Hepatoencephalopathy prognosis marker, detection method and system

Abstract

The dominant hepatic encephalopathy is a serious complication of liver cirrhosis and affects lipid and lipoprotein metabolism. The invention relates to a biomarker for prognosis of hepatic encephalopathy. The biomarkers comprise high-density lipoprotein cholesterol, an end-stage liver disease model score, age, gender, upper gastrointestinal hemorrhage, spontaneous bacterial peritonitis, dominant hepatic encephalopathy grading, ascites, aspartic transaminase, glutamic-pyruvic transaminase, total bilirubin, serum albumin, total cholesterol, low-density lipoprotein cholesterol and serum creatinine. The specific value of neutrophil to lymphocyte, the prothrombin time and the prothrombin activity level are one or more. Compared with patients with alcoholic hepatitis, liver diseases, hepatic failure and the like in the prior art, the marker provided by the invention can remarkably improve the evaluation accuracy of the prognosis of the dominant hepatic encephalopathy due to the specificity of people.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a prognostic marker, a detection method and a system 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 vein shunt. HE can be divided into overt HE (OHE) and covert HE (CHE). Among them, patients with overt HE have more obvious symptoms and their disease progresses faster. Therefore, it is particularly important to assess the risk of prognosis in patients with overt HE.

[0003] The clinical features of overt hepatic encephalopathy include sleep disorders, personality changes, cognitive impairment, coma, etc. Studies have shown that the incidence of overt hepatic encephalopathy in patients with cirrhosis is 30-45%, with an extremely high mortality rate, a 1-year survival rate of <50%, and a 3-year survival rate of <25%. In addition, overt hepatic encephalopathy is one of the main factors causing frequent hospitalization of patients, and it is also one of the main factors causing extrahepatic failure. There are few methods for predicting hepatic encephalopathy or the prognosis risk of hepatic encephalopathy disclosed in the prior art, and the steps used in some methods are relatively complicated and cannot be used frequently. For example, the method for predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt surgery disclosed in CN117011242A includes the following steps: obtaining a three-dimensional model of the liver and a three-dimensional model of the spleen; performing morphological analysis on the three-dimensional model of the liver and the three-dimensional model of the spleen, respectively, to obtain a first liver morphological feature and a first spleen morphological feature; standardizing the original liver volume in the first liver morphological feature by the portal vein geometry feature to obtain a second liver morphological feature; standardizing the original spleen volume in the first spleen morphological feature by the splenic vein geometry feature to obtain a second spleen morphological feature; obtaining liver and spleen morphological features based on the second liver morphological feature and the second spleen morphological feature; performing high-dimensional imaging genomics feature analysis on the three-dimensional scanning model of the liver and spleen to obtain high-dimensional imaging genomics features of the liver and spleen; performing classification prediction based on the liver and spleen morphological features and the liver and spleen high-dimensional imaging genomics features to obtain the probability of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt surgery. Since there are few methods for predicting hepatic encephalopathy or the prognosis risk of hepatic encephalopathy, some patients are currently recommended to use prediction methods for diseases with similar clinical symptoms, such as the hepatitis B-related chronic acute liver failure prognosis assessment product disclosed in CN117890605A, which contains the biomarker myeloperoxidase-DNA complex; or the method for predicting liver disease mortality 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 rate of patients with alcoholic hepatitis.

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

[0005] At present, there are many types 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 assess liver functional damage rather than directly assess central nervous system damage, which also leads to insufficient marker specificity. This lack of specificity means that even if liver function shows abnormalities, it may not accurately reflect the prognosis of hepatic encephalopathy.

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

[0007] Due to the complex mechanism of hepatic encephalopathy and the significant differences in the responses of different patients to liver damage and ammonia levels, and this individual difference has not been fully considered in the evaluation of existing markers, it is difficult to obtain indicators that can effectively evaluate hepatic encephalopathy through limited experimental means. It can be said that the pathogenesis and prognostic risk factors of hepatic encephalopathy are physiologically significantly different from those of liver function decline, which has also led to the fact that technicians in this field have never considered using the end-stage liver disease model scoring 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 association between these markers and the prognostic risk of hepatic encephalopathy. This lack will make it difficult for clinicians to clearly select prognostic risk markers when using these markers to assess the prognostic risk of hepatic encephalopathy, thus limiting their clinical application and practical implications.

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

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

[0011] In addition, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making the present invention, but due to space limitations, not all details and contents are listed in detail. However, this does not mean that the present invention does not have the characteristics of these prior arts. On the contrary, the present invention already has all the characteristics of the prior art, and the applicant reserves the right to add relevant prior art to the background technology. Summary of the invention

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

[0013] One of the purposes of this application is to provide a method for evaluating the condition of a patient with hepatic encephalopathy.

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

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

[0016] One of the purposes of this application is to provide a system for evaluating the condition of a patient with hepatic encephalopathy.

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

[0018] One of the purposes of this application is to propose a method for predicting the prognosis and mortality of patients with hepatic encephalopathy.

[0019] One of the purposes of the present application is to propose a composition for predicting the prognosis and mortality of patients with hepatic encephalopathy.

[0020] One of the purposes of this application is to provide a kit for predicting the prognosis and mortality of patients with hepatic encephalopathy.

[0021] One of the purposes of the present application is to propose a composition for predicting the prognosis and mortality rate of patients with hepatic encephalopathy or a kit comprising the composition for use in formulating an individualized treatment plan for patients with hepatic encephalopathy.

[0022] One of the purposes of this application is to propose a treatment plan for hepatic encephalopathy.

[0023] One of the objectives of this application is to propose a system for predicting the prognosis and 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 treatment. Finding simple, cheap, and rapid prognostic markers for hepatic encephalopathy can reduce mortality and improve prognosis.

