Method and system for preventing and controlling prevalence of multiple drug-resistant bacteria, server and storage medium

Abstract

The invention discloses a method and a system for preventing and controlling prevalence of multiple drug-resistant bacteria, a server and a storage medium. The method comprises the following steps: S1, collecting specimen detection data of parts to which respiratory tracts, urinary tracts, blood and drainage tubes of hospitalized patients belong; and S2, according to the specimen detection data of the part, constructing a patient space-time relationship network. And S3, based on the patient space-time relationship network, establishing a risk weight calculation model, and calculating a propagation risk value. And S4, establishing a role level propagation relation network, and tracing data of positive patients of the same fungus to obtain source patients and infection paths. And S5, generating a prevention and control decision instruction, and triggering a grading intervention measure when the propagation risk value exceeds a preset threshold value. According to the invention, the risk of each inpatient area or department of the whole hospital can be graded, a risk early warning report is formed, and risk data sharing and real-time forecasting of clinical departments and sensing control management departments are realized in the first time.

Description

Technical Field

[0001] The present invention belongs to the technical field of hospital multidrug-resistant bacterial infection prevention and control, and specifically relates to a method, system, server and storage medium for multidrug-resistant bacterial epidemic prevention and control. Background Art

[0002] Hospital-acquired infections (HAIs) are a widespread public health problem in all medical institutions. The irrational use of antimicrobial drugs and the adaptive mutation of bacteria have led to the emergence of multidrug-resistant bacteria. Literature research indicates that most patients with severe COVID-19 infection are at risk of secondary infections during hospitalization. The bacteria causing these secondary infections are mostly multidrug-resistant. Patients with such infections often have more complex conditions, are difficult to cure, and require higher-level medications and specialized nursing care, placing a heavy financial burden on patients and medical institutions. The prevention and control of multidrug-resistant bacteria has become a global public health and safety issue.

[0003] The prior art discloses an invention patent entitled "A method, device, equipment, and storage medium for monitoring clustered infection routes" (authorization announcement number: CN114171148B). The method includes: querying the identity information of the user who initiates the infection route locking request and determining the user authority of the user based on the identity information, querying the hospitalization process information, transfer information, drug-resistant bacteria test information, bed distribution information, and medical staff care information of the patient pointed to by the infection route locking request; determining the patient zero; and determining and outputting the transmission relationship path based on the patient zero case and all cases of multidrug-resistant bacteria detection with time-space relationships according to the bacteria detection date and the occurrence time of the time-space relationship.

[0004] Currently, my country's monitoring methods for multidrug-resistant bacteria are relatively backward and limited, relying solely on positive reports from microbiology laboratories and manual epidemic surveys conducted by infection control professionals, as well as in-person supervision and intervention in clinical departments. A single infection management staff member typically faces a workload of more than a dozen wards and nearly a hundred patients, resulting in a severe shortage of staff and difficulty ensuring the accuracy, comprehensiveness, and traceability of investigations and interventions. This results in the current multidrug-resistant bacteria prevention and control efforts being unable to, at a macro level, control the overall epidemic trends of multidrug-resistant bacteria and accurately predict high-risk areas for multidrug-resistant bacteria. At a micro level, it is impossible to provide a reasonable and scientific explanation for how each patient became infected with multidrug-resistant bacteria, let alone warn the next patient who may be infected. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned technical problems existing in the prior art and to provide a method, system, server and storage medium for epidemic prevention and control of multidrug-resistant bacteria.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for epidemic prevention and control of multidrug-resistant bacteria, characterized by comprising the following steps:

[0008] S1. Establish a dynamic monitoring module based on the classification of patients' susceptible parts, and collect real-time test data of specimens from the respiratory tract, urinary tract, blood, and drainage tubes of hospitalized patients.

[0009] S2. Construct a spatiotemporal relationship network of patients based on the test data of the specimens at the corresponding parts of the body. The spatiotemporal relationship network of patients includes:

[0010] Contact relationship data based on three transmission pathways: caregiver-mediated transmission, spatial proximity transmission, and environmental residual transmission;

[0011] Patient attribute dataset: daily recorded strain detection information, medical operation records, and susceptibility factor assessment results of the body temperature dataset.

[0012] S3. Based on the spatiotemporal relationship network of patients, a risk weight calculation model is established, and the transmission risk value is calculated using the following formula: P = Σ(W × β × T); where W is the basic coefficient of the transmission pathway, β is the risk coefficient of the susceptible site, and T is the time intensity factor.

[0013] S4. Establish a role-level transmission relationship network to trace the data of patients with the same fungus positive to identify the source patient and transmission path. The role-level transmission relationship network includes: dynamic role conversion rules for positive patients, latent carriers, first-level contacts, and second-level contacts, and an algorithm for determining the termination of relationship lines based on contact duration and strain genotype matching.

[0014] S5. Generate prevention and control decision instructions, and trigger graded intervention measures when the transmission risk value exceeds the preset threshold.

