Red flag index model for CD early diagnosis and application thereof
By combining CD characteristics and other symptoms in the central China region in the Hongqi index model and using logistic regression and partial regression coefficients for weighted scores, the problem of poor diagnosis performance in the existing Hongqi index model in the central China region was solved, and higher sensitivity and specificity were achieved, improving the accuracy of early CD diagnosis.
Patent Information
- Application Number
- CN202510008541.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-03
- Publication Date
- 2025-05-27
AI Technical Summary
When the existing Hongqi index model is used for CD risk analysis in the central China population, the sensitivity and specificity are significantly reduced, especially the specificity is reduced to 68.42%, resulting in poor diagnostic efficacy.
Two improved red flag index models were proposed. By combining the original red flag index items and other symptoms of the subjects, logistic regression was used to analyze the correlation between disease diagnosis and various items, and entries related to CD diagnosis were screened out, and they were weighted and scored using partial regression coefficients to calculate the improved red flag index.
It improves the sensitivity and specificity of the Hongqi Index model in central China, enhances the accuracy of early diagnosis of CD, and reduces the occurrence of missed diagnosis and misdiagnosis.
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Figure CN120048480A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of early diagnosis of Crohn's disease (CD), and particularly relates to a red flag index model for early diagnosis of CD and its application. Background Art
[0002] Crohn's disease (CD) is a type of inflammatory bowel disease. Due to the non-specific early symptoms and signs, misdiagnosis and delayed diagnosis are common. Failure to diagnose in time and the resulting disease progression may lead to serious consequences such as surgery. Research shows that early intervention can effectively improve the prognosis of the disease. In order to identify CD in the early stage of the disease, the World IBD Organization proposed the concept of "Red Flags Index (RFI)" in 2015. By scoring the clinical symptoms present in patients, a red flag index ≥ 8 indicates a high likelihood of having CD, aiming to assist in the early diagnosis of CD (Table 1).
[0003] Table 1. Original Red Flags Index
[0004]
[0005] Although the above original red flag index model can be applicable within a certain range, when it is used for the CD risk analysis of the population in Central China, its sensitivity and specificity will both decrease significantly, especially the specificity decreases significantly, and the specificity decreases to only 68.42%, indicating that this model is not applicable in Central China. At present, there is no domestic clinical research to verify the effectiveness of the red flag index in assisting the diagnosis of CD, and the preliminary research data of multiple centers in this region show that the diagnostic efficacy of the original red flag index is not good, and there is still room for improvement in this model. Therefore, fill the gap in domestic research related to the red flag index, and improve the original red flag index according to the characteristics of CD in the region (especially in Hubei region or even Central China), and establish a CD early diagnosis screening tool that is more suitable for domestic disease characteristics. Summary of the Invention
[0006] In view of the above problems, the present invention provides a red flag index model for early diagnosis of CD and its application, mainly to solve the problem of poor diagnostic efficacy of the current original red flag index.
[0007] To solve the above problems, the present invention adopts the following technical solutions:
[0008] The first aspect of the present invention relates to a red flag index model for early diagnosis of CD and its application in constructing a model for early diagnosis of CD.
[0009] The red flag index model for early diagnosis of CD includes any of the following scoring models:
[0010] Red Flag Index Model One:
[0011]
[0012] Or,
[0013] Red Flag Index Model Two:
[0014]
[0015] Among them, for the scoring of the scoring model, if any item is satisfied, the corresponding score is assigned; otherwise, no score is assigned. The sum of the scores of each item is the Red Flag Index. For the early diagnosis of CD, the risk of getting the disease can be known based on the score of this model, providing a reference for clinical work to better understand the situation of the person being questioned. This model is especially suitable for the population in Central China, and further especially suitable for the population in Hubei Province, and has high sensitivity and specificity.
