Intelligent early warning system for deep venous catheter related blood flow infection

Abstract

The invention relates to the technical field of infectious disease monitoring, in particular to an intelligent early warning system for deep venous catheter related blood flow infection, which is characterized in that a deep venous catheter data collection module is used for collecting patient data related to a deep venous catheter; the deep venous catheter data processing module is used for carrying out qualification processing on the collected patient data; the blood flow infection intelligent analysis module is used for calculating a physiological abnormality score, a catheter-caused complication infection risk index and a puncture infection risk index according to the processed patient data; the blood flow infection intelligent diagnosis module is used for performing intelligent diagnosis on whether a patient has blood flow infection or not in combination with a clinical diagnosis standard and a medical knowledge base; the risk assessment module is used for performing hierarchical risk assessment according to the intelligent diagnosis direction; and the early warning module is used for performing early warning operation of the corresponding level according to the risk level. Early recognition and graded intervention of blood flow infection can be realized, so that the infection occurrence rate is reduced, and the clinical infection prevention and control efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of infectious disease monitoring technology, and in particular to an intelligent early warning system for deep vein catheter-related bloodstream infections. Background Technology

[0002] Deep vein catheters (DVCs) are widely used in intensive care and long-term intravenous therapy, playing a crucial role in ensuring the stability of intravenous access and the continuity of drug infusion. However, in clinical practice, DVC-related bloodstream infections have become a common and high-risk type of hospital-acquired infection, potentially prolonging hospital stays, increasing treatment costs, and impacting overall patient prognosis.

[0003] Current clinical prevention and monitoring mechanisms for bloodstream infections still face numerous technical bottlenecks in practical application. On the one hand, deep vein catheter-related bloodstream infections often lack typical symptoms in their early stages, and related vital sign changes are somewhat insidious, making early identification difficult using a single physiological parameter. On the other hand, risk factors such as catheter type, insertion time, and patient's underlying medical conditions are complex, and effective means for systematic management and dynamic assessment are lacking. Furthermore, the standardization of puncture procedures, staff compliance, and the completeness of clinical records also influence infection risk, but structured data collection and quantitative analysis methods for these factors are still immature.

[0004] In existing technical solutions, the diagnosis of catheter-related bloodstream infections (DRCs) largely relies on a comprehensive assessment of microbial culture, laboratory indicators, and clinical experience, limiting the timeliness of diagnosis and the ability to intervene proactively. While some auxiliary systems based on electronic medical records or monitoring devices can provide a degree of risk indication, they still have limitations in data dimensions, information fusion, and dynamic modeling, making it difficult to fully reflect the combined impact of individual patient differences and actual clinical behavior on infection risk. In summary, under the current circumstances, risk management of catheter-related infections mainly depends on human experience and fragmented data, with a prominent problem of information fragmentation, making it difficult to build a comprehensive and dynamic risk prediction mechanism. Furthermore, because early physiological changes in infection often lack specificity, clinical intervention often lags behind the actual onset of infection, affecting the timeliness and effectiveness of prevention and control. Summary of the Invention

[0005] This application provides an intelligent early warning system for deep vein catheter-related bloodstream infections, capable of early identification and graded intervention of bloodstream infections, thereby reducing the infection incidence and improving the efficiency of clinical infection control. This application provides the following technical solution: In a first aspect, this application provides an intelligent early warning system for deep vein catheter-related bloodstream infections, the system comprising a deep vein catheter data collection module, a deep vein catheter data processing module, an intelligent bloodstream infection analysis module, an intelligent bloodstream infection diagnosis module, a risk assessment module, and an early warning module; The deep vein catheter data collection module is used to collect patient data related to deep vein catheters; The deep vein catheter data processing module is used to process the collected patient data for quality control. The bloodstream infection intelligent analysis module is used to calculate physiological abnormality scores based on processed patient data. Catheter-related infection risk index and puncture infection risk index ; The intelligent bloodstream infection diagnosis module is used to calculate physiological abnormality scores. Catheter-related infection risk index and puncture infection risk index By combining clinical diagnostic criteria and a medical knowledge base, the system can intelligently diagnose whether a patient has developed a bloodstream infection. The risk assessment module is used to perform graded risk assessment based on the direction of intelligent diagnosis, classifying patients into low-risk, medium-risk, and high-risk categories. The early warning module is used to perform corresponding early warning operations based on the risk level.

[0006] In one specific implementation scheme, the patient data includes the patient's physiological parameters, complication data, and daily clinical data and catheter usage data. The deep vein catheter data collection module includes a physiological parameter unit, a complication data unit, and a clinical data unit. After collecting the patient data, the patient data is transmitted to the deep vein catheter data processing module for preprocessing, and then imported into the bloodstream infection intelligent analysis module for data calculation.

[0007] In one specific implementation, the physiological parameter unit collects the patient's physiological parameters, including body temperature, blood pressure, heart rate, respiratory rate, blood oxygen saturation, white blood cell count, C-reactive protein, and other indicators, through a medical device interface and sensors.

[0008] In one specific implementation scheme, the complication data unit collects patient complication data related to catheters through an electronic medical record system and manual input by medical staff. This includes the diagnostic criteria for catheter-related bloodstream infections, infection site, infection time, infection severity, treatment measures, complication rate, and implementation status of infection control measures.

[0009] In one specific implementation scheme, the clinical data unit collects patients' daily clinical data and catheter usage data through a mobile clinical terminal and a clinical information system, including catheter insertion time, catheter maintenance frequency, clinical operation records, skin disinfection status, catheter fixation status, clinical staff operation standardization, clinical staff training records, and clinical quality assessment results.

