CN112837818B - Model for evaluating liver fibrosis degree of hepatitis B patient - Google Patents
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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
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Abstract
The invention provides a model for evaluating liver fibrosis degree of a hepatitis B patient, wherein the model is a regression equation obtained by carrying out Logistic regression analysis on liver ALT, AST, PTA, LSM. The model is used for evaluating the degree of liver fibrosis of a patient, clinically, liver puncture is not needed to be carried out on the patient, and the correlation and rule of fibrosis are defined by utilizing the correlation analysis of the fibrosis result of liver-penetrating pathology biopsy of the liver-penetrating patient and the clinical noninvasive detection result data so as to evaluate whether the patient has obvious liver fibrosis or not, determine whether the patient has antiviral indications or not and timely give antiviral treatment.
Description
Technical Field
The invention relates to the field of hepatology, in particular to a model for evaluating liver fibrosis degree of a hepatitis B patient.
Background
Liver fibrosis can be caused by various chronic injuries of liver, can further cause liver cirrhosis and liver cancer, seriously affects the life quality and survival time of patients, is very important for early diagnosis of liver fibrosis, and the gold standard of liver fibrosis is still liver histopathology at present, so that the liver fibrosis is not accepted by patients, and can not be found in time, and can not be dynamically observed, so that the clinical application of the liver fibrosis is limited, and a high-accuracy detection method is clinically required to replace liver pathology biopsy to more easily evaluate the liver fibrosis of the patients. At present, whether the patient is liver fibrosis or not can not be accurately distinguished by using various tests and examinations singly in clinic, so that how to develop a liver fibrosis examination mode which is rapid, low in cost and high in detection accuracy is a technical subject with clinical application value.
The invention analyzes clinical data of chronic liver disease patients to build a mathematical model to increase the accuracy of general examination for diagnosis of liver fibrosis. And evaluating the efficacy of diagnosing the liver fibrosis degree based on the Logistic regression model jointly constructed by various test data by adopting a calibration curve and ROC curve method.
Disclosure of Invention
The invention analyzes clinical data of chronic liver disease patients to build a mathematical model to increase the accuracy of general examination for diagnosis of liver fibrosis. And evaluating the efficiency of diagnosing the liver fibrosis degree based on the Logistic regression model jointly constructed by various test data by adopting a calibration curve and ROC curve method.
The technical scheme adopted by the invention is as follows: a model for assessing the degree of liver fibrosis in a patient with hepatitis b, the model being a regression equation obtained by Logistic regression analysis of ALT, AST, PTA, LSM, wherein ALT is alanine aminotransferase, AST is aspartate aminotransferase, PTA is prothrombin activity, LSM refers to the elasticity value obtained by a liver fibrosis detector.
Further, the model is specifically: aapl= -0.01×alt+0.021×ast-0.042×pta+0.356×lsm.
The invention also provides a model for evaluating the liver fibrosis degree of the hepatitis B patient and application of the visualization tool in evaluating the liver fibrosis degree of the hepatitis B patient.
Further, it was judged that no significant liver fibrosis occurred when the value of AAPL was lower than 0.607, and it was judged that significant fibrosis occurred when the value of AAPL was higher than 0.607, and root rate was calculated according to nomogram visualization tool.
The invention also provides a construction method of the model for evaluating the liver fibrosis degree of the hepatitis B patient, which is characterized by comprising the following steps:
s1, obtaining a liver tissue sample and carrying out pathological fibrosis stage, and dividing patients into a group without significant fibrosis (liver fibrosis stage 0 to stage 1) and a group with significant liver fibrosis (liver fibrosis stage 2 or more) according to the stage result;
s2, collecting examination results of the two groups of patients: glutamate Aminotransferase (ALT), aspartate Aminotransferase (AST), total Bilirubin (TBIL), direct Bilirubin (DBIL), indirect Bilirubin (IBIL), albumin (ALB), glutamyl transpeptidase (GGT), total Bile Acid (TBA), alpha Fetoprotein (AFP), platelets (PLT), clotting time (PT), prothrombin activity (PTA); calculating A/G (ALB/GLB) value, and checking instantaneous liver elasticity value (LSM) and Controlled Attenuation Parameter (CAP);
s3, testing the correlation between ALT, AST, TBIL, DBIL, IBIL, ALB, GGT, TBA, AFP, PT, PTA, PLT, A/G, LSM, CAP and whether liver fibrosis occurs obviously;
s4, constructing a mathematical model of whether fibrosis is remarkable or not by adopting a binary Logistic stepwise regression analysis.