[0016] The Red Flag Index Model can also be embodied in the form of a model formula: Red Flag Index Model One, Red Flag Index = 3ω 1 + 2ω 2 + 2ω3 + 2ω4 + 1ω5, where: ω 1 is whether there is an unhealed or complex perianal fistula or abscess or perianal lesion other than hemorrhoids, if yes, it is 1, otherwise it is 0; ω 2 is whether the weight has decreased in the past 3 months, if yes, it is 1, otherwise it is 0; ω 3 is whether there has been mild fever in the past 3 months, if yes, it is 1, otherwise it is 0; ω 4 is whether there is mucus stool and / or bloody stool, if yes, it is 1, otherwise it is 0; ω 5 is whether there is a decrease in appetite, if yes, it is 1, otherwise it is 0. Red Flag Index Model Two, Red Flag Index = 2.68ω 1 + 1.54ω 2 + 2.16ω 3 + 1.63ω 4 + 0.74ω 5 , where: ω 1 is whether there is an unhealed or complex perianal fistula or abscess or perianal lesion other than hemorrhoids, if yes, it is 1, otherwise it is 0; ω 2 is whether the weight has decreased in the past 3 months, if yes, it is 1, otherwise it is 0; ω 3 is whether there has been mild fever in the past 3 months, if yes, it is 1, otherwise it is 0; ω 4 is whether there is mucus stool and / or bloody stool, if yes, it is 1, otherwise it is 0; ω 5 is whether there is a decrease in appetite, if yes, it is 1, otherwise it is 0.
[0017] In this aspect, each condition can also be as follows, and any feature in each paragraph can be selected independently:
[0018] One of them is that, if based on the red flag index model 1, when the red flag index ≥ 4, it indicates a high risk of Crohn's disease; when the red flag index < 4, it indicates a low risk or non-high risk of Crohn's disease. If based on the red flag index model 2, when the red flag index ≥ 2.5, it indicates a high risk of Crohn's disease; when the red flag index < 2.5, it indicates a low risk or non-high risk of Crohn's disease.
[0019] Another one is that when using the aforementioned red flag index model 1 or red flag index model 2, it is not required to be limited to specific values. When each value is adjusted proportionally, it should still be within the scope of the present invention. For example, taking the red flag index model 1 as an example, the scores for each item can also be 30, 20, 20, 20, 10 in sequence. At this time, the threshold of the red flag index score is 40. When the red flag index ≥ 40, it indicates a high risk of Crohn's disease.
[0020] Another one is that the judgment of the diseases involved in each index in the scoring model can be based on the clinical judgment criteria as the diagnostic basis, or can be adjusted based on the clinical criteria. For example, for mild fever, the standard can be 37.3℃ < axillary temperature ≤ 38℃, but it is not limited to this, and the clinical criteria can be used as a reference.
[0021] Another one is that the model data source in the research is from Wuhan Union Hospital, Taihe Hospital in Shiyan City, and the First People's Hospital of Jingzhou City. Therefore, it can better reflect the disease characteristics of Hubei region and even Central China region. Thus, the aforementioned red flag index model can effectively analyze the disease characteristics of Central China region and even Hubei region.
[0022] The second aspect of the present invention relates to a model for assisting in the early diagnosis of CD and its application in constructing a product for early diagnosis and analysis of CD.
[0023] The model for assisting in the early diagnosis of CD includes the following modules:
[0024] Information input module: used to input 5 items of data, which are respectively: the first item is whether there is an unhealed or complex perianal fistula, abscess, or perianal lesion other than hemorrhoids; the second item is whether there is a weight loss in the past 3 months; the third item is whether there is a mild fever in the past 3 months; the fourth item is whether there is mucus stool or bloody stool; the fifth item is whether there is a decrease in appetite;
[0025] Data analysis module: used to determine whether the 5 items of data meet the respective conditions. If they meet, points are assigned according to the scoring model; if they do not meet, no points are assigned, and the red flag index is calculated;
[0026] Information comparison module: if the red flag index ≥ the critical value, it is determined that the risk of CD is high.
