CN116052899A

CN116052899A - Microbial epidemiology and drug sensitivity analysis system

Info

Publication number: CN116052899A
Application number: CN202211525463.0A
Authority: CN
Inventors: 赵锋
Original assignee: Hangzhou Manwei Smart Technology Co ltd
Current assignee: Hangzhou Manwei Smart Technology Co ltd
Priority date: 2022-11-30
Filing date: 2022-11-30
Publication date: 2023-05-02

Abstract

The invention discloses a microbial epidemiology and drug sensitivity analysis system, which comprises: the data importing and adjusting module is used for acquiring the drug sensitivity data of the patient and storing the drug sensitivity data into the drug sensitivity database; the drug sensitive standard code database configuration module is used for constructing a drug sensitive standard code database; the data analysis module is used for calling the patient drug sensitivity data from the drug sensitivity database and processing the patient drug sensitivity data according to the user requirements to obtain patient sub drug sensitivity data; and carrying out target demand calculation on the patient sub-drug sensitivity data according to the standard of the drug sensitivity standard code database to obtain a target value, processing the target value to form a chart, and displaying the chart.

Description

Microbial epidemiology and drug sensitivity analysis system

Technical Field

The invention relates to the technical field of drug sensitivity analysis and statistics, in particular to a microbial epidemiology and drug sensitivity analysis system.

Background

With the increasing prominence of bacterial resistance, the clinical application of antibacterial drugs is getting more and more important, who is free software developed and recommended by the world health organization using visual basic language for bacterial resistance monitoring, and currently, the latest version is WHONET 22.6.15. The software or the system adopts a universal code and file format to assist each clinical laboratory in analyzing, monitoring and processing local drug resistance monitoring data, and integrates the information into a drug resistance monitoring data file of the whole country or even the whole world so as to promote the sharing of resources.

However, only the drug resistance monitoring data file in DBF and excel formats can be exported by utilizing WHONET, and then other data platforms (such as Microsoft-OFFICE or other software) are adopted for personal requirement analysis. Because there is no corresponding/easy-to-debug computing logic, not only is it time consuming to personalize the computation to the needs, but the establishment of the "deduplication mode and standard" that is considered for the computation also requires a significant amount of effort.

Disclosure of Invention

The invention aims to provide a microbial epidemiology and drug sensitivity analysis system which can rapidly, efficiently and safely analyze, acquire insight and manage microbial drug sensitivity data, provide basis for the treatment of an empirical antibacterial drug and guide clinical science and accurate medication.

In order to achieve the above object, the present invention is realized by the following technical scheme:

a microbial epidemiology and drug sensitivity analysis system, comprising: the data importing and adjusting module 200 is used for acquiring the drug sensitivity data of the patient and storing the drug sensitivity data in the drug sensitivity database.

The drug sensitive standard code database configuration module 300 is used for constructing a drug sensitive standard code database;

the data analysis module 500 calls the patient drug sensitive data from the drug sensitive database, and processes the patient drug sensitive data according to the user requirement to obtain the patient sub drug sensitive data.

And carrying out target demand calculation on the patient sub-drug sensitivity data according to the standard of the drug sensitivity standard code database to obtain a target value, processing the target value to form a chart, and displaying the chart.

Optionally, the patient drug susceptibility data includes data in DBF format and excel format derived from who.

Optionally, the data importing and adjusting module 200 is specifically configured to generate an intermediate file, where a first row in each column of the intermediate file is provided with an attribute field, and the intermediate file includes a fixed column and a dynamic column; identifying the patient drug sensitive data, dividing the patient drug sensitive data into a fixed column and a dynamic column, matching the dynamic column of the patient drug sensitive data with the dynamic column of the intermediate file, and adding the dynamic column of the patient drug sensitive data into the dynamic column of the intermediate file to realize the storage of the patient drug sensitive data.

Optionally, the patient drug sensitive data originally stored in the patient drug sensitive database is referred to as history data, and when additional patient drug sensitive data needs to be added to the history data, the data import and adjustment module 200 is further configured to obtain an attribute field of a dynamic column of the additional patient drug sensitive data. Adding a dynamic column with the same attribute field as the historical data into a corresponding dynamic column of the historical data, adding a dynamic column with a different attribute field as the historical data into the historical data, and adding the dynamic column data of the additional patient drug sensitive data into the newly added dynamic column; the data import and adjustment module 200 is also used to delete the history data and add new patient drug sensitive data to the intermediate file.

Optionally, the attribute field includes one or any combination of a patient identification code, a name, a gender, an age, a department, a sample number, a date of examination, a sample type, a pathogen name, gram, and drug sensitive test result data.

Optionally, the drug sensitive standard code database stores international standard microorganism names, classifications and codes, specimen types and codes, strain names, classifications and codes, antimicrobial drug names and codes, detection methods and codes and break-over points of antimicrobial drugs.

