CN113362965A - System and method for monitoring drug resistance of pathogenic bacteria in hospital - Google Patents
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Abstract
The invention discloses a hospital pathogenic bacteria drug resistance monitoring system and a method, comprising an acquisition module, a calculation module and an evaluation module, wherein the acquisition module is used for collecting related data information and transmitting the collected related information to the calculation module; the calculation module is used for cleaning and standardizing related acquired information, obtaining a drug resistance index by using a related algorithm and transmitting the drug resistance index into the evaluation module; the evaluation module evaluates according to the magnitude of the drug resistance index, obtains a related disease control risk analysis report, collects related data information, arranges the information and calculates the information to obtain the drug resistance index, and comprehensively judges the drug resistance of bacteria and the applicability of antibacterial drugs by using the drug resistance index, thereby effectively reducing the abuse condition of antibiotics and the risk probability of generating 'super bacteria'.
Description
Technical Field
The invention relates to the technical field of biology, in particular to a system and a method for monitoring drug resistance of pathogenic bacteria in hospitals.
Background
At present, hospital infection management systems (namely hospital infection systems) are generally on-line in hospitals and are mainly used for reporting infection cases, monitoring and counting data of infection, bacterial drug resistance and the like in hospitals. At present, data indexes for drug resistance monitoring mainly comprise drug resistance rate, sensitivity rate, antibacterial drug utilization rate, multi-drug resistance bacteria composition ratio, pathogenic bacteria MIC value distribution and the like.
In 2011, two researchers in Laxminarayan R and Klugman KP in the united states proposed the resistance index (DRI). It uses the ratio of each drug to weight, and integrates the drug resistance of all available drugs into a comprehensive index. Thus, a value may describe the average effectiveness of an infection or use of antibiotics in a region. The DRI index is usually represented by an "adaptive index" which takes into account the specific prescription made after adjustment according to the local bacterial resistance. Its value is between 0 and 1, the greater the value indicates the more severe the resistance situation, 1 reflects that the current prescription is 100% effective for the local bacterial resistance model. After the antibiotic administration mode is fixed at a certain baseline year, the change of the antibiotic effectiveness with time can be described. If the mode of administration does not change with changes in antibiotic resistance, then "fixed DRI" may be used.
At present, the commonly used bacterial drug resistance monitoring statistical indexes cannot integrate and calculate the drug resistance of various drugs, so that the description and comparison of the bacterial drug resistance conditions at different time and different places are difficult, and the characteristics and the change conditions of the drug resistance cannot be explained and analyzed through certain quantitative single comprehensive indexes. And some professional terms are often difficult to be understood by the general public, so that the communication between experts and policy makers and the general public is greatly hindered. The newly proposed resistance index DRI was only used primarily in describing the integrated bacterial resistance transitions monitored in the united states, and further studies were needed to apply it in different situations.
Disclosure of Invention
Aiming at the defects in the technology, the invention provides a system and a method for monitoring drug resistance of pathogenic bacteria in hospitals. After relevant data information is collected, the information is sorted and then calculated, so that a drug resistance index is obtained, and the drug resistance condition of bacteria is judged by using the drug resistance index, so that the abuse condition of antibiotics and the risk probability of producing 'super bacteria' are effectively reduced.
In order to achieve the aim, the invention provides a hospital pathogenic bacteria drug resistance monitoring system which comprises an acquisition module, a calculation module and an evaluation module, wherein the acquisition module is used for collecting related data information and transmitting the acquired related information into the calculation module; the calculation module is used for cleaning and standardizing related acquired information, obtaining a drug resistance index by using a related algorithm and transmitting the drug resistance index into the evaluation module; the evaluation module evaluates according to the magnitude of the drug resistance index and obtains a relevant disease control risk analysis report, thereby effectively reducing the antibiotic abuse condition and the risk probability of generating 'super bacteria'.
Preferably, the acquisition module comprises a patient unit, an antibiotic information unit and a pathogenic bacteria unit; the patient unit collects personal information and disease information of the patient; the method comprises the steps that an antibiotic information unit acquires used antibiotic information of a patient, wherein the used antibiotic information comprises information of the type, time and quantity of used antibiotics; the pathogenic bacteria unit is used for acquiring the drug resistance detection result of pathogenic bacteria.
Preferably, the calculation module comprises a data processing unit and a data calculation unit, the data processing unit performs data cleaning and standardization processing on the acquired information to form a unified data type and a unified data format, the acquired data information is transmitted to the data calculation unit, and the data calculation unit calculates the data information to obtain the drug resistance index.
