CN110911008A - Method for establishing biliary tract occluded alignment chart prediction model and application thereof - Google Patents
Method for establishing biliary tract occluded alignment chart prediction model and application thereof Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 21
- 210000003445 biliary tract Anatomy 0.000 title description 5
- 206010010317 Congenital absence of bile ducts Diseases 0.000 claims abstract description 81
- 201000005271 biliary atresia Diseases 0.000 claims abstract description 81
- 238000007477 logistic regression Methods 0.000 claims abstract description 15
- 206010056528 Neonatal cholestasis Diseases 0.000 claims abstract description 11
- 210000002966 serum Anatomy 0.000 claims abstract description 10
- 230000001587 cholestatic effect Effects 0.000 claims abstract description 9
- 208000011580 syndromic disease Diseases 0.000 claims abstract description 8
- 208000006454 hepatitis Diseases 0.000 claims abstract description 7
- 231100000283 hepatitis Toxicity 0.000 claims abstract description 7
- 101710107035 Gamma-glutamyltranspeptidase Proteins 0.000 claims description 39
- 101710173228 Glutathione hydrolase proenzyme Proteins 0.000 claims description 39
- 102000006640 gamma-Glutamyltransferase Human genes 0.000 claims description 39
- 102000002260 Alkaline Phosphatase Human genes 0.000 claims description 21
- 108020004774 Alkaline Phosphatase Proteins 0.000 claims description 21
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Abstract
The invention belongs to the field of biological medicine and molecular biology, relates to a method for establishing a disease-related factor scoring parameter analysis model, and particularly relates to a method for establishing a biliary atresia nomogram prediction model and application thereof; the independent variables include sex, body weight, DB, log (ALP) and log (GGT). The established Nomorgram diagnostic nomogram based on multiple logistic regression analysis helps to determine the risk of biliary atress from the scores in clinical practice. Further, the established Nomorgram diagnostic nomogram based on multiple logistic regression analysis can be used to analyze scoring parameters of serum markers of biliary atresia and cholestatic infant hepatitis syndrome, facilitating the identification of BA from other forms of Neonatal Cholestasis (NC) of different etiology.
Description
Technical Field
The invention relates to the field of biotechnology, relates to a method for analyzing a disease-related factor scoring parameter analysis model, and particularly relates to a method for establishing a biliary atresia nomogram prediction model and application thereof.
Background
Biliary Atresia (BA) is reported to be a rare but serious neonatal disease that, if undiagnosed and treated, can rapidly progress to biliary cirrhosis and liver failure, and can even die within 2-3 years after birth. According to statistics, the total neonatal morbidity rate of BA in east Asia is 8000 ten-thousandths, which is obviously higher than that in the United states, the subsidiary pediatric hospital of the university of Fudan, which is one of the largest pediatric hospitals in China, and up to 400 neonatal patients are diagnosed with BA every year, treated by hepatic portal surgery (Kasai), and treated by conventional postoperative medicaments, such as antibiotics, hormones, ursodeoxycholic acid and the like. Studies have disclosed that BA patients surviving with autologous liver have a 2-year survival rate of 53.7%, indicating that early diagnosis and treatment are key to restoring biliary tract blood flow and achieving good clinical results, however, misdiagnosis of BA can lead to inappropriate treatment and unnecessary surgery, as 602 cases of BA surgery were analyzed in studies, of which only 86% were pathologically confirmed as Biliary Atresia (BA) after surgery, and therefore, establishing a reliable BA predictive model for biliary atresia is crucial for early detection and diagnosis of the disease. Unfortunately, at present, definitive diagnosis and validation of suspected neonatal BA in clinical practice often requires liver biopsy and intraoperative biliary angiography (IOC) during surgery, and these diagnostic methods have proven to be invasive, time consuming and expensive.
