LU504512B1 - Prognostic risk assessment system for severe liver fibrosis related to metabolic associated fatty liver disease and its application - Google Patents
Prognostic risk assessment system for severe liver fibrosis related to metabolic associated fatty liver disease and its application Download PDFInfo
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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- G—PHYSICS
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Abstract
The present invention discloses a severe liver fibrosis prognosis risk assessment system and its application related to metabolic associated fatty liver disease, belonging to the field of bioinformatics analysis. The system includes a computing unit that utilizes a prognostic risk assessment model for severe liver fibrosis to calculate the probability of risk; The prognostic risk assessment model of severe liver fibrosis takes the serum index level, age, sex and whether with type 2 diabetes as the input variables; The serum indicators include monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high-density protein, and glycated hemoglobin. The present invention models based on inflammation, metabolic indicators related to the occurrence and development of MAFLD and uses non-invasive detection methods to grade liver fibrosis related to MAFLD patients, conducts in-depth research on its risk association with severe liver fibrosis to help identify the severity of liver fibrosis in MAFLD patients as soon as possible.
Description
DESCRIPTION LU504512
PROGNOSTIC RISK ASSESSMENT SYSTEM FOR SEVERE LIVER FIBROSIS
RELATED TO METABOLIC ASSOCIATED FATTY LIVER DISEASE AND ITS
The present invention relates to the field of bioinformatics analysis, in particular to a prognostic risk assessment system and application for severe liver fibrosis related to metabolic associated fatty liver disease.
Metabolic associated fatty liver disease (MAFLD) is a newly proposed disease category derived from non-alcoholic fatty liver disease and is considered a liver manifestation of metabolic syndrome; its proposal downplays the importance of alcohol in the definition of non-alcoholic fatty liver disease (NAFLD) and emphasizes the metabolic risk factors behind the pathological progression of fatty liver disease. In the
United States, MAFLD is associated with an increase in all-cause mortality. At present, many studies reported that the triggering factors of inflammation are rooted in the liver (lipid overload, lipotoxicity, oxidative stress) and the extrahepatic (intestinal liver axis, adipose tissue, skeletal muscle) system, leading to a unique immune mediated pathological mechanism in MAFLD. NAFLD is not only confined to liver-related morbidity and mortality, but also a multisystem disease, affecting extra-hepatic organs and regulatory pathways. For example, MAFLD increases risk of type 2 diabetes mellitus (T2DM), cardiovascular (CVD) and cardiac diseases, and chronic kidney disease (CKD)
The incidence of significant fibrosis is higher in lean patients with fatty liver disease and metabolic risk abnormalities of = 2. Liver inflammation is a hallmark of patients with fatty liver disease; many studies have demonstrated the involvement of immune cells in promoting the development of fibrosis and cirrhosis related to fatty liver diseagd)504512 mechanically. The inflammatory mechanism involves the entire spectrum of fatty liver disease, but especially in the more advanced stages of the disease, including cirrhosis and the transition to HCC. Inflammation involves several cellular and molecular mechanisms, including cellular aging, mitochondrial dysfunction, autophagy and mitochondrial autophagy defects, activation of inflammasomes, dysregulation of the ubiquitin proteasome system, activation of DNA damage responses, and ecological dysregulation (alteration of host microbiota composition). From the perspective of the development of steatohepatitis, excessive adipose tissue in the liver is the core of inflammatory metastasis. The size of adipocytes exposed to nutrient intensive diets increases until they reach a structurally critical state. Therefore, the vascularization of adipose tissue is reduced, and adipocytes are exposed to hypoxia conditions, which further causes inflammation, leading to the transformation from simple liver steatosis to liver fibrosis, cirrhosis and even hepatocellular carcinoma.
Liver fibrosis is a crucial step in the progression of various chronic liver diseases to cirrhosis and an important link that affects the prognosis of patients with chronic liver diseases. Although there is currently no definitive and effective intervention for liver fibrosis, both clinicians and patients urgently need early diagnosis and evaluation.