Preferably, the data calculation unit comprises a first calculation unit and a second calculation unit, the first calculation unit comprises a fixed drug resistance index algorithm formula, the second calculation unit is internally provided with an adaptive drug resistance index algorithm formula, and the first calculation unit and the second calculation unit perform related calculation according to the type of the data information acquired by the data processing unit.
Preferably, the evaluation module performs related evaluation on the drug resistance index calculated by the data calculation unit, and performs different measures according to the numerical value of the drug resistance index.
The application also discloses a method for monitoring the drug resistance of pathogenic bacteria in hospitals, which comprises the following steps:
s1, collecting the related information by the collecting module;
s2: the calculation module performs correlation arrangement on the information collected by the collection module and then performs calculation to obtain a drug resistance index;
s3: and the evaluation module carries out early warning of different degrees according to the size of the drug resistance index.
Preferably, in step S1, the relevant information includes personal information of the patient, disease information, antibiotic information including information on the type of antibiotic used, the time of use, and the amount of use, and detection of drug resistance of pathogenic bacteria.
Preferably, in step S2, the method further includes a data processing step, in which after the relevant information is acquired from the acquisition module, the information is classified, and the information is classified and screened according to the types of pathogenic bacteria, so as to obtain the relevant information of different pathogenic bacteria, and then the relevant information is converted into a unified data type and data format.
Preferably, in step S2, the calculation of the data includes two calculation methods, the first is the fixed DRI algorithm: wherein
Is composed of
The drug resistance ratio of pathogenic bacteria i to drug k at time t,
the proportion of drug k used to treat pathogen i in baseline years(ii) a The second is the adaptive DRI algorithm:
wherein
The drug resistance ratio of pathogenic bacteria i to drug k at time t,
the proportion of drug k used to treat pathogen i at time t.
Preferably, in step S3, the fixed DRI and the adaptive DRI at different years of hospital level in the area are obtained by using the drug resistance index algorithm, the relationship between the drug resistance index and the characteristics of the patient, the treatment condition, the infection condition and the like is obtained, the risk factors of drug resistance are analyzed, the clinical treatment of the patient is guided according to the size of the drug resistance index, and the use specification of antibiotics is established. (ii) a
The invention has the beneficial effects that: compared with the prior art, the system and the method for monitoring the drug resistance of the pathogenic bacteria in the hospital, provided by the invention, are used for collecting personal case information, drug resistance test results, antibiotic use and other information of patients with infection diagnosis for the hospital, summarizing data to the system in a data interface integration mode, carrying out standardized processing on the data, and solving the normative problem of professional medical terms such as clinical diagnosis, pathogen names, antibiotic names and the like by using a drug resistance index algorithm; the importance of drug resistance of a pathogen and the change trend of the drug resistance of the pathogen to different antibiotics along with the time are researched in a targeted manner; finally, the conclusion is popularized and applied to a hospital through scientific analysis, clinical medication is scientifically guided, and the abuse condition of antibiotics and the risk probability of producing 'super bacteria' are effectively reduced; the fixed DRI and the adaptive DRI of different years at the hospital level in the region are obtained by applying a drug resistance index algorithm, and the index has very strong scientificity and applicability through application evaluation and can be used for guiding clinical treatment and policy making.
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FIG. 1 is a system framework diagram of the present invention;
FIG. 2 is a flow chart of the steps of the present invention;
FIG. 3 shows the variation of the adaptive index of 5 indicator bacteria according to the present invention;
FIG. 4 is a comparison of the adaptive and fixed resistance index DRI of 5 indicator bacteria of the present invention.
Description of the main symbols:
1. acquisition module 2, calculation module 3, evaluation module 11, patient unit 12, antibiotic information unit
13. Pathogen unit 21, data processing unit 22, data calculation unit.
Detailed Description
In order to more clearly express the invention, the invention is further described below with reference to the accompanying drawings and examples; it will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Referring to fig. 1 and 2, the invention discloses a hospital pathogenic bacteria drug resistance monitoring system, which comprises an acquisition module 1, a calculation module 2 and an evaluation module 3, wherein the acquisition module 1 collects related data information and transmits the collected related information to the calculation module 2; the calculation module 2 carries out data cleaning and standardization processing on the related acquired information, obtains a drug resistance index by using a related algorithm, and transmits the drug resistance index into the evaluation module 3; the evaluation module 3 evaluates according to the magnitude of the drug resistance index and obtains a relevant disease control risk analysis report, thereby effectively reducing the antibiotic abuse condition and the risk probability of generating 'super bacteria'. In the embodiment, firstly, the acquisition module is used for acquiring information of related data, accurate calculation and evaluation can be performed only after the related data information is collected, after the information is collected, the calculation module is used for calculating the related data information to obtain a drug resistance index, after the drug resistance index is obtained, the evaluation module can evaluate according to the drug resistance index, judge the drug resistance degree of the bacteria, determine the epidemic characteristics of the drug-resistant bacteria, and simultaneously, the related prevention and control part establishes a corresponding coping mechanism.