There are studies that propose the use of the serum activity of gamma-glutamyl transpeptidase (GGT) for the diagnosis of BA. In fact, studies have disclosed that GGT >300 units/liter, or its serum activity increases by 6 units/liter per day, with the accuracy of differentiation between BA and neonatal hepatitis being 85% and 88%, respectively. El-Guindi and colleagues reported that the diagnostic sensitivity of the serum activity of GGT to BA was 76.7% at cutoff (>286 units/liter) with a specificity of 80%; it has been found that serum GGT activity also shows good ability to identify BA and other causes in the Chinese population, however, practice has confirmed that gamma-glutamyl transpeptidase alone (the reliability, accuracy and reproducibility of GGT activity are questionable, for example, healthy newborn at birth has higher GGT levels and the normal range of GGT levels may vary with age; in fact, GGT corrected for age changes has improved accuracy in predicting BA, and so far, the construction of a predictive model of GGT combined with other BA-related factors has not been reported.
Based on the current situation of the prior art, the inventor of the application intends to provide a method for establishing a biliary atresia disease related factor score parameter analysis model, in particular to a method for establishing a biliary atresia nomogram prediction model, wherein the established model can be used for analyzing score parameters of serum markers of biliary atresia and cholestatic infant hepatitis syndrome, and is helpful for the identification of BA and neonatal cholestatic syndrome (NC) with different etiologies.
Disclosure of Invention
The invention aims to provide a method for establishing a biliary atresia disease related factor score parameter analysis model based on the current situation of the prior art, in particular to a method for establishing a biliary atresia nomogram prediction model, wherein the established model can be used for analyzing score parameters of serum markers of biliary atresia and cholestatic infant hepatitis syndrome, and is beneficial to the identification of biliary atresia BA and neonatal cholestatic syndrome (NC) with different causes.
The invention utilizes rms package to establish a Nomorgram diagnosis nomogram based on multiple logistic regression analysis, wherein independent variables are selected, and weight scores of all factors are quantized; the independent variables include sex, body weight, DB, log (ALP) and log (GGT). The established Nomorgram diagnostic nomogram based on multiple logistic regression analysis helps to determine the risk of biliary atress from the scores in clinical practice. Further, the established Nomorgram diagnostic nomogram based on multiple logistic regression analysis can be used to analyze scoring parameters of serum markers of complex signs of biliary atresia and cholestatic infant hepatitis, facilitating the identification of BA from other forms of Neonatal Cholestasis (NC) of different etiology.
The Nomorgram diagnostic nomogram of the present invention is shown in fig. 1A.
Specifically, the method for establishing the biliary tract occlusion nomogram prediction model comprises the following steps:
1) determining predictors, gender, weight, DB, log (ALP) and log (GGT), creating a nomogram for BA prediction;
2) preparing a Receiver Operating Characteristic (ROC) graph;
3) preparing a calibration curve of the prediction model;
4) decision Curve Analysis (DCA) of the BA prediction nomograms was determined.
In the present invention, the log (ALP) and log (GGT) employ alkaline phosphatase (ALP) and gamma-glutamyltranspeptidase (GGT);
in the present example, 1,728 neonatal patients were collected and the sex, weight, DB, log (ALP), log (GGT) and other variables were determined, wherein the sex, weight, DB, log (ALP), log (GGT) were significantly different between the BA group and the non-BA group (P <0.05), and the age, TB, ALT, AST were not significantly different between the two groups (P > 0.05);
in the invention, univariate logistic regression analysis is adopted to determine the independent variable related to BA: the BA group and the non-BA group have significant difference (P <0.05) in the variables of sex, weight, DB, logarithm (ALP), logarithm (GGT) and the like, the logarithm (GGT) has good independent prediction performance, the AUC is more than 0.8, however, the AUC of DB and logarithm (ALP) is less than 0.6;
in the invention, on the basis of multiple logistic regression analysis, a nomogram for predicting BA is established by utilizing sex, weight, DB, logarithm (ALP) and logarithm (GGT), the relation between the factors and BA is evaluated by using the multiple logistic regression analysis, the BA ratio of the factors is calculated, and the result shows that the sex, the weight, the DB, the logarithm (ALP) and the logarithm (GGT) are obviously related to the BA, and the nomogram prediction model of the BA is established by taking the factors as prediction factors.