Therefore, it is necessary to strengthen research on the early diagnosis and evaluation of liver fibrosis, in order to provide better basis for clinical diagnosis, treatment, and prognosis judgment. In recent years many scholars have conducted extensive research on the diagnosis and evaluation of liver fibrosis, especially in the field of non-invasive diagnosis and evaluation, which includes some non-invasive serum diagnostic models and imaging methods and although they have high diagnostic value for liver fibrosis, the accuracy is still unsatisfactory. At present, the gold standard for diagnosing liver fibrosis is liver biopsy under B-ultrasound. However, due to the invasive nature of liver biopsy, it is not easy for patients to accept, and the process is relatively cumbersome and time-consuming. At present, liver biopsy and pathological reports require a long time to obtain, and it takes at least 4-5 working days to complete all the procedures until the report is obtained. This will increase the time cost and anxiety of patients to a certain extent, and "liver biopsy" is difficult to popularize due to its invasive nature. Therefore, LiV504512 is extremely important to seek a convenient, economical, and minimally invasive method for screening for severe fibrosis of metabolic related fatty liver.
The purpose of the present invention is to provide a prognostic risk assessment system and application for severe liver fibrosis related to metabolic related fatty liver, in order to solve the problems existing in the aforementioned prior art. The present invention models based on inflammation and metabolic indicators related to the occurrence and development of MAFLD, providing a basis for the prognosis diagnosis of severe liver fibrosis related to MAFLD.
To achieve the above purpose, the present invention provides the following scheme:
The present invention provides a product that can be used for prognosis diagnosis of severe liver fibrosis. The product includes a reagent for detecting serum indicators, including monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high-density protein, and glycosylated hemoglobin;
The severe liver fibrosis refers to severe liver fibrosis related to metabolic related fatty liver.
The present invention also provides the application of reagents for detecting serum markers in the preparation of prognostic diagnostic products for severe liver fibrosis, including monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high-density proteins, and glycosylated hemoglobin; The severe liver fibrosis refers to severe liver fibrosis related to metabolic related fatty liver.
The present invention also provides a system for assessing the prognostic risk of severe liver fibrosis, comprising a computing unit that utilizes a prognostic risk assessment model for severe liver fibrosis to calculate the probability of risk;
The prognostic risk assessment model of severe liver fibrosis takes the serum index level, age, sex and whether with type 2 diabetes as the input variables to obtain a single score;
The ratio score of monocytes to lymphocytes in the individual score [4504512 22.528533945 x MLR value; Age score = 2.5 x Age - 87.5; Gender score: female score is 0, male score is 12.65466; BMI score = 0.270370445 x BMI value - 4.055556673;
Glycated hemoglobin score = -2.299808066 x HbA1c value + 25.297888721; High density protein score = 4.322856547 x HDL; Score of type 2 diabetes: none, score 0, score 8.616423 if any; Triglyceride score = -1.668173996 x TG + 20.01808795; Alanine aminotransferase score = 0.509161731 x ALT value. Add up the individual scores to obtain the total score, denoted as R;
The serum indicators include monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high-density protein, and glycated hemoglobin;
The criteria for determining whether the subject belongs to type 2 diabetes is that if the subject meets any of the following, it will be determined as type 2 diabetes: (1) Have a history of diabetes, and the diabetes includes type 2 diabetes, ICD-10 type E11 diabetes and type E14 diabetes; (2) Currently undergoing insulin therapy and/or using oral hypoglycemic drugs; (3) Serum glucose level = 11.1 mmol/l; (4) Glycated hemoglobin level = 48mmol/mol;
The model calculates the risk probability using the following equation:
Risk probability = 5.85 x 10 - 7 x R®- 0.000186804 x R? + 0.019725652 x R - 0.677104617;
The severe liver fibrosis refers to severe liver fibrosis related to metabolic associated fatty liver disease.
Furthermore, the system also includes a detection unit for detecting the serum indicator level.
Furthermore, the system also includes an information acquisition unit, which is used to perform the operation of acquiring the detection information of the subject, and the detection information includes the serum indicator level, age, gender and whether with type 2 diabetes.
Furthermore, the system also includes an evaluation unit, which is used tdJ504512 determine the risk probability of severe liver fibrosis prognosis in subjects based on the calculation results of the calculation unit, and provide reasonable prevention and treatment suggestions.
Furthermore, the system also includes a result display unit for displaying the conclusions drawn by the evaluation unit.
Furthermore, the result display unit displays the results through screen display, sound broadcasting, or printing.
The present invention also provides a computer readable storage medium, which includes a stored computer program, wherein the device in which the computer readable storage medium is controlled to execute the pre post risk assessment model for severe liver fibrosis while the computer program is running.
The invention also provides the application of physiological indicators in the construction of a risk assessment model for the prognosis of severe liver fibrosis. The physiological indicators include the serum indicator level, age, sex and whether withtype 2 diabetes; The severe liver fibrosis refers to severe liver fibrosis related to metabolic associated fatty liver disease.