Optionally, the data analysis module 500 is specifically configured to: the user requirements comprise screening conditions and deduplication conditions;

screening and de-duplicating the patient drug sensitive data in the drug sensitive database according to the screening condition and the de-duplication condition to obtain the patient sub-drug sensitive data, and storing the patient sub-drug sensitive data in a sub-database;

grabbing data in the drug sensitivity standard code database according to the microorganisms, the detection method and the specimen types in the patient sub drug sensitivity data, and constructing a data table corresponding to the break points;

selecting a target demand, screening and counting the patient sub-drug sensitive data in the sub-database according to the standard of the target demand in the data table corresponding to the break point to obtain the target value corresponding to the target demand, processing the target value to form a chart corresponding to the target demand, and displaying the chart.

Optionally, the target requirements include the following: clinical isolated strain distribution, ESBL analysis of klebsiella pneumoniae and escherichia coli, main carbapenem drug-resistant strain and MRSA drug-resistant transition analysis, sample quantity analysis of a department of examination, drug-resistant rate or sensitivity rate comparison analysis of specific strains, high-hazard high-drug-resistant strain analysis in different departments, sample-like distribution of clinical isolated bacteria and drug-resistant and sensitivity comparison analysis of different departments or different times of the same department of isolated strains.

Optionally, the method further comprises: and the application database building module 400 is used for storing the chart, downloading the chart, deriving the chart and sharing the chart.

Optionally, the method further comprises: the login and rights management module 100 is used for registering a user and verifying account information of the user.

Optionally, the method further comprises: the customer service management module 600 is used for discovering abnormal, error reporting and other abnormal operation conditions, and popping up the customer contact module.

The invention has at least one of the following advantages:

the system can analyze data from different dimensionalities, different ways of the deduplication standard, more complete data and higher data analysis speed and efficiency.

Along with the increasing prominence of bacterial drug resistance, the clinical application of antibacterial drugs is more and more emphasized, and an epidemiological and drug sensitivity analysis system (hereinafter referred to as the system) is used as an interpretation analysis tool of drug sensitivity test results, so that the system can help hospitals and departments to know historical data and give drug resistance and drug sensitivity data to guide medication.

Drawings

FIG. 1 is a block diagram of a microbiological epidemiology and drug sensitivity analysis system according to one embodiment of the present invention;

FIG. 2 is a schematic workflow diagram of a microbiological epidemiology and drug sensitivity analysis system according to an embodiment of the invention;

FIG. 3 is a graph showing the distribution of clinical isolates as desired according to one embodiment of the present invention;

FIG. 4 is a graph showing ESBL analysis of Klebsiella pneumoniae and Escherichia coli as target requirements according to an embodiment of the present invention;

FIG. 5 is a graph showing the target requirement of Klebsiella pneumoniae for imipenem and meropenem drug resistance transition analysis according to an embodiment of the present invention;

FIG. 6 is a diagram showing the target demand for analysis of the sample amount in the department of clinical laboratory according to an embodiment of the present invention;

FIG. 7 is a graph showing the comparison analysis of the target requirement for specific strain drug resistance or sensitivity according to an embodiment of the present invention;

FIG. 8 is a graph showing the analysis of drug-resistant strains with high risk and target requirement in different departments according to an embodiment of the present invention;

FIG. 9 is a graph showing the distribution of a sample-like substance of a clinical isolate according to an embodiment of the present invention;

FIG. 10 is a graph showing the comparison of drug resistance and sensitivity of isolated strains at different departments or at different times in the same department according to an embodiment of the present invention;

fig. 11 is a schematic interface diagram when the analysis results in fig. 3 to 10 are all displayed simultaneously.

Detailed Description

The following provides a further detailed description of a microbiological epidemiology and drug sensitivity analysis system in accordance with the present invention, taken in conjunction with the accompanying drawings and detailed description. The advantages and features of the present invention will become more apparent from the following description. It should be noted that the drawings are in a very simplified form and are all to a non-precise scale, merely for the purpose of facilitating and clearly aiding in the description of embodiments of the invention. For a better understanding of the invention with objects, features and advantages, refer to the drawings. It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for illustration purposes only and should not be construed as limiting the invention to the extent that any modifications, changes in the proportions, or adjustments of the sizes of structures, proportions, or otherwise, used in the practice of the invention, are included in the spirit and scope of the invention which is otherwise, without departing from the spirit or essential characteristics thereof.

Referring to fig. 1 and 2, the system for epidemiology and drug sensitivity analysis of microorganisms according to the present embodiment includes: the login and rights management module 100 is used for registering a user and verifying account information of the user. The embodiment can carry out software authorization use in a single account management mode.

The embodiment can activate (1) the personal PC stand-alone activation (2) the cloud activation in the following 2 modes, and can activate the whole system only after confirming the information of the authorized account.

Specifically, the login and rights management module 100 has a login verification system: the integrated micro-letter public number platform ensures the information security of the account by using the micro-letter user identity authorization, mobile phone number (short message verification) and account password setting mode; account activation and verification requires that the user can be activated for use after verification of rights by the cloud system.

