Decompensated liver cirrhosis patient long-term prognosis prediction system and method

Abstract

The invention discloses a long-term prognosis prediction system and method for decompensated liver cirrhosis patients, the prediction system is based on human serum albumin Cys-34 site modification typing, and comprises a data collection module, a model construction module and an evaluation prediction module, the data collection module is used for collecting clinical data of the decompensated liver cirrhosis patients, the model construction module is used for constructing a model, and the evaluation prediction module is used for evaluating the clinical data of the decompensated liver cirrhosis patients. Comprise anthropometry data, vital signs, laboratory data, and the level of the original subtype of the human serum albumin and the effective albumin concentration obtained by specific detection; the model construction module is used for constructing a model for predicting death of acute decompensated patients suffering from cirrhosis one year after admission of hospitals; and the evaluation and prediction module is used for evaluating the prediction performance of the model through the subject working curve. According to the present invention, the index relationship between the effective albumin concentration and the liver cirrhosis acute decompensation long-term prognosis is established, such that the important clinical significance is provided, the clinical management can be further optimized, and the drug treatment can be guided.

Description

Technical Field

[0001] The present invention relates to the field of disease prognosis prediction, and in particular to a system and method for predicting the long-term prognosis of patients with decompensated liver cirrhosis. Background Art

[0002] Cirrhosis caused by various factors such as hepatitis virus, alcohol, fat-related liver disease, autoimmune liver disease, etc., once complications such as portal hypertension-related gastrointestinal bleeding, hepatic encephalopathy, hepatorenal syndrome, ascites, etc. occur, it marks the transition from the compensated stage of liver function to the decompensated stage of liver function. Once liver decompensation occurs, it not only increases the hospitalization rate and seriously affects the quality of life of patients, but also significantly increases the mortality rate. Acute decompensation (AD) is the acute occurrence of complications such as hepatic encephalopathy, ascites, esophageal variceal bleeding, bacterial infection, etc., which will lead to an increased risk of acute-chronic liver failure (ACLF) or death. However, among patients who survive after the disease worsens, some patients with improved conditions can survive for a relatively long time, but due to the lack of definite susceptibility factors or inducing events, there is still a high risk of death without transplantation. Therefore, there is an urgent need for prognostic factors that can predict the long-term survival of patients with decompensated cirrhosis in the clinic, which will help high-risk populations and disease management.

[0003] Human serum albumin (HSA) is the most abundant protein in human plasma, accounting for about 50% of the total plasma protein. Human serum albumin is not only the main component for maintaining plasma colloidal osmotic pressure, but also has the functions of binding, transporting substances, anti-oxidation, anti-coagulation and anti-thrombosis, regulating immune function, maintaining capillary integrity, promoting cardiac positive inotropy, and neuroprotection. The biological multifunctional characteristics of human serum albumin are derived from its unique molecular structure. In patients with cirrhosis, as the disease progresses to the decompensated stage, the liver's function of synthesizing human serum albumin is weakened, and the increase in human serum albumin metabolism or fluid dilution leads to characteristic refractory hypoalbuminemia. In addition to the decrease in quantity, the quality of serum albumin also changes in decompensated cirrhosis. Under the influence of systemic inflammation and oxidative stress, the molecular structure of circulating human serum albumin will be damaged, resulting in many isomers, such as cysteine ​​and oxidation, which have been shown to reduce the binding ligand and antioxidant functions of human serum albumin and are related to the severity of cirrhosis. Since in cirrhosis, not only the overall amount of human serum albumin decreases, but also the proportion of various forms of post-translational modifications of human serum albumin molecules increases, resulting in a decrease in the proportion of original albumin with intact structure, the content of human serum albumin representing complete structure and function is also far lower than the total serum albumin routinely measured in clinical practice. The effective albumin (eAlb) concentration refers to the ratio of human serum albumin containing completely preserved reduced free mercaptoalbumin at the 34th cysteine ​​residue. The effective albumin concentration represents the main antioxidant component in the blood circulation and is closely related to the short-term mortality and development of ACLF in patients with acute decompensation of cirrhosis. However, the potential value of effective albumin concentration for the long-term prognosis of patients with acute decompensation of cirrhosis remains to be confirmed. Exploring the relationship between effective albumin concentration and the long-term prognosis of acute decompensation of cirrhosis has great clinical significance, which can further optimize clinical management and guide drug treatment. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for predicting the long-term prognosis of patients with decompensated liver cirrhosis, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned purpose, one aspect of the present invention provides a long-term prognosis prediction system for patients with decompensated liver cirrhosis. The prediction system is based on human serum albumin Cys-34 site modification typing, and includes a data collection module, a model building module, and an evaluation prediction module, wherein:

[0006] The data collection module is used to collect clinical data of patients with decompensated cirrhosis, including anthropometric data, vital signs, laboratory data, and levels of human serum albumin original isoforms and effective albumin concentration obtained by specific tests;

[0007] The model building module is used to build a prediction model for the death of patients with acute decompensation of liver cirrhosis one year after admission;

[0008] The prediction evaluation module is used to evaluate the prediction performance of the model through the receiver operating curve.

[0009] Furthermore, the level of original subtypes of human serum albumin obtained by the specific detection is specifically evaluated by ultra-high performance liquid chromatography-mass spectrometry analysis technology to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different subtypes of human serum albumin, and obtain the relative peak area ratio of each subtype of human serum albumin based on the mass spectrometry peak diagram, and calculate the effective albumin concentration based on the relative content of original albumin and the serum albumin index detected by clinical laboratory.

[0010] Furthermore, biomarkers of assessed factors included effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

[0011] Furthermore, the prediction model formula is:

[0012] Survival score = 0.0394*total bilirubin + 1.3635*international normalized ratio - 1.6341*effective albumin concentration - 0.1417*hemoglobin. Score ≥ 0.21 is a high-risk group, and score < 0.21 is a low-risk group.

[0013] Another aspect of the present invention provides a method for predicting the long-term prognosis of patients with decompensated liver cirrhosis, the prediction method is based on human serum albumin Cys-34 site modification typing, and comprises the following steps:

[0014] Step S1, collecting clinical data of patients with decompensated liver cirrhosis, including anthropometric data, vital signs, laboratory data, and levels of human serum albumin original isoforms and effective albumin concentration obtained by specific detection;

[0015] Step S2, constructing a competing risk model to predict the death of patients with acute decompensation of cirrhosis one year after admission;

[0016] Step S3, evaluating the prediction performance of the model through the receiver operating curve.

[0017] Furthermore, the level of original subtypes of human serum albumin obtained by the specific detection is specifically evaluated by ultra-high performance liquid chromatography-mass spectrometry analysis technology to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different subtypes of human serum albumin, and obtain the relative peak area ratio of each subtype of human serum albumin based on the mass spectrometry peak diagram, and calculate the effective albumin concentration based on the relative content of original albumin and the serum albumin index detected by clinical laboratory.

[0018] Furthermore, biomarkers of assessed factors included effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

[0019] Furthermore, the prediction model formula is:

[0020] Survival score = 0.0394*total bilirubin + 1.3635*international normalized ratio - 1.6341*effective albumin concentration - 0.1417*hemoglobin. Score ≥ 0.21 is a high-risk group for death, and score < 0.21 is a low-risk group for death.

[0021] Compared with the prior art, the present system and method have the following advantages:

[0022] The present invention has developed a method for predicting the clinical outcomes of patients with acute decompensation of cirrhosis one year after hospitalization, which can evaluate the risk of death in patients with cirrhosis in a relatively long time window. The present invention has developed a new biomarker - the clinical application of effective albumin concentration in patients with acute decompensation of cirrhosis, which clarifies that effective albumin concentration is an important risk factor for the long-term prognosis of patients with acute decompensation of cirrhosis, and expands the important correlation between effective albumin concentration and the progression of cirrhosis. The present invention has developed a clinical prediction model for effective albumin concentration to predict long-term mortality in patients with acute decompensation of cirrhosis. The model is better than the current prediction score used for routine evaluation in patients with decompensated cirrhosis, and can further improve the predictive performance of conventional scores. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a method for predicting long-term prognosis in patients with decompensated cirrhosis.

[0024] Figure 2 ROC curve diagram for evaluating the performance of the prognostic prediction model for the test cohort.