The present invention discloses the following technical effects:
The present invention provides indicators that can be used for the prognosis diagnosis of severe liver fibrosis related to MAFLD, providing a basis for the prognosis diagnosis of severe liver fibrosis related to MAFLD.
The present invention also provides a prognostic risk assessment system for severe liver fibrosis related to MAFLD, which can be used to evaluate the prognostic risk of patients with severe liver fibrosis related to MAFLD.
The present invention models based on inflammation and metabolic indicators related to the occurrence and development of MAFLD and uses non-invasive detection methods to grade liver fibrosis in MAFLD patients, and conducts in-depth research on its risk association with liver fibrosis to help identify liver fibrosis in MAFLD patients as early as possible.
BRIEF DESCRIPTION OF THE FIGURES LU504512
In order to provide a clearer explanation of the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given to the accompanying drawings required in the embodiments. It is evident that the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technical personnel in the art, other accompanying drawings can also be obtained based on these drawings without any creative effort.
Figure 1 is a flowchart of the population selected for metabolic associated fatty liver disease at UK Biobank;
Figure 2 shows the nomogram prediction model established using age, sex, type 2 diabetes, HbA1c, HDL, TG, ALT and MLR;
Figure 3 shows the use of R package "pROC" to plot the subject's work curve.
The various exemplary embodiments of the present invention are now explained in detail, which should not be considered as a limitation of the present invention, but should be understood as a more detailed description of certain aspects, characteristics, and implementation schemes of the present invention.
It should be understood that the terms described in the present invention are only intended to describe specific embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any stated value or intermediate value within the stated range, as well as any other stated value or intermediate value within the stated range, is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded from the range.
Unless otherwise stated, all technical and scientific terms used in this article hav&/504512 the same meanings as those commonly understood by conventional technical personnel in the field described in the present invention. Although the present invention only describes preferred methods and materials, any methods and materials similar or equivalent to those described herein can also be used in the implementation or testing of the present invention. All literature mentioned in this manual is incorporated by reference to disclose and describe methods and/or materials related to the literature. In case of conflict with any incorporated literature, the content of this manual shall prevail.
Without departing from the scope or spirit of the present invention, it is evident to those skilled in the art that various improvements and variations can be made to the specific embodiments of the present invention specification. The other embodiments obtained from the specification of the present invention are apparent to technical personnel. The specification and embodiments of the present invention are only illustrative.
The terms "including", "including", "possessing", "containing", and so on used in this article are all open-ended terms, meaning including but not limited to.
The meanings represented by the English abbreviations in the embodiments of the present invention are as follows:
MLR: Monocyte to lymphocyte ratio;
ALT: alanine aminotransferase;
BMI: Body mass index;
TG: Triglycerides;
HDL: high-density lipoprotein;
HbA1c: Glycated hemoglobin;
AST: aspartate aminotransferase;
FLI: Fatty liver index;
FIB-4: Liver Fibrosis Score-4;
MAFLD: Metabolic associated fatty liver disease 1. Experimental data source and pretreatment
(1) Selecting 117393 individuals with metabolic associated fatty liver disease frobtJ504512
UK Biobank (see Figure 1 for the specific process) and defined liver fibrosis in MAFLD based on the FIB-4 index.
FIB-4 Score (Fibrosis 4 Score) is a non-invasive method for evaluating liver fibrosis in patients with chronic liver disease. The values needed include age, ALT (alanine transaminase), AST (aspartate transaminase), and PLT (platelet). The calculation formula is:
FIB — 4 = SF
The FIB-4 index of different liver diseases has different critical values for defining liver fibrosis. For non-alcoholic fatty liver, the critical values for liver fibrosis below grade 2 or above grade 3-4 are FIB-4 < 1.3 and FIB-4 > 2.67, respectively. Metabolic associated fatty liver disease (MAFLD) is a newly proposed disease category derived from non-alcoholic fatty liver disease and is considered a liver manifestation of metabolic syndrome; Its proposal downplays the importance of alcohol in the definition of non-alcoholic fatty liver disease (NAFLD) and emphasizes the metabolic risk factors behind the pathological progression of NAFLD. The relationship between FIB-4 index and the level and degree of liver fibrosis is shown in Table 1.