The login and rights management module 100 performs personal PC stand-alone activation: the system captures the unique identification code of the personal PC computer system by matching an account with a computer matching mode, and generates a two-dimensional code of the unique identification of the computer after encryption (encryption, decryption and verification are carried out in an MD5 salt mode) along with the mobile phone with authenticated starting page. Scanning the two-dimensional code through WeChat to enter a cloud system; the cloud system recognizes the information in the two-dimension code, and returns an activation verification code to be input and then the single account is activated for use after the information is matched by the same encryption mode (PC and background) through PC information and user authorization information (such as a software purchasing system, background authorization or other purposes). In addition, the account password setting and the periodic activation authority management mode are simultaneously established in the module, so that the safety and the convenience in use are ensured.

When the login and rights management module 100 performs cloud server activation, the following steps are performed: the cloud system can be activated only by confirming the information of the authorized account by using the information of the authorized account, the verification of the mobile phone and the setting mode of the account password, if the verification code information is correct, the system activation is completed, and the import and adjustment module 200 included in the embodiment of the data can be accessed after the activation; otherwise, the verification code error information is prompted, and the customer service system module 600 included in the embodiment is skipped. The customer service system module 600 is used for discovering abnormal, error reporting and other abnormal operation conditions, and popping up a customer contact module to help solve the problems of system login, activation and the like, and improve user experience.

The data importing and adjusting module 200 in this embodiment is configured to obtain the drug sensitivity data of the patient, and store the drug sensitivity data in the drug sensitivity database.

Specifically, the data importing and adjusting module 200 is specifically configured to generate an intermediate file, where a first row in each column of the intermediate file is provided with an attribute field, and the intermediate file includes a fixed column and a dynamic column; identifying the patient drug sensitive data, dividing the patient drug sensitive data into a fixed column and a dynamic column, matching the dynamic column of the patient drug sensitive data with the dynamic column of the intermediate file, and adding the dynamic column of the patient drug sensitive data into the dynamic column of the intermediate file to realize the storage of the patient drug sensitive data.

In this embodiment, the patient sensitization data includes data in DBF format and excel format derived from who. That is, each medical institution currently develops and recommends software for bacterial resistance monitoring according to the WHOET (world health organization using visual basic language), and the latest version of the present invention is WHONET 22.6.15) structure derived "patient drug sensitive data" (in this embodiment, patient drug sensitive data specifically refers to sample post-detection data of a patient, and each piece of data may include, but is not limited to, patient information, sample type, detection time, detection method, department of delivery, detection result, etc.), which have universal codes and file formats.

The patient drug sensitive data may be in a large number of forms with (1) different data formats: database data in excel and dbf formats; (2) different time periods; (3) different hospital sources; (4) data from different sources.

In this embodiment, a local data interaction mode is adopted to read patient drug sensitive data into a memory, and attribute fields used for analysis are specified according to fields of original data (patient drug sensitive data before the memory is not imported).

The data importing and adjusting module 200 may generate an intermediate file in CSV format when importing a database for construction, generate a fixed column and a dynamic column, and insert the newly added dynamic column into the intermediate file by comparing the dynamic columns of the data fields imported each time, so as to maximally accommodate the drug sensitivity analysis results of different periods, different hospitals, different inspection methods, etc.

In this embodiment, the patient drug sensitive data originally stored in the patient drug sensitive database is referred to as history data, and when additional patient drug sensitive data needs to be added to the history data, the data import and adjustment module 200 is further configured to obtain an attribute field of a dynamic column of the additional patient drug sensitive data. Adding a dynamic column with the same attribute field as the historical data into a corresponding dynamic column of the historical data, adding a dynamic column with a different attribute field as the historical data into the historical data, and adding the dynamic column data of the additional patient drug sensitive data into the newly added dynamic column; the data import and adjustment module 200 is also used to delete the history data and add new patient drug sensitive data to the intermediate file.

It will be appreciated that in this embodiment, the attribute field includes at least one of a patient identification code, a name, a sex, an age, a department of administration, a sample number, a date of examination, a sample type, a pathogen name, gram, and drug sensitive test result data, or any combination thereof.

Therefore, in the present embodiment, the data import and adjustment module 200 can freely overwrite and append data. When the addition is selected, the data import and adjustment module 200 adds the data of the current import to the data of the previous import. If the imported data has a new field (newly added attribute field column), the system defaults to add the new field, and takes the "null" value at the position corresponding to the new field column in the history data, i.e. no data exists in the history data corresponding to the new field column. And the system can append imported data multiple times.

Namely, the data import and adjustment module 200 can implement automatic matching and import of data: by adopting a default attribute field matching mode, searching for a file attribute field of the drug sensitive data of the imported patient, and automatically configuring corresponding fields (corresponding to the matched patient identification code, name, sex, age, department, sample number, inspection date, sample type, strain name, gram, drug sensitive data and the like according to the requirement of the data analysis module 500) can be completed. If the user finds that the matching data is problematic, the data can be input, adjusted, modified and re-imported in the data import and adjustment module 200 when adjustment is needed.

In summary, the data importing and adjusting module 200 of the present embodiment adapts to the importing of the related type of patient drug sensitive data by identifying and compatible with the xls, xlsx, dbf file format of patient drug sensitive data. And distinguishing a fixed column and a dynamic column of the imported data by using the intermediate file, and comparing the situation of newly adding the dynamic column of the imported data each time to realize coverage or adding data quantity. Accelerating data matching by default attribute field matching,

Through compiling and extracting analysis logic, data statistics efficiency is increased, human intervention is reduced, and data insight is rapidly output.