[0015] Furthermore, the risk coefficients of susceptible sites are: respiratory tract β = 1.8; urinary tract β = 1.3; blood β = 2.0; drainage tube β = 1.5; latent carrier / other β = 1.

[0016] Furthermore, the calculation formula of the basic coefficient of the transmission path includes:

[0017] Transmission through nursing staff: W1 = 0.3 × (1 + e^(-0.5 × (N-2))), where N is the number of common nursing shifts;

[0018] Adjacent transmission via spatial pathway: W2 = 0.2 × (1 + 0.3 × D), where D is the number of adjacent beds;

[0019] Transmission via environmental residues: W3 = 0.15×(1-0.1×C), where C is the number of times the bed unit is disinfected.

[0020] Furthermore, the formula for the time intensity factor includes:

[0021] Transmission through caregivers: T1 = 1-e^(-0.1×t), where t is the cumulative duration of shared care;

[0022] Adjacent propagation in pathway space: T2 = min(1, adjacent overlap duration / 72);

[0023] Transmission via environmental residual: T3 = e^(-(Δt-48) / 24), where Δt is the interval between beds used by the previous and next patients.

[0024] Furthermore, the dynamic role conversion rules include: when a second-level contact tests positive, the patient at the previous level is automatically marked as a latent carrier; when a latent carrier tests negative for the same part for at least two consecutive times, his / her carrier status is automatically lifted.

[0025] Furthermore, the carry-over exemption applies to:

[0026] 1) Carrier clearance within 14 days before the first positive case; 2) Carrier clearance after the target bacteria are detected in the patient; 3) Carrier clearance after close contact relationship is established.

[0027] The specific cancellation rules are as follows:

[0028] For 1), if the specimen was sent for testing within 14 days before detection, and ≥2 times within 7 days, and the specimen was sent for testing from the same location as the target bacteria, and the detection results were all non-target strains, then the carriage is resolved;

[0029] For 2), if the specimens are submitted for testing ≥2 times within 7 days, and the specimens submitted are the same species as the specimens in which the target bacteria were detected, and the detection results are all non-target strains, then the carriage is resolved;

[0030] For 3), if the specimens are sent for testing ≥2 times within 7 days, and the specimens sent for testing are from the same part of the body, and the detection results are all non-target strains, then the carriage is resolved.

[0031] Further, the graded interventions include:

[0032] When the transmission routes are combined and the transmission risk value is ≥0.4, prevention and control measures such as isolating patients and disinfecting the environment are initiated.

[0033] When the route of transmission is environmental residual transmission and the transmission risk value is ≥0.25, initiate prevention and control measures such as bed disinfection and patient screening.

[0034] When the transmission route is through nursing personnel or spatially adjacent transmission, and the transmission risk value is ≥0.3, initiate prevention and control measures such as environmental disinfection and patient screening.

[0035] A multi-drug resistant bacteria epidemic prevention and control system based on a multi-drug resistant bacteria epidemic prevention and control method is characterized by comprising:

[0036] Data acquisition module, used to obtain patient diagnosis and treatment data, microbiological testing data and nursing operation records in the hospital information system in real time;

[0037] The relationship network construction module is used to build a dynamic communication network model that includes patients' spatiotemporal relationships, communication pathways, and role hierarchies;

[0038] a risk assessment engine for performing calculations of transmission risk values;

[0039] Decision support module for generating reports for tiered interventions.

[0040] A server, comprising:

[0041] a memory storing a computer program;

[0042] The processor implements the steps of the method for preventing and controlling the epidemic of multidrug-resistant bacteria when executing the computer program.

[0043] A computer-readable storage medium stores a computer program, characterized in that when the computer program is executed by a processor, the computer program implements the steps of a method for preventing and controlling the epidemic of multidrug-resistant bacteria.

[0044] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0045] (1) The present invention collects data on the patient's hospitalization process and hospital bed distribution data, establishes a spatiotemporal relationship network based on the above data combined with the transmission pathway, and monitors non-positive patient samples through susceptibility factors. A patient relationship hierarchy is established, and data on patients with positive fungi of the same type are traced back to the source patient and the transmission pathway, and the clinical department is alerted to intervene in the patient in a timely manner. The risks of each ward or department in the hospital are further graded to form a risk warning report, so that risk data sharing and real-time forecasting can be achieved between clinical departments and infection control management departments at the first opportunity.

[0046] (2) The present invention uses retrospective analysis and prospective prediction as the main strategies, deeply analyzes the epidemic situation of all bacteria (mainly drug-resistant bacteria) in the hospital from the perspectives of time, space and patient groups, and establishes a hierarchical management mechanism for drug-resistant bacteria.