[0027] In this aspect, each condition can also be as follows, and any feature in each paragraph can be selected independently:
[0028] One of them is that when using the Red Flag Index Model 1, the critical value is 4. When the Red Flag Index ≥ 4, it is determined that the risk of CD disease is high; when the Red Flag Index < 4, it is determined that the risk of CD disease is low or not high risk. When using the Red Flag Index Model 2, the critical value is 2.5. When the Red Flag Index ≥ 2.5, it is determined that the risk of CD disease is high; when the Red Flag Index < 2.5, it is determined that the risk of CD disease is low or not high risk.
[0029] Another one is that in the second item: the standard for weight loss within 3 months is that the weight loss within 3 months ≥ 5% of the usual weight; in the third item: for mild fever, it can be based on 37.3°C < axillary temperature ≤ 38°C as the standard, but it is not limited to this, and the clinical standard can be used as a reference.
[0030] The third one is that based on this, it also provides the application of the model for assisting in the early diagnosis of CD in the construction of CD early diagnosis analysis products. The CD early diagnosis analysis product can be an analysis system. After obtaining 5 specific data of a specific object, the risk situation of the specific object suffering from CD can be known. This model is especially suitable for the early analysis and screening of CD in Central China.
[0031] The fourth one is the application of the model for assisting in the early diagnosis of CD in the construction of a CD risk population screening system. The CD risk population screening system is used to screen the people who are prone to or have suffered from CD in the population.
[0032] The third aspect of the present invention relates to a method for constructing a Red Flag Index scoring model for the early diagnosis of CD, which is characterized by including the following steps: S1, confirm the subjects; S2, based on the original Red Flag Index, combine other symptoms of the subjects and use logistic regression to analyze the correlation between the disease diagnosis of the subjects and each item. Other characteristics include chronic diarrhea, mucus stools and / or bloody stools, loose stools, nausea and vomiting, acid reflux and heartburn, abdominal distension, decreased appetite, etc.; S3, according to the items with the significance p value ≤ 0.05 in the logistic regression analysis, screen out the items related to CD diagnosis; S4, use the partial regression coefficient to assign weights to the obtained items and calculate the Youden index as the threshold of the Red Flag Index scoring model for the early diagnosis of CD. The confirmed subjects are generally limited to the target area, and the diseased population in the target area is selected as the subjects, so as to better construct an analysis model applicable to the relevant area.
[0033] The initial analysis items include digestive tract symptoms such as chronic diarrhea, mucus stools (and) or bloody stools, loose stools, nausea and vomiting, acid reflux and heartburn, abdominal distension, decreased appetite, constipation and bad breath, erythema, nodules, arthritis, etc. and extraintestinal manifestations of IBD. Calculate the OR value to screen out the indicators related to CD diagnosis. Calculating the OR value and so on can be carried out by existing schemes, which will not be elaborated here.
[0034] In the present disclosure, a red flag index model for early diagnosis of CD is provided, which can analyze the risk of CD, estimate whether it is a high-risk of CD through risk scoring, and a model for assisting early diagnosis of CD based on the red flag index model is given, and the CD risk score can be accurately obtained through the analysis of the obtained biological indicators. Description of the Drawings
[0035] Figure 1 For the analysis of data by SPSS 26.0 and the results of logistic regression analysis;
[0036] Figure 2 ROC curve for verifying the quality of the improved red flag index model with original score assignment;
[0037] Figure 3 ROC curve for verifying the quality of the improved red flag index model with weighted score assignment. Detailed Description of the Invention
[0038] In order to better introduce the solution of the present invention, the present invention will be further described below in combination with specific research examples.
[0039] Research Methods
[0040] 1. Design a red flag index questionnaire based on the original red flag index model, including components such as age, gender, past medical history, smoking and alcohol history, original red flag index items, and other symptoms.
[0041] 2. Screen subjects who came to the outpatient department of the Department of Gastroenterology of Wuhan Union Hospital, the First People's Hospital of Jingzhou City, and Taihe Hospital of Shiyan City from August 1, 2023 to August 1, 2024 and had lower gastrointestinal symptoms such as chronic diarrhea or a history of gastrointestinal ulcers indicated by previous endoscopy. Exclusion criteria: already diagnosed with ulcerative colitis; combined with major systemic diseases or shock, coma; mental abnormalities and unable to cooperate; taking drugs with gastrointestinal adverse reactions in the past 3 months.
