A system and method for long-term prognosis prediction of patients with decompensated cirrhosis

Abstract

The application discloses a kind of decompensated cirrhosis patient long-term prognosis prediction system and method, the prediction system is based on human serum albumin Cys-34 point position modification typing, including data collection module, model construction module, evaluation prediction module, wherein: data collection module is used to collect the clinical data of decompensated cirrhosis patient, including anthropometry data, vital signs, laboratory data and the level of human serum albumin original subtype and effective albumin concentration obtained by specific detection;Model construction module is used to construct the prediction model of the death of cirrhosis acute decompensation patient after 1 year of admission;Evaluation prediction module is used to evaluate the prediction performance of model by receiver operating characteristic curve.The present application has great clinical significance to establish the index relationship between effective albumin concentration and long-term prognosis of cirrhosis acute decompensation, and can further optimize clinical management and guide drug treatment.

Description

Technical Field

[0001] This invention relates to the field of disease prognosis prediction, and more particularly to a system and method for predicting the long-term prognosis of patients with decompensated cirrhosis. Background Technology

[0002] Cirrhosis caused by various factors such as hepatitis viruses, alcohol, fat-related liver disease, and autoimmune liver disease, once complications such as portal hypertension-related gastrointestinal bleeding, hepatic encephalopathy, hepatorenal syndrome, and ascites occur, marks the transition from the compensated to the decompensated stage of liver function. Once liver function decompensation occurs, it not only increases hospitalization rates and severely impacts patients' quality of life, but also significantly raises the mortality rate. Acute decompensation (AD) is the acute occurrence of complications such as hepatic encephalopathy, ascites, esophageal and gastric variceal bleeding, and bacterial infection, leading to an increased risk of acute-on-chronic liver failure (ACLF) or death. However, among patients who survive after disease deterioration, some whose condition improves can survive for a relatively long time, but due to the lack of identifiable predisposing factors or precipitating events, they still have a high risk of transplant-free mortality. Therefore, there is an urgent clinical need for prognostic factors that can predict the long-term survival of patients with decompensated cirrhosis, which would be helpful for high-risk groups and disease management.

[0003] Human serum albumin (HSA) is the most abundant protein in human blood plasma, accounting for approximately 50% of total plasma protein. HSA is not only a major component in maintaining plasma colloid osmotic pressure, but also plays a vital role in binding and transporting substances, antioxidation, anticoagulation and antithrombosis, regulating immune function, maintaining capillary integrity, promoting positive cardiac inotropic activity, and neuroprotection. The multifunctional biological properties of HSA stem from its unique molecular structure. In patients with cirrhosis, as the disease progresses to the decompensated stage, the liver's ability to synthesize HSA weakens. Combined with increased HSA metabolism or fluid dilution, this leads to characteristic refractory hypoalbuminemia. Besides the reduced quantity, the quality of serum albumin also changes in decompensated cirrhosis. Systemic inflammation and oxidative stress can damage the molecular structure of circulating HSA, producing numerous isoforms, such as cysteine ​​and oxidation. These are shown to reduce HSA's ligand binding and antioxidant functions and are correlated with the severity of cirrhosis. Because cirrhosis involves not only a decrease in the total amount of human serum albumin but also an increase in the proportion of various forms of post-translational modifications of the human serum albumin molecule, the proportion of structurally intact original albumin decreases. Consequently, the level of human serum albumin, representing complete structure and function, is far lower than the total serum albumin measured routinely in clinical practice. Effective albumin (eAlb) concentration refers to the proportion of human serum albumin containing intact, preserved reduced free thiol albumin at the 34th cysteine ​​residue. EAlb concentration represents a major antioxidant component in the bloodstream and is closely related to short-term mortality and the development of ACLF in patients with acute decompensated cirrhosis. However, the potential value of eAlb concentration for the long-term prognosis of patients with acute decompensated cirrhosis remains to be confirmed. Exploring the relationship between eAlb concentration and the long-term prognosis of acute decompensated cirrhosis has significant clinical implications, potentially leading to optimized clinical management and guidance for drug therapy. Summary of the Invention

[0004] The purpose of this invention is to provide a system and method for predicting the long-term prognosis of patients with decompensated cirrhosis, in order to solve the problems mentioned in the background art.

