CN111192687A - Line graph prediction model for advanced appendicitis and application thereof - Google Patents
Line graph prediction model for advanced appendicitis and application thereof Download PDFInfo
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- CN111192687A CN111192687A CN201811353042.8A CN201811353042A CN111192687A CN 111192687 A CN111192687 A CN 111192687A CN 201811353042 A CN201811353042 A CN 201811353042A CN 111192687 A CN111192687 A CN 111192687A
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- 206010003011 Appendicitis Diseases 0.000 title claims abstract description 96
- 102100032752 C-reactive protein Human genes 0.000 claims abstract description 46
- 108010074051 C-Reactive Protein Proteins 0.000 claims abstract description 44
- 108010058861 Fibrin Fibrinogen Degradation Products Proteins 0.000 claims abstract description 40
- 239000000208 fibrin degradation product Substances 0.000 claims abstract description 40
- 238000007477 logistic regression Methods 0.000 claims abstract description 16
- 210000002966 serum Anatomy 0.000 claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 230000000750 progressive effect Effects 0.000 claims description 39
- 239000011734 sodium Substances 0.000 claims description 19
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 claims description 5
- 229910001415 sodium ion Inorganic materials 0.000 claims description 5
- 238000011088 calibration curve Methods 0.000 claims description 4
- 238000013211 curve analysis Methods 0.000 claims description 4
- 238000003556 assay Methods 0.000 claims 1
- 230000002452 interceptive effect Effects 0.000 claims 1
- 201000010099 disease Diseases 0.000 abstract description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 239000003814 drug Substances 0.000 abstract 1
- 238000003745 diagnosis Methods 0.000 description 6
- 230000035945 sensitivity Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000002591 computed tomography Methods 0.000 description 3
- 210000000265 leukocyte Anatomy 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 2
- 238000002604 ultrasonography Methods 0.000 description 2
- 206010000084 Abdominal pain lower Diseases 0.000 description 1
- 208000007743 Acute Abdomen Diseases 0.000 description 1
- 206010002091 Anaesthesia Diseases 0.000 description 1
- 206010061818 Disease progression Diseases 0.000 description 1
- 208000005577 Gastroenteritis Diseases 0.000 description 1
- 206010024652 Liver abscess Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 206010034649 Peritoneal abscess Diseases 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 206010037660 Pyrexia Diseases 0.000 description 1
- 206010040047 Sepsis Diseases 0.000 description 1
- 206010047700 Vomiting Diseases 0.000 description 1
- 230000003187 abdominal effect Effects 0.000 description 1
- 230000037005 anaesthesia Effects 0.000 description 1
- 238000007486 appendectomy Methods 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 230000005750 disease progression Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 208000003243 intestinal obstruction Diseases 0.000 description 1
- 230000005865 ionizing radiation Effects 0.000 description 1
- 238000002595 magnetic resonance imaging Methods 0.000 description 1
- 230000001394 metastastic effect Effects 0.000 description 1
- 206010061289 metastatic neoplasm Diseases 0.000 description 1
- 206010034674 peritonitis Diseases 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 230000008673 vomiting Effects 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
Abstract
The invention belongs to the field of biological medicine and molecular biology, relates to the establishment and application of a disease-related factor scoring parameter analysis model, and particularly relates to a progression appendicitis nomogram prediction system which comprises model software, wherein the model software consists of a back-end database, a model algorithm and a front-end graphic user interface; the model algorithm is realized by computer programming of a score analysis model, a prediction factor is analyzed by logistic regression, and a model is established by adopting Nomogram; the prediction factors comprise fibrin degradation products, C-reactive protein and Na +. The established nomogrm diagnostic Nomogram based on multiple logistic regression analysis is helpful for determining the risk index of advanced appendicitis according to scores in clinical practice; furthermore, the kit can be used for analyzing the scoring parameters of serum markers of advanced appendicitis and early appendicitis, and is beneficial to the identification of the advanced appendicitis and the early appendicitis.