[0042] 3. The subjects fill in the red flag index questionnaire, and the researcher calculates the red flag index. After the visit, the subjects are regularly followed up for the disease diagnosis results and laboratory, imaging and other examination results.
[0043] 4. Group according to the subject's disease diagnosis (CD / non-CD) and red flag index level (≥8 / <8), and test the correlation between the red flag index and the diagnosis of Crohn's disease.
[0044] 5. Ask the patients whether there are other digestive tract symptoms and extra-intestinal manifestations of IBD when the disease onset (the initial version also included constipation, bad breath, erythema, nodules, arthritis, etc.), calculate the OR value, screen out the indicators related to the diagnosis of CD, and finally determine to combine the original red flag index items and other symptoms existing in the subjects, including chronic diarrhea, mucous stools (and / or) bloody stools, unformed stools, nausea and vomiting, acid reflux and heartburn, abdominal distension, decreased appetite, and use logistic regression to analyze the correlation between the disease diagnosis of the subjects and each item.
[0045] 6. Screen out the items most relevant to the diagnosis of CD and assign weighted values to them, add them up to obtain the modified red flag index, use the ROC curve to establish the threshold and test the test performance of the modified red flag index.
[0046] Result analysis
[0047] 1. A total of 323 initial questionnaires were collected in the study. 30 questionnaires that were not clearly diagnosed or lost to follow-up were excluded, and 293 valid questionnaires were screened out. There were slightly more male than female subjects, and the majority were young and middle-aged people aged 16 - 40.
[0048] 2. Follow up the subjects and count the disease diagnosis. Among them, there were more CD patients, and other diagnoses included irritable bowel syndrome, chronic gastritis, intestinal polyps, intestinal tuberculosis, etc.
[0049] Table 3. Disease diagnosis of the subjects
[0050]
[0051] 3. Count the red flag index, and group according to the disease diagnosis CD / non - CD and the red flag index ≥ 8 / <8, and perform chi - square test, suggesting that the red flag index is related to the onset of Crohn's disease, with significant differences. Sensitivity: 72.50%; Specificity: 68.42%, relative risk RR = 2.25, suggesting that the risk of subjects with RFI ≥ 8 having CD is 2.25 times that of the population with RFI < 8.
[0052] Table 4. Cross - tabulation of red flag index x disease diagnosis
[0053]
[0054] 4. Count other symptoms existing in the subjects at the same time, including chronic diarrhea, mucous stools (and / or) bloody stools, unformed stools, nausea and vomiting, acid reflux and heartburn, abdominal distension, decreased appetite, and the results are expressed as "yes" or "no". Take the disease diagnosis (CD / non - CD) as the dependent variable, and the 8 items of the original red flag index and 7 other symptoms as the independent variables, and conduct binary logistic regression analysis.
[0055] The regression equation is as follows:
[0056] Logit(P|y = CD) = ω 0 + ω 1肛周病变 + ω 2 x IBD亲属 + ω 3 x 体重下降 + ω 4 x 慢性腹痛 + ω 5 x 夜间腹泻
[0057] + ω 6 x 发热 + ω 7 x 餐后腹痛 + w 8 x 排便紧迫 + ω 9 x 腹泻 + ω 10 x 粘液便 + ω 11 x 大便不成形 + ω 12 x 恶心 + ω 13 x 反酸 + ω 14 x 腹胀 + ω 15 x 食欲下降 ;
[0058] where ω 0 is a constant, and ω 1 , ω 2 , ω 3 , ω 4 , ω 5 , ω 6 , ω 7 , ω 8 , ω 9 , ω 10 , ω 11 , ω 12 , ω 13 , ω 14 , ω 15 are the partial regression coefficients corresponding to each independent variable, respectively.
[0059] 5. Analyze the data using SPSS 26.0, and the logistic regression analysis results are shown in Table 5 ( Figure 1 ).
