Regional new tuberculosis drug resistance dynamic monitoring and early warning algorithm

Abstract

The invention relates to a dynamic monitoring and early warning algorithm for drug resistance of new regional tuberculosis. The method comprises the following steps: firstly, selecting a pulmonary tuberculosis drug resistance type needing to be monitored and a corresponding drug; and secondly, determining a time interval of traditional measurement on the monitoring data, and processing a monitoring result according to the determined time interval. And finally, giving a standard of a drug-resistant risk level, determining an early warning threshold value, carrying out statistical test and time sequence analysis on the uploaded data, judging whether a drug-resistant tuberculosis prevalence risk exists or not, and carrying out dynamic assessment on the risk level if the risk exists.

Description

Technical Field

[0001] The present invention relates to the technical field of infectious disease control, and in particular to a dynamic monitoring and early warning algorithm for drug resistance of newly-emerged tuberculosis in a region. Background Art

[0002] Drug-resistant tuberculosis is resistant to anti-tuberculosis drugs. Currently, the number of anti-tuberculosis Western medicines used in clinical practice is limited, and few new anti-tuberculosis drugs have been put into clinical treatment in recent decades. However, the proportion of drug-resistant tuberculosis continues to increase, which has increased the burden of tuberculosis treatment. Drug-resistant tuberculosis is divided into primary resistance and secondary resistance. Secondary resistance tuberculosis is gradually generated during the medication process. This type of drug-resistant tuberculosis can be avoided to a certain extent through standardized medication. Primary drug-resistant tuberculosis is when the tuberculosis bacteria infected by the patient are drug-resistant bacteria. Monitoring of the population receiving initial treatment for tuberculosis will indicate whether there is an outbreak of drug-resistant tuberculosis in the region. At present, there is no monitoring method in Jilin Province to monitor and warn of primary drug-resistant tuberculosis.

[0003] 1. Technical issues to be solved

[0004] In view of the deficiencies of existing research, the present invention provides a dynamic monitoring and early warning algorithm for new tuberculosis drug resistance in a region, which solves the problems raised in the background technology.

[0005] (II) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a dynamic monitoring and early warning algorithm for new tuberculosis drug resistance in a region, the steps include the following:

[0007] S1: Determine the drugs for sputum culture drug susceptibility testing and the types of tuberculosis drug resistance that need to be monitored;

[0008] S2: Perform sputum culture drug sensitivity test on newly admitted pulmonary tuberculosis patients to determine the time interval for data uploading;

[0009] S3: Give the drug resistance risk level standard, determine the warning threshold, and perform statistical tests on the uploaded data to determine whether there is a risk of drug-resistant tuberculosis epidemic;

[0010] S4: If the results indicate a risk, the risk level will be dynamically assessed; if the results are normal, daily monitoring will be conducted.

[0011] 1. According to the algorithm for dynamic monitoring and early warning of drug resistance of newly-emerged tuberculosis in the region of claim 1, it is characterized in that: the drugs for drug sensitivity test in S1 are: streptomycin, isoniazid, rifampicin, ethambutol, levofloxacin, moxifloxacin, linezolid, amikacin, capreomycin, prothionamide, pasoniazid, para-aminosalicylic acid, cycloserine, rifabutin, kanamycin, clofazimine. The four types of drug resistance are: single drug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to any one of isoniazid, rifampicin, ethambutol, and streptomycin. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to two or more of isoniazid, rifampicin, ethambutol, and streptomycin, except for resistance to isoniazid and rifampicin at the same time. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to at least isoniazid and rifampicin. Extensive drug resistance: The Mycobacterium tuberculosis infecting tuberculosis patients is not only resistant to rifampicin, but also resistant to any of levofloxacin, moxifloxacin and linezolid.

[0012] 2. According to the dynamic monitoring and early warning algorithm for drug resistance of newly-emerged tuberculosis in the region described in claim 1, it is characterized in that: in described step S2, the patient with newly-treated pulmonary tuberculosis is one of the following conditions: a patient who has never been treated with anti-tuberculosis drugs for tuberculosis; a patient who is taking medication according to the standard chemotherapy regimen but has not completed a full course of treatment; a patient who has not completed 1 month of irregular chemotherapy. The sputum culture drug sensitivity test is: microplate drug sensitivity test technology, which is a ratio method based on the sensitivity of the drug measured in a liquid culture medium. The data upload time interval is 30 days.