The acquisition module comprises a patient unit 11, an antibiotic information unit 12 and a pathogenic bacteria unit 13; the patient unit 11 collects personal information of the patient and disease information; the antibiotic information unit 12 acquires information on the antibiotics used by the patient, including information on the type, time, and amount of antibiotics used; the pathogenic bacteria unit 13 is used for acquiring the detection result of the drug resistance of pathogenic bacteria. In this embodiment, in order to ensure that the data obtained by the calculation module is real and effective, the acquisition module needs to be used to acquire various information, and in the acquisition process, the personal information and the disease information of the patient need to be acquired first, so that the tracking and observation of the individual can be realized; the pathogenic bacteria unit collects pathogenic bacteria infected by the patient, the antibiotic information unit collects antibiotic information used by the patient, the mutual correspondence between the pathogenic bacteria and the antibiotic is realized on a bacterial level, and the patient can be used as an observation target on an individual level to know whether the antibiotic plays a role or not according to the close condition of the patient; in the collection stage, there are two calculation modes for the usage rate of the antibacterial agent, one is to calculate the composition ratio of the antibacterial agent, and the other is to calculate the DDD frequency of the antibacterial agent every thousand hospitalization days (DDDs is a drug classification system of anatomy-treatment-chemistry made by WHO in 1969, and a unit for analyzing the administration frequency of a Defined Daily Dose (DDD) is determined), wherein the composition ratio of the antibacterial agent: number of patients with a certain antibacterial agent/number of total antibacterial agent-using patients x 100%. DDD frequency of certain antibacterial drugs per thousand hospitalization days: the DDD frequency/cumulative hospitalization day number of a certain antibacterial drug is 100, wherein the DDD frequency (DDDs) needs the DDD value of the drug and the total sales amount of the drug in the same period, the DDD value cannot be extracted due to the loss of the value in a general hospital, the total sales amount of the drug is related to the hospital, and the secret data of the traditional prescription is not easy to obtain; therefore, when the conditions are not met, the antibacterial drug utilization rate is obtained by adopting another calculation mode, and the accuracy and the practicability of the data are ensured.
The calculation module 2 comprises a data processing unit 21 and a data calculation unit 22, the data processing unit 21 performs data cleaning and standardization processing on the acquired information to form a unified and standard data type and data format, the acquired data information is transmitted to the data calculation unit 22, and the data calculation unit 22 calculates the data information to obtain the drug resistance index. The data calculation unit 22 includes a first calculation unit and a second calculation unit, the first calculation unit includes a DRI algorithm formula for fixing the drug resistance index, the second calculation unit includes an adaptive DRI algorithm formula for the drug resistance index, and the first calculation unit and the second calculation unit perform related calculation according to the type of the data information acquired by the data processing unit. In this embodiment, after the relevant signals are obtained, the information is first screened to clean the interference data, for example, when the relevant calculation is performed on staphylococcus aureus, only the relevant information of staphylococcus aureus is retained, and the information of other bacteria is filtered out, so that when the drug resistance index of staphylococcus aureus is calculated, the calculation result is more accurate; the two algorithms are set to meet different statistical requirements; by fixed DRI is meant that DRI is calculated using baseline year antibiotic usage to assess the degree of overall resistance that would occur assuming no change in dosing pattern, i.e., the degree of attenuation in antibiotic effectiveness. The calculation algorithm is as follows: the pathogen resistance rate (the current year) is the antimicrobial drug composition ratio (the past year) or the pathogen resistance rate (the current year) is the antimicrobial drug DDD (the past year) per thousand hospitalization days. Adaptive DRI refers to the DRI calculated using the actual antibiotic usage of the year, reflecting the overall effectiveness of the actual antibiotic medication change, and can be used to assess the disease burden of the medication change reduction. The calculation algorithm is as follows: the pathogen resistance rate (the current year) is the antimicrobial composition ratio (the current year) or the pathogen resistance rate (the current year) is the antimicrobial DDD (the current year) per thousand hospitalized days.