Table 1 Comparison Table of the relationship between FIB-4 Index, Liver Fibrosis
Level and Liver Fibrosis Degree
Liver Fibrosis Level | Below Level 2 Level 2-3 Level 3-4 and proc jen ee
Liver Fibrosis | No/Mild Fibrosis Moderate Severe Fibrosis oa fr (2) Obtaining information on other variable factors
Race information: This variable information can be obtained from the baseline questionnaire content, which includes white people (British white, Irish white, and other white people), black people (Caribbean black, African black, and other black people)U504512
South Asians (Indians, Pakistanis, Bengali, and others) Mixed race (white mixed with
Caribbean black, African black, Asian mixed, and others), Chinese, and other races.
Considering the extremely small proportion of people from other races than white in the
UK Biobank database, we ultimately defined racial information as "white", "mixed race", "yellow", "black", and "other" when included in the analysis.
Physical activity level: this variable is derived from the baseline questionnaire. The metabolic equivalent task score (MET Score) is used to reflect the comprehensive physical activity level of participants, and the parameters are obtained by comprehensively considering the physical consumption factors such as daily commuting, work intensity and fithess exercise. This study measured the level of physical activity of each participant by the number of hours per week (MET hours/week) that each participant engaged in moderate or above physical activity. According to the results, it is divided into two categories: one is "the individual's weekly physical activity level is higher than the recommended medium to high level physical labour level", and the other group is "the individual's weekly physical labour level is lower than the recommended medium intensity physical labour level", which meets these two standards. The baseline table defines it as "yes".
Baseline type 2 diabetes is defined by one of the following criteria: 1) Self-reported type 2 diabetes or unspecified diabetes history; 2) Hospital diagnosis of type 2 or unspecified diabetes before the baseline assessment visit (ICD-10 E11, E14); 3)
Currently undergoing insulin therapy and/or using oral hypoglycemic drugs; 4) Serum glucose level = 11.1 mmol/l (200 mg/dl); 5) HbA1c = 48 mmol/mol (6.5%).
The baseline table for each variable is shown in Table 2.
Table 2 Baseline Table LU504512
No/Mild
Moderate Fibrosis Severe Fibrosis
Characteristic Fibrosis n = 46,175 n = 2,676 n = 68,542
Gender (male) 41,788 (61%) 34,674 (75%) 2,185 (82%) <45 9,297 (14%) 745 (1.6%) 21 (0.8%)
Ag e 45-55 25,489 (37%) 5,719 (12%) 215 (8.0%) (ye 55-65 27,083 (40%) 23,991 (52%) 1,161 (43%) ar) >65 6,673 (9.7%) 15,720 (34%) 1,279 (48%)
White 62,547 (91%) 42,739 (93%) 2,469 (92%)
Mixed 2,654 (3.9%) 1,728 (3.7%) 103 (3.8%)
Race
Rac e Yellow 2,312 (3.4%) 1,270 (2.8%) 78 (2.9%)
Black 369 (0.5%) 176 (0.4%) 10 (0.4%)
Other 660 (1.0%) 262 (0.6%) 16 (0.6%) 30.7 (28.4,
BMI (kg/m?) 30.2 (28.1, 32.8) 30.3 (28.1, 33.0) 33.8)
Physical activity level 31,194 (46%) 23,877 (52%) 1,371 (51%) (Yes)
Drin Never 2,852 (4.2%) 1,656 (3.6%) 87 (3.3%) king Previo
Stat usSmo 2,756 (4.0%) 1,656 (3.6%) 125 (4.7%) us ked
No/Mild LU504512
Moderate Fibrosis Severe Fibrosis
Characteristic Fibrosis n = 46,175 n = 2,676 n = 68,542
Current ly 62,934 (92%) 42,863 (93%) 2,464 (92%)
Smokin g
ALT (U/L) 27 (20, 35) 26 (20, 34) 28 (19, 43) 2.18 (1.61,
TG (mmol/L) 2.10 (1.56, 2.86) 1.97 (1.40, 2.77) 2.97) 1.21 (1.05,
HDL (mmol/L) 1.22 (1.05, 1.42) 1.19 (1.01, 1.43) 1.41) 5.45 (5.22,
HbA1c (%) 5.48 (5.23, 5.76) 5.49 (5.22, 5.83) 5.74) 0.24 (0.19,
MLR 0.26 (0.20, 0.33) 0.28 (0.22, 0.37) 0.31)
Type 2 diabetes 23,277 (4.8%) 2,757 (6.0%) 265 (9.9%) 2. Univariate and multivariate logistic regression of severe liver fibrosis
In order to explore the risk factors associated with severe liver fibrosis in the MAFLD population, the univariate and multivariate logistic regression method need to be used to include age, gender, BMI, race, drinking status, exercise, whether there is type 2 diabetes, HbA1c, HDL, TG, ALT and MLR into the univariate analysis, and the event was defined as severe liver fibrosis. In univariate analysis, age, gender, BMI, drinking status, exercise, presence or absence of type 2 diabetes, HbA1c, TG, ALT, and MLR were found to be associated with severe hepatic fibrosis in MAFLD individuals (P < 0.05). In clinical practice, race is often also considered as an influencing factor. In combination with the univariate factor and clinical practice content, although there is no difference in tH&J504512 univariate factor analysis, race is still included in the multivariate analysis. Therefore, age, gender, BMI, race, drinking status, exercise, presence or absence of type 2 diabetes, HbA1c, HDL, TG, ALT and MLR are also included as covariates in COX regression analysis for multivariate analysis. The event is defined as severe liver fibrosis.