[0047] (3) The present invention establishes a real-time, automatic, and intelligent epidemic investigation mechanism for any positive bacteria in the hospital, especially multi-drug resistant bacteria, and can conduct a qualitative analysis of the source of the pathogens of any infected patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below in conjunction with the accompanying drawings:

[0049] Figure 1 This is a diagram of the role hierarchy in the present invention;

[0050] Figure 2 This is an example diagram of the role hierarchy relationship in the present invention;

[0051] Figure 3 This is a rule diagram for adding or removing relationship lines in the present invention. DETAILED DESCRIPTION

[0052] The present invention provides a method, system, server and storage medium for the prevention and control of the epidemic of multidrug-resistant bacteria. It constructs a set of epidemic prevention and control measures for multidrug-resistant bacteria that integrates the epidemic situation of bacterial flora, strain epidemic investigation, risk assessment, process intervention and other prevention and control functions, and achieves the new era of multidrug resistance prevention and control goals based on dynamic monitoring, risk management, global overview and precise prevention and control, greatly improving the biosafety prevention and control capabilities of medical institutions.

[0053] A method for epidemic prevention and control of multidrug-resistant bacteria includes: monitoring the test results of specimens from the respiratory tract, urinary tract, blood, and drainage tubes of hospitalized patients based on the susceptibility factors of the patient's location; constructing a spatiotemporal relationship network of patients based on their basic information and three transmission pathways; and recording data on each patient's attributes, relationships, and various factors daily. The epidemic characteristics of drug-resistant bacteria, the main transmission pathways, and the patient's susceptibility factors are condensed into rules, which are further analyzed in combination with information from the patient's hospitalization process information database to establish a relationship network of multidrug-resistant bacterial infection cases. The source of drug-resistant bacteria present in the hospital is accurately traced, and it is accurately determined whether patients with positive drug-resistant bacteria are transmitting the disease. If so, the source patient and transmission pathway are also determined.

[0054] The strain objects solved by the present invention cover bacterial classifications of different particles, including: classification by bacterial genus: Pseudomonas, Staphylococcus, Klebsiella, etc.; classification by bacterial genus: Pseudomonas, Staphylococcus, Klebsiella, etc.; classification by bacterial genus: Pseudomonas, Staphylococcus, Klebsiella, etc.; classification refers to the bacterial classification table.

[0055] The strain detection sites of the present invention are classified into four categories, including: respiratory tract, urinary tract, blood, and drainage tube.

[0056] The specific specimen classification is as follows Table 1:

[0057]

[0058] Table 1

[0059] The present invention constructs a spatiotemporal relationship network of patients based on the detection data of specimens from the corresponding parts of the body, which includes: (1) contact relationship data based on three transmission pathways: nursing staff-mediated transmission, spatial adjacent transmission, and environmental residual transmission; (2) patient attribute data sets: daily recorded strain detection information, medical operation records, and susceptibility factor assessment results of the body temperature data set.

[0060] Susceptibility factors were divided into four categories based on the site of strain detection. By analyzing a large number of hospital infection case hospitalization data, key factors that may affect the occurrence of infection at the site of strain detection were identified. Finally, the dataset variables were determined, including:

[0061] 1. Respiratory tract susceptibility factors:

[0062] 1. Respiratory tract disease progresses:

[0063] 1.1 Diagnosis with respiratory tract related description

[0064] The diagnosis contains one of the keywords "lung % infection", "pneumonia", "lower respiratory tract % infection", "% bacterial % lung", "empyema", "fever", "COPD", "chronic obstructive pulmonary disease", and "COPD".

[0065] 1.2 Course of disease / imaging with lung-related descriptions:

[0066] 1.2.1 Moderate / large amounts of sputum (coughing / aspirating moderate / large amounts of sputum);

[0067] 1.2.2 Presence of rales: The course of disease contains "wet rales" / "rales" / "bubbles" / "positive wet rales" in one sentence, and there is no description of "none" / "not" / "not";

[0068] 1.2.3 There is phlegm sound ("phlegm sound" can be heard);

[0069] 1.2.4 There is seepage: There is a sentence in the image that says “seepage” and there is no description of “none” / “not yet” / “not”.

[0070] 1.3 Send respiratory specimens

[0071] For specific specimen classification, please refer to the contents of Table 1 above.

[0072] 1.4 Abnormal body temperature

[0073] Abnormal body temperature: The highest body temperature of the day is ≥38.5℃.

[0074] 2. Respiratory tract defense breach:

[0075] 2.1 Use of a ventilator

[0076] (Not counting within 48 hours after intubation)

[0077] Method for extracting ventilator data from medical records and nursing records:

[0078] A. Extraction method of medical records

[0079] The key words to be extracted are “ventilator mode”;

[0080] The keyword “non-invasive ventilator” was excluded;

[0081] B. Methods of extracting nursing records

[0082] The keywords to be extracted are “ventilator”, “tracheotomy”, and “endotracheal intubation”;

[0083] Exclude the keyword “non-invasive ventilator”.