[0025] One aspect of the present application relates to a biomarker for assessing the prognostic risk of hepatic encephalopathy. Biomarkers included high-density lipoprotein cholesterol (HDL-C), Model for End-Stage Liver Disease (MELD) score, age, gender, upper 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 (SERUM Preferably, the biomarkers include one or more of the following: Rreatinine, Cr), neutrophil-lymphocyte ratio (NLR), prothrombin time (PT), and prothrombin activity (PTA) levels. Preferably, the biomarkers include a variety of end-stage liver disease model score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, dominant 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 levels. Preferably, the biomarkers include high-density lipoprotein cholesterol, age, sex, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, dominant 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. Preferably, the biomarkers include age, ascites, end-stage liver disease model score, high-density lipoprotein cholesterol and neutrophil-lymphocyte ratio. Preferably, the biomarkers include high-density lipoprotein cholesterol. The biomarkers include end-stage liver disease model score. The biomarkers include neutrophil-lymphocyte ratio.More preferably, the biomarkers include high-density lipoprotein cholesterol and Model for End-Stage Liver Disease score.

[0026] Another aspect of the present application relates to the use of biomarkers in assessing the prognostic risk of hepatic encephalopathy.

[0027] According to a preferred embodiment, the use comprises biomarkers for assessing the prognostic risk of hepatic encephalopathy with ascites.

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

[0029] Another aspect of the present application relates to a kit for predicting the risk of transplant-free (TF) prognosis of hepatic encephalopathy. The kit comprises reagents for detecting biomarkers for assessing the risk of prognosis of hepatic encephalopathy. Preferably, the biomarker comprises high-density lipoprotein cholesterol. Preferably, the biomarker comprises a model for end-stage liver disease. Preferably, the biomarker comprises high-density lipoprotein cholesterol and a model for end-stage liver disease in a terminal patient. The kit comprises reagents for detecting high-density lipoprotein cholesterol. The kit also comprises a model for end-stage liver disease score sheet in a terminal patient.

[0030] Another aspect of the present application relates to the use of a kit for predicting hepatic encephalopathy transplant-free mortality, comprising the steps of:

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

[0032] b) determining the absolute or relative quantitative value 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 biomarkers in the body fluid sample from step a), and / or determining the indication of one or more of the model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, and neutrophil-lymphocyte ratio of the subject.

[0033] Another aspect of 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 comprising: 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 a first category of hepatic encephalopathy prognosis without a high risk of transplant mortality; when the high-density lipoprotein cholesterol is not less than 0.5 mmol / L, the patient belongs to a first category of hepatic encephalopathy prognosis without a low risk of transplant mortality.

[0034] On the other hand, the present application relates to a system for assessing the prognostic risk of hepatic encephalopathy, wherein the system is used to collect the patient's Model for End-Stage Liver Disease score, wherein when the Model for End-Stage Liver Disease score is not less than 17, the patient belongs to the first category of hepatic encephalopathy prognosis without a high risk of transplant mortality; when the Model for End-Stage Liver Disease score is less than 17, the patient belongs to the first category of hepatic encephalopathy prognosis without a low risk of transplant mortality.

[0035] Another aspect of the present application relates to a method for assessing the prognostic risk of hepatic encephalopathy in a subject. The method comprises: measuring 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 obtained from the subject and / or determining the subject's end-stage liver disease model score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, and one or more indications; assessing the prognosis of hepatic encephalopathy. The risk is associated 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, prothrombin activity obtained from body fluids and / or the prognostic risk of hepatic encephalopathy is associated with the level of one or more indicators of the subject's model for end-stage liver disease score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, overt hepatic encephalopathy grade, ascites, and neutrophil-lymphocyte ratio. Preferably, the method comprises: measuring high-density lipoprotein cholesterol in body fluids obtained from the subject and / or determining the model for end-stage liver disease score of the subject; associating the prognostic risk of hepatic encephalopathy with high-density lipoprotein cholesterol obtained from body fluids and / or associating the prognostic risk of hepatic encephalopathy with the model for end-stage liver disease score of the subject.

[0036] Another aspect of the present application relates to a system for assessing the prognostic risk of hepatic encephalopathy. The system includes a data acquisition module for collecting one or more of high-density lipoprotein cholesterol, end-stage liver disease model 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 associating the prognostic mortality rate of hepatic encephalopathy with the level in the acquired body fluid components and / or the prognostic mortality rate of hepatic encephalopathy without transplantation with the end-stage liver disease model 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 assessing the prognosis risk of hepatic encephalopathy, the system comprising a data acquisition module for collecting one or more of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio (INR), neutrophil-lymphocyte ratio and prothrombin time levels; and a data processing module for associating the prognosis mortality rate of hepatic encephalopathy with the acquired alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio, neutrophil-lymphocyte ratio and prothrombin time. The prognosis risk is measured and / or calculated using a healthy group as a control or a time point of the patient's prognosis. Alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio, neutrophil-lymphocyte ratio and / or prothrombin time levels are used as controls. When the alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio, neutrophil-lymphocyte ratio and / or prothrombin time levels of the corresponding patients collected by the data acquisition module are elevated, the data processing module generates an indication of an increased risk of mortality from hepatic encephalopathy in the patient.

[0038] On the other hand, the present application relates to a system for assessing the prognostic risk of hepatic encephalopathy, the system comprising a data acquisition module for collecting one or more of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and serum albumin levels; and a data processing module for associating the prognostic mortality rate of hepatic encephalopathy with the acquired indications of one or more of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and serum albumin, wherein, taking a healthy group as a control or taking the low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and / or serum albumin levels measured at a time point of the patient's prognosis as a control, when the low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and / or serum albumin levels of the corresponding patient collected by the data acquisition module are reduced, the data processing module generates an indication that the patient's risk of hepatic encephalopathy without transplantation mortality is increased.

[0039] Another aspect of 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 prognosis risk of hepatic encephalopathy based on the evaluation model involved in the present application.

[0040] According to a preferred embodiment, with the healthy group as the control or the high-density lipoprotein cholesterol level measured at a time point of the patient's prognosis as the control, a decrease in high-density lipoprotein cholesterol represents an increased risk of mortality without transplantation in patients with hepatic encephalopathy. With the healthy group as the control or the high-density lipoprotein cholesterol level measured at a time point of the patient's prognosis as the control, an increase in high-density lipoprotein cholesterol represents a reduced risk of mortality without transplantation in patients with hepatic encephalopathy.