The formula for the calculation is as follows:
the biliary tract blocked nomogram prediction model established by the invention comprises model software, wherein the model software consists of a back-end database, a model algorithm and a front-end graphical user interface, and the back end of the database stores the information of a detected person and inquires and edits; the model algorithm is realized by computer programming of a grading analysis model; the graphical user interface provides a window for user interaction with the computer, and provides interfaces for information input, querying, editing, scoring analysis, and result printout.
The invention provides a method for establishing a biliary atresia nomogram prediction model, wherein the established model can be used for analyzing scoring parameters of serum markers of biliary atresia and cholestatic infant hepatitis syndrome, and is beneficial to the identification of biliary atresia BA and neonatal cholestatic syndrome (NC) with different causes.
Table 1 shows the ability to verify internally and externally the method of the invention for the identification of biliary atresia BA from Neonatal Cholestasis (NC) of different etiology.
Drawings
Fig. 1, probability of BA prediction using nomogram based on multiple logistic regression, wherein,
creating nomogram 1 for BA prediction using gender, weight, DB, log (ALP) and log (GGT)5 predictors, (a) creation of nomogram; (B) a Receiver Operating Characteristic (ROC) graph; (C) a calibration curve of the prediction model; decision curve analysis of BA prediction nomograms (DCA).
For the purpose of facilitating understanding, the invention will hereinafter be described in detail by means of specific drawings and detailed description. It is to be expressly understood that the description is illustrative only and is not intended as a definition of the limits of the invention.
Detailed Description
Example 1
1,728 neonatal patients were collected, of which 1,512 (87.5%) were diagnosed with BA, 216 (12.5%) were confirmed to be non-BA, the mean age was 73.8(SD, 24.8) days, the BA group was 73.7(SD, 24.9) days, and the non-BA group was 74.4(SD, 24.3) days. non-BA patients were mostly male (80.6%), with the BA group gender distribution being essentially the same (51% male, 49% female); table 1 also lists details of other characteristics including body weight, TB, DB, ALT, AST, log (ALP) and log (GGT). The gender, weight, DB, logarithm (ALP) and logarithm (GGT) of the BA group and the non-BA group are all significantly different (P <0.05), and the age, TB, ALT and AST of the two groups are not significantly different (P > 0.05);
independent variables associated with BA were determined using univariate logistic regression analysis: the BA group and the non-BA group have significant difference in variables such as sex, weight, DB, logarithm (ALP), logarithm (GGT) and the like (P < 0.05). The logarithm (GGT) has good independent predictive performance with AUC greater than 0.8. However, AUC for DB and log (ALP) is less than 0.6;
on the basis of multiple logistic regression analysis, a nomogram for predicting BA is established by utilizing sex, weight, DB, logarithm (ALP) and logarithm (GGT), the relation between the factors and BA is evaluated by using the multiple logistic regression analysis, the BA ratio of the factors is calculated, and the result shows that the sex, the weight, the DB, the logarithm (ALP) and the logarithm (GGT) are obviously related to the BA, and the nomogram prediction model of the BA is established by taking the factors as prediction factors.
As shown in fig. 1A, there are 8 rows in the nomogram, with rows 2 through 6 representing the variables involved. The points of the five variables are added to the total points in row 7 and correspond to the risk probability in the BA prediction in row 8, and the nomogram shows the risk percentage for BA. For the nomograms obtained, the area under ROC curve (AUC) value was 0.898, which was greater than the log (GGT) AUC value of 0.848, log (ALP) AUC value of 0.572, and DB AUC value of 0.567 in the BA prediction (fig. 1B, table 1).
A calibration blot with 1,000 boottrap resamples is shown in fig. 1C, showing that the nomogram predicted probability for BA is similar to the actual probability for BA, indicating a better agreement of the prediction with the actual observations in terms of the probability for BA (as shown in fig. 1C); the result also shows that the identification capability of the nomogram on BA prediction can be popularized to other people groups, and the nomogram has clinical applicability; further, the present invention analyzes the specific normalized net gain of nomograms and logarithms (GGTs) at different threshold probabilities and shows that the net benefit of DCA is 9.4% at 80% of the threshold probability better than that of the logarithmic (GGT) and 30.2% of the baseline model.