[0084] 2.2 Fiberoptic bronchoscopy

[0085] The method of extracting the "bronchoscope" operation record from the medical record: in the medical record table in the Oracle database, the field name "DOC_TITLE" contains "bronchoscope", "fiberbronchus", "fiber trachea", "fiber electronic bronchus", "fiber electronic trachea", "electronic fiber bronchus", and "electronic fiber trachea".

[0086] Respiratory tract susceptibility judgment of the present invention:

[0087] On that day (00:00:00-23:59:59), the patient met any one of 1.1, 2.1, 2.2, any one of 1.2.1, 1.2.2, 1.2.3, 1.2.4, and any one of 1.3 and 1.4.

[0088] 2. Urinary tract susceptibility factors:

[0089] 1. Urinary tract disease progresses:

[0090] 1.1 Diagnosis with urinary tract related description

[0091] The diagnosis contains one of the key words "urine % infection", "urinary % inflammation", "bacterial % urine", and "fever".

[0092] 1.2 Send urinary tract specimens

[0093] For specific specimen classification, please refer to the contents of Table 1 above.

[0094] 1.3 Abnormal body temperature

[0095] Abnormal body temperature: The highest body temperature of the day is ≥38.5℃.

[0096] 2. Urinary tract intubation: use of urinary tract catheter

[0097] (Not counting within 7 days after intubation)

[0098] Methods for extracting urinary tract catheterization data from medical records and nursing records:

[0099] The keyword to be extracted is "urinary tract catheterization".

[0100] Urinary tract susceptibility judgment of the present invention:

[0101] On that day (00:00:00-23:59:59), the patient had either 1.1 or 2, and either 1.2 or 1.3.

[0102] 3. Blood susceptibility factors:

[0103] 1. Progression of blood disease:

[0104] 1.1 Diagnosis with blood-related description

[0105] The diagnosis contains one of the keywords "blood infection", "phlebitis", "arteritis", "sepsis", "bacteremia", "septicemia", and "fever".

[0106] 1.2 Sending blood samples

[0107] For specific specimen classification, please refer to the contents of Table 1 above.

[0108] 1.3 Abnormal body temperature

[0109] Abnormal body temperature: The highest body temperature of the day is ≥38.5℃.

[0110] 2. Vascular rupture prevention: use central venous catheter

[0111] (Not counting within 7 days after intubation)

[0112] Methods for extracting central venous catheterization data from medical records and nursing records:

[0113] The key words to be extracted are “central venous catheterization”.

[0114] Blood susceptibility judgment of the present invention:

[0115] On that day (00:00:00-23:59:59), the patient contained 2, and either 1.2 or 1.3.

[0116] 4. Susceptibility factors of drainage tube-related sites:

[0117] 1. The condition of the drainage tube-related area has progressed:

[0118] 1.1 Send drainage fluid sample

[0119] For specific specimen classification, please refer to the contents of Table 1 above.

[0120] 1.2 Abnormal body temperature

[0121] Abnormal body temperature: The highest body temperature of the day is ≥38.5℃.

[0122] 2. Drainage tube breach: Use drainage tube

[0123] (Not counting within 72 hours after intubation)

[0124] Method for extracting drainage tube data from medical records and nursing records:

[0125] The keyword to be extracted is "drainage tube".

[0126] Susceptibility judgment of the drainage tube of the present invention:

[0127] On that day (00:00:00-23:59:59), the patient had either 1.1 or 2, and either 1.2 or 1.3.

[0128] The three types of transmission pathways of the present invention include:

[0129] Path 1: The same shift and the same nurse, and both parties have level 2 care needs, with the nurse as the medium for communication;

[0130] Path 2: Simultaneous adjacent beds (adjacent hospital beds at the same time), which is spatial adjacent transmission;

[0131] Route ③: Using the same inpatient bed successively within 48 hours is environmental residual transmission.

[0132] Considering that a patient may have multiple identities across multiple transmission routes, for example, patient A is both a primary contact and a secondary contact. When patient A tests positive, if the strain is the same, how can we determine whether the infection was caused by the primary or secondary contact? To clarify the transmission route, a transmission risk weighting algorithm is constructed based on the contact's susceptibility factors and transmission pathways. For example, the risk coefficient for respiratory, urinary, blood, and drainage susceptibility factors is β. The transmission pathways are then superimposed to weight the infection probability and determine the infection route.

[0133] The corresponding risk coefficient β values for the susceptible factors of respiratory tract, urinary tract, blood, and drainage tube are shown in Table 2:

[0134] Susceptible areas β value respiratory tract 1.8 urinary tract 1.3 blood 2.0 drainage tube 1.5 Concealed Carry / Other 1

[0135] Table 2

[0136] The single-pathway risk calculation formula is as follows:

[0137] 1. Transmission route ①: Transmission by nursing staff;

[0138] Formula: Risk value P1 = W1 × β × T1;

[0139] W1 (basic transmission coefficient): W1 = 0.3 × (1 + e^(-0.5 × (N-2))), where N is the number of common nursing shifts;

[0140] Example: When N=3, W1=0.3×(1+e^(-0.5×1))=0.3×(1+0.606)=0.482.