With continued reference to fig. 1 and 2, the drug sensitive standard code database configuration module 300 provided in this embodiment is used to construct a drug sensitive standard code database. The drug sensitive standard code database stores the microorganism names, classifications and codes, specimen types and codes, strain names, classifications and codes, antimicrobial drug names and codes, detection methods and codes and break points of antimicrobial drugs of international standards.

The drug sensitivity standard code database configuration module 300 incorporates drug sensitivity calculation related criteria, and constructs a drug sensitivity standard code library using international criteria such as (1) microorganism name, classification, code (2) specimen type, code (3) strain name, classification, code (4) antimicrobial drug name, code (5) detection method, and code (6) break point of antimicrobial drug (it is understood that the break point is a reference criterion for determining the sensitivity of the microorganism to the antimicrobial drug, and thus classifying the microorganism including drug resistance, sensitivity, etc.,. For example, above the threshold is drug resistance, below the threshold is drug intolerance, or above the threshold is drug intolerance, below the threshold is sensitivity), etc. The drug sensitive standard code library is a data table corresponding to break points constructed by different drugs, different microorganisms and different detection methods.

According to the database established by the American, european and Chinese standards. By default, the system is provided with a standard drug sensitive standard code base without a separate configuration file. If the drug sensitive standard code library needs to be modified, the code. Xlsx mode can be directly modified under the data/excel in the folder of the system, the data and codes of the proper drug sensitive standard code library can be modified and edited again in the module, and the modified file takes effect immediately after restarting the system, and is analyzed quickly.

With continued reference to fig. 1 and 2, the data analysis module 500 provided in this embodiment invokes the patient drug sensitive data from the drug sensitive database, and processes the patient drug sensitive data according to the user requirement to obtain patient sub-drug sensitive data.

Specifically, the data analysis module 500 is specifically configured to: the user requirements include screening conditions (e.g., different time periods, different departments) and deduplication conditions (e.g., counting according to different criteria, possibly counting according to each data count, each individual patient count, or in other different ways).

And screening and de-duplicating the patient drug sensitive data in the drug sensitive database according to the screening condition and the de-duplication condition to obtain the patient sub-drug sensitive data, and storing the patient sub-drug sensitive data in a sub-database.

And (3) grabbing data (the data refer to counting standards, namely break points which can be antimicrobial medicines for example) in the drug sensitivity standard code database according to the microorganisms, the detection method and the sample types in the patient sub drug sensitivity data, and constructing a data table corresponding to the break points.

In this embodiment, the target requirements include at least the following eight types: clinical isolated strain distribution, ESBL analysis of klebsiella pneumoniae and escherichia coli, main carbapenem drug-resistant strain and MRSA drug-resistant transition analysis, sample quantity analysis of a department of examination, drug-resistant rate or sensitivity rate comparison analysis of specific strains, high-hazard high-drug-resistant strain analysis in different departments, sample-like distribution of clinical isolated bacteria and drug-resistant and sensitivity comparison analysis of different departments or different times of the same department of isolated strains.

With continued reference to fig. 1 and 2, the present embodiment further includes: and the application database building module 400 is used for storing the chart, downloading the chart, deriving the chart and sharing the chart.

Specifically, the database editing in the database creation module 400 includes: the user can upload the picture to perform layout after entering the additional data editing function. Meanwhile, a custom header can be added to the picture, so that the content expression of the picture is clearer. After clicking the 'confirm', all editing effects can be automatically stored in the system, and the 'view data' can be clicked directly to see the pictures and the words when the system is used next time. Downloading, exporting and sharing data. In order to facilitate academic and information exchange of users, the module can directly download analysis graphs with insight significance according to screening conditions confirmed by the users.

The downloaded content includes, but is not limited to: the conditions of deletion, download time, location, other computer-derived values.

The above target demand counts may be selected from the following conditions:

condition screening-global: global condition setting can be performed according to requirements, and the overall data insight and the 8-big sub-module data insight conclusion can be quickly seen at the first time, as shown in fig. 11.

Date and time: selecting the data in full time period or different time periods as screening condition of counting

Duplicate removal conditions: different deduplication modes as screening conditions for counts

Department default to all departments, and single or multiple departments can be selected as required to be used as screening condition of counting

Condition screening-submodule: the analysis condition of the eight sub-modules can be set according to the requirements,

date and time: selecting the whole period, different periods and period of data as a screening condition deduplication condition of counting: different deduplication modes as screening conditions for counts

Strain type: defaulting to all strain types, selecting single-selected or multi-selected strain types as required, and selecting as screening condition of counting

Specimen type: defaulting to all specimen types, selecting single or multiple specimen types as required, and selecting as screening condition of counting

Microorganism: all microorganisms are defaulted, and single-select or multi-select microorganisms can be selected as required to be used as screening conditions for counting

Different drug resistance types: defaulting to all common types, selecting single-choice or multi-choice drug-resistant types as required, and taking the single-choice or multi-choice drug-resistant types as screening conditions of counting.