[0041] According to a preferred embodiment, taking the healthy group as a control or the model for end-stage liver disease score calculated at a time point of the patient's prognosis as a control, a decrease in the model for end-stage liver disease score represents a decrease in the risk of transplant-free mortality in patients with hepatic encephalopathy. Taking the healthy group as a control or the model for end-stage liver disease score calculated at a time point of the patient's prognosis as a control, an increase in the model for end-stage liver disease score represents an increase in the risk of transplant-free mortality in patients with hepatic encephalopathy.

[0042] According to a preferred embodiment, taking the healthy group as a control or 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 score as a control, a decrease in the model for end-stage liver disease score and an increase in high-density lipoprotein cholesterol represent a decrease in the risk of mortality without transplantation in patients with hepatic encephalopathy. Taking the healthy group as a control or 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 score as a control, an increase in the model for end-stage liver disease score and a decrease in high-density lipoprotein cholesterol represent an increase in the risk of mortality without transplantation in patients with hepatic encephalopathy.

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

[0044] Another aspect of the present application relates to an assessment model for the prognosis risk of hepatic encephalopathy. The assessment criteria in the assessment model include: patients with a low risk of no transplant mortality in the second category of hepatic encephalopathy prognosis, high-density lipoprotein cholesterol is not less than 0.5mmol / L and the model for end-stage liver disease score is less than 17; patients with a medium risk of no transplant mortality in the second category of hepatic encephalopathy prognosis, high-density lipoprotein cholesterol is less than 0.5mmol / L or the model for end-stage liver disease score is not less than 17; and / or patients with a high risk of no transplant mortality in the second category of hepatic encephalopathy prognosis, high-density lipoprotein cholesterol is less than 0.5mmol / L and the model for end-stage liver disease score is not less than 17.

[0045] Another aspect of the present application relates to an assessment model for the prognosis risk of hepatic encephalopathy. The assessment criteria in the assessment model include: patients with a high risk of mortality from transplantation without the prognosis of the first category of hepatic encephalopathy, high-density lipoprotein cholesterol less than 0.5mmol / L or a model for end-stage liver disease score of not less than 17; and / or patients with a low risk of mortality from transplantation without the prognosis of the first category of hepatic encephalopathy, high-density lipoprotein cholesterol not less than 0.5mmol / L or a model for end-stage liver disease score less than 17.

[0046] Preferably, when the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5mmol / L, the data processing module generates an indication that the patient is at high risk of hepatic encephalopathy without transplant mortality. When the high-density lipoprotein cholesterol collected by the data acquisition module is not less than 0.5mmol / L, the data processing module generates an indication that the patient is at low risk of hepatic encephalopathy without transplant mortality. When the Model for End-Stage Liver Disease score collected by the data acquisition module is less than 17, the data processing module generates an indication that the patient is at low risk of hepatic encephalopathy without transplant mortality. When the Model for End-Stage Liver Disease score collected by the data acquisition module is not less than 17, the data processing module generates an indication that the patient is at high risk of hepatic encephalopathy without transplant mortality.

[0047] Another aspect of the present application relates to a system for assessing the prognostic risk of hepatic encephalopathy, the system comprising a data acquisition module for collecting high-density lipoprotein cholesterol and end-stage liver disease model score; and a data processing module for associating the prognostic mortality rate of hepatic encephalopathy with the acquired high-density lipoprotein cholesterol and end-stage liver disease model indications, wherein the data processing module is configured as follows: when the high-density lipoprotein cholesterol collected by the data acquisition module is not less than 0.5mmol / L and the end-stage liver disease model score is less than 17, the data processing module generates an indication of a low risk of transplant-free mortality in the second category of hepatic encephalopathy patients. When the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5mmol / L or the end-stage liver disease model score is not less than 17, the data processing module generates an indication of a medium risk of transplant-free mortality in the second category of hepatic encephalopathy patients. When the high-density lipoprotein cholesterol collected by the data acquisition module is less than 0.5mmol / L and the end-stage liver disease model score is not less than 17, the data processing module generates an indication of a high risk of transplant-free mortality in the second category of hepatic encephalopathy patients. Preferably, the data acquisition module comprises a blood lipid detection unit and an end-stage liver disease model scoring unit. More preferably, the blood lipid detection module collects high-density lipoprotein cholesterol levels. The end-stage liver disease model scoring unit collects the end-stage liver disease model score. Preferably, hepatic encephalopathy includes overt hepatic encephalopathy.

[0048] Another aspect of 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 score from the subject; and correlating the hepatic encephalopathy prognosis mortality rate with the level in the obtained body fluid component and / or correlating the hepatic encephalopathy prognosis mortality rate with the model score level, and evaluating the hepatic encephalopathy prognosis mortality risk based on a first-class evaluation method or a second-class evaluation method; wherein, the first-class evaluation method: when high-density lipoprotein cholesterol is not less than 0.5mmol / L and the Model for End-Stage Liver Disease score is less than 17, the hepatic encephalopathy prognosis risk is predicted to belong to the second-class hepatic encephalopathy prognosis without transplantation low risk of mortality; when high-density lipoprotein cholesterol is less than 0.5mmol / L or the Model for End-Stage Liver Disease score is not less than 17, the hepatic encephalopathy prognosis risk is predicted to belong to the second-class hepatic encephalopathy prognosis without transplantation low risk of mortality. At 17 o'clock, the predicted prognostic risk of hepatic encephalopathy belongs to the second category of hepatic encephalopathy prognosis with medium risk of transplant mortality; when high-density lipoprotein cholesterol is less than 0.5mmol / L and the end-stage liver disease model score is not less than 17, the predicted prognostic risk of hepatic encephalopathy belongs to the second category of hepatic encephalopathy prognosis with high risk of transplant mortality; or the second category evaluation method: when high-density lipoprotein cholesterol is not less than 0.5mmol / L or the end-stage liver disease model score is less than 17, the predicted prognostic risk of hepatic encephalopathy belongs to the first category of hepatic encephalopathy prognosis with low risk of transplant mortality; when high-density lipoprotein cholesterol is less than 0.5mmol / L or the end-stage liver disease model score is not less than 17, the predicted prognostic risk of hepatic encephalopathy belongs to the first category of hepatic encephalopathy prognosis with high risk of transplant mortality.