[0013] 3. The algorithm for dynamic monitoring and early warning of drug resistance of newly diagnosed tuberculosis in a region according to claim 1 is characterized in that: the standard of drug resistance risk level and the risk judgment method in step S3 are: the drug sensitivity data of the nth month is defined as Where k is the drug resistance type, and its values ​​are as follows: single drug resistance is 1, multiple drug resistance is 2, multidrug resistance is 3, and extensive drug resistance is 4.

[0014] If there are any two or more types of resistance The data is organized in the following format:

[0015]

[0016] Among them: A ij is the actual number of cases, i,j=1,2; T ij is the theoretical number of cases, i,j=1,2; n Ri is the total number of rows, i=1,2; n Cj is the total number of the column, j=1,2; n is the total number of cases.

[0017] A statistical test was performed on the actual number of cases and the theoretical number of cases. The statistical test formula is as follows:

[0018]

[0019] The test is a two-sided test, α is 0.05, and the χ 2 The distribution table shows that the boundary value is 3.96. 2 When the value is greater than 3.96, it means that the difference is statistically significant, indicating the possibility of prevalence of drug-resistant tuberculosis.

[0020] Subsequently, the data of the first n-1 months are analyzed in time series to predict the drug resistance data of the nth month. The formula is as follows:

[0021]

[0022] in, Predicted value at time point n, c: constant term, φ1, φ2, …φ p : model parameters, p: model order, ∈ n : Noise error term.

[0023] Finally, the predicted value With actual value Perform a difference test and determine whether it is statistically significant. The formula is as follows:

[0024]

[0025] in, and S: standard deviation of the drug sensitivity data in the previous n months. v: degrees of freedom. 0.05，v >t, then P<0.05, the difference is statistically significant; if t 0.05，v >t, P<0.05, and the difference was not statistically significant.

[0026] If both statistical test results show statistical significance, the third-level risk of drug resistance in the area is triggered. If only one analysis shows statistical significance, it triggers the potential epidemic stage. If both analyses show no statistical significance, daily monitoring will continue. After triggering the third-level warning, the drug sensitivity data of the two consecutive months will continue to be compared. If any two or more types of drug resistance appear three times in a row, If the risk is statistically significant, the risk level is raised to level 2, and the risk level is raised to level 1. If there is no significant upward trend in the drug sensitivity data for six consecutive times in the risk level, the risk level is lifted.

[0027] (III) Beneficial effects

[0028] The present invention provides a dynamic monitoring and early warning algorithm for new tuberculosis drug resistance in a region.

[0029] Beneficial effects:

[0030] The present invention mainly considers the tuberculosis initial treatment population as the monitoring point, and obtains monitoring data through sputum culture drug sensitivity test. According to the unified monitoring scheme, the monitoring data is uploaded regularly, and the data analysis and threshold theory are used to judge whether there is an explosive epidemic of drug-resistant tuberculosis. The new tuberculosis drug resistance monitoring method with the initial treatment population as the monitoring point makes the monitoring system simpler and more convenient, and saves manpower and material resources. When the monitoring reaches the early warning value, the explosive epidemic of drug-resistant tuberculosis can be promptly and quickly prompted. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is the actual uploaded data diagram of drug sensitivity of a hospital in Province A of the present invention;

[0032] Figure 2 The drug sensitivity data of a hospital in Province A of the present invention is 2 Result graph; DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0034] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0035] See also Figure 1-2 , the algorithm for dynamic monitoring and early warning of new tuberculosis drug resistance in a region includes the following steps:

[0036] S1: Determine the drugs for sputum culture drug susceptibility testing and the types of tuberculosis drug resistance that need to be monitored;

[0037] S2: Perform sputum culture drug sensitivity test on newly admitted pulmonary tuberculosis patients to determine the time interval for data uploading;

[0038] S3: Give the drug resistance risk level standard, determine the warning threshold, and perform statistical tests on the uploaded data to determine whether there is a risk of drug-resistant tuberculosis epidemic;

[0039] S4: If the results indicate a risk, the risk level will be dynamically assessed; if the results are normal, daily monitoring will be conducted.