(for example, CRAB is a common drug-resistant type (Acinetobacter baumannii resistant to carbapenems), and the systematic data screening conditions are (1) Acinetobacter baumannii (2) imipenem resistance-meropenem resistance according to break point (3) -counts of other screening conditions according to break point (4), and the like).

In order to demonstrate the preliminary analysis of the overall data at a first time, there is a "global condition screening" that allows the user to form preliminary data at a first time after the preliminary conditions are present. But the user can refine the conditions he needs (global conditions: only a few conditions, 8 big submodules have more detailed screening conditions) according to the 8 big submodules.

Typically (without global conditions changing) the sub-databases formed are used to make calculations, and if the conditions do not match the global screening conditions, the data is re-crawled to create new "sub-databases".

"Global screening and sub-screening": the core aims at seeing the 'preliminary analysis' of the whole data at the time of data import, the sub-screening core aims at seeing further details (possibly part of conditions can be continuously adjusted or optimized) on the global data.

To facilitate understanding of the operation of the data analysis module 500, the following is illustrated:

when the target requirement of the user is to view the clinical isolate distribution, as shown in fig. 3, for example, screening conditions are set as follows:

screening date: (the screening date in this example is 2020-10-01,2021-12-31);

conditions for duplication removal (in this example, the duplication removal conditions are for the patient)

Microorganism type (gram for short), (microorganism type in this example: all microorganisms by default)

Clinical department (clinical department in this example is general surgery)

Specimen type: (the specimen types in this example are all specimen types by default)

According to the screenable conditions described above, 2048 patient sub-drug susceptibility data were counted (and stored in a sub-database).

After counting the corresponding folding points, 600 pieces of patient sub-drug sensitive data are judged to be drug resistant, namely, 100 pieces of repeated patient sub-drug sensitive data are removed, namely, 500 pieces of patients belong to drug resistant results, and 425 pieces of patient sub-drug sensitive data belong to escherichia coli (in the example, the target requirement is to count escherichia coli, and specifically, count the number of pieces of data resistant to escherichia coli). The final result is 425 counts. According to the detection time or other time of the patient sub-drug sensitivity data, the 425 pieces of data can be made according to the trend of different times, and a two-dimensional or three-dimensional chart is made for display.

As shown in FIG. 3, the mathematical chart shown in FIG. 3 was prepared by taking different microorganism strains as the abscissa and the number of the patient sub-sensitization data existing for each strain as the ordinate, and it can be seen from the table that there are 251 strains belonging to Escherichia coli, the ratio of which is 12.3%. Purpose and logic: based on the condition screening, counts under different microbial species as grouping conditions. And calculating the number, the sequence, the calculation proportion and the like of the strains according to the counting result.

Guidance meaning: and intuitively obtaining the detected strains, the number and the duty ratio of the ten first strains in the sorting order according to the screening conditions, and quickly knowing the distribution condition of pathogenic bacteria in the data range area.

When the target demand of the user is ESBL analysis on klebsiella pneumoniae and escherichia coli, as shown in fig. 4, screening conditions are set:

screening date: (the screening date in this example is 2020-10-01,2021-12-31);

Clinical department (clinical department in this example is general surgery)

According to the screenable conditions described above, sub-drug susceptibility data (stored in a sub-database) for the patient are counted (479).

Specific counting conditions are set: a break point correspondence table is set, specifically as shown in table 1 below:

TABLE 1 break point correspondence table employed when the target demand of the user is ESBL analysis of Klebsiella pneumoniae and Escherichia coli

Drug resistance

MIC method > = data of break point class corresponding to specific strain; or, the KB method is less than or equal to the data of the drug resistance value of the break point category corresponding to the fixed strain: if the drug sensitivity data is less than or equal to the data.

According to the above counting conditions, 241 pieces of data were counted for klebsiella pneumoniae, wherein 110 pieces of klebsiella pneumoniae belonging to ESBL account for 45.6%. The ESBL-containing ratio of Escherichia coli was 50.8%. As shown in fig. 4, a mathematical circular chart is generated and displayed based on the count result.

Purpose and logic: and counting the positive condition of ESBL according to the drug resistance count and the proportion of ceftriaxone or cefotaxime of klebsiella pneumoniae and escherichia coli, and guiding clinical experience to take drugs. Two different microorganisms of klebsiella pneumoniae and escherichia coli are used as screening conditions, and the counts of drug resistance to ceftriaxone or cefotaxime are screened according to a folding point correspondence table. The number of strains, the calculation ratio, and the like are calculated according to the counting result.

Guidance meaning: after the third generation of cephalosporins in China is widely used, drug-resistant bacteria which cause ESBL (ultra-broad spectrum beta-lactamase) are spread in China worldwide. ESBL, once generated, is responsible for multiple drug resistance, and has a tremendous impact on the medical environment and patient. Because the ESBL-carrying plasmid often carries genes that are resistant to other drugs at the same time. The analysis can obtain the positive rate of the klebsiella pneumoniae and the escherichia coli ESBL according to the screening conditions, and can quickly give clinical guidance significance.