[0141] T1 (time intensity factor): T1 = 1-e^(-0.1×t), where t is the cumulative co-care time (hours);

[0142] Example: When t=6 hours, T1=1-e^(-0.6)=1-0.548≈0.452.

[0143] Complete example: P1 = 0.482 × β × 0.452.

[0144] 2. Transmission path ②: spatial adjacent transmission;

[0145] Formula: Risk value P2 = W2 × β × T2;

[0146] W2 (basic transmission coefficient): W2 = 0.2 × (1 + 0.3 × D), where D is the number of adjacent beds (D = 1 in the same ward and D = 0.5 in adjacent wards);

[0147] Example: Adjacent wards in the same ward, D = 1, W2 = 0.2 × 1.3 = 0.26;

[0148] T2 (time intensity factor): T2 = min (1, adjacent overlap duration (hours) / 72);

[0149] Example: When there are three consecutive days (72 hours), T2 = 1.0.

[0150] Complete example: P2 = 0.26 × β × 1.0.

[0151] 3. Transmission route ③: Environmental residual transmission;

[0152] Formula: Risk value P3 = W3 × β × T3;

[0153] W3 (basic transmission coefficient): W3 = 0.15 × (1-0.1 × C), where C is the number of bed unit disinfections;

[0154] Example: When not sterilized (C = 0), W3 = 0.15;

[0155] T3 (time intensity factor): T3 = e^(-(Δt-48) / 24), where Δt is the interval between the previous and next patient bed use (hours);

[0156] Example: When the interval is 24 hours, T3 = e^(-(24-48) / 24) = e^(1) = 2.718 (take 1.0 if it exceeds 1);

[0157] Complete example: P3 = 0.15 × β × 1.0.

[0158] When patients A and B have both transmission pathways ① and ②, ① and ② are combined into the multi-pathway calculation formula, and ③ is not included in the combination.

[0159] Specifically, the combined calculation of transmission paths ①+② is:

[0160] Formula: Merger risk P_merge = 1-(1-P1)×(1-P2);

[0161] Assuming that the two transmission pathways are independent, the combined risk is the probability that at least one pathway will be successful.

[0162] Example:

[0163] If P1=0.3, P2=0.2; then P_merge=1-(1-0.3)(1-0.2)=1-0.7×0.8=1-0.56=0.44.

[0164] The present invention generates prevention and control decision instructions, and triggers graded intervention measures when the transmission risk value exceeds a preset threshold. The threshold determination rules are shown in Table 3:

[0165]

[0166]

[0167] Table 3

[0168] When A has multiple transmission pathways (D->A, C->B->A, F->A, etc.) and the parent positive strain is the same, the transmission route is determined by calculating the risk value P of the parent patient. If A does not have a parent positive patient and is detected within 48 hours of admission, the patient is identified as an imported case and becomes the source patient.

[0169] The present invention establishes a role-level communication relationship network, traces the data of patients with positive fungi, and obtains the source patients and transmission paths.

[0170] Among them, the role-level communication relationship network is set to a maximum of three layers: positive / implicit -> first level -> second level. Figure 1 .

[0171] Ordinary patients have no superiors or subordinates, and second-level patients have no lower-level patients; a patient may have multiple roles, see examples Figure 2 .

[0172] A change in role or relationship is a new relationship line. For specific judgment, see "Rules for adding / canceling relationship lines" and Figure 3 .

[0173] The latent carrier status is when a patient with the original first-level status is converted from the first-level status to the latent carrier status because the patient at the lower level (original second-level status) reports positive.

[0174] Among them, every patient's attributes, relationships, various factors and other data are recorded every day. Specifically including:

[0175] 1. Ordinary patients: patient number, patient role;

[0176] 2. Positive patients: patient number, patient role, and relationship line.

[0177] Positive patient relationship lines can find superiors:

[0178] ① Report by a level 1 character

[0179] Reported positive strain, reporting time, reporting location, reporting ward / bed, superior positive patient, relationship number, relationship start time, relationship end time, relationship end reason, relationship line start time, relationship line end time, role start time, role end time;

[0180] ② Report by the second-level character

[0181] Reported positive strain, reported positive time, reported positive site, reported positive ward / bed, superior positive patient, superior level patient, relationship number, relationship start time, relationship end time, relationship end reason, relationship line start time, relationship line end time, role start time, role end time.

[0182] Positive patient relationship line failed to find a superior:

[0183] The reported positive strain, the time of reporting the positive infection, the reported positive location, the reported positive ward / bed, and whether it was imported from outside the hospital.

[0184] 3. First-level patients: patient number, patient role, and relationship line.