[0005] To achieve the above-mentioned objectives, one aspect of the present invention provides a long-term prognosis prediction system for patients with decompensated cirrhosis. The prediction system is based on human serum albumin Cys-34 site modification typing and includes a data collection module, a model construction module, and an evaluation and 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 the level of the original subtype of human serum albumin and the effective albumin concentration obtained by specific detection.

[0007] The model building module is used to build a predictive model for death one year after admission in patients with acute decompensated cirrhosis.

[0008] The evaluation prediction module is used to assess the predictive performance of the model using receiver operating characteristic (ROC) curves.

[0009] Furthermore, the level of the original human serum albumin subtype obtained by the specific detection is specifically assessed by ultra-high performance liquid chromatography coupled with mass spectrometry to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different human serum albumin subtypes, obtain the relative peak area ratio of each human serum albumin subtype 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 the clinical laboratory.

[0010] Further assessment factors include biomarkers such as effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

[0011] Furthermore, the prediction model formula is as follows:

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

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

[0014] Step S1: Collect clinical data of patients with decompensated cirrhosis, including anthropometric data, vital signs, laboratory data, and the level of the original subtype of human serum albumin and the effective albumin concentration obtained by specific detection.

[0015] Step S2: Construct a competing risk model to predict the mortality of patients with acute decompensated cirrhosis one year after admission.

[0016] Step S3: Evaluate the predictive performance of the model using the receiver operating characteristic curve.

[0017] Furthermore, the level of the original human serum albumin subtype obtained by the specific detection is specifically assessed by ultra-high performance liquid chromatography coupled with mass spectrometry to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different human serum albumin subtypes, obtain the relative peak area ratio of each human serum albumin subtype 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 the clinical laboratory.

[0018] Further assessment factors include biomarkers such as effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

[0019] Furthermore, the prediction model formula is as follows:

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

[0021] Compared with existing technologies, this system and method have the following advantages:

[0022] This invention develops a method for predicting clinical outcomes in patients with acute decompensated cirrhosis one year after admission, enabling the assessment of mortality risk over a longer time window. This invention also develops a novel biomarker—effective albumin concentration—for the clinical application in patients with acute decompensated cirrhosis, clarifying that effective albumin concentration is a significant risk factor for long-term prognosis and expanding its important association with disease progression. Furthermore, this invention develops a clinical prediction model for effective albumin concentration to predict long-term mortality in patients with acute decompensated cirrhosis. This model outperforms current predictive scores used for routine assessment in patients with decompensated cirrhosis and can further improve the predictive performance of routine scores. Attached Figure Description

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

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

[0025] Figure 3 ROC curves were used to evaluate the effectiveness of the cohort prognostic prediction model. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] like Figure 1 The diagram shown is a flowchart of the method of the present invention. The specific steps of the method will be explained in detail with reference to this specific embodiment.

[0028] Step S1, collect the clinical data of patients with decompensated cirrhosis, including anthropometric data, vital signs, laboratory data, and the levels of original human serum albumin subtypes and effective albumin concentration obtained by specific tests.

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

[2018] Ethics Review No.

[162] .

[0030] The inclusion criteria of the present invention are: (1) aged 18 - 80 years; (2) cirrhosis: diagnosed with cirrhosis either previously or during this admission. The diagnosis of cirrhosis is based on liver histopathological diagnosis or comprehensive judgment through imaging (magnetic resonance, CT, B-mode ultrasound), digestive endoscopy (esophageal-gastric fundus varices, signs of portal hypertension), liver elastography, and corresponding clinical manifestations and / or laboratory test indicators of cirrhosis. This admission is non-selective, and the reasons for admission are one or several combinations of the following decompensated cirrhosis events: ascites (grades 2 - 3), upper gastrointestinal bleeding, jaundice (total bilirubin ≥ 5 mg / dL), hepatic encephalopathy, bacterial / fungal infection.