The evaluation module 3 performs related evaluation on the drug resistance index calculated by the data calculation unit, and performs different measures according to the numerical value of the drug resistance index. More specifically, clinical medication is guided according to the DRI condition, and the application condition of antibiotics is scientifically managed; obtaining the relationship between DRI and patient characteristics, treatment condition, infection condition and the like, and analyzing the risk factors of drug resistance; the disease burden caused by drug resistance, such as the influence on the hospitalization time and the mortality of the patients, is obtained.
The invention also discloses a method for monitoring the drug resistance of the pathogenic bacteria in the hospital, which is applied to the system for monitoring the drug resistance of the pathogenic bacteria in the hospital, and comprises the following steps: s1, collecting the related information by the collecting module; s2: the calculation module performs correlation arrangement on the information collected by the collection module and then performs calculation to obtain a drug resistance index; s3: and the evaluation module carries out early warning of different degrees according to the size of the drug resistance index. In the embodiment, the three steps are set, so that the drug resistance of pathogens in the whole hospital is effectively monitored, and the occurrence probability of 'super bacteria' is reduced.
In step S1, the relevant information includes personal information of the patient, disease information, antibiotic information including information on the type of antibiotic used, the time of use, and the amount of use, and detection of drug resistance of pathogenic bacteria. In step S2, a data processing step is further included, in which after the relevant information is acquired from the acquisition module, the information is classified and processed, information is classified and screened according to the types of pathogenic bacteria, so as to acquire relevant information of different pathogenic bacteria, and then the relevant information is converted into a unified data type and data format. In step S2, when the data is calculated, two calculation methods are included,
the first is the fixed DRI algorithm:
wherein
For the drug resistance ratio of pathogenic bacteria i to drug k at time t,
the proportion of drug k used to treat pathogen i in the baseline year;
the second is the adaptive DRI algorithm:
wherein
The drug resistance ratio of pathogenic bacteria i to drug k at time t,
the proportion of drug k used to treat pathogen i at time t. The drug resistance index is obtained by substituting related numbers into the two groups of formulas for calculation, and different calculation formulas are based on different sampling modes, so that the calculation result is more accurate.
In step S3, the fixed DRI and the adaptive DRI of different years in hospital level in the area are obtained by using the drug resistance index algorithm, the relationships between the drug resistance index and the characteristics of the patient, the treatment condition, the infection condition and the like are obtained, the risk factors of drug resistance are analyzed, the clinical treatment of the patient is guided according to the size of the drug resistance index, and the use specification of antibiotics is established.
The present application is illustrated by the following specific embodiments:
and the pathogenic bacteria drug resistance rate is obtained by acquiring pathogenic bacteria drug resistance test results, and is equal to the ratio of the number of antibiotics in the drug resistance results to the total number of antibiotics. If 19 antibiotics of the pathogenic bacteria are detected together, 9 results are drug resistance, and the drug resistance rate is 47.37%.
In order to evaluate the applicability of the drug resistance index in hospitals, a certain three hospitals are selected to carry out adaptability evaluation, and Klebsiella pneumoniae, pseudomonas aeruginosa, staphylococcus aureus, acinetobacter and Escherichia coli are taken as index bacteria. 1031 clinical infection cases are collected in total, the clinical infection cases come from three years of 2008, 2010 and 2013, and the fixed property and adaptive drug resistance index DRI of 5 index bacteria in different years are calculated respectively. The results show that the adaptability index DRI of 3 index bacteria such as Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus and the like is in a descending trend in 3 years, and the immobility and the adaptability index DRI of the Acinetobacter are in a descending trend, which indicates that the drug resistance situation is the most severe, and the introduction of novel antibacterial drugs and the research and development of the novel antibacterial drugs are urgently needed. The adaptation index DRI of escherichia coli is rising and the immobility index is falling, indicating that more antibacterial drugs are available to the escherichia coli for improved treatment over time.
The invention has the advantages that:
1. the fixed DRI and the adaptive DRI of different years at the hospital level in the region are obtained by applying a drug resistance index algorithm, and the index has very strong scientificity and applicability through application evaluation and can be used for guiding clinical treatment and policy making.
2. According to DRI of the levels of pathogens, departments, infection types and the like in various hospitals in the region, the drug-resistant epidemic characteristics and key prevention and control departments are discovered.
3. Clinical medication is guided according to DRI conditions, the application condition of antibiotics is scientifically managed, and drug abuse is avoided.
4. And obtaining the relation between the DRI and the characteristics of the patient, the treatment condition, the infection condition and the like, and analyzing the risk factors of the drug resistance.
