poorer. Monitoring the HDL-C level can help clinicians identify high-risk patients and promote early treatment to improve the prognosis of patients.

[0141] In this application, Table 1 shows the clinical baseline characteristics of patients with overt hepatic encephalopathy in the training cohort and validation cohort. Table 2 shows the baseline characteristics of surviving and deceased patients in the training cohort. Table 3 shows the univariate and multivariate Cox regression analyses of the 1-year mortality without transplantation in patients with overt hepatic encephalopathy in the training cohort. Table 4 shows the clinical characteristics of patients with different HDL-C levels in the training cohort.

[0142] Table 1 Clinical baseline characteristics of patients with overt hepatic encephalopathy in the training cohort and validation cohort

[0143]

[0144]

[0145] Note: Data are presented as n (%), mean ± standard deviation, or median (interquartile range).

[0146] Table 2 Baseline characteristics of surviving and deceased patients in the training cohort

[0147]

[0148] Note: Data are presented as n (%), mean ± standard deviation, or median (interquartile range).

[0149] Table 3 Univariate and multivariate Cox regression analyses of the 1-year mortality without transplantation in patients with overt hepatic encephalopathy in the training cohort

[0150]

[0151] Note: Data are presented as n (%), mean ± standard deviation, or median (interquartile range).

[0152] Table 4 Clinical characteristics of patients with different HDL-C levels in the training cohort

[0153]

[0154]

[0155] Note: Data are presented as n (%), mean ± standard deviation, or median (interquartile range).

[0156] It should be noted that the above specific embodiments are exemplary. Those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and within the protection scope of the present invention. Those skilled in the art should understand that the description and drawings of the present invention are illustrative and do not constitute a limitation on the claims. The protection scope of the present invention is defined by the claims and their equivalents. The description of the present invention contains multiple inventive concepts. For example, "preferably" and "according to a preferred embodiment" indicate that the corresponding paragraphs disclose an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, the features guided by "preferably" are only optional and should not be construed as must-be-set. Therefore, the applicant reserves the right to waive or delete the relevant preferred features at any time.

Claims

Use of a reagent for determining the abundance of high-density lipoprotein cholesterol in the preparation of a kit for evaluating the prognostic risk of hepatic encephalopathy.

2. The use according to claim 1, characterized in that, High-density lipoprotein cholesterol is used in combination with the Model for End-Stage Liver Disease score.

3. The use according to claim 1, wherein High-density lipoprotein cholesterol is used in combination with age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, neutrophil-lymphocyte ratio, prothrombin time, and prothrombin activity level.

4. The use according to claim 1, wherein The use includes evaluating the prognostic risk of hepatic encephalopathy with ascites using a biomarker.

5. The use according to claim 1, characterized in that, Evaluating the prognostic risk of hepatic encephalopathy includes predicting the likelihood of patient death within one year.

6. The use according to claim 1, wherein The evaluation includes: 1) Collecting a blood sample from a patient; 2) Detecting the abundance of high-density lipoprotein cholesterol in the blood sample; wherein, when the high-density lipoprotein cholesterol is less than 0.5 mmol / L, the patient belongs to the high-risk group of non-transplant mortality in the prognosis of type I hepatic encephalopathy; when the high-density lipoprotein cholesterol is not less than 0.5 mmol / L, the patient belongs to the low-risk group of non-transplant mortality in the prognosis of type I hepatic encephalopathy.

7. A system for evaluating the prognostic risk of hepatic encephalopathy, characterized in that, The system includes a data acquisition module for collecting data on high-density lipoprotein cholesterol and the Model for End-Stage Liver Disease score; and a data processing module for correlating the prognostic mortality of hepatic encephalopathy with the obtained high-density lipoprotein cholesterol and the indicators of the Model for End-Stage Liver Disease, wherein the data processing module is configured to: generate an indication of low risk of non-transplant mortality in a hepatic encephalopathy patient when the high-density lipoprotein cholesterol collected by the data acquisition module is not less than 0.5 mmol / L and the Model for End-Stage Liver Disease score is less than 17; generate an indication of medium risk of non-transplant mortality in a hepatic encephalopathy patient when the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5 mmol / L or the Model for End-Stage Liver Disease score is not less than 17; and generate an indication of high risk of non-transplant mortality in a hepatic encephalopathy patient when the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5 mmol / L and the Model for End-Stage Liver Disease score is not less than 17.

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