[0185] First-level patient relationship line: previous-level patient, previous-level carrier strain, relationship number, relationship start time, relationship end time, relationship end reason, relationship line start time, relationship line end time, role start time, role end time, whether it turned positive.

[0186] 4. Secondary patients: patient number, patient role, and relationship line.

[0187] Secondary patient relationship line: positive patient, positive carrier strain, previous level patient, relationship number, relationship start time, relationship end time, relationship end reason, relationship line start time, relationship line end time, role start time, role end time, whether converted to positive.

[0188] 5. Invisible patients: patient number, patient role, and relationship line.

[0189] Invisible patient relationship line: subordinate positive patient, positive carrier strain, relationship number, relationship start time, relationship end time, relationship end reason, relationship line start time, relationship line end time, whether converted to positive, superior positive patient, positive carrier strain, relationship number, relationship start time, relationship end time, relationship end reason, relationship line start time, relationship line end time, role start time, role end time, whether converted to positive.

[0190] The present invention adds a new relationship line rule: Patients can have multiple roles, and the new relationship lines have the following three situations:

[0191] Case 1: New pathway (the three pathway types mentioned above);

[0192] Case 2: The previous level role is added (for example, the previous level role was originally a level one role, and a positive patient role was added after the positive test result was reported, so this patient has both level one and level two roles);

[0193] Case 3: The number of patients at the previous level increases.

[0194] Rules for canceling relationship lines:

[0195] 1. Case 1: The patient at the previous level turns negative (becomes an ordinary patient).

[0196] Note: 1. Only the character on a specific relationship line is transformed into a regular patient; this does not apply to all the roles on the current relationship line. 2. This only applies to the determination of whether the overlapping paths (Path ① and Path ②) have ended; Path ③ does not determine whether the relationship has ended.

[0197] 2. Case 2: The relationship between the upper-level patient and the lower-level patient is terminated (the relationship no longer exists or is changed to another relationship).

[0198] Note: 1. Applicable to the judgment of overlapping and spacing relationships.

[0199] Determination of negative test result:

[0200] Carry-over rules apply to:

[0201] 1) The carrier was released within 14 days before the first positive case;

[0202] 2) carriage resolution after the target bacteria are detected in the patient;

[0203] 3) The carrier is released after a close contact relationship is established.

[0204] Lifting rules:

[0205] 1. Applicable to 1) Carrier exemption within 14 days before the first positive case:

[0206] If the patient has submitted the specimen for testing within 14 days before detection, and has submitted the specimen for testing ≥2 times within 7 days (including 2 or more hospitalizations), and the specimen site for testing is the same specimen site as when the target bacteria was detected, and the detection results are all non-target strains, then the carriage is resolved (the period from the time of testing closest to the positive detection to the time of testing closest to the positive detection is the non-carriage period, and the period from the time of testing closest to the positive detection to the time of the first positive test submission is the pre-detection carriage period).

[0207] II. Applicable to 3) Release of carrier status after close contact is established (including release of carrier status for patients who are latent carriers):

[0208] If the patient submits for testing ≥2 times within 7 days (including 2 or more hospitalizations), and the specimens submitted are from the same part of the body, and the detection results are all non-target strains, the carrier status will be released (the second time of testing is the starting time of release).

[0209] 3. Applicable to 2) Elimination of carriage after detection of target bacteria in patients:

[0210] If the patient submits for examination ≥2 times within 7 days (including 2 or more hospitalizations), and the specimens submitted are of the same species as the specimens when the target bacteria were detected, and the detection results are all non-target strains, then the carriage is resolved (the second time of submission is the starting time of liberation).

[0211] For example, if a patient has both sputum and blood samples positive for pan-resistant aeruginosa, then two sputum samples and two blood samples need to be sent for testing within 7 days and no target bacteria are detected in either test before the patient is considered free of pan-resistant aeruginosa.

[0212] Note:

[0213] 1. In the above cancellation rules, the same specimen collected within the same date (00:00:00-23:59:59 on XX / XX / XX) is counted as one specimen;

[0214] 2. If multiple identical specimens are submitted on the same date and the test results are inconsistent, any specimen found to contain the target bacteria will be counted as having detected the target bacteria on that day. For example, as shown in the following test scenario, a single specimen, "Blood-Whole Blood (B1-WB)," was submitted for testing on February 21, 2018. If any of the four specimens contains the target bacteria, then the Blood-Whole Blood (B1-WB) specimen will be counted as having detected the target bacteria on February 21, 2018.

[0215] In this clinical scenario, the system identifies patient A (bed 10) as having tested positive for carbapenem-resistant Klebsiella pneumoniae (CRKP). The system then traces back to patient B, identifying path ③ (patient B in the same bed is a latent carrier of CRKP, patient C is a positive test, and patient A also tests positive, meaning B is latent in the C->B->A transmission chain). Patient B is automatically flagged as a source of infection and a contact screening instruction is sent to the respiratory department. Simultaneously, patients D and F are also found to have tested positive for CRKP, and they share paths ① and ② with patient A, respectively, and are also flagged as sources of infection. The system then calculates the risk value P for each of the three paths, D->A, C->B->A, and F->A. If the paths D->A, C->B->A, and F->A meet these criteria, prevention and control measures are implemented, and the patient with the highest risk value is selected as the source of infection, determining the transmission route.