[0025] Figure 3 ROC curve diagram for evaluating the effectiveness of the prognostic prediction model in the validation cohort. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] like Figure 1 The flowchart of the method of the present invention is shown, and the specific steps of the method are explained in detail in combination with this specific embodiment.

[0028] Step S1, collecting clinical data of patients with decompensated liver cirrhosis, including anthropometric data, vital signs, laboratory data, and levels of human serum albumin original isoforms and effective albumin concentration obtained by specific detection.

[0029] This study prospectively and non-selectively screened patients with decompensated cirrhosis admitted to the Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine between June 2016 and September 2019, and finally enrolled 333 cases for study analysis. All procedures involved were implemented in accordance with the principles of the Declaration of Helsinki and the International Conference on Harmonization and Good Clinical Practice Guidelines. This protocol was reviewed and approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, with the ethics approval number:

[2018] Lunshen No.

[162] .

[0030] The inclusion criteria of the present invention are: (1) age 18-80 years old; (2) cirrhosis: cirrhosis diagnosed in the past or this hospitalization. The diagnosis of cirrhosis is based on liver tissue pathology or comprehensive judgment through imaging (magnetic resonance imaging, CT, B-ultrasound), gastrointestinal endoscopy (esophageal-gastric varices, signs of portal hypertension), liver elasticity examination, and corresponding clinical manifestations of cirrhosis and / or laboratory test indicators. This admission is non-selective, and the reason for admission is one or more of the following combinations of cirrhosis decompensation events: ascites (grade 2-3), upper gastrointestinal bleeding, jaundice (total bilirubin ≥5mg / dL), hepatic encephalopathy, bacterial / fungal infection.

[0031] The exclusion criteria of the present invention are: (1) age < 18 years or age > 80 years; (2) after liver transplantation; (3) chronic acute liver failure; (4) receiving commercial albumin solution infusion, plasma infusion, plasma exchange, artificial liver, etc. within 15 days before enrollment; (5) hepatocellular carcinoma at any stage and other advanced malignant tumors; (6) combined with acute or chronic extrahepatic diseases of other systems that affect short-term prognosis, such as chronic kidney disease in the uremic stage (requiring dialysis treatment), chronic left heart failure, obstructive pulmonary disease, chronic respiratory failure, etc.; (7) taking immunosuppressants for long-term reasons other than liver disease, such as nephrotic syndrome, rheumatic system diseases, anti-rejection drugs after organ transplantation. reaction, etc.; (8) Long-term use of anticoagulants, such as warfarin for atrial fibrillation; (9) This admission is elective, such as only to complete scheduled diagnosis and treatment, including but not limited to liver biopsy puncture, splenectomy, TIPS surgery, HVPG measurement, endoscopic ligation, MDT consultation, simple follow-up, etc.; (10) HIV antibody positive; (11) Pregnant or lactating women; (12) The patient refuses to participate in this study and refuses to sign the informed consent form; (13) None of the above conditions are met, but the patient is temporarily unable to sign the informed consent form due to coma and other reasons, and there is no legal agent to sign it within a certain period of time. According to the patient's condition, the patient may not be able to wake up and sign the informed consent form.

[0032] Within 48 hours after enrollment, 5 mL of peripheral venous blood was collected from the patients and the serum was immediately separated and frozen in a refrigerator at -80 degrees Celsius. The baseline anthropometric data, medical history information, vital signs, and laboratory data of the enrolled patients were collected through the electronic medical record information system. Clinical data collection included: (1) gender, age, height, weight, and blood pressure;

[0033] (2) History of hypertension, history of diabetes, history of etiology of cirrhosis, history of decompensation of cirrhosis, decompensation events during hospitalization, acute-on-chronic liver failure during hospitalization, admission time, discharge time, and survival outcome 1 year after admission; (3) Laboratory examination indicators: white blood cell count (WBC), hemoglobin (Hb), platelet count (Plt), alanine aminotransferase