[0060] Corresponding to the regression equation:
[0061] Logit(P|y = CD) = - constant 1.814 + 2.687x 肛周病变 - 0.231x IBD亲属 + 1.542x 体重下降
[0062] +0.155x 慢性腹痛 -0.437x 夜间腹泻 +2.163x 发热 +0.068x 餐后腹痛 -0.044x 排便紧迫 -0.338x 腹泻 +1.631x 粘液便 +0.179x 大便不成形 +0.092x 恶心 -0.366x 反酸 -0.572x 腹胀 +0.743x 食欲下降 ;
[0063] Or assume
[0064] T = - constant 1.814 + 2.687x 肛周病变 -0.231x IBD亲属 +1.542x 体重下降 +0.155x 慢性腹痛
[0065] -0.437x 夜间腹泻 +2.163x 发热 +0.068x 餐后腹痛 -0.044x 排便紧迫 -0.338x 腹泻 +1.631x 粘液便 +0.179x 大便不成形 +0.092x 恶心 -0.366x 反酸 -0.572x 腹胀 +0.743x 食欲下降 ;
[0066] Then Prob(y = CD) = (1 + e T ) / e T .
[0067] 6. In the logistic regression analysis, the significance p - values of the 2nd item "whether there is a first - degree relative with a confirmed IBD", the 4th item "chronic abdominal pain (> 3 months)", the 5th item "nocturnal diarrhea", the 7th item "no abdominal pain 30 - 45 minutes after a meal, especially after eating vegetables", the 8th item "no sense of urgency to defecate", the 9th item "chronic diarrhea", the 11th item "loose stools", the 12th item "nausea and vomiting", the 13th item "acid reflux and heartburn", and the 14th item "abdominal distension" are much greater than 0.05, indicating that these items have little correlation with the diagnosis of CD. Therefore, the above items are excluded. The scoring values of the remaining significantly different items are shown in Table 6 below.
[0068] Table 6. Modified Red Flag Index items and original scores
[0069]
[0070]
[0071] Then, the remaining significantly different items are weighted and scored (the scores are taken as integers) using the partial regression coefficients to serve as the weighted scoring model of the improved Red Flag Index.
[0072] Table 7. Items of the improved Red Flag Index and weighted scoring
[0073]
[0074] 7. Draw the ROC curve to test the quality of the related model in Table 6 ( Figure 2 ), indicating that the overall model quality is good; the area under the curve AUC = 0.885, and the model classification performance is good. Calculate the Youden index, and the corresponding RFI value at its maximum is 2.5, that is, 2.5 is a more appropriate threshold for distinguishing CD / non-CD. When the improved Red Flag Index ≥ 2.5, it indicates high risk of CD, and when the improved Red Flag Index < 2.5, it indicates low risk of CD.
[0075] Draw the ROC curve to test the quality of the related model in Table 7 ( Figure 3 ), indicating that the overall model quality is good; the area under the curve AUC = 0.882, and the model classification performance is good. Calculate the Youden index, and the corresponding RFI value at its maximum is 4, that is, 4 is a more appropriate threshold for distinguishing CD / non-CD. When the improved Red Flag Index ≥ 4, it indicates high risk of CD, and when the improved Red Flag Index < 4, it indicates low risk of CD.
[0076] 8. Calculate the test efficacy of the improved Red Flag Index based on the original scoring in Table 6, sensitivity: 75%; specificity 85.71%; positive predictive value 86.33%; negative predictive value 74.02%. Calculate the test efficacy of the improved Red Flag Index with weighted scoring based on Table 7, sensitivity: 68.75%; specificity 87.97%; positive predictive value 87.30%; negative predictive value 70.06%. The specificity, positive predictive value, and negative predictive value of the two improved index models are all better than those of the original Red Flag Index. Compared with the original model, the improved Red Flag Index is more suitable for the CD disease characteristics in Central China. Applying the improved Red Flag Index can help clinicians identify the risk signals at the initial stage of CD onset and reduce the missed diagnosis and misdiagnosis of CD.