When the target requirement of the user is that the klebsiella pneumoniae has drug resistance transition analysis on imipenem and meropenem, as shown in fig. 5, screening conditions are set:

screening date: (the screening date in this example is 2020-10-01,2021-12-30)

Duplicate removal conditions: (the deduplication condition in this example is with the patient)

Cycle: (the period of this example is quarter)

Common strains: (the strain of this example is Klebsiella pneumoniae)

Clinical department: (the clinical department in this example is general surgery)

According to the screenable conditions described above, patient sub-sensitization data (stored in sub-databases) are counted (267).

Specific counting conditions are set: (the folding point correspondence table in this example is specifically a table for screening counts for "imipenem and meropenem" resistance. The number of strains, the calculation ratio, etc. are calculated based on the count result), and the specific folding point correspondence table is shown in table 2 below.

Table 2 break point correspondence table when the target requirement of the user is analysis of drug resistance transitions of klebsiella pneumoniae to imipenem and meropenem

Drug resistance:

And (3) analyzing the drug-resistant quarter change trend of imipenem and meropenem of all sample-isolated klebsiella pneumoniae according to the strain deduplication mode of patients from 10 months in 2020 to 2021. According to the screenable conditions, 57 pieces of data of klebsiella pneumoniae have resistance to imipenem in the first quarter of 2021, and the proportion is 56.1%; there were 0 data on meropenem resistance, with a proportion of 0%. The generation of the mathematical graph from the count results is shown in fig. 5.

Purpose and logic: according to the screening conditions and time periods, counting, proportion and time transition conditions of different strain combinations (Klebsiella pneumoniae, acinetobacter baumannii, pseudomonas aeruginosa, escherichia coli and Staphylococcus aureus) on imipenem, meropenem, methicillin resistance and the like are arranged. Purpose and logic: and counting the positive condition of ESBL according to the drug resistance count and the proportion of ceftriaxone or cefotaxime of klebsiella pneumoniae and escherichia coli, and guiding clinical experience to take drugs. Two different microorganisms of klebsiella pneumoniae and escherichia coli are used as screening conditions, and the counts of drug resistance to ceftriaxone or cefotaxime are screened according to a folding point correspondence table. The number of strains, the calculation ratio, and the like are calculated according to the counting result.

Guidance meaning: can rapidly give clinical guidance significance.

When the target requirement of the user is analysis of the sample amount of the department for examination, as shown in fig. 6, screening conditions are set:

screening date: (the screening date in this example is 2020-10-01,2021-12-31)

Microbial type (gram): ( The microorganism types in this example are: defaulting to all microorganisms )

According to the screenable conditions described above, sub-drug susceptibility data (stored in sub-databases) for the patient are counted (9039).

According to the screening conditions and the deduplication conditions of the insight submodule, strain types are used as grouping conditions, and under counting logics such as combination of analysis logics of the analysis module and the drug sensitivity standard code database configuration module, the number of strains, the sequencing and the calculation proportion are calculated to generate a corresponding mathematical chart as shown in figure 6.

As seen in FIG. 6, from 10 months 2020 to 2021, the total amount of specimens from the strain was determined by removing the strain from the patient.

As seen in FIG. 6, there were counted 9039 pieces of data, 1924 pieces of data belonging to the general surgery department, according to the screenable conditions described above.

Purpose and logic: counts under different departments as grouping conditions were selected according to conditions. And calculating the number, the sequence, the calculation proportion and the like of the strains according to the counting result.

Guidance meaning: the specimen inspection quantity of the isolated bacteria of each department can be obtained according to the screening conditions, so that a hospital manager and a clinical staff can be assisted in quickly knowing the situations of delivering specimens of different departments, and an inspection strategy can be quickly formulated.

When the target requirement of the user is the specific strain drug resistance rate or sensitivity rate comparison analysis,

as shown in fig. 7, screening conditions are set:

screening date: (the screening date in this example is 2020-10-01,2021-12-30)

Type of microorganism: (the type of microorganism in this example is Klebsiella pneumoniae)

According to the screenable conditions described above, sub-drug susceptibility data (and stored in a sub-database) for the patient are counted (3534).

The table of the break point correspondence in this example is shown in table 3 below:

TABLE 3 break point correspondence table when the user's target demand is specific strain drug resistance or sensitivity versus analysis

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Sensitivity:

the MIC method < = sensitive value data of the break point category corresponding to the specific strain is drug sensitive data < = data; or (b)

KB method > = sensitive value data of break point class corresponding to specific strain: drug sensitive data > = data.

Drug resistance:

MIC method > = data of break point class corresponding to specific strain; or (b)

The KB method is less than or equal to the data of the drug resistance value of the break point category corresponding to the fixed strain: if the drug sensitivity data is less than or equal to the data.

The mathematical graph is then generated as shown in fig. 7, as seen in fig. 7: from 10 in 2020 to 12 in 2021, the drug resistance of each drug of all specimen-isolated klebsiella pneumoniae was analyzed according to the way that the strain was detected for weight loss by the patient. The ceftazidime/avibactam has 1 piece of data, and the proportion is 100%.

Purpose and logic: and according to the condition screening module, counting and proportion conditions under the condition that different medicines are used as grouping conditions.