[0040] 1. According to the algorithm for dynamic monitoring and early warning of drug resistance of newly-emerged tuberculosis in the region of claim 1, it is characterized in that: the drugs for drug sensitivity test in S1 are: streptomycin, isoniazid, rifampicin, ethambutol, levofloxacin, moxifloxacin, linezolid, amikacin, capreomycin, prothionamide, pasoniazid, para-aminosalicylic acid, cycloserine, rifabutin, kanamycin, clofazimine. The four types of drug resistance are: single drug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to any one of isoniazid, rifampicin, ethambutol, and streptomycin. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to two or more of isoniazid, rifampicin, ethambutol, and streptomycin, except for resistance to isoniazid and rifampicin at the same time. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to at least isoniazid and rifampicin. Extensive drug resistance: The Mycobacterium tuberculosis infecting tuberculosis patients is not only resistant to rifampicin, but also resistant to any of levofloxacin, moxifloxacin and linezolid.

[0041] 2. According to the dynamic monitoring and early warning algorithm for drug resistance of newly-emerged tuberculosis in the region described in claim 1, it is characterized in that: in described step S2, the patient with newly-treated pulmonary tuberculosis is one of the following conditions: a patient who has never been treated with anti-tuberculosis drugs for tuberculosis; a patient who is taking medication according to the standard chemotherapy regimen but has not completed a full course of treatment; a patient who has not completed 1 month of irregular chemotherapy. The sputum culture drug sensitivity test is: microplate drug sensitivity test technology, which is a ratio method based on the sensitivity of the drug measured in a liquid culture medium. The data upload time interval is 30 days.

[0042] 3. The algorithm for dynamic monitoring and early warning of drug resistance of newly diagnosed tuberculosis in a region according to claim 1 is characterized in that: the standard of drug resistance risk level and the risk judgment method in step S3 are: the drug sensitivity data of the nth month is defined as Where k is the drug resistance type, and its values ​​are as follows: single drug resistance is 1, multiple drug resistance is 2, multidrug resistance is 3, and extensive drug resistance is 4.

[0043] If there are any two or more types of resistance The data needs to be organized in the following format:

[0044]

[0045] Among them: A ij is the actual number of cases, i,j=1,2; T ij is the theoretical number of cases, i,j=1,2; n Ri is the total number of rows, i=1,2; n Cj is the total number of the column, j=1,2; n is the total number of cases.

[0046] A statistical test was performed on the actual number of cases and the theoretical number of cases. The statistical test formula is as follows:

[0047]

[0048] The test is a two-sided test, α is 0.05, and the χ 2 The distribution table shows that the boundary value is 3.96. 2 When the value is greater than 3.96, it means that the difference is statistically significant, indicating the possibility of prevalence of drug-resistant tuberculosis.

[0049] Subsequently, the data of the first n-1 months are analyzed in time series to predict the drug resistance data of the nth month. The formula is as follows:

[0050]

[0051] in, Predicted value at time point n, c: constant term, φ1, φ2, …φ p : model parameters, p: model order, ∈ n : Noise error term.

[0052] Finally, the predicted value With actual value Perform a difference test and determine whether it is statistically significant. The formula is as follows:

[0053]

[0054] in, and S: standard deviation of the drug sensitivity data in the previous n months. v: degrees of freedom. 0.05，v >t, then P<0.05, the difference is statistically significant; if t 0.05，v >t, P<0.05, and the difference was not statistically significant.

[0055] If both statistical test results show statistical significance, the third-level risk of drug resistance in the area is triggered. If only one analysis shows statistical significance, it triggers the potential epidemic stage. If both analyses show no statistical significance, daily monitoring will continue. After triggering the third-level warning, the drug sensitivity data of the two consecutive months will continue to be compared. If any two or more types of drug resistance appear three times in a row, If the risk is statistically significant, the risk level is raised to level 2, and the risk level is raised to level 1. If there is no significant upward trend in the drug sensitivity data for six consecutive times in the risk level, the risk level is lifted.