[0049] Another aspect of the present invention relates to a method for constructing a scoring model for predicting hepatic encephalopathy transplant-free mortality. The construction method comprises the following steps:

[0050] (1) obtaining one or more of the levels 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 subject's body fluids, and / or obtaining one or more of the subject's end-stage liver disease model score, age, gender, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, dominant hepatic encephalopathy grade, and ascites;

[0051] (2) Based on the results of step (1), independent risk factors with prognostic value were screened by Cox proportional hazards regression analysis, and a scoring model for predicting transplant-free mortality in hepatic encephalopathy was constructed.

[0052] Beneficial effects of this technical solution:

[0053] Different types of liver diseases have significant differences in epidemiological characteristics. Hepatic encephalopathy is closely related to clinical symptoms such as liver dysfunction, ammonia metabolism disorders, and nervous system damage, which involves complex neurophysiological changes, while the direct influencing factors of alcoholic hepatitis, liver disease, and liver failure are liver cell damage and inflammation, and there are significant differences in the pathological mechanisms of the two. At the same time, the prognosis of hepatic encephalopathy is affected by multiple factors, including ammonia levels, liver function, kidney function, and neural repair, while alcoholic hepatitis, liver disease, and liver failure mainly use liver function indicators as evaluation parameters for the prognostic level. Therefore, the prognostic model of hepatic encephalopathy needs to be established in a specific patient population, and the model established in the alcoholic hepatitis, liver disease, and liver failure population may have large prediction errors when predicting hepatic encephalopathy.

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

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

[0056] Multivariate Cox analysis was used to explore independent predictive factors associated with transplant-free mortality, and the prognostic value of these factors was evaluated using the area under the receiver operating characteristic curve (AUC). Finally, high-density lipoprotein cholesterol was found to have a good prognostic value. Further in-depth analysis was then conducted, including subgroup analysis and survival analysis. The indicator was further validated using 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 shunt, manifested as dysfunction of the central nervous system. Its pathogenesis involves multiple factors such as ammonia poisoning and changes in neurotransmitters. Limited by traditional cognition, research design, barriers to interdisciplinary knowledge, complexity of physiological mechanisms, and limitations of data and sample size, the existing technology has not combined the research of HDL-C, which is mainly focused on the cardiovascular field, with the cross-disciplinary fields of hepatology and neurology. This application gradually discovered the potential impact of HDL-C on hepatic encephalopathy through step-by-step calculation and analysis of clinical data. The process is as follows: This study included multiple demographic characteristics and commonly used clinical laboratory indicators, and through univariate and multivariate risk regression analysis, it was finally found that three laboratory indicators were independent predictors of patients' 1-year transplant-free mortality. The three laboratory indicators are MELD score, HDL-C and NLR levels. Compared with the MELD score, it was found that HDL-C and MELD scores had higher prognostic values ​​at 1, 3, and 12 months, and the AUC (indicator for evaluating prognostic value) values ​​of the two were similar, with no significant statistical difference, indicating that HDL-C has strong prognostic value and significance. In addition, compared with NLR, HDL-C performed significantly higher than NLR at 1, 3, and 12 months (with significant statistical difference, p<0.05). Thus, we determined that HDL-C is an important risk factor affecting hepatic encephalopathy. Therefore, the potential effect of HDL-C on hepatic encephalopathy is an unexpected new finding of the study.

[0058] Furthermore, the present application further verified the prognostic value of high-density lipoprotein cholesterol level and end-stage liver disease model score through the receiver operating characteristic (ROC) curve, and gave further evaluation limits of high-density lipoprotein cholesterol level and end-stage liver disease model score. The present application found that the optimal cutoff value of high-density lipoprotein cholesterol was 0.5mmol / L. Based on the indicator of high-density lipoprotein cholesterol alone, it was found that the 1-year transplant-free mortality rate of patients with high-density lipoprotein cholesterol <0.5mmol / L was significantly higher than that of patients with high-density lipoprotein cholesterol ≥0.5mmol / L (38.7% vs. 10.3%, p<0.0001; Figure 3 B). We further combined the model for end-stage liver disease score and divided the patients into three groups: low (HDL-C ≥ 0.5mmol / L and MELD < 17), medium (HDL-C < 0.5mmol / L or MELD ≥ 17), and high-risk (HDL-C < 0.5mmol / L and MELD ≥ 17). The 1-year transplant-free mortality rates in the low-, medium-, and high-risk subgroups were 7.5%, 20.2%, and 51.5%, respectively. The combination of the two indicators can further clearly distinguish low-risk and high-risk patients.

[0059] Combining these two most important scores for prognosis, patients can be further divided into three risk levels: low, medium, and high. When these two risk factors are not included, the patient mortality rate is about 10%. When these two risk factors (HDL-C < 0.5mmol / L and MELD ≥ 17) exist at the same time, the patient mortality rate is ≥ 50%. The clinical revelation of this result is that patients in the medium-risk to high-risk groups can be treated in a targeted manner to reduce the mortality rate.

[0060] At the same time, from an economic perspective (or the perspective of clinical application value and transformation), the data acquisition method for the markers proposed in this application is simple, and the high-density lipoprotein cholesterol level and the end-stage liver disease model score are both clinical indicators, which are easy to obtain and the process of obtaining them is simple and fast; the markers proposed in this application can simply and quickly calculate the patient's risk of death and guide doctors to carry out the next step of treatment. In actual application, this marker combination can be widely used in grassroots hospitals. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0062] Figure 2is the predictive value of independent risk factors in the training cohort and the relationship between high-density lipoprotein cholesterol and prognosis, where (A), (B), and (C) are the ROC curves for predicting transplant-free mortality by high-density lipoprotein cholesterol, neutrophil-lymphocyte ratio, and Model for End-Stage Liver Disease score at 1 month, 3 months, and 12 months of prognosis; (D), (E), and (F) are the relationships between high-density lipoprotein cholesterol and transplant-free mortality at 1 month, 3 months, and 12 months, respectively. The red line represents the hazard ratio (HR) reference, and the red area represents the 95% CI;

[0063] Figure 3 The scatter plot and risk stratification of patients with overt HE in the training cohort, (A) Distribution of patients with overt HE who died and survived; (B) 1-year survival rate of patients with HDL-C < 0.5 mmol / L and ≥ 0.5 mmol / L; (C) 1-year survival rate of patients with MELD < 17 and ≥ 17; (D) Survival probability of patients in low-, medium-, and high-risk groups; (E) The predictive ability of different indicators for the 1-year transplant-free mortality of patients with HE and ascites; (F) Survival probability of patients with HE and ascites in 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 are the 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 for predicting transplant-free mortality 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 low-, intermediate-, and high-risk groups. DETAILED DESCRIPTION

[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 the specific circumstances. The experimental procedures described in the following examples are conventional procedures unless otherwise specified. The reagents used in the following examples, etc., can be obtained through commercial channels unless otherwise specified.