The results suggest that age, sex, type 2 diabetes, HbA1c, HDL, TG, ALT, BMI and MLR are independent risk factors for MAFLD with severe liver fibrosis (P < 0.05) (Table 3).
Table 3: Relationship between clinical prediction models and MAFLD related sevetéJ504512 liver fibrosis
Univariate regression Multivariate regression
OR 95%Cl P value OR 95%CI P value
Age 1.14 1.13-1.15 <0.001 1.15 1.14-1.16 <0.001
Gender 223 202-246 <0.001
Male vs. Female 223 202-246 <0.001 2.03 1.82-227 <0.001
BMI 0.98 0.97-0.99 <0.001 1.02 1-1.03 <0.001
Race
White ref. ref.
Yellow 0.93 0.73-1.16 0.52 1.19 0.94-1.5 0.15
Black 0.78 0.39-1.38 0.44 1.22 0.64-23 0.55
Mixed Race 1.00 0.82122 0.98 12 098-147 0.08
Other 0.74 0.43-1.17 0.23 1.32 0.79-218 0.29
Dinking Status
Never ref. ref.
Previously Smoked 1.47 1.12-1.94 0.006 1.31 0.99-174 0.06
Currently Smoking 1.21 0.98-1.51 0.08 0.96 0.77-1.2 0.7
Physical activity level
Yes vs. No 1.14 1.05-1.23 <0.001 1.06 098-114 0.16 type 2 diabetes 1.98 1.74-2.25 <0.001 1.61 1.38-1.88 <0.001
HbA1c 1.16 1.10-1.22 <0.001 0.87 0.82-0.93 <0.001
HDL 1.08 0.95-1.23 0.24 1.29 1.12-149 <0.001
TG 0.89 0.85-0.92 <0.001 0.91 088-095 <0.001
ALT 1.02 1.02-1.02 <0.001 1.03 1.03-1.03 <0.001
MLR 11.82 9.16-15.17 <0.001 3.54 264-475 <0.001
3. Establish a nomogram model for predicting prognosis LU504512
Age, sex, type 2 diabetes, HbA1c, HDL, TG, ALT and MLR were used to establish nomogram prediction prognosis model. The results are shown in Figure 2. The C-index of this model is 0.766. The formula for extracting nomogram using R package nomogramEx, with a score of monocyte to lymphocyte ratio = 22.528533945 x MLR value; Age score = 2.5 x Age - 87.5; Gender score: female score is 0, male score is 12.65466; BMI score = 0.270370445 x BMI value - 4.055556673; Glycated hemoglobin score = -2.299808066xHbA1c value + 25.297888721; High density protein score = 4.322856547xHDL; Score of type 2 diabetes: none, score 0, score 8.616423 if any;
Triglyceride score = -1.668173996 x TG + 20.01808795; Alanine aminotransferase score = 0.509161731 x ALT value. The scores of each variable are shown in Figure 2, and the total score is obtained by adding the scores of each variable; Probability of severe liver fibrosis = 5.85 x 107 x Total score * + -0.000186804xTotal score ? + 0.019725652 x Total score - 0677104617. For example, a woman with MAFLD is 47 years old, white, BMI 45.24kg/m2, ALT 23.84U/L, TG 3.65 mmol/L, HbA1c 5.81%, no type 2 diabetes, MLR 0.29. The total score is 87.68828 points, and according to the formula, the probability of her merging with severe liver fibrosis is less than 0.1%.