Guidance meaning: and obtaining the drug resistance rate of various drugs of the selected strain according to the screening conditions. Clinical and hospital managers can quickly know the drug resistance condition in the data range under different conditions, and can quickly guide the clinic and make a management decision of antibiotics in the hospital.

When the target requirement of the user is that the drug-resistant strain with high hazard is analyzed in different departments, as shown in fig. 8, screening conditions are set:

Screening time: (the screening date in this example is 2020-10-01,2021-12-30)

Specific drug resistant strains: (the deduplication condition in this example is CRKP), in some other examples, one or a combination of the definitions (CRAB, CRPA, CRE, MRSA) may also be defined for a particular resistant strain.

According to the screenable conditions described above, patient sub-sensitization data (stored in a sub-database) is counted (136).

The fold point correspondence table in this example is shown in table 4 below:

table 4 fold point correspondence table when the target requirement of the user is analysis of highly dangerous and highly resistant strains in different departments

Drug resistance:

Then, a mathematical chart is generated as shown in fig. 8, and the examination departments of carbapenem-resistant klebsiella pneumoniae (CRKp) are ordered according to the strain duplication removal mode of the patient examined from 2020 to 2021, wherein the serious medical department has 78 pieces of data, and the proportion is 57.8%. The emergency medical discipline has 31 pieces of data, accounting for 59.6%.

Purpose logic of this example: according to the condition screening module, the counting and proportion conditions of the drug resistance of different drugs under different departments as grouping conditions are defined (CRKP, CRAB, CRPA, CRE, MRSA) according to specific drug resistant strains.

Guidance meaning: the main carbapenem-resistant strains and MRSA are distributed in ten departments before detection. The analysis can obtain the distribution situation of main carbapenem drug-resistant strains (very difficult to treat) and MRSA (methicillin-resistant staphylococcus aureus) drug-resistant strains in different departments according to the screening conditions, and can rapidly guide the clinical and hospital antibiotics management decision-making.

When the target requirement of the user is the specimen-like distribution of the clinical isolates, as shown in fig. 9, screening conditions are set:

screening date: (the screening date in this example is 2020-10-01,2021-12-30)

Gram type: (the type in this example is: default to all)

According to the screenable conditions described above, there were counted (2048) patient sub-sensitization data (stored in sub-databases).

Thereafter, a mathematical chart is generated as shown in FIG. 9, and it can be seen from FIG. 9 that the general surgery is performed for two years from 10 in 2020 to 12 in 2021, and the specimen distribution of all strains is analyzed in accordance with the strain deduplication mode detected by the patient. Bile has 591 data with a 28.9% ratio.

Purpose and logic: according to the condition screening module, the sample types are used as the counts under the grouping condition. And calculating the number, the sequence, the calculation proportion and the like of the strains according to the counting result.

Guidance meaning: obtaining the specimen source distribution of the isolated bacteria according to the screening conditions, and after knowing the distribution, rapidly guiding a hospital manager to make a management decision of antibiotics of the hospital.

When the target requirement of the user is drug resistance and sensitivity comparison analysis of different departments of the isolated strain or different times of the same department, as shown in fig. 10, screening conditions are set:

Common strains: (the common strain in this example is Klebsiella pneumoniae)

Clinical department and corresponding analysis date: ( In this example, department a: general surgery; department a screening date: 2020-10-01,2021-12-30; department B: critical medicine department (4F): department B screening date: 2020-10-01,2021-12-30 )

According to the screenable conditions described above, a sub-patient drug susceptibility data set (6264) is counted (and stored in a sub-database).

The fold point correspondence table in this example is shown in table 5 below:

Table 5 break point correspondence table when the user's target requirement is to isolate the strain for drug resistance and sensitivity comparison analysis in different departments or at different times in the same department

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Sensitivity:

Drug resistance:

Thereafter, a mathematical chart is generated as shown in fig. 10, and it can be seen from fig. 10 that the general surgery department is specific gravity medical department, and from 10 in 2020 to 12 months in 2021, the drug resistance count of each drug whose all data are klebsiella pneumoniae is analyzed in a single patient weight removal manner. According to the above screenable conditions, klebsiella pneumoniae has 186 pieces of drug resistance data (58.1% in proportion) in nitrofurantoin general surgery, but 111 pieces of drug resistance data (71.2% in proportion) in severe medical department.

Purpose and logic: and comparing different departments according to the condition screening module, and taking the drug resistance conditions of different drugs as the counts under grouping conditions. And calculating the number, the sequence, the calculation proportion and the like of the strains according to the counting results of the drug resistance rate and the sensitivity rate.

Guidance meaning: and (3) comparing the drug resistance and the sensitivity of different departments of the selected strain of the isolated strain according to the screening conditions or obtaining a comparison histogram of the drug resistance and the sensitivity of the same department at different times according to the screening conditions. The comparison conditions of different time periods and different drug-resistant bacteria in different departments can be quickly known, on one hand, clinical understanding of department drug-resistant characteristics (correct antibiotics are selected) can be assisted, and hospital management can be assisted to quickly formulate a targeted (department, medicine and drug-resistant bacteria) comprehensive management policy from time transition.