[0068] High-density lipoprotein (HDL) is a type of lipoprotein in the blood, characterized by its high density and high protein content. It helps collect excess cholesterol from tissues and cells and transports it back to the liver for metabolism and excretion. HDL has the functions of reverse cholesterol transport, anti-oxidation, anti-inflammatory, anti-thrombotic and immunomodulatory. HDL is the main lipid component of serum and can bind lipid components such as cholesterol and phospholipids. Because the liver plays a vital role in lipid synthesis, transport and metabolism, patients with cirrhosis often have abnormal lipid levels. HDL dysfunction may lead to the occurrence of inflammatory diseases such as cardiovascular disease, diabetes and kidney disease. Patients with advanced cirrhosis 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 increased mortality in patients with alcoholic hepatitis and non-cholestatic cirrhosis, while 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. Inflammatory response caused by infection is a common risk factor for the development of overt hepatic encephalopathy. HDL-C plays an important role in inhibiting endogenous inflammation, and the reduction of HDL-C levels is related to systemic inflammatory response. HDL-C exerts anti-inflammatory effects by attaching to and dissolving lipopolysaccharide and weakening the expression of adhesion molecules.

[0069] There is no relevant study in the prior art to confirm the prognostic value of HDL-C and transplant-free mortality in patients with overt hepatic encephalopathy. The method of identifying patients with high mortality based on HDL-C levels is unclear.

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

[0071] Results showed that patients with overt hepatic encephalopathy who died had lower HDL-C levels than those who survived. The prognostic value of HDL-C was good (AUC at 1 year: 0.745), which was 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 that of the MELD score (0.724 vs. 0.724). The optimal thresholds for 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 the 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 with a high risk of transplant mortality without a first category of hepatic encephalopathy prognosis ranges from 50 to 100%. More preferably, the relative incidence of death in patients with a high risk of transplant mortality without a first category of hepatic encephalopathy prognosis is greater than 50%, 60%, 70%, 80% or 90%. Preferably, the relative incidence of death in patients with a high risk of transplant mortality without a first category of hepatic encephalopathy prognosis is less than 50%. More preferably, the relative incidence of death in patients with a high risk of transplant mortality without a first category of hepatic encephalopathy prognosis is less than 10%, 20%, 30% or 40%.

[0073] Preferably, the relative incidence of death in patients with low risk of transplant mortality in the second category of hepatic encephalopathy prognosis without transplant mortality is not less than 10%. The relative incidence of death in patients with medium risk of transplant mortality in the second category of hepatic encephalopathy prognosis without transplant mortality is in the range of 10-50%. The relative incidence of death in patients with high risk of transplant mortality in the second category of hepatic encephalopathy prognosis without transplant mortality is greater than 50%. More preferably, the relative incidence of death in patients with medium risk of transplant mortality in the second category of hepatic encephalopathy prognosis without transplant mortality is in the range of 10-20%, 20-30%, 30-40%, 40-50%. The relative incidence of death in patients with high risk of transplant mortality in the second category of hepatic encephalopathy prognosis without transplant mortality is greater than 60%, 70%, 80%, 90% or other values ​​in the range of 50-100%. Particularly preferably, the relative incidence of death in patients with medium risk of transplant mortality in the second category of hepatic encephalopathy prognosis without transplant mortality is 20%, 30%, 40%, 50% or other values ​​in the range of 10-50%. The relative incidence of death in patients with high risk of hepatic encephalopathy without transplantation mortality is 60%, 70%, 80%, 90% or other values ​​within the range of 50 to 100%.

[0074] Based on the above results, HDL-C is closely related to the 1-year transplant-free mortality rate in patients with overt hepatic encephalopathy. In general, 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 one-year transplant-free mortality in patients with hepatic encephalopathy and ascites.

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

[0076] A 95% confidence interval (95% CI) means that in repeated experiments, there is a 95% probability that the interval contains the true parameter value.

[0077] Restricted Cubic Splines (RCS) are used to represent the nonlinear 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 rate or time to event. The Kaplan-Meier curve can show the difference in survival / mortality between patients in the control group and patients in the experimental group.

[0079] In the present invention, the prognostic risk is, for example, a mortality risk or a survival rate risk. Generally speaking, the mortality rate and the survival rate total 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 rate of hepatic encephalopathy proposed in the present application is equivalent to the kit for predicting the survival rate of hepatic encephalopathy. The biomarker for predicting the mortality rate of hepatic encephalopathy proposed in the present application is equivalent to the biomarker for predicting the survival rate of hepatic encephalopathy. The evaluation model for predicting the mortality rate of hepatic encephalopathy proposed in the present application is equivalent to the evaluation model for predicting the survival rate of hepatic encephalopathy. The system for predicting the mortality rate of hepatic encephalopathy proposed in the present application is equivalent to the device for predicting the survival rate of hepatic encephalopathy. The system for predicting the mortality rate of hepatic encephalopathy proposed in the present application is equivalent to the system for predicting the survival rate of hepatic encephalopathy. The method for predicting the mortality rate of hepatic encephalopathy proposed in the present 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 a circuit, 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 storage medium of the following: a flash memory, a hard disk, a multimedia card micro memory, a card-type memory (e.g., an SD or XD memory), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a disk, an optical disk, etc.

[0081] Example 1

[0082] 1. Materials and methods.

[0083] (1) Study population.

[0084] Patients with overt HE who visited Beijing Ditan Hospital from January 2010 to August 2016 (n=1640) 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 with co-infection with human immunodeficiency virus; 4) patients with severe mental illness or use of psychiatric medications; 5) patients with neurological diseases, use of psychotropic drugs or long-term alcoholism; 6) patients with latent HE. Finally, 821 patients with overt HE were included in the training cohort. Using the same inclusion and exclusion criteria, 480 patients were included as the validation cohort from September 2016 to December 2020 ( Figure 1 ).