[0056] Implementation examples:

[0057] Taking the drug sensitivity data of a hospital in Province A as an example, the specific implementation steps are as follows:

[0058] S1: Confirm that the drugs for sputum culture drug sensitivity test are: streptomycin, isoniazid, rifampicin, ethambutol, levofloxacin, moxifloxacin, linezolid, amikacin, capreomycin, prothionamide, pasoniazid, aminosalicylic acid, cycloserine, rifabutin, kanamycin, clofazimine. The types of drug resistance of pulmonary tuberculosis that need to be monitored are: Single drug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to any one of isoniazid, rifampicin, ethambutol, and streptomycin. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to two or more of isoniazid, rifampicin, ethambutol, and streptomycin, except for isoniazid and rifampicin. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to at least isoniazid and rifampicin. Extensive drug resistance: Mycobacterium tuberculosis infected in tuberculosis patients is not only resistant to rifampicin, but also resistant to any of levofloxacin, moxifloxacin and linezolid).

[0059] S2: Perform sputum culture drug sensitivity test on newly admitted pulmonary tuberculosis patients to determine the time interval for data uploading;

[0060] Patients with newly diagnosed pulmonary tuberculosis are those who meet one of the following conditions: patients who have never used anti-tuberculosis drugs for tuberculosis; patients who are taking standard chemotherapy regimens regularly but have not completed the full course of treatment; patients who have not received regular chemotherapy for 1 month. Sputum culture drug sensitivity test is: microplate drug sensitivity test technology, which is a ratio method based on liquid culture medium to determine drug sensitivity. The interval for uploading monitoring data is 30 days.

[0061] S3: Provide the drug resistance risk level standard, determine the warning threshold, and conduct statistical tests on the uploaded data to determine whether there is a risk of drug-resistant tuberculosis epidemic.

[0062] The calculation is based on the drug resistance data of a hospital in Province A in March and April.

[0063] like Figure 1 The total number of cases in March was 46, of which single-drug resistant Multidrug resistance Multidrug resistance and quasi-extensively drug-resistant The data were 5, 13, 7 and 0 respectively; the drug resistance data in April were Q4 = 95. By calculating the corresponding differences, it can be seen that the difference between single-drug resistance and multi-drug resistance data is greater than 0, and the difference between multi-drug resistance and quasi-extensive drug resistance is less than 0, indicating that the number of single-drug resistance and multi-drug resistance cases has increased, while the number of multi-drug resistance and quasi-extensive drug resistance cases has decreased. Therefore, it is necessary to conduct statistical tests on the single-drug resistance and multi-drug resistance data in March and April to determine whether there is a risk of epidemic of drug-resistant tuberculosis.

[0064] First, organize the single drug resistance data in the following format:

[0065]

[0066] And calculate its χ 2 Value, χ 2 The difference is 4.919>3.96, indicating that the difference is statistically significant. Subsequently, the single drug resistance data in April was predicted based on the single drug resistance data in the first three months, and the predicted value of the single drug resistance data in April was 3. A difference test was conducted between the predicted value and the actual value of the single drug resistance data in April, t=14.795, t 0.05，4 >t, P<0.05, indicating that the difference was statistically significant.

[0067] Multidrug resistance data are organized as follows

[0068]

[0069] Calculate its χ 2 The value is 4.673>3.96, which means that the difference is statistically significant. The statistical test showed that t = 5.974, t 0.05，4 >t, P<0.05, and the difference is statistically significant.

[0070] According to the drug resistance risk level standard, the region is now at a level 3 drug resistance risk.

[0071] Monthly drug susceptibility data 2 result Figure 2 As shown. After that, the monthly drug sensitivity data were compared with the drug sensitivity data of the previous month and statistically tested. After six consecutive comparisons, there was no significant upward trend in the drug sensitivity data, that is, there was still no significant upward trend in the drug sensitivity data in October. At this time, the risk level was lifted and daily monitoring was resumed.