[0010] In one specific implementation scheme, the physiological abnormality score The calculation formula is: In the formula, Indicates the physiological abnormality score. This represents the measured value of the i-th physiological parameter. This represents the standard value of the i-th physiological parameter. This represents the weight of the i-th physiological parameter. This represents the total number of physiological parameters.

[0011] In one specific feasible implementation, the catheter raises an infection risk index for complications. The calculation formula is: In the formula, This indicates the risk index of infection complications caused by catheterization. Indicates the basic risk of catheter type. This represents the patient's risk correction factor. This indicates the risk factor for the duration of catheter placement. , , These represent the weights of the baseline risk of catheter type, the patient risk correction factor, and the risk factor of catheter indwelling time, respectively.

[0012] In one specific feasible implementation, the puncture infection risk index The calculation formula is: In the formula, This indicates the risk index of infection from puncture. This indicates the basic risk value of the puncture procedure itself. This represents the compliance rate of the i-th puncture operation. This represents the weight corresponding to the compliance rate of the i-th puncture operation. , , These represent the medical staff's theoretical examination score, simulated puncture operation assessment score, and experience coefficient, respectively. , , These represent the medical staff's theoretical examination score, simulated puncture operation assessment score, and experience coefficient, respectively. This represents the correction factor for patient-related risk factors.

[0013] In one specific feasible implementation, in the risk assessment module, when the physiological abnormality score... A score below 20, and a risk index for catheter-related complications and infections. <0.5%, with a risk index of infection during puncture. A score <0.05% is considered low-risk, indicating a low likelihood of the patient currently developing a deep vein catheter-related bloodstream infection; when the physiological abnormality score... When the score is between 20 and 60, the risk index for catheter-related complications and infections is considered high. Between 0.5% and 5%, or the risk index for puncture infection. A score between 0.05% and 1% is considered a medium-risk status, indicating a potential risk of bloodstream infection and requiring close monitoring; when the physiological abnormality score... A score of ≥60, or an infection risk index for catheter-related complications. ≥5%, or the risk index of puncture infection A rate of ≥1% indicates a high-risk status, meaning the patient is highly likely to develop a deep vein catheter-related bloodstream infection and requires immediate intervention.

[0014] In a specific feasible implementation, the early warning module sends routine clinical reminders to low-risk patients via mobile clinical terminals or clinical information systems, while also including the patient's information in the daily observation list; for medium-risk patients, it sends early warning information to the responsible nurse and attending physician via pop-ups and text messages, prompting them to increase the frequency of vital sign monitoring, strengthen local catheter clinical care, and develop personalized clinical plans; for high-risk patients, the highest level of early warning is triggered, which, in addition to pushing emergency alerts to medical staff, automatically generates a list of infection management recommendations and simultaneously initiates a multidisciplinary consultation process.

[0015] In summary, the beneficial effects of this application include at least the following: (1) By calculating the physiological abnormality score As a directional standard for early warning of infection risk, when calculating physiological abnormality scores When the risk level is low, routine monitoring measures should be implemented; when the calculated physiological abnormality score... In high-risk areas, enhanced monitoring and intervention measures should be implemented to ensure that the system can both detect potential infection risks early and reduce the incidence of infection.

[0016] (2) Calculate the infection risk index of catheter-related complications As a directional standard for assessing the risk of catheter-related infections, the calculated infection risk index for catheter-related complications... In low-risk situations, routine clinical procedures should be followed; when calculating the catheter-related infection risk index... In high-risk situations, optimized catheter maintenance and enhanced infection control measures were implemented, enabling the system to accurately identify high-risk catheter usage and reduce the occurrence of complications.

[0017] (3) Calculate the risk index of puncture infection As a directional standard for assessing the risk of infection during puncture procedures, when the calculated puncture infection risk index... When the risk level is low, standard precautions should be taken; when the calculated risk index for puncture infection is... In high-risk situations, strengthening operational training and strictly adhering to aseptic techniques can ensure that the system meets the standardized puncture procedure requirements, thereby reducing the risk of puncture-related infections.

[0018] By establishing modules for deep vein catheter (DVC) data collection, processing, intelligent bloodstream infection analysis, intelligent diagnosis, risk assessment, and early warning, and through inter-module collaboration, the system achieves comprehensive monitoring and real-time early warning of DVC-related bloodstream infections. This improves the accuracy of DVC infection risk assessment, elevating the system to an intelligent, precise, and automated level of infection control. This is further enhanced by constructing a physiological abnormality scoring system. Catheter-related infection risk index and puncture infection risk index The system integrates three major quantitative models, including patient physiological parameters, catheter usage, and clinical operation records, to achieve standardized data processing and dynamic analysis. This enables the system to identify early abnormal signals of infection in real time, accurately classify low, medium, and high risk levels, and take corresponding measures such as routine monitoring, enhanced clinical and emergency interventions. This effectively solves the problems of difficulty in early warning of bloodstream infections, delayed risk assessment, and lack of targeted intervention measures, thereby reducing the incidence of deep vein catheter-related bloodstream infections and improving the timeliness of clinical infection control.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, the preferred embodiments of this application are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0020] Figure 1 This is a structural block diagram of the intelligent early warning system for deep vein catheter-related bloodstream infections in this application embodiment. Detailed Implementation

[0021] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0022] Reference Figure 1 This is a structural block diagram of a deep vein catheter-related bloodstream infection intelligent early warning system provided in one