[0034] (ALT), aspartate aminotransferase (AST), total bilirubin (TB), serum albumin (Alb), serum creatinine (Cr), prothrombin time (PT), international normalized ratio (INR), and C-reactive protein (CRP). The MELD and CLIF-C AD scores of the patients were calculated based on laboratory data. The redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin was evaluated by ultra-performance liquid chromatography-mass spectrometry, and 7 human serum albumin subtypes were identified, including native albumin, nitrogen-terminal truncated albumin (HSA-DA), carbon-terminal truncated albumin (HSA-L), glycosylated albumin (HSA-Glyc), cysteine ​​albumin (HSA-Cys), glycosylated cysteine ​​albumin (HSA-Glyc-Cys), sulfonylated albumin (HSA-SO 2 / SO 3 ), and the relative peak area ratio of each subtype of human serum albumin was obtained according to the mass spectrometry peak diagram. The effective albumin concentration was calculated according to the original albumin relative content and the serum albumin index detected by the clinical laboratory: effective albumin concentration (g / dL) = [serum albumin (g / dL) × original albumin (%)] / 100.

[0035] Statistical methods All statistical tests were performed using R software (version 4.3.2). The Shapiro test was used to test normal distribution. Continuous variables with normal distribution were expressed as mean ± standard deviation (SD), and skewed distributions were expressed as median (interquartile range, IQR). Categorical variables were described as counts (percentages). Comparisons between groups were performed using Student t test, Mann-Whitney U test or χ2 test. 2Test. Analysis of variance or Kruskal-Wallis test was used for more than two groups. Competing risk regression analysis was used to identify risk factors associated with 1-year mortality in patients with acute decompensation of cirrhosis, with transplantation as a competing risk, followed by a backward stepwise method between variables with p<0.05 in univariate analysis to select the optimal variable for further multivariate analysis. Parameters with strong correlations (Spearman correlation coefficients exceeding 0.5) were not included in the multivariate analysis due to multicollinearity. The receiver operating characteristic curve (ROC) was used to evaluate the prognostic performance of the model, assuming that the survival outcome of patients who had undergone liver transplantation was death and that no one would survive at the end of the follow-up period. Gray's method was used to estimate the cumulative incidence function, stratify the risk of death, and determine the optimal threshold based on the Youden index. In all analyses, the significance level was set at a two-sided p<0.05.

[0036] Among the 333 patients with decompensated cirrhosis included in the final analysis, according to the survival outcome 1 year after admission, 240 patients survived, 69 patients died, and 24 patients underwent liver transplantation. The baseline characteristics of the survivors and non-survivors with 1-year survival outcome were compared (Table 1). It was found that the mean arterial pressure, hemoglobin, white blood cell count, C-reactive protein, aspartate aminotransferase, total bilirubin, serum albumin, international normalized ratio, MELD score, CLIF-C AD score, original albumin, HSA-DA, HSA-Glyc, HSA-Glyc-Cys, HSA-SO 2 / SO 3 , and effective albumin concentrations were statistically different.

[0037] Table 1. Baseline demographic, clinical, and laboratory data of patients with decompensated cirrhosis

[0038]

[0039]

[0040] Step S2, constructing a prediction model for predicting the death of patients with acute decompensation of liver cirrhosis one year after admission.

[0041] Multivariate competing risk analysis identified the following independent predictors of 1-year mortality in hospitalized patients with acute decompensation of cirrhosis: effective albumin concentration, total bilirubin, international normalized ratio, and hemoglobin. The sub-distribution hazard ratios were: effective albumin concentration (sHR 0.195; 95% CI, 0.073-0.521; p = 0.001), total bilirubin (sHR 1.040; 95% CI, 1.012-1.069; p = 0.005), INR (sHR 3.910; 95% CI, 2.040-7.495; p < 0.001), and MAP (sHR 0.868; 95% CI, 0.771-0.977; p = 0.0019) (Table 2).

[0042] Table 2. Multivariate independent risk factors for mortality 1 year after admission in patients with acute decompensation of cirrhosis.

[0043]

[0044]

[0045] The regression coefficients of the screened independent risk factors were used to construct a prognostic prediction model. The model formula is: survival score

[0046] =0.0394*total bilirubin+1.3635*international normalized ratio-1.6341*effective albumin concentration-0.1417*hemoglobin. Score ≥0.21 is a high-risk group for death, and score <0.21 is a low-risk group for death. Figure 2 The ROC curve comparison of the prognostic model, effective albumin concentration, and MELD score of the test cohort is shown. The cut-off value of the model obtained by the optimal Youden index was 0.21, and its area under the curve was 0.77, which was better than the MELD score and CLIF-C AD score.