Description
Technical Field
The invention relates to the field of biotechnology, relates to a new application of a disease-related factor scoring parameter analysis model, and particularly relates to a prediction model for establishing a progressive appendicitis nomogram and an application thereof.
Background
The prior art discloses that appendicitis is the most common cause of surgical acute abdomen in children, and the incidence rate is high. Accurate diagnosis and timely, appropriate treatment are critical to achieving a good prognosis, and conversely, delayed diagnosis and treatment can lead to rapid disease progression, leading to a number of complications, including liver abscesses, peritoneal abscesses, diffuse peritonitis, intestinal obstruction, sepsis and other serious clinical conditions. Also, misdiagnosis of appendicitis may lead to unnecessary appendectomies and their associated increased mortality.
The characteristic clinical manifestations of appendicitis include vomiting, metastatic right lower abdominal pain, fever, etc., however, these clinical manifestations are often atypical in pediatric patients, overlap with many other diseases, are confusing, including pneumonia and gastroenteritis, etc., which present challenges to the initial diagnosis of appendicitis in children. In addition, abdominal examinations are another challenge, and children are often reluctant to fit or not clearly express, making it difficult to perform effective physical examinations. There are a number of disadvantages to current imaging procedures for diagnosing appendicitis in children, for example, Computed Tomography (CT) exposure of the child to ionizing radiation may increase the risk of radiation-related cancer; magnetic Resonance Imaging (MRI) has higher diagnostic accuracy for appendicitis than CT and Ultrasound (US), however, it is expensive, and the infant patient often needs anesthesia due to long examination time, and the clinical application value is not large, etc.; therefore, there is an urgent need to develop a non-invasive model for diagnosing appendicitis, particularly in the case of children with advanced appendicitis who require immediate surgery.
Previous studies have shown that serum markers, including C-reactive protein (CRP), White Blood Cell (WBC) counts, Fibrin Degradation Products (FDP), can be used for appendicitis diagnosis and help to assess the severity of the disease, however, the diagnostic sensitivity and specificity of individual serum markers is low, e.g., clinical studies have shown that WBC counts in up to 20% of children's appendicitis patients can be within the normal range, and it is therefore necessary to develop diagnostic models that combine indices.
Based on the current state of the art, the inventors of the present application propose to provide a nomogram prediction model for advanced appendicitis and its uses.
Disclosure of Invention
The invention aims to provide a model for analyzing scoring parameters of appendicitis serum markers based on the current situation of the prior art, and also relates to a new application of the appendicitis nomogram prediction model.
The method is based on the theory of Nomogram, which is to rapidly, intuitively and accurately display a complex calculation formula in a graphical mode, namely drawing Nomogram and aiming at explaining the relation between different variables in a drawing method; in the medical field, nomogrm has an advantage in that the medical examination level of a specific patient can be calculated personalizedly, and thus has great value in clinical practice.
A general regression model can plot its corresponding Nomogram. The invention utilizes rms package to establish a Nomogram diagnosis Nomogram based on multiple logistic regression analysis, wherein independent variables are selected, and weight scores of all factors are quantized; the independent variables included in the present invention include: fibrin Degradation Products (FDP), C-reactive protein (CRP) and Na +. The established Nomorgram diagnostic nomogram based on multiple logistic regression analysis helps to determine the risk of progressive appendicitis based on score in clinical practice. Further, the established Nomogram diagnostic Nomogram based on multiple logistic regression analysis can be used for analyzing scoring parameters of serum markers of advanced appendicitis and early appendicitis, and is beneficial to identifying advanced appendicitis and early appendicitis.
The Nomogram diagnostic of the present invention is shown in fig. 1.