[0077] Table 8. Disease diagnosis cross-tabulation of the improved Red Flag Index based on weighted scoring
[0078]
[0079] The model provided by this study fills the gap in domestic research on the Red Flag Index, improves the original Red Flag Index according to the characteristics of CD in Central China (especially Hubei), and particularly establishes an early diagnosis and screening tool for CD that is more suitable for the disease characteristics in China, especially suitable for the early screening of CD in Hubei and even Central China.
[0080] Those skilled in the art can clearly make various modifications to the above embodiments without departing from the general spirit and concept of the present invention. For the details not elaborated in the text, reference can be made to the prior art. All of them fall within the protection scope of the present invention. The protection scheme of the present invention shall be subject to the claims appended hereto.
Claims
1. The red flag index model for early diagnosis of CD is characterized by: Include any of the following scoring models: Red Flag Index Model 1: or, Red Flag Index Model 2: If any one of the items is met, points will be awarded; otherwise, no points will be awarded. The sum of the scores of each item is the red flag index.
2. The red flag index model for early diagnosis of CD according to claim 1, characterized in that: Based on Red Flag Index Model 1, a red flag index ≥ 4 indicates a high risk of Crohn's disease, and a red flag index < 4 indicates a low risk of Crohn's disease; based on Red Flag Index Model 2, a red flag index ≥ 2.5 indicates a high risk of Crohn's disease, and a red flag index < 2.5 indicates a low risk of Crohn's disease.
3. Use of the red flag index model for early diagnosis of CD in claim 1 or 2 in constructing a model for early diagnosis of CD.
4. A model for assisting early diagnosis of CD, characterized in that: include: Information entry module: used to enter 5 items of data, namely: the first item is whether there is unhealed or complicated perianal fistula or abscess or perianal lesion other than hemorrhoids; the second item is whether there is weight loss in the past 3 months; the third item is whether there is mild fever in the past 3 months; the fourth item is whether there is mucus or blood in the stool; the fifth item is whether there is decreased appetite; Data analysis module: used to determine whether the five data items meet various conditions. If they meet the conditions, they will be scored according to the scoring model. If they do not meet the conditions, no points will be assigned, and the red flag index will be calculated. Information comparison module: If the red flag index ≥ the critical value, the patient is judged to be at high risk of CD.
5. The model for assisting early diagnosis of CD according to claim 4, characterized in that: When the Red Flag Index Model 1 is used, the critical value is 4. When the Red Flag Index ≥ 4, it is judged as a high risk of CD, and the Red Flag Index < 4 indicates a low risk of Crohn's disease. When the Red Flag Index Model 2 is used, the critical value is 2.
5. When the Red Flag Index ≥ 2.5, it is judged as a high risk of CD, and the Red Flag Index < 2.5 indicates a low risk of Crohn's disease.
6. The model for assisting early diagnosis of CD according to claim 4, characterized in that: In the second item, the standard for weight loss within 3 months is a weight loss of ≥ 5% of the usual weight within 3 months.
7. Use of any one of the models for assisting early diagnosis of CD according to claims 4 to 6 in constructing an analytical product for early diagnosis of CD.
8. Use of any one of the models for assisting early diagnosis of CD according to claims 4 to 6 in constructing a system for screening people at risk of CD.
9. A method for constructing a red flag index scoring model for early diagnosis of CD, characterized in that: The steps include: S1. Confirm the subjects; S2. Based on the original red flag index and combined with other symptoms of the subjects, logistic regression was used to analyze the correlation between the subject's disease diagnosis and each item. Other characteristics included chronic diarrhea, mucus and / or bloody stools, loose stools, nausea and vomiting, acid reflux, abdominal distension, and decreased appetite. S3. Items related to CD diagnosis were screened out based on items with significant p value ≤ 0.05 in logistic regression analysis; S4. Use partial regression coefficients to weight and score the items and calculate the Youden index as the threshold of the red flag index scoring model for early diagnosis of CD.
10. The method for constructing a red flag index scoring model for early diagnosis of CD according to claim 9, characterized in that: Based on the original red flag index, the analysis items involving other symptoms of the subjects, including gastrointestinal symptoms and extraintestinal manifestations of IBD, were combined to calculate the OR value to screen out indicators related to the diagnosis of CD.