As shown in table 1, the Nomorgram nomogram of the present invention shows a stronger differential diagnostic power, with a sensitivity of 85.7%, a specificity of 80.3%, and a PPV of 0.969 at the optimal critical value; further, the discriminatory power of the Nomorgram nomograms was compared with individual risk predictors, in particular GGT, and the study subjects were divided into two subgroups according to GGT (GGT <300 units/liter and ≧ 300 units/liter), diagnostic sensitivity and specificity for GGT <300 units/liter being 0 and 0.960, respectively, and diagnostic sensitivity and specificity for the Nomorgram nomograms being 0.448 and 0.951, respectively; furthermore, the Nomorgram nomograms show the consistency of performance of the models and validation sets in nomograms, however, the sensitivity, specificity and PPV values (0.786, 0.795 and 0.966) of GGT alone in the model group did not reproduce well in the validation group (0.885, 0.124 and 0.706 respectively), and therefore the analysis results show that the Nomorgram nomograms are used for differential diagnosis of BA over simple GGT.
TABLE 1 internal and external verification of the diagnostic ability of the method of the invention
Abbreviations: AUC, area under the Receiver Operating Characteristic (ROC) curve; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value;
note: is based on the combination of gender, weight, DB, log (ALP) and log (GGT); external verification is based on a threshold.
Claims (7)
1. A method for establishing a biliary atresia nomogram prediction model is characterized by comprising the following steps:
(1) determining a prediction factor, gender, weight, direct bilirubin DB, logarithmic ALP and logarithmic GGT, and establishing a nomogram for BA prediction;
(2) preparing a Receiver Operating Characteristic (ROC) graph;
(3) preparing a calibration curve of the prediction model;
(4) decision Curve Analysis (DCA) of the BA prediction nomograms was determined.
2. The method of claim 1, wherein a nomogram for predicting BA is established using sex, weight, DB, log ALP and log GGT on the basis of multiple logistic regression analysis, the relationship between the factors and BA is evaluated using multiple logistic regression analysis, the BA ratio of the factors is calculated, a prediction factor significantly related to BA is obtained, a nomogram prediction model for BA is established according to the following calculation formula,
3. the method of claim 1, wherein said log ALP and log GGT) are alkaline phosphatase ALP and gamma-glutamyltranspeptidase GGT.
4. The method of claim 1, wherein the independent variables associated with the BA are determined using univariate logistic regression analysis.
5. The method of claim 1 or 2, wherein the nomogram (Nomorgram) has a threshold sensitivity of 85.7%, a specificity of 80.3% and a PPV of 0.969.
6. The method of claim 1, wherein the use of the established biliary atresia nomogram prediction model for analyzing scoring parameters for serum markers of biliary atresia and cholestatic infant hepatitis syndrome.
7. The method of claim 1, characterized by the use of the established biliary atresia nomogram prediction model for differentiating BA from other forms of Neonatal Cholestasis (NC) of different etiology.
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| CN112748249A (en) * | 2020-12-18 | 2021-05-04 | 深圳市绘云生物科技有限公司 | Application of neonatal biliary tract occlusion diagnostic marker |
| CN112908467A (en) * | 2021-01-19 | 2021-06-04 | 武汉大学 | Multivariable dynamic nomogram prediction model and application thereof |
| CN113707272A (en) * | 2021-08-02 | 2021-11-26 | 复旦大学附属中山医院 | Evaluation model for suitable population for interventional therapy of radial artery access liver cancer and construction method |
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| CN112748249A (en) * | 2020-12-18 | 2021-05-04 | 深圳市绘云生物科技有限公司 | Application of neonatal biliary tract occlusion diagnostic marker |
| CN112908467A (en) * | 2021-01-19 | 2021-06-04 | 武汉大学 | Multivariable dynamic nomogram prediction model and application thereof |
| CN113707272A (en) * | 2021-08-02 | 2021-11-26 | 复旦大学附属中山医院 | Evaluation model for suitable population for interventional therapy of radial artery access liver cancer and construction method |
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