Further, the specific operation of evaluating the correlation in step S3: ALT, AST, TBIL, DBIL, IBIL, ALB, GGT, TBA, AFP, PT, PTA, PLT, A/G, LSM, CAP by performing LASSO regression analysis to find meaningful variables (see FIGS. 1-A, 1-B), screening the variables, and further performing multi-factor logistic regression analysis, wherein the variables with P <0.05 are included, and fit the equation.
The construction method according to claim 5, wherein the independent variable inclusion and rejection criteria in step S4 are P <0.05 and P > 0.10, respectively.
The construction method according to claim 5, wherein the model constructed in step S4 is: AAPL= -0.01×ALT+0.021×AST-0.042×PTA+0.356×LSM, and a nomogram graph was made with this model to calculate probability.
The invention has the beneficial effects that: the invention constructs a model for evaluating the liver fibrosis degree of a hepatitis B patient, the model carries out a regression equation obtained by Logistic regression analysis by ALT, AST, PTA, LSM, and a nomogram graph is manufactured by the model to calculate probability. The model is used for evaluating the degree of liver fibrosis of a patient, clinically, liver puncture is not needed to be carried out on the patient, and the correlation and rule of fibrosis are defined by utilizing the correlation analysis of the fibrosis result of liver-penetrating pathology biopsy of the liver-penetrating patient and the clinical noninvasive detection result data so as to evaluate whether the patient has obvious liver fibrosis or not, determine whether the patient has antiviral indications or not and timely give antiviral treatment.
Drawings
FIG. 1 is a drawing of a LASSO regression analysis (A, B are drawing of LASSO analysis)
FIG. 2 is a calibration chart of AAPL
FIG. 3 is a ROC graph of AAPL
FIG. 4 is a graph comparing AAPL, APRI, FIB-4 clinical decision curves
FIG. 5 is a graph showing the clinical effect of AAPL
FIG. 6 is a nomogram of AAPL
FIG. 7 is a graph comparing ROC curves of AAPL, APRI, FIB-4
Detailed Description
In order to more clearly demonstrate the technical scheme, objects and advantages of the present invention, the present invention is described in further detail below with reference to the specific embodiments and the accompanying drawings.
Examples
1 materials and methods
1.1 case Material
The invention is incorporated into 324 chronic hepatitis B patients who take liver puncture biopsies in hospitals in Guangdong province in 2017-2019, the ages are 16-70, and the average ages are as follows: age 38, male: 226, female: 98, all patients had no clinical manifestations and laboratory basis of decompensated liver disease, and no pregnancy, kidney disease, blood system disease.
1.2 liver biopsy and stage of pathological fibrosis
All enrolled patients were under abdominal ultrasound guidance for descending liver biopsy. Liver tissue biopsy adopts a 1s percutaneous liver puncture method, and a plastic specimen tube is immediately placed in the plastic specimen tube for frozen biopsy after specimen collection. The liver tissue is placed in a plastic embedding box, neutral formaldehyde fixation, gradient ethanol dehydration, xylene transparency, paraffin immersion and embedding, slicing, hematoxylin-eosin staining and reticular fiber staining are carried out. Quality assessment of liver tissue specimens and liver histopathological diagnosis were independently performed by 1 experienced pathologist. Liver histopathological diagnosis referring to the consensus of liver fibrosis diagnosis and efficacy evaluation in 2002, liver histopathological fibrosis stage includes stage 5 such as stage 5S 0, stage 1, stage 2, stage 3 and stage 4, and patients are divided into two groups according to the stage results thereof, namely, a group without significant fibrosis (S0, S1) and a group with significant liver fibrosis (. Gtoreq.S 2). The no significant fibrosis group was 101 cases and the significant fibrosis group was 223 cases.