[0072] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0073] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. Dynamic monitoring and early warning algorithm for new tuberculosis drug resistance in a region, characterized by: The steps include: S1: Determine the drugs for sputum culture drug susceptibility testing and the types of tuberculosis drug resistance that need to be monitored; S2: Perform sputum culture drug sensitivity test on newly admitted pulmonary tuberculosis patients to determine the time interval for data uploading; S3: Give the drug resistance risk level standard, determine the warning threshold, and perform statistical tests on the uploaded data to determine whether there is a risk of drug-resistant tuberculosis epidemic; S4: If the results indicate a risk, the risk level will be dynamically assessed; if the results are normal, daily monitoring will be conducted.

2. The algorithm for dynamic monitoring and early warning of new tuberculosis drug resistance in a region according to claim 1, characterized in that: The drugs for drug sensitivity test in S1 are: streptomycin, isoniazid, rifampicin, ethambutol, levofloxacin, moxifloxacin, linezolid, amikacin, capreomycin, prothionamide, pasoniazid, aminosalicylic acid, cycloserine, rifabutin, kanamycin, clofazimine. The four types of drug resistance are: single resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to any one of isoniazid, rifampicin, ethambutol, and streptomycin. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to two or more of isoniazid, rifampicin, ethambutol, and streptomycin, except for isoniazid and rifampicin resistance at the same time. Multidrug resistance: Mycobacterium tuberculosis infected by tuberculosis patients is resistant to at least isoniazid and rifampicin. Extensive drug resistance: The Mycobacterium tuberculosis infecting tuberculosis patients is not only resistant to rifampicin, but also resistant to any of levofloxacin, moxifloxacin and linezolid.

3. The algorithm for dynamic monitoring and early warning of new tuberculosis drug resistance in a region according to claim 1, characterized in that: In step S2, the newly diagnosed pulmonary tuberculosis patient is one of the following: a patient who has never been treated with anti-tuberculosis drugs for tuberculosis; a patient who is taking a standard chemotherapy regimen and has not completed the full course of treatment; a patient who has not received regular chemotherapy for 1 month. The sputum culture drug sensitivity test is a microplate drug sensitivity test technology, which is a ratio method based on liquid culture medium to determine the sensitivity of the drug. The data upload interval is 30 days.

4. The algorithm for dynamic monitoring and early warning of new tuberculosis drug resistance in a region according to claim 1 is characterized by: The standard and risk judgment method of drug resistance risk level in step S3 are as follows: the drug sensitivity data of the nth month is defined as Where k is the drug resistance type, and its values ​​are as follows: single drug resistance is 1, multiple drug resistance is 2, multidrug resistance is 3, and extensive drug resistance is 4. If there are any two or more types of resistance The data is organized in the following format: Among them: A ij is the actual number of cases, i,j=1,2; T ij is the theoretical number of cases, i,j=1,2; n Ri is the total number of rows, i=1,2; n Cj is the total number of the column, j=1,2; n is the total number of cases. A statistical test was performed on the actual number of cases and the theoretical number of cases. The statistical test formula is as follows: The test is a two-sided test, α is 0.05, and the χ 2 The distribution table shows that the boundary value is 3.

96. 2 When the value is greater than 3.96, it means that the difference is statistically significant, indicating the possibility of prevalence of drug-resistant tuberculosis. Subsequently, the data of the first n-1 months are analyzed in time series to predict the drug resistance data of the nth month. The formula is as follows: in, Predicted value at time point n, c: constant term, φ1, φ2, …φ p : model parameters, p: model order, ∈ n : Noise error term. Finally, the predicted value With actual value Perform a difference test and determine whether it is statistically significant. The formula is as follows: in, and S: standard deviation of the drug sensitivity data in the previous n months. v: degrees of freedom. 0.05，v >t, then P<0.05, the difference is statistically significant; if t 0.05，v >t, P<0.05, and the difference was not statistically significant. If both statistical test results show statistical significance, the third-level risk of drug resistance in the area is triggered. If only one analysis shows statistical significance, it triggers the potential epidemic stage. If both analyses show no statistical significance, daily monitoring will continue. After triggering the third-level warning, the drug sensitivity data of the two consecutive months will continue to be compared. If any two or more types of drug resistance appear three times in a row, If the risk is statistically significant, the risk level is raised to level 2, and the risk level is raised to level 1. If there is no significant upward trend in the drug sensitivity data for six consecutive times in the risk level, the risk level is lifted.

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