[0031] The exclusion criteria of the present invention are: (1) aged < 18 years or aged > 80 years; (2) after liver transplantation; (3) acute-on-chronic liver failure; (4) received 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 and chronic extrahepatic diseases of other systems that affect short-term prognosis, such as end-stage chronic kidney disease (requiring dialysis treatment), chronic left heart failure, obstructive pulmonary disease, chronic respiratory failure, etc.; (7) long-term use of immunosuppressive agents due to non-liver disease reasons, such as nephrotic syndrome, rheumatic system diseases, anti-rejection after organ transplantation, etc.; (8) long-term use of anticoagulant drugs, such as taking warfarin for atrial fibrillation, etc.; (9) this admission is a selective admission, such as only for completing scheduled diagnoses and treatments, including but not limited to liver biopsy, splenectomy, TIPS, HVPG measurement, endoscopic ligation, MDT consultation, simple reexamination, etc.; (10) positive for AIDS antibody; (11) pregnant or lactating women; (12) patients refuse to participate in this study and refuse 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, etc., and there is no legal representative to sign it on their behalf, and it is judged according to the condition that the patient may not be able to wake up to sign the informed consent form.

[0032] Within 48 hours of enrollment, 5 mL of peripheral venous blood was collected from each patient, and the serum was immediately separated and frozen at -80°C. Anthropometric data, medical history, vital signs, and laboratory data of the enrolled patients were collected using the electronic medical record information system. Clinical data collection included: (1) gender, age, height, weight, and blood pressure;

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

[0034] The following parameters were measured: 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). MELD and CLIF-C AD scores were calculated based on laboratory data. The redox state of the thiol group at the 34th cysteine ​​residue of human serum albumin was assessed using ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS / MS), identifying seven human serum albumin subtypes: plain albumin, N-terminal truncated albumin (HSA-DA), C-terminal truncated albumin (HSA-L), glycosylated albumin (HSA-Glyc), cysteine-modified albumin (HSA-Cys), glycosylated cysteine-modified albumin (HSA-Glyc-Cys), and sulfonated albumin (HSA-SO2 / SO3). The relative peak area ratios of each human serum albumin subtype were obtained from the mass spectrometry peak diagrams. The effective albumin concentration is calculated based on the relative content of raw albumin and the serum albumin index measured by the clinical laboratory: Effective albumin concentration (g / dL) = [serum albumin (g / dL) × raw albumin (%)] / 100.

[0035] Statistical methods: All statistical tests were performed using R software (version 4.3.2). The Shapiro test was used to test for normality. For continuous variables, normality was expressed as mean ± standard deviation (SD), and skewed distributions were expressed as the median (IQR). Categorical variables were described using counts (percentages). Comparisons between groups were performed using the Student t-test, Mann-Whitney U test, or χ² test. 2Tests were performed. Analysis of variance or the Kruskal-Wallis test was used for two or more groups. Competing risk regression analysis was used to identify risk factors associated with 1-year mortality in patients with acute decompensated cirrhosis, with transplantation as a competing risk. A stepwise backward approach was then used among variables with p < 0.05 in univariate analyses to select the optimal variable for further multivariate analysis. Parameters with strong correlations (Spearman correlation coefficients greater than 0.5) were not included in multivariate analysis due to multicollinearity. Receiver operating curves (ROCs) were used to assess the prognostic performance of the model, assuming that the survival outcome for liver transplant recipients was death and that no one survived at the end of the follow-up period. The cumulative incidence function was estimated using the Gray method, mortality risk was stratified, and the optimal threshold was determined based on the Youden index. In all analyses, the significance level was set at two-sided p < 0.05.

[0036] This invention included 333 patients with decompensated cirrhosis in the final analysis. Based on one-year survival outcomes after admission, 240 survived, 69 died, and 24 underwent liver transplantation. Comparison of baseline characteristics between survivors and non-survivors at one year (Table 1) revealed statistically significant differences in 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, raw albumin, HSA-DA, HSA-Glyc, HSA-Glyc-Cys, HSA-SO2 / SO3, and effective albumin concentration at admission.

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

[0038]

[0039]

[0040] Step S2: Construct a predictive model to predict death one year after admission in patients with acute decompensated cirrhosis.

[0041] Independent predictors of 1-year mortality in hospitalized patients with acute decompensated cirrhosis were identified through multivariate competing risk analysis: effective albumin concentration, total bilirubin, international normalized ratio (INR), and hemoglobin. Their 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 death in patients with acute decompensated cirrhosis one year after admission.