In summary, the clinical isolated strain distribution, ESBL analysis of klebsiella pneumoniae and escherichia coli, main carbapenem drug-resistant strain and MRSA drug-resistant transition analysis, sample quantity analysis of a department of examination, drug-resistant rate or sensitivity rate comparison analysis of specific strains, analysis of drug-resistant strains with high harmfulness in different departments, sample-like distribution of clinical isolated bacteria and drug-resistant and sensitivity comparison analysis of isolated strains in different departments or different times in the same department, and the 8 charts can be simultaneously displayed, namely, the drug-sensitive data of patients can be rapidly provided with data insight results in 8 charts.

The system also has the safety: the security professionals of the data and the platform are confirmed by using a single edition and background authentication multiple mode: the only Excel and DBF databases derived from WHONET can be used for rapid and comprehensive analysis of microbial drug resistance data.

The system can assist doctors/clients to quickly improve corresponding data and support in response to related requirements.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

It should be noted that the apparatus and methods disclosed in the embodiments herein may be implemented in other ways. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, the functional modules in the embodiments herein may be integrated together to form a single part, or the modules may exist alone, or two or more modules may be integrated to form a single part.

While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.

Claims

1. A microbial epidemiology and drug sensitivity analysis system, comprising:

the data importing and adjusting module (200) is used for acquiring the drug sensitivity data of the patient and storing the drug sensitivity data into the drug sensitivity database;

the drug sensitive standard code database configuration module (300) is used for constructing a drug sensitive standard code database;

the data analysis module (500) is used for calling the patient drug sensitivity data from the drug sensitivity database and processing the patient drug sensitivity data according to the user requirements to obtain patient sub drug sensitivity data;

2. The microbial epidemiology and drug susceptibility analysis system of claim 1, wherein the patient drug susceptibility data includes data in DBF format and excel format derived from who.

3. The microbiological epidemiology and drug sensitivity analysis system of claim 2, wherein the data import and adjustment module (200) is specifically configured to generate an intermediate file, a first row in each column of the intermediate file being provided with an attribute field, the intermediate file comprising a fixed column and a dynamic column;

identifying the patient drug sensitive data, dividing the patient drug sensitive data into a fixed column and a dynamic column, matching the dynamic column of the patient drug sensitive data with the dynamic column of the intermediate file,

and adding the dynamic column of the patient drug sensitive data into the dynamic column of the intermediate file to realize the storage of the patient drug sensitive data.

4. The microbial epidemiology and drug susceptibility analysis system of claim 3, wherein the patient drug susceptibility data originally stored in the patient drug susceptibility database is referred to as historical data, the data import and adjustment module (200) is further configured to obtain a dynamic column of attribute fields of the supplemental patient drug susceptibility data when supplemental patient drug susceptibility data is required to be added to the historical data,

Adding a dynamic column with the same attribute field as the historical data into a corresponding dynamic column of the historical data, adding a dynamic column with a different attribute field as the historical data into the historical data, and adding the dynamic column data of the additional patient drug sensitive data into the newly added dynamic column; the data import and adjustment module (200) is also configured to delete the historical data and add new patient drug sensitive data to the intermediate file.

5. The microbial epidemiology and drug susceptibility analysis system of claim 4, wherein the attribute field includes one or any combination of a patient identification code, name, gender, age, department, sample number, date of examination, sample type, pathogen name, gram and drug susceptibility test result data.

6. The microbial epidemiology and drug susceptibility analysis system of claim 5, wherein the drug susceptibility standard code database stores international standard microbial names, classifications and codes, specimen types and codes, strain names, classifications and codes, antimicrobial drug names and codes, detection methods and codes and break points of antimicrobial drugs.

7. The microbial epidemiology and drug susceptibility analysis system of claim 6, wherein the data analysis module (500) is specifically configured to, the user requirements including screening conditions and deduplication conditions;

screening condition and duplication removal condition screening and duplication removal operation are carried out on the patient drug sensitive data in the drug sensitive database according to the screening condition and the duplication removal condition to obtain the patient sub drug sensitive data, and the patient sub drug sensitive data is stored in a sub database;

8. The microbial epidemiology and drug sensitivity analysis system of claim 7,

the target requirements include the following: the distribution of the clinical isolated strains,

ESBL analysis of Klebsiella pneumoniae and Escherichia coli,

the main carbapenem drug-resistant strain and MRSA drug-resistant transition analysis,

the sample amount analysis of the department of clinical laboratory is carried out,

specific strain drug resistance rate or sensitivity rate comparison analysis,

the drug-resistant strain with high hazard is analyzed in different departments,

sample-like distribution of clinically isolated bacteria

And (5) separating the drug resistance and sensitivity comparison analysis of the strain in different departments or in different times in the same department.

9. The microbial epidemiology and drug sensitivity analysis system of claim 1,

further comprises: and the application database building module (400) is used for storing the chart, downloading the chart, deriving the chart and sharing the chart.

10. The microbial epidemiology and drug sensitivity analysis system of claim 1, further comprising: a login and rights management module (100) for registering a user and verifying account information of the user;

further comprises: and the customer service management module (600) is used for finding out abnormal, error reporting and other abnormal operation conditions and popping up the customer contact module.