5. The disease burden caused by drug resistance, such as the influence on the hospitalization time and the mortality of the patients, is obtained.
The above disclosure is only for a few specific embodiments of the present invention, but the present invention is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (10)
1. A monitoring system for drug resistance of pathogenic bacteria in hospitals is characterized by comprising an acquisition module, a calculation module and an evaluation module which are sequentially connected; the acquisition module collects related data information and transmits the acquired related information to the calculation module; the calculation module carries out data cleaning and standardization processing on related acquired information, substitutes the acquired information into an algorithm formula to calculate and obtain a drug resistance index, and transmits the drug resistance index into the evaluation module; the evaluation module evaluates according to the magnitude of the drug resistance index and obtains a relevant disease control risk analysis report, thereby effectively reducing the antibiotic abuse condition and the risk probability of generating 'super bacteria'.
2. The hospital pathogenic bacteria resistance monitoring system according to claim 1, wherein the collection module includes a patient unit, an antibiotic information unit, and a pathogenic bacteria unit; the patient unit collects personal information and disease information of the patient; the method comprises the steps that an antibiotic information unit acquires used antibiotic information of a patient, wherein the used antibiotic information comprises information of the type, time and quantity of used antibiotics; the pathogenic bacteria unit is used for acquiring the drug resistance detection result of pathogenic bacteria.
3. The hospital pathogenic bacteria drug resistance monitoring system according to claim 1, wherein the calculation module comprises a data processing unit and a data calculation unit, the data processing unit performs data cleaning and standardization processing on the acquired information to form a unified data type and a unified data format, the acquired data information is transmitted to the data calculation unit, and the data calculation unit calculates the data information to obtain the drug resistance index.
4. The hospital pathogenic bacteria drug resistance monitoring system according to claim 3, wherein the data calculation unit includes a first calculation unit and a second calculation unit, the first calculation unit includes a fixed drug resistance index algorithm formula, the second calculation unit includes an adaptive drug resistance index algorithm formula, and the first calculation unit and the second calculation unit perform related calculation according to the type of the data information acquired by the data processing unit.
5. The hospital pathogenic bacteria drug resistance monitoring system according to claim 1, wherein the evaluation module performs a relative evaluation on the drug resistance index calculated by the data calculation unit, and performs different measures according to the numerical value of the drug resistance index.
6. A method for monitoring drug resistance of pathogenic bacteria in hospitals is characterized by comprising the following steps:
s1, collecting the related information by the collecting module;
s2: the calculation module performs correlation arrangement on the information collected by the collection module and then performs calculation to obtain a drug resistance index;
s3: and the evaluation module carries out early warning of different degrees according to the size of the drug resistance index.
7. The hospital pathogenic bacteria drug resistance monitoring method according to claim 6, wherein in step S1, the related information includes personal information of the patient, disease information, antibiotic information and drug resistance detection of pathogenic bacteria, wherein the antibiotic information includes information on the kind of antibiotic used, the time of use and the amount of use.
8. The method for monitoring drug resistance of pathogenic bacteria in hospitals according to claim 6, wherein in step S2, the method further comprises a data processing step, after acquiring relevant information from the acquisition module, classifying the information, classifying and screening the information according to the types of pathogenic bacteria, thereby acquiring relevant information of different pathogenic bacteria, and then converting the relevant information into a uniform data type and data format.
9. The method for monitoring nosocomial pathogen resistance according to claim 6, wherein the calculation of the data in step S2 includes two calculation methods, the first method is the fixed DRI algorithm:
wherein
For the drug resistance ratio of pathogenic bacteria i to drug k at time t,
the proportion of drug k used to treat pathogen i in the baseline year; the second is the adaptive DRI algorithm:
wherein
The drug resistance ratio of pathogenic bacteria i to drug k at time t,
the proportion of drug k used to treat pathogen i at time t.
10. The method for monitoring drug resistance of pathogenic bacteria in hospitals according to claim 6, wherein in step S3, the fixed DRI and the adaptive DRI of hospital levels in the area are obtained by using a drug resistance index algorithm, the relationship between the drug resistance index and the characteristics of patients, the treatment condition, the infection condition and the like is obtained, the risk factors of drug resistance are analyzed, the clinical treatment of patients is guided according to the size of the drug resistance index, and the use specification of antibiotics is established.
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| CN115116623A (en) * | 2022-06-28 | 2022-09-27 | 深圳市儿童医院 | ICD disease coding-based antibacterial drug use index evaluation method and terminal |
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