[0043]

[0044]

[0045] The regression coefficients of the selected independent risk factors are 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. A score ≥ 0.21 indicates a high-risk mortality group, and a score < 0.21 indicates a low-risk mortality group. Figure 2 The figure shows a comparison of ROC curves for the prognostic model of the test cohort, effective albumin concentration, and MELD score. The cut-off value of this model, obtained using the optimal Youden index, is 0.21, and its area under the curve is 0.77, which is superior to both the MELD score and the CLIF-C AD score.

[0047] Step S3: Evaluate the predictive performance of the model using receiver operating characteristic (ROC) curves.

[0048] like Figure 3 The figure shows a comparison of ROC curves for the prognostic model, effective albumin concentration, and MELD score in the validation cohort. The predictive performance of the model was validated in the validation cohort: Of the 263 patients with acute decompensated cirrhosis, 183 survived, 60 died, and 20 underwent liver transplantation at one year follow-up. Substituting the model into the validation cohort analysis yielded an AUC of 0.70, indicating that the model has good predictive ability for expected survival outcomes.

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

Claims

1. A long-term prognosis prediction system for patients with decompensated cirrhosis, characterized in that, 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 and prediction module, wherein: The data collection module is used to collect clinical data of patients with acute decompensated cirrhosis, including anthropometric data, vital signs, laboratory data, and the levels of primitive human serum albumin subtypes and effective albumin concentrations obtained through specific detection. The levels of primitive human serum albumin subtypes obtained through specific detection are assessed by ultra-high performance liquid chromatography coupled with mass spectrometry to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different human serum albumin subtypes, and obtain the relative peak area ratio of each subtype of human serum albumin based on the mass spectrometry peak diagram. The effective albumin concentration is calculated based on the relative content of primitive albumin and the serum albumin index detected by the clinical laboratory. The calculation formula is: Effective albumin concentration (g / dL) = [serum albumin (g / dL) × primitive albumin (%)] / 100. The model building module is used to construct a predictive model for death one year after admission in patients with acute decompensated cirrhosis; the formula for the predictive model is: Survival score = 0.0394 * total bilirubin + 1.3635 * international normalized ratio - 1.6341 * effective albumin concentration - 0.1417 * hemoglobin. A score ≥ 0.21 indicates a high risk of death, and a score < 0.21 indicates a low risk of death. The evaluation prediction module is used to assess the predictive performance of the model using receiver operating characteristic (ROC) curves.

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

3. A method for predicting the long-term prognosis of patients with decompensated cirrhosis, characterized in that, The prediction method is based on human serum albumin Cys-34 site modification typing and includes the following steps: Step S1: Collect clinical data of patients with decompensated cirrhosis, including anthropometric data, vital signs, laboratory data, and the levels of the original human serum albumin subtypes and the effective albumin concentration obtained by specific detection. The levels of the original human serum albumin subtypes obtained by specific detection are specifically assessed by ultra-high performance liquid chromatography coupled with mass spectrometry to evaluate the redox state of the thiol group on the 34th cysteine ​​residue of human serum albumin, identify different human serum albumin subtypes, and obtain the relative peak area ratio of each human serum albumin subtype according to the mass spectrometry peak diagram. The effective albumin concentration is calculated based on the relative content of the original albumin and the serum albumin index detected by the clinical laboratory. The calculation formula is: Effective albumin concentration (g / dL) = [serum albumin (g / dL) × original albumin (%)] / 100. Step S2: Construct a predictive model for predicting death one year after admission in patients with acute decompensated cirrhosis; the formula for the predictive model is: Survival score = 0.0394 * total bilirubin + 1.3635 * international normalized ratio - 1.6341 * effective albumin concentration - 0.1417 * hemoglobin. A score ≥ 0.21 indicates a high risk of death, and a score < 0.21 indicates a low risk of death. Step S3: Evaluate the predictive performance of the model using the receiver operating characteristic curve.

4. The method for predicting the long-term prognosis of patients with decompensated cirrhosis according to claim 3, characterized in that, The assessment factors and biomarkers include effective albumin concentration, international normalized ratio, hemoglobin, and total bilirubin.

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