1.3 detection of blood liver function, AFP, PLT, coagulation function
Taking 4ml of whole blood after 8h of empty stomach, centrifuging for 10min at 3500r/min, collecting serum, analyzing ALT, AST, TBIL, DBIL, IBIL, ALB, GLB, GGT, TBA by a Roche cobas c702 full-automatic biochemical analyzer and a matched reagent, and calculating an A/G (ALB/GLB) value; roche cobas 602 full-automatic electrochemiluminescence analyzer and matched reagent for analyzing AFP; SYSME XE-2100 full-automatic blood cell analyzer and matched reagent analyze PLT; the STAR-EVOLUTION full-automatic coagulation analyzer and the matched reagent analyze PT and PTA, all projects participate in the clinical examination of the national health commission to obtain qualified quality evaluation results among ventricles, and have traceability certificates; every 24 hours of indoor quality control, two horizontal quality control objects are detected, and the out-of-control rule adopts 13S and 22S, R S.
1.4 liver LSM, CAP
LSM and CAP are detected by a fibriScan (liver fibrosis detector) instrument, the method comprises the steps of placing a patient on the back, placing hands behind the brain, placing a probe on the right anterior axillary line to the detection area between 7 th, 8 th and 9 th intercostal lines after the couplant is smeared, continuously detecting for 10 times, taking the median as a final result, and taking the elastic value as LSM and the unit as kPa. Fat degree is expressed in CAP, and the unit is B/m.
2. Statistical method
Data were processed using SPSS 26.0, R software 4.0 software. Patients find meaningful variables by performing LASSO regression analysis on ALT, AST, TBIL, DBIL, IBIL, ALB, GGT, TBA, AFP, PT, PTA, PLT, A/G, LSM, CAP according to whether fibrosis is significant or not, and further perform a multi-factor logistic regression analysis fit equation (p < 0.05) after screening the variables. The construction of a mathematical model for diagnosing whether fibrosis is obvious or not based on a difference detection index adopts two kinds of Logistic stepwise regression analysis, independent variable inclusion and rejection standards are P <0.05 and P > 0.10 respectively, and a calibration curve and ROC curve method are adopted to evaluate the performance of the regression model.
3. Results
The invention is incorporated into 324 patients with chronic hepatitis B liver penetration, and the patients are divided into two groups (0, 1) according to whether obvious liver fibrosis occurs or not, and clinical data of the patients are collected.
3.1 there was a correlation with the occurrence of overt liver fibrosis according to LASSO cues LSM, CAP, ALT, AST, DBIL, IBIL, ALB, A/G, AFP, PLT, PT, PTA, GGT, TBA. See fig. 1 (A, B).
3.2 due to obvious correlation between PT and PTA, eliminating PT, carrying out multi-factor logistic regression analysis on LSM, CAP, AFP, PTA, ALT, AST, DBIL, IBIL, ALB, A/G, GGT, PLT, TBA of patients and whether obvious liver fibrosis occurs or not, wherein the obvious liver fibrosis occurs and ALT, AST, PTA, LSM are obviously correlated, the difference has statistical significance (P is less than 0.05), and the regression equation (AAPL): aapl= -0.01×alt+0.021×ast-0.042×pta+0.356×lsm. A visualization tool nomogram was made according to AAPL to calculate the probability of developing overt liver fibrosis.
The new model effect is verified through the clinical influence curves of the localization plot of fig. 2, the ROC curve of fig. 3 and the AAPT of fig. 5, and the clinical decision curve comparison is carried out on AAPL, APRI, FIB-4 in fig. 4, the ROC curve comparison of the new model and the old model (AAPL, APRI, FIB-4) is carried out in fig. 7, and the new model is evaluated to be better than the old model.