[0047] In step S3, the prediction performance of the model is evaluated by the receiver operating curve (ROC).

[0048] like Figure 3 The figure shows the ROC curve comparison of the validation cohort prognostic model, effective albumin concentration, and MELD score. To verify the predictive performance of the model: Through the validation cohort, 263 patients with acute decompensation of liver cirrhosis were followed up for 1 year after admission, with 183 patients surviving, 60 patients dying, and 20 patients undergoing liver transplantation. The above model was brought into the validation cohort for analysis, and the AUC was 0.70, indicating that the model has a good ability to predict the expected survival outcome.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A long-term prognosis prediction system for patients with decompensated liver cirrhosis, characterized in that: The prediction system is based on human serum albumin Cys-34 site modification typing, including a data collection module, a model building module, and an evaluation prediction module, wherein: The data collection module is used to collect clinical data of patients with decompensated cirrhosis, including anthropometric data, vital signs, laboratory data, and levels of human serum albumin original isoforms and effective albumin concentration obtained by specific tests; The model building module is used to build a prediction model for the death of patients with acute decompensation of liver cirrhosis one year after admission; The prediction evaluation module is used to evaluate the prediction performance of the model through the receiver operating curve.

2. The long-term prognosis prediction system for patients with decompensated liver cirrhosis according to claim 1, characterized in that: The level of the original subtype of human serum albumin obtained by the specific detection is to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin by ultra-high performance liquid chromatography-mass spectrometry analysis technology, identify different subtypes of human serum albumin, and obtain the relative peak area ratio of each subtype of human serum albumin based on the mass spectrometry peak diagram, and calculate the effective albumin concentration based on the relative content of original albumin and the serum albumin index detected by clinical laboratory.

3. The long-term prognosis prediction system for patients with decompensated liver cirrhosis according to claim 1, characterized in that: Assessment factors included effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

4. The long-term prognosis prediction system for patients with decompensated liver cirrhosis according to claim 1, characterized in that: The prediction model formula is: Survival score = 0.0394*total bilirubin + 1.3635*international normalized ratio - 1.6341*effective albumin concentration - 0.1417*hemoglobin. Score ≥ 0.21 is a high-risk group for death, and score < 0.21 is a low-risk group for death.

5. A method for predicting long-term prognosis of patients with decompensated liver cirrhosis, characterized in that: The prediction method is based on human serum albumin Cys-34 site modification typing, and comprises the following steps: Step S1, collecting clinical data of patients with decompensated liver cirrhosis, including anthropometric data, vital signs, laboratory data, and levels of human serum albumin original isoforms and effective albumin concentration obtained by specific detection; Step S2, constructing a prediction model for predicting the death of patients with acute decompensation of liver cirrhosis one year after admission; Step S3, evaluating the prediction performance of the model through the receiver operating curve.

6. The method for predicting long-term prognosis of patients with decompensated liver cirrhosis according to claim 5, characterized in that: The level of the original subtype of human serum albumin obtained by the specific detection is specifically evaluated by ultra-high performance liquid chromatography-mass spectrometry analysis technology to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different subtypes of human serum albumin, and obtain the relative peak area ratio of each subtype of human serum albumin based on the mass spectrometry peak diagram, and calculate the effective albumin concentration based on the relative content of original albumin and the serum albumin index detected by clinical laboratory.

7. The method for predicting long-term prognosis of patients with decompensated liver cirrhosis according to claim 5, characterized in that: The biomarkers evaluated included effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

8. The method for predicting long-term prognosis of patients with decompensated liver cirrhosis according to claim 5, characterized in that: The prediction model formula is: Survival score = 0.0394*total bilirubin + 1.3635*international normalized ratio - 1.6341*effective albumin concentration - 0.1417*hemoglobin. Score ≥ 0.21 is a high-risk group for death, and score < 0.21 is a low-risk group for death.

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