[0085] (2) Data collection and clinical definitions.

[0086] We collected baseline demographic and laboratory data from the electronic medical record database, including age, sex, comorbidities, ALT, AST, ALB, TBIL, triglyceride (TG), TC, Cr, HDL-C, and LDL-C levels. In addition, we 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 were collected within 48 hours after admission. Endpoint events were 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 MELD score was calculated as follows: 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 the independent risk indicator, and the DeLong test was used to determine whether there was a statistical difference between the various prognostic indicators. The RCS curve was used to visualize whether there was a nonlinear correlation between HDL-C and 1-month, 3-month, and 12-month transplant-free mortality. The 1-year survival rate was estimated using the Kaplan-Meier method, and the log-rank test was used. A forest plot was 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 patients with overt HE in the two groups. The median age of all patients was 55 years (range, 47-63 years), including 962 males (73.9%) and 339 females (26.1%). In the training cohort, 679 (82.7%) were diagnosed with overt HE grade II, and 142 (17.3%) were diagnosed with overt HE grade III-IV. During the 1-year follow-up, 200 (24.4%) deaths occurred in the training cohort and 130 (27.0%) deaths occurred in the validation cohort. There were no significant differences in the baseline characteristics of the two subgroups.

[0092] In addition, we analyzed the characteristics of the patients who survived and died in the training cohort. As shown in Table 2, compared with the surviving patients (median 54.0 years), the patients who died were older (median 56.5 years); compared with the surviving patients (median 28.2 IU / L), the patients who died had higher ALT (median 51.8 IU / L); compared with the surviving patients (median 44.8 μmol / L), the patients who died had higher TBIL (median 174.1 μmol / L); compared with the surviving patients (median 43.9 IU / L), the patients who died had higher AST (median 97.4 IU / L); compared with the surviving patients (median 67.4 μmol / L), the patients who died had higher Cr (median 88 .9μmol / L); compared with surviving patients (median 13.0), non-surviving patients had higher MELD scores (median 23.2); compared with surviving patients (median 53.8%), non-surviving patients had lower PTA and worse prognosis (median 34.8%); compared with surviving patients (median 1.5), non-surviving patients had higher INR (median 1.9); compared with surviving patients (median 3.7), non-surviving patients had higher NLR (median 6.5); compared with surviving patients (median 16.9s), non-surviving patients had higher PT (median 22.4s), among which the p values ​​of the above two groups of data were all less than 0.001. In addition, non-surviving patients were more likely to have GIB, SBP and ascites. It is worth noting that the levels of LDL-C, TC, HDL-C and ALB in non-surviving patients were lower than those in surviving patients (all p<0.001).

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

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

[0095] In the training cohort, univariate Cox analysis showed that age, sex, 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 found in univariate analysis were included in 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 factors in patients with overt HE.

[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. AUC represents the area under the ROC curve, which is a value between 0 and 1 and is used to measure the predictive ability of the classification model. 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. Figure 2 As shown in A to 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 In the results shown in A to C, the AUC of HDL-C at 1 month, 3 months and 12 months were all greater than 0.7, indicating that the HDL-C level has a good prognostic value for predicting short-term and long-term overt hepatic encephalopathy patients (mortality without transplantation), especially at 1 month (0.792>0.777>0.745).

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

[0101] like Figure 2 As shown in A to C, at 1, 3, and 12 months, HDL-C (AUC at 1, 3, and 12 months were 0.792, 0.777, and 0.745, respectively) performed significantly better than NLR (AUC at 1, 3, and 12 months were 0.701, 0.701, and 0.684, respectively) (all p < 0.05), indicating that HDL-C is more accurate than NLR in predicting the transplant-free mortality of patients with overt hepatic encephalopathy. This result indicates that HDL-C is more advantageous than a single inflammatory indicator (such as NLR) in reflecting the prognosis and development of hepatic encephalopathy (or hepatic encephalopathy mortality).

[0102] like Figure 2 As shown in D~F, RCS curve ( Figure 2 The vertical axes D to F represent the transplant-free mortality rate (HR), showing that the HDL-C level showed an "L-shaped" relationship with the transplant-free mortality rate at 1 month, 3 months and 12 months, which indicated that the relationship between the HDL-C level and the risk of death was nonlinear (p<0.001): when the HDL-C level was low (<0.05mmol / L), the risk of death increased significantly, while when the HDL-C level was high (≥0.05mmol / L), the risk of death decreased significantly.

[0103] like Figure 2 As shown in D to F, the RCS curves at 1, 3, and 12 months showed a consistent L-shaped trend, which indicated that HDL-C levels were nonlinearly correlated with transplant-free mortality at different time points.

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

[0105] (4) Optimal thresholds for HDL-C and MELD scores

[0106] based on Figure 2 C shows the ROC curve at 12 months, which determined the cut-off values ​​of HDL-C and MELD score, and then determined the optimal thresholds of HDL-C and MELD score.

[0107] Figure 3A shows a scatter plot showing the relationship between HDL-C, MELD score and 12-month transplant-free mortality, wherein red represents the death group (poor prognosis) and blue represents the survival group (good prognosis).

[0108] like Figure 3 As shown in A, in the training cohort, as the MELD score increases, the number of death groups (red) increases relatively; as the HDL-C level decreases, the number of death groups (red) increases relatively. According to the distribution results of surviving and dead patients, most of the death groups (red) have high MELD scores and low HDL-C levels. Most of the patients in the survival group have 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 HDL-C ≥ 0.5 mmol / L, as shown in Table 4 , it can be seen that: patients with HDL-C < 0.5 mmol / L had increased MELD scores (median 19.7) compared with those with HDL-C ≥ 0.5 mmol / L (median 11.7);

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

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

[0113] Patients with HDL-C < 0.5 mmol / L had elevated ALT (median 42.3 IU / L) compared with those with HDL-C ≥ 0.5 mmol / L (median 25.8 IU / L);

[0114] Patients with HDL-C < 0.5 mmol / L had elevated AST (median 81.2 IU / L) compared with those with HDL-C ≥ 0.5 mmol / L (median 38.5 IU / L);

[0115] Patients with HDL-C < 0.5 mmol / L had increased TBIL (median 124.1 μmol / L) compared with those with HDL-C ≥ 0.5 mmol / L (median 34.2 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 increased Cr (median 76.1 μmol / L);

[0117] Patients with HDL-C < 0.5 mmol / L had increased NLR (median 5.1) compared with those with HDL-C ≥ 0.5 mmol / L (median 3.5);

[0118] Patients with HDL-C < 0.5 mmol / L had increased PT (median 20.9 s) compared with those with HDL-C ≥ 0.5 mmol / L (median 15.8 s);

[0119] Patients with HDL-C < 0.5 mmol / L had an increased INR (median 1.8) compared with those with HDL-C ≥ 0.5 mmol / L (median 1.3).