According to the diagnostic criteria of this patent, the woman also does not have severe liver fibrosis. 4. Diagnostic Effectiveness Analysis
Using UK Biobank data for subject work characteristic curve analysis, the R package "pROC" was used to plot the subject work curve, as shown in Figure 3, with
AUC=0.766. Result: From Figure 3, it can be seen that the model can accurately predict whether MAFLD individuals have severe liver fibrosis, and the model indicators are easy to obtain or detect. This is beneficial for timely screening and corresponding intervention of MAFLD patients with severe liver fibrosis in clinical practice, and delaying further disease progression.
In summary, this model has shown high clinical predictive value for severe livét/504512 fibrosis in the metabolic associated fatty liver disease cohort of the British Biobank.
The described embodiments above are only a description of the preferred method of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made by ordinary technical personnel in the field to the technical solution of the present invention should fall within the scope of protection determined in the claims of the present invention.
Claims (10)
1. A product that can be used for prognosis diagnosis of severe liver fibrosis, characterized in that the product includes reagents for detecting serum indicators, including monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high-density protein and glycated hemoglobin; the severe liver fibrosis refers to severe liver fibrosis related to metabolic associated fatty liver disease.
2. An application of reagents for detecting serum markers in the preparation of prognostic diagnostic products for severe liver fibrosis, characterized in that the serum markers include monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high-density proteins and glycated hemoglobin; the severe liver fibrosis refers to severe liver fibrosis related to metabolic associated fatty liver disease.
3. A system for assessing the prognosis risk of severe liver fibrosis, characterized in that the system includes a computing unit that utilizes a severe liver fibrosis prognosis risk assessment model to calculate the risk probability; the prognostic risk assessment model of severe liver fibrosis takes the serum index level, age, sex and whether with type 2 diabetes as the input variables to obtain a single score; the ratio score of monocytes to lymphocytes in the individual score = 22.528533945 x MLR value; age score = 2.5 x Age - 87.5; gender score: female score is 0, male score is 12.65466; BMI score = 0.270370445 x BMI value - 4.05555667 3; glycated hemoglobin score = -2.299808066 x HbA1c value + 25.297888721; high density protein score =
4.322856547 x HDL; score of type 2 diabetes: none, score 0, score 8.616423 if any; triglyceride score = -1.668173996 x TG + 20.01808795; alanine aminotransferase score = 0.509161731 x ALT value; add up the individual scores to obtain the total score, denoted as R;
the serum indexes include monocyte to lymphocyte ratio, alanine aminotransferasèU504512 triglycerides, high-density protein and glycated hemoglobin; the criteria for determining whether the subject has type 2 diabetes is that if the subject meets any of the following, it will be determined as type 2 diabetes: (1) have a history of diabetes, and the diabetes includes type 2 diabetes, ICD-10 type E11 diabetes and type E14 diabetes; (2) currently undergoing insulin therapy and/or using oral hypoglycemic drugs; (3) serum glucose level=11.1mmol/l; (4) glycated hemoglobin level=z48mmol/mol; the model calculates the risk probability using the following equation: risk probability = 5.85 x 10 — 7 x RS - 0.000186804 x R? + 0.019725652x R -
0.677104617; the severe liver fibrosis refers to severe liver fibrosis related to metabolic associated fatty liver disease.
4. The system according to claim 3, characterized in that the system further includes a detection unit for detecting the serum index level.
5. The system according to claim 4, which is characterized in that the system also includes an information acquisition unit, which is used to perform the operation of acquiring the detection information of the subject, and the detection information includes the serum index level, age, gender and whether with type 2 diabetes.
6. The system according to claim 5, characterized in that the system further includes an evaluation unit, which is used to determine the risk probability of severe liver fibrosis prognosis in subjects based on the calculation results of the calculation unit, and provide reasonable prevention and treatment suggestions.
7. The system according to claim 6, characterized in that the system further includés/504512 a result display unit for displaying the conclusions drawn by the evaluation unit.
8. The system according to claim 7, characterized in that the result display unit displays the results through screen display, sound broadcasting, or printing.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored computer program, wherein the device where the computer-readable storage medium is located is controlled to execute the severity liver fibrosis prognosis risk assessment system as claimed in claim 3 while the computer program is running.
10. An application of physiological indicators in the construction of a risk assessment model for the prognosis of severe liver fibrosis, which is characterized in that the physiological indicators include the serum index level, age, sex and whether with type 2 diabetes as described in claim 3; the severe liver fibrosis refers to severe liver fibrosis related to metabolic associated fatty liver disease.
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