The invention provides a progression appendicitis nomogram prediction system, which comprises model software, wherein the model software consists of a back-end database, a model algorithm and a front-end graphical user interface;
the model algorithm is realized by computer programming of a score analysis model, a prediction factor is analyzed by logistic regression, and a model is established by adopting Nomogram;
the prediction factors comprise fibrin degradation products, C-reactive protein and Na +.
The back end of the database stores the information of the detected person and inquires and edits.
The graphical user interface provides a user and computer interaction window, and provides interfaces for information input, query, editing, score analysis and result printout.
The system can score serum markers, analyze the progressive appendicitis odds ratios of the factors, calculate the probability of progressive appendicitis, and further predict the risk index of progressive appendicitis. Wherein, the formula of the calculation of the P value is as follows:
ln(P/1-P)＝39.1367+0.0769×FDP-0.2969×Na+0.0196×CRP；
wherein FDP is fibrin degradation product, CRP is C-reactive protein, and Na is sodium ion.
Correspondingly, the invention provides application of a progressive appendicitis Nomogram prediction model, wherein the prediction factors are analyzed through logistic regression, a diagnosis model is established by adopting Nomogram, and serum markers are scored; the prediction factors comprise fibrin degradation products, C-reactive protein and Na +.
The application comprises the following steps:
(1) preparing a subject running characteristic map;
(2) preparing a calibration curve of the prediction model;
(3) and (3) determining a decision curve analysis of the progressive appendicitis prediction nomogram.
Wherein, the predictor and its weight are usually determined before step (1).
The nomogram prediction model of advanced appendicitis established by the invention can be used for analyzing scoring parameters of serum markers of advanced appendicitis and early appendicitis.
On the basis, the established line graph prediction model of the advanced appendicitis can be used for distinguishing and identifying the advanced appendicitis and the early appendicitis.
Specifically, the new application of the invention in establishing the line chart prediction model of advanced appendicitis comprises the following steps:
1) determining the predictors, Fibrin Degradation Products (FDPs), C-reactive protein (CRP) and Na +, creating a nomogram for the prediction of advanced appendicitis;
2) preparing a Receiver Operating Characteristic (ROC) graph;
3) preparing a calibration curve of the prediction model;
4) decision Curve Analysis (DCA) to determine the progression appendicitis prediction nomogram.
In one embodiment of the present invention, 669 clinical data of appendicitis patients are collected and the variables Fibrin Degradation Product (FDP), C-reactive protein (CRP) and Na + are determined, wherein the Fibrin Degradation Product (FDP), C-reactive protein (CRP) and Na + of the advanced appendicitis group and the early appendicitis group are significantly different (P <0.05), and the age and sex of the two groups are not significantly different (P > 0.05).
Independent variables associated with advanced appendicitis were determined using univariate logistic regression analysis: the progressive appendicitis group and the early appendicitis group have significant difference on the variables such as Fibrin Degradation Products (FDP), C-reactive protein (CRP), Na + and the like (P is less than 0.05), the Fibrin Degradation Products (FDP) and the C-reactive protein (CRP) have good independent prediction performance, and the AUC is more than 0.8.
On the basis of multiple logistic regression analysis, a nomogram for predicting progressive appendicitis is established by utilizing Fibrin Degradation Products (FDP), C-reactive protein (CRP) and Na +, the relation between the factors and progressive appendicitis is evaluated by using the multiple logistic regression analysis, the ratio of progressive appendicitis ratios of the factors is calculated, and the results show that the Fibrin Degradation Products (FDP), the C-reactive protein (CRP) and the Na + are obviously related to the progressive appendicitis and are used as prediction factors to establish a nomogram prediction model of the progressive appendicitis. The formula for the calculation is as follows:
ln(P/1-P)＝39.1367+0.0769×FDP-0.2969×Na+0.0196×CRP。
the established advanced appendicitis nomogram prediction model comprises model software, wherein the model software consists of a back-end database, a model algorithm and a front-end graphic user interface, and the back end of the database stores 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 user and computer interaction window, and provides interfaces for information input, querying, editing, scoring analysis, and result printout.