TABLE 1 comparison of the results of the tests for the two groups of patients
3.4 comparison of the detection efficacy of the inventive model (AAPL) with the detection indicators
The results of the efficacy comparison are shown in table 2 and fig. 3, and the results suggest that AAPL has higher detection efficacy for whether obvious liver fibers occur, and AUC, sensitivity and specificity are respectively: 0.86 82.5 percent and 78.2 percent, which is obviously higher than the detection efficiency of single index.
TABLE 2AAPT vs. ROC curves for various detection indicators
According to the invention, the AAPL regression model is evaluated, as shown in fig. 3, various indexes are compared with the ROC curve of the model AAPL, so that the AAPL is used as a patient to evaluate whether obvious liver fibrosis occurs better than a single index, the indexes are all non-invasive detection indexes commonly used in clinic, the patient acceptance is high, the clinical value is high for evaluating whether the patient has obvious liver fibrosis, and the antiviral treatment can be performed according to the nonogram graph result and the viral load detection result. Therefore, the combined detection index model with high correlation degree for the chronic hepatitis B patients constructed by the invention can accurately evaluate whether the patients have obvious hepatic fibrosis or not, and can provide basis for clinical early diagnosis and treatment.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (2)
1. A model for assessing the degree of liver fibrosis in a patient with hepatitis b, wherein the model is a regression equation obtained by Logistic regression analysis of liver ALT, AST, PTA, LSM values; wherein ALT is alanine aminotransferase, AST is aspartate aminotransferase, PTA is prothrombin activity, LSM is the elasticity value obtained by a liver fibrosis detector;
the model is specifically as follows: aapl=0.356×lsm-0.01×alt+0.021×ast-0.042×pta, where AAPL represents a predicted value of liver fibrosis in a liver patient.
2. A method of constructing a model for assessing the degree of liver fibrosis in a patient having hepatitis b according to claim 1 comprising the steps of:
s1, acquiring a liver tissue sample, carrying out pathological fibrosis stage on the liver tissue sample, and dividing patients into a group without obvious fibrosis and a group with obvious liver fibrosis according to the stage result;
s2, collecting examination results of the two groups of patients: glutamate Aminotransferase (ALT), aspartate Aminotransferase (AST), total Bilirubin (TBIL), direct Bilirubin (DBIL), indirect Bilirubin (IBIL), albumin (ALB), glutamyl transpeptidase (GGT), total Bile Acid (TBA), alpha Fetoprotein (AFP), platelets (PLT), clotting time (PT), prothrombin activity (PTA); calculating A/G (ALB/GLB) value, and checking instantaneous liver elasticity value (LSM) and Controlled Attenuation Parameter (CAP);
s3, testing the correlation between ALT, AST, TBIL, DBIL, IBIL, ALB, GGT, TBA, AFP, PLT, PT, PTA, A/G, LSM, CAP and whether liver fibrosis occurs obviously;
s4, constructing a mathematical model of whether fibrosis is remarkable or not by adopting two-classification Logistic stepwise regression analysis, and calculating probability by using a nomogram;
the specific operation of evaluating the correlation in the step S3: collecting ALT, AST, TBIL, DBIL, IBIL, ALB, GGT, TBA, AFP, PLT, PT, PTA, A/G, LSM, CAP, performing LASSO regression analysis on the variables to find meaningful variables, screening the variables, performing multi-factor Logistic regression analysis, and incorporating a variable fitting equation if P is less than 0.05;
the independent variable inclusion and rejection criteria in the step S4 are P <0.05 and P > 0.10 respectively.
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| CN104866720A (en) * | 2015-05-19 | 2015-08-26 | 何清 | Non-creative scoring model for ULN chronic hepatitis B and hepatic fibrosis and establishment method therefor |
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