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

[0121] Biochemical indicators or indicators calculated based on biochemical levels (MELD score, ALT, AST, TBIL, Cr, NLR, PT, INR) are associated with the patient's liver function, inflammatory response, and coagulation function. The increase in the above indicators reflects liver dysfunction and the systemic effects it causes, 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 increased ALT can reflect liver cell damage or inflammation, and hepatic encephalopathy is often accompanied by progressive deterioration of liver function. It can be said that increased ALT is closely related to the prognosis of hepatic encephalopathy.

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

[0123] The above results suggest that the optimal threshold of HDL-C of 0.5mmol / L can significantly distinguish the prognosis status of hepatic encephalopathy, which can distinguish including but not limited to the changing trend of 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 and the changing trend of the risk of GIB. For example: There is a risk of increased risk of one or more parameters of ALT, AST, TBIL, Cr, NLR, PT, and INR in the prognosis of hepatic encephalopathy patients with HDL-C <0.5mmol / L. The risk of ascites in the prognosis of hepatic encephalopathy patients with HDL-C <0.5mmol / L is also significantly increased. The risk of SBP in the prognosis of hepatic encephalopathy patients with HDL-C <0.5mmol / L is also significantly increased. Based on the above indications, HDL-C in this application can be used to predict the risk of complications in patients with hepatic encephalopathy. HDL-C in this application can also be used to assist medical staff in evaluating the prognosis and treatment effects of patients with hepatic encephalopathy. In the present application, HDL-C can also be used to evaluate the improvement / deterioration of liver function in the prognosis of patients with hepatic encephalopathy.

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

[0125] The optimal cutoff values ​​for HDL-C and MELD score (eg Figure 3 A ) and a Kaplan-Meier curve showing the 1-year transplant-free mortality rate in the training cohort.

[0126] like Figure 3 As 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] like Figure 3 As shown in C, the 1-year transplant-free mortality rate was 47% in patients with MELD score ≥17, while the mortality rate was 10.7% in patients with MELD score <17 (p<0.0001).

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

[0129] In this study, ascites was an independent adverse prognostic factor in patients with overt HE. We further evaluated the prognostic value of HDL-C levels in patients with HE and ascites and assessed the risk stratification of these patients. Figure 3 As shown in E, the AUCs of 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. The results showed that HDL-C, MELD score and NLR could predict the 1-year mortality of patients with hepatic encephalopathy and ascites. Among them, the prognostic value of HDL-C was significantly higher than that of NLR (p<0.05), and was equivalent to the prognostic value of MELD score (p>0.05).

[0130] like Figure 3 As shown in F, the 1-year transplant-free mortality rates of patients with low-, medium-, and high-risk HE with ascites were 9.1%, 22.5%, and 51.8%, respectively (p<0.0001). The results showed that the 1-year transplant-free mortality rate of patients with low-risk HE with ascites was less than 10%, the 1-year transplant-free mortality rate of patients with medium-risk HE with ascites was between 10% and 50%, and the 1-year transplant-free mortality rate of patients with high-risk HE with ascites exceeded 50%.

[0131] (6) Subgroup analysis

[0132] Figure 4 Forest plot obtained by Cox proportional hazards model analysis. We performed a comprehensive multi-faceted stratified analysis to show the relationship between HDL-C level and 1-year transplant-free mortality in different subgroups, including demographic factors (age and sex), complications (ascites), liver function parameters (AST and ALB), inflammatory markers (NLR), lipid index (TC), renal function (Cr), and MELD score. Figure 4The results of subgroup analysis of HDL-C were presented with age (≤55 or >55 years), sex (male or female), ascites (yes or no), 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. All the above continuous variables were categorized according to the cutoff value.

[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 located on the left side of the ineffective line (vertical line), indicating that in the above subgroups, HDL-C was negatively correlated with the 1-year transplant-free mortality rate, that is, HDL-C reduction or low HDL-C level had a poorer prognosis in each subgroup.

[0134] Figure 4 It shows that in each subgroup [age (55 years as the limit), gender, ascites (with or without), AST (50U / L as the limit), ALB (45g / L as the limit), TC (50mmol / L as the limit), MELD score (17 as the limit)], HDL-C level is a protective factor for the prognosis of patients in each subgroup. (Note: aHR<1 means that the factor is a protective factor; aHR>1 means that the factor is a risk factor.)

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

[0136] like Figure 5 As 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), all p<0.05.

[0137] like Figure 5 As shown in D, the 1-year transplant-free mortality rate in patients with HDL-C < 0.5 mmol / L was significantly higher than that in patients with HDL-C ≥ 0.5 mmol / L (39.6% vs 10%, p < 0.0001).

[0138] like Figure 5As shown in E, the 1-year transplant-free mortality rate in patients with MELD score ≥17 was significantly higher than that in patients with MELD score <17 (44.2% vs. 14.3%, p<0.0001).

[0139] like Figure 5 As shown in F, the 1-year transplantation-free mortality rates of patients in the low-risk group (HDL-C ≥ 0.5 mmol / L and MELD < 17), intermediate group (HDL-C < 0.5 mmol / L or MELD ≥ 17), and high-risk group (HDL-C < 0.5 mmol / L and MELD ≥ 17) were 10.1%, 20.8%, and 51.2%, respectively (p < 0.0001).