The invention provides an established progressive appendicitis nomogram prediction model and a new application thereof, and the established model can be used for analyzing scoring parameters of serum markers of progressive appendicitis and early appendicitis and is beneficial to the identification of the progressive appendicitis and the early appendicitis.
Drawings
FIGS. 1-4 use nomograms based on multiple logistic regression to predict the probability of progressive appendicitis.
FIG. 1 creation of a nomogram.
FIG. 2 is a graph of Receiver Operating Characteristics (ROC).
FIG. 3 is a calibration curve of a predictive model.
FIG. 4 Decision Curve Analysis (DCA) of BA prediction nomograms.
Among these, nomograms for the prediction of progressive appendicitis were created using Fibrin Degradation Products (FDP), C-reactive protein (CRP) and Na +3 predictors.
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
Clinical data was collected as a model training set for 411 infants with appendicitis, in which 200 (48.66%) of the children were diagnosed and diagnosed with early appendicitis and 211 (51.34%) of the patients were diagnosed and diagnosed with advanced appendicitis. Most appendicitis patients were male (67.15%), however, there was no significant difference in gender distribution between the early and advanced appendicitis groups.
Independent variables associated with advanced appendicitis were determined using univariate logistic regression analysis: the progressive appendicitis group and the early appendicitis group have significant differences in the variables of Fibrin Degradation Products (FDP), C-reactive protein (CRP), Na + and the like (P < 0.05). Fibrin Degradation Products (FDP), C-reactive protein (CRP), have good independent predictive properties with AUC greater than 0.8.
On the basis of multiple logistic regression analysis, a nomogram for predicting progressive appendicitis is established by utilizing Fibrin Degradation Products (FDP), C-reactive protein (CRP) and Na +, the relation between the factors and progressive appendicitis is evaluated by using the multiple logistic regression analysis, the ratio of the factors to the ratio of the predicted progressive appendicitis is calculated, and the results show that the Fibrin Degradation Products (FDP), the C-reactive protein (CRP) and the Na + are obviously related to the progressive appendicitis and are used as prediction factors to establish a nomogram prediction model of the progressive appendicitis.
As shown in fig. 1, there are 6 rows in the nomogram, with rows 2 through 4 representing the variables included. The points of the three variables are added to the total points in row 5 and correspond to the risk probability in the prediction of progression 6 appendicitis, and the nomogram shows the percentage risk for progression appendicitis. For the nomograms obtained, the area under the ROC curve (AUC) value was 0.860, which is greater than the AUC value of Fibrin Degradation Product (FDP) of 0.818, the AUC value of C-reactive protein (CRP) of 0.803, and the AUC value of Na + of 0.785 (fig. 2, table 1).
A calibration blot with 1,000 Bootstrap resamples is shown in fig. 3, showing that the nomogram predicted probability for progressive appendicitis is similar to the actual probability for progressive appendicitis, indicating better agreement of the prediction with the actual observations in terms of probability for progressive appendicitis (fig. 3). The results also show that the discrimination ability of the nomogram on the prediction of progressive appendicitis can be popularized to other people, and the nomogram has certain clinical applicability. The Nomogram and the specific normalized net gain of CRP at different threshold probabilities were also analyzed, with the net gain of the Nomogram being superior to that of CRP (fig. 4).
Table 1 is a diagnostic capability validation of the novel use of the present invention.
As shown in table 1, the Nomogram of the present invention shows a stronger differential diagnostic ability with a sensitivity of 75.7%, a specificity of 85.4%, and a PPV of 0.835 at the optimal cut-off. In addition, the training set and the verification set have better performance consistency in the nomogram. Therefore, Nomogram has certain clinical application value in diagnosing advanced appendicitis.
TABLE 1 diagnostic ability verification of the novel use of the present invention
Abbreviations: AUC, area under the Receiver Operating Characteristic (ROC) curve; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value.