[0216] Implementation case: CRKP transmission and tracing

[0217] Scene Setting

[0218] Patient A (Bed 10): Respiratory tract infection (β=1.8), CRKP positive on the 5th day of admission.

[0219] Potential routes of transmission:

[0220] D→A (pathway ①);

[0221] C→B→A (pathway ③→pathway ③, B is a latent carrier, β=1.0);

[0222] F→A (pathway ②).

[0223] Risk Calculation:

[0224] 1. Transmission chain D→A (path ①):

[0225] Exposure data: N = 3, t = 8 hours;

[0226] W1=0.3×(1+e^(-0.5(3-2)))≈0.482;

[0227] T1=1-e^(-0.1×8)≈0.551;

[0228] PD→A=0.482×1.8×0.551≈0.480.

[0229] 2. Transmission chain C→B→A (pathway ③→pathway ③)

[0230] C→B (environmental communication):

[0231] W3=0.15×(1-0.1×0)=0.15

[0232] T3≈4.48 (take the upper limit as 1.0)

[0233] PC→B=0.15×1.0×1.0=0.15

[0234] B→A (environmental communication):

[0235] W3=0.15×(1-0.1×1)=0.135

[0236] T3=e^1≈2.718 (take the upper limit of 1.0)

[0237] PB→A=0.135×1.8×1.0≈0.243

[0238] Whole chain risk: PC→B→A=PC→B×PB→A=0.15×0.243≈0.036

[0239] 3. Transmission chain F→A (path ②)

[0240] Contact Data:

[0241] D = 1 (same ward), Δt = 48 hours

[0242] W2=0.2×(1+0.3×1)=0.26

[0243] T2≈0.667

[0244] PF→A=0.26×1.8×0.667≈0.312

[0245] V. Risk Ranking and Prevention and Control Decision-making

[0246] Transmission chain risk value risk level treatment measures

[0247] D→A 0.480 High-risk closed nursing unit + full staff screening

[0248] F→A 0.312 Disinfection of medium-risk environments + contact monitoring

[0249] C→B→A 0.036 Low risk only marks latent carrier B

[0250] System judgment:

[0251] Main source of infection: Patient D (highest risk);

[0252] Hidden carriers: Automatically push B's screening instructions to the respiratory ward workstation (secondary warning) and the infection control department workstation (first-level warning);

[0253] Priority treatment: Give priority to ward where D is located.

[0254] The present invention discloses a prevention and control system, comprising:

[0255] The data acquisition module is used to obtain patient diagnosis and treatment data (including hospital patient hospitalization process data and bed data), microbiological detection data and nursing operation records in the hospital information system in real time.

[0256] The monitoring module conducts personalized analysis based on the bed composition and patient susceptibility of different medical institutions, and collects data related to hospital infections caused by multidrug-resistant bacteria from systems such as HIS, LIS, RIS, nursing systems, EMR, and disinfection supply systems in real time, thereby realizing real-time monitoring of the entire process of hospitalized patients.

[0257] The monitoring modules are shown in Table 4:

[0258]

[0259]

[0260] Table 4

[0261] The relationship network construction module is used to establish a dynamic communication network model that includes patients' spatiotemporal relationships, communication pathways, and role hierarchies.

[0262] A risk assessment engine is used to perform the calculation of the transmission risk value.

[0263] Decision support module for generating reports for tiered interventions.

[0264] The early warning module triggers an alert when a latent carrier appears, accurately sending it to management personnel and the patient's clinical department, enabling both hospital and department levels to participate in the monitoring and prevention of multidrug-resistant bacteria. A tiered early warning strategy: Level 1 alerts are sent to the infection control department's workstation, while level 2 alerts are sent to the ward's medical and nursing workstations.

[0265] Some embodiments of the present application provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0266] Some embodiments of the present application provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation of the first aspect described above is implemented.

[0267] The above are only specific embodiments of the present invention, but the technical features of the present invention are not limited thereto. Any simple changes, equivalent substitutions, or modifications based on the present invention to solve substantially the same technical problems and achieve substantially the same technical effects are all included in the scope of protection of the present invention.