[0140] The results of the study showed that low HDL-C levels can be used as a reliable indicator to predict the 1-year transplant-free mortality of patients with overt hepatic encephalopathy. Patients with HDL-C < 0.5 mmol / L and MELD ≥ 17 have a significantly worse prognosis. Monitoring HDL-C levels can help clinicians identify high-risk patients and promote early treatment to improve patient outcomes.

[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 patients who survived and died in the training cohort. Table 3 shows the univariate and multivariate Cox regression analysis of the 1-year transplant-free mortality rate 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 expressed as n(%), mean±standard deviation or median (interquartile range).

[0146] Table 2 Baseline characteristics of patients who survived and died in the training cohort

[0147]

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

[0149] Table 3 Univariate and multivariate Cox regression analysis of 1-year transplant-free mortality in patients with overt hepatic encephalopathy in the training cohort

[0150]

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

[0156] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also belong to the disclosure scope of the present invention and fall within the protection scope of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation of the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably" and "according to a preferred embodiment", both of which indicate that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept. Throughout the text, the features guided by "preferably" are only an optional method and should not be understood as being required. Therefore, the applicant reserves the right to abandon or delete the relevant preferred features at any time.

Claims

1. A biomarker for assessing the prognostic risk of hepatic encephalopathy, characterized in that: The biomarker is one of the following groups (1), (2), (3) or (4): (1) multiple of the Model for End-Stage Liver Disease score, age, sex, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, grade of overt hepatic encephalopathy, 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; (2) multiple of high-density lipoprotein cholesterol, age, sex, upper gastrointestinal bleeding, spontaneous bacterial peritonitis, grade of overt hepatic encephalopathy, 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; (3) high-density lipoprotein cholesterol; (4) High-density lipoprotein cholesterol and Model for End-stage Liver Disease score.

2. Use of the biomarker as claimed in claim 1 in assessing the prognostic risk of hepatic encephalopathy.

3. Use of the biomarker according to claim 2 in assessing the prognosis risk of hepatic encephalopathy, characterized in that: The use includes biomarkers to assess the prognostic risk of hepatic encephalopathy with ascites.

4. Use of the biomarker according to claim 2 in assessing the prognosis risk of hepatic encephalopathy, characterized in that: The assessment of the prognostic risk of hepatic encephalopathy includes predicting the possibility of patient death within 1 year.

5. A kit for predicting transplant-free mortality in hepatic encephalopathy, characterized in that: The kit comprises reagents for detecting the biomarker according to claim 1.

6. Use of a reagent for determining high-density lipoprotein cholesterol abundance in the preparation of a kit for assessing the prognostic risk of hepatic encephalopathy, characterized in that: The assessment includes: 1) Collecting blood samples from patients; 2) Detecting the abundance of high-density lipoprotein cholesterol in blood samples; Among them, when high-density lipoprotein cholesterol is less than 0.5mmol / L, the patient belongs to the first category of hepatic encephalopathy prognosis with high risk of transplant mortality; when high-density lipoprotein cholesterol is not less than 0.5mmol / L, the patient belongs to the first category of hepatic encephalopathy prognosis with low risk of transplant mortality.

7. A system for assessing the prognostic risk of hepatic encephalopathy, characterized in that: The system is used to collect the patient's Model for End-Stage Liver Disease score, wherein: When the Model for End-Stage Liver Disease score was 17 or higher, the patient was classified as a high-risk patient for mortality without transplantation in category I with hepatic encephalopathy prognosis; When the Model for End-Stage Liver Disease score is less than 17, the patient belongs to the first category of patients with hepatic encephalopathy prognosis and low risk of mortality without transplantation.

8. A system for assessing the prognostic risk of hepatic encephalopathy, characterized in that: The system comprises a data collection module for collecting one or more of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, model for end-stage liver disease, international normalized ratio, neutrophil-lymphocyte ratio and prothrombin time levels; and A data processing module for associating the mortality rate of hepatic encephalopathy prognosis with one or more of the acquired indicators of alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio, neutrophil-lymphocyte ratio and prothrombin time, wherein: Taking the healthy group as a control or the alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio, neutrophil-lymphocyte ratio and / or prothrombin time levels measured and / or calculated at a time point of the patient's prognosis as a control, when the alanine aminotransferase, total bilirubin, aspartate aminotransferase, serum creatinine, end-stage liver disease model, international normalized ratio, neutrophil-lymphocyte ratio and / or prothrombin time levels of the corresponding patient collected by the data acquisition module are elevated, the data processing module generates an indication of an increased risk of mortality from hepatic encephalopathy in the patient.

9. A system for assessing the prognostic risk of hepatic encephalopathy, characterized in that: The system comprises a data collection module for collecting one or more of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity, and serum albumin levels; and A data processing module for associating the mortality rate of hepatic encephalopathy with one or more of the acquired indicators of low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and serum albumin, wherein: Taking the healthy group as a control or the low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and / or serum albumin levels measured at a time point of the patient's prognosis as a control, when the low-density lipoprotein cholesterol, total cholesterol, high-density lipoprotein cholesterol, prothrombin activity and / or serum albumin levels of the corresponding patient collected by the data acquisition module are reduced, the data processing module generates an indication that the patient's risk of hepatic encephalopathy without transplantation mortality is increased.

10. A system for assessing the prognostic risk of hepatic encephalopathy, characterized in that: The system comprises a data acquisition module for collecting high-density lipoprotein cholesterol and model for end-stage liver disease scores; and A data processing module for associating the prognosis mortality rate of hepatic encephalopathy with the acquired high-density lipoprotein cholesterol and the indication of the end-stage liver disease model, wherein the data processing module is configured to: When the high-density lipoprotein cholesterol collected by the data collection module is not less than 0.5 mmol / L and the Model for End-Stage Liver Disease score is less than 17, an indication of a low risk of mortality without transplantation is generated for the patient with hepatic encephalopathy; When the high-density lipoprotein cholesterol collected by the data collection module is less than 0.5 mmol / L or the Model for End-Stage Liver Disease score is not less than 17, an indication of a medium risk of transplant-free mortality in patients with hepatic encephalopathy is generated; and When the high-density lipoprotein cholesterol collected by the data collection module is less than 0.5 mmol / L and the Model for End-Stage Liver Disease score is not less than 17, an indication of a high risk of mortality without transplantation in patients with hepatic encephalopathy is generated.

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