Note: the Model is based on a combination of Fibrin Degradation Products (FDP), C-reactive protein (CRP) and Na +.§The verification is based on a threshold.
Claims (10)
1. The progressive appendicitis nomogram prediction system comprises model software and is characterized in that the model software consists of a back-end database, a model algorithm and a front-end graphical user interface;
the model algorithm is realized by computer programming of a score analysis model, a prediction factor is analyzed by logistic regression, and a model is established by adopting Nomogram;
the prediction factors comprise fibrin degradation products, C-reactive protein and Na +.
2. The advanced appendicitis nomogram prediction system of claim 1, wherein said database backend stores subject information, query editions.
3. The progressive appendicitis nomogram prediction system of claim 1, wherein the graphical user interface provides a user and computer interactive window.
4. The progressive appendicitis nomogram prediction system of claim 1, wherein the graphical user interface provides an information input, query, edit, score analysis and result printout interface.
5. The progressive appendicitis nomogram prediction system of claim 1, wherein the P value is calculated by the formula:
ln(P/1-P)＝39.1367+0.0769×FDP-0.2969×Na+0.0196×CRP；
wherein FDP is fibrin degradation product, CRP is C-reactive protein, and Na is sodium ion.
6. Use of a progressive appendicitis Nomogram predictive model in an analytical system for preparing scoring parameters for serum markers of progressive appendicitis and early appendicitis, said analytical system analyzing the predictive factors by logistic regression and building a diagnostic model using nomogr to score serum markers; the prediction factors comprise fibrin degradation products, C-reactive protein and Na +.
7. The use of claim 6, wherein the assay system scores serum markers by:
(1) preparing a subject running characteristic map;
(2) preparing a calibration curve of the prediction model;
(3) and (3) determining a decision curve analysis of the progressive appendicitis prediction nomogram.
8. The use of claim 6, wherein step (1) is preceded by determining parameters of the predictor.
9. The use of claim 8 wherein a nomogram predictive model of progressive appendicitis is established for analyzing scoring parameters for serum markers of progressive appendicitis and early appendicitis.
10. The use of claim 8 wherein a nomogram predictive model of progressive appendicitis is established to distinguish progressive appendicitis from early appendicitis.
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Cited By (3)
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| CN112017743A (en) * | 2020-08-20 | 2020-12-01 | 姚香怡 | Automatic generation platform and application of disease risk evaluation report |
| CN112542247A (en) * | 2020-08-17 | 2021-03-23 | 中山大学孙逸仙纪念医院 | Method and system for predicting probability of complete remission of pathology after breast cancer neoadjuvant chemotherapy |
| CN112908467A (en) * | 2021-01-19 | 2021-06-04 | 武汉大学 | Multivariable dynamic nomogram prediction model and application thereof |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112542247A (en) * | 2020-08-17 | 2021-03-23 | 中山大学孙逸仙纪念医院 | Method and system for predicting probability of complete remission of pathology after breast cancer neoadjuvant chemotherapy |
| WO2022036869A1 (en) * | 2020-08-17 | 2022-02-24 | 中山大学孙逸仙纪念医院 | Method and system for predicting pathological complete remission probability of breast cancer after neoadjuvant chemotherapy |
| CN112542247B (en) * | 2020-08-17 | 2024-03-08 | 中山大学孙逸仙纪念医院 | Method and system for predicting complete remission probability of pathology after breast cancer neoadjuvant chemotherapy |
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| WO2022036673A1 (en) * | 2020-08-20 | 2022-02-24 | 姚香怡 | Automatic disease risk evaluation and prediction report generation platform and application |
| CN112017743B (en) * | 2020-08-20 | 2024-02-20 | 姚香怡 | Automatic generation platform and application of disease risk evaluation report |
| CN112908467A (en) * | 2021-01-19 | 2021-06-04 | 武汉大学 | Multivariable dynamic nomogram prediction model and application thereof |
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