Claims

1. A method for preventing and controlling the epidemic of multidrug-resistant bacteria, characterized in that: The steps include: S1. Establish a dynamic monitoring module based on the classification of susceptible areas of patients, and collect real-time test data from respiratory tract, urinary tract, blood, and drainage tube specimens of hospitalized patients; S2. Construct a patient spatiotemporal relationship network based on the specimen detection data of the corresponding body parts. The patient spatiotemporal relationship network includes: Contact relationship data based on three transmission pathways: caregiver-mediated transmission, spatial proximity transmission, and environmental residual transmission; Patient attribute dataset: daily recorded strain detection information, medical operation records, and susceptibility factor assessment results of the body temperature dataset; S3. Based on the spatiotemporal relationship network of patients, a risk weight calculation model is established, and the transmission risk value is calculated using the following formula: P = Σ(W × β × T); Among them, W is the basic coefficient of the transmission pathway, β is the risk coefficient of the susceptible site, and T is the time intensity factor; S4. Establish a role-level communication relationship network to trace the data of patients with the same fungus-positive disease to obtain the source patient and the transmission path; The role-level communication relationship network includes: Dynamic role conversion rules for positive patients, latent carriers, primary contacts, and secondary contacts; Relationship line release judgment algorithm based on contact duration and strain genotype matching degree; S5. Generate prevention and control decision instructions, and trigger graded intervention measures when the transmission risk value exceeds the preset threshold.

2. The method for preventing and controlling the epidemic of multidrug-resistant bacteria according to claim 1, characterized in that: The values of the risk coefficients of the susceptible parts are: respiratory tract β = 1.8; urinary tract β = 1.3; blood β = 2.0; Drainage tube β = 1.5; latent carriage / other β = 1.

3. The method for preventing and controlling the epidemic of multidrug-resistant bacteria according to claim 2, characterized in that: The calculation formula of the basic coefficient of the transmission path includes: Transmission through nursing staff: W1 = 0.3 × (1 + e^(-0.5 × (N-2))), where N is the number of common nursing shifts; Adjacent transmission via spatial pathway: W2 = 0.2 × (1 + 0.3 × D), where D is the number of adjacent beds; Transmission via environmental residues: W3 = 0.15×(1-0.1×C), where C is the number of times the bed unit is disinfected.

4. The method for preventing and controlling the epidemic of multidrug-resistant bacteria according to claim 3, characterized in that: The formula for the time intensity factor includes: Transmission through caregivers: T1 = 1-e^(-0.1×t), where t is the cumulative duration of shared care; Adjacent propagation in pathway space: T2 = min(1, adjacent overlap duration / 72); Transmission via environmental residual: T3 = e^(-(Δt-48) / 24), where Δt is the interval between beds used by the previous and next patients.

5. The method for preventing and controlling the epidemic of multidrug-resistant bacteria according to claim 1, characterized in that: The dynamic role conversion rules include: when a second-level contact tests positive, the patient at the previous level is automatically marked as a latent carrier; when a latent carrier tests negative for the same part for at least two consecutive times, the carrier status is automatically lifted.

6. The method for preventing and controlling the epidemic of multidrug-resistant bacteria according to claim 5, characterized in that: Carryover applies to: 1) The carrier was released within 14 days before the first positive case; 2) carriage resolution after the target bacteria are detected in the patient; 3) Release of carrier status after close contact relationship is established; The specific cancellation rules are as follows: For 1), if the specimen was sent for testing within 14 days before detection, and ≥2 times within 7 days, and the specimen was sent for testing from the same location as the target bacteria, and the detection results were all non-target strains, then the carriage is resolved; For 2), if the specimens are submitted for testing ≥2 times within 7 days, and the specimens submitted are the same species as the specimens in which the target bacteria were detected, and the detection results are all non-target strains, then the carriage is resolved; For 3), if the specimens are sent for testing ≥2 times within 7 days, and the specimens sent for testing are from the same part of the body, and the detection results are all non-target strains, then the carriage is resolved.

7. The method for preventing and controlling the epidemic of multidrug-resistant bacteria according to claim 1, characterized in that: The tiered interventions include: When the transmission routes are combined and the transmission risk value is ≥0.4, the prevention and control measures of isolating patients and disinfecting the environment will be initiated; When the transmission route is environmental residual and the transmission risk value is ≥0.25, bed disinfection and patient screening prevention and control measures will be initiated; When the transmission route is through nursing personnel or spatially adjacent transmission, and the transmission risk value is ≥0.3, initiate prevention and control measures such as environmental disinfection and patient screening.

8. A multi-drug resistant bacteria epidemic prevention and control system based on a multi-drug resistant bacteria epidemic prevention and control method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, used to obtain patient diagnosis and treatment data, microbiological testing data and nursing operation records in the hospital information system in real time; The relationship network construction module is used to build a dynamic communication network model that includes patients' spatiotemporal relationships, communication pathways, and role hierarchies; a risk assessment engine for performing calculations of transmission risk values; Decision support module for generating reports for tiered interventions.

9. A server, characterized in that: include: a memory storing a computer program; A processor, when executing the computer program, implements the steps of the method for preventing and controlling the epidemic of multidrug-resistant bacteria according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for preventing and controlling the epidemic of multidrug-resistant bacteria according to any one of claims 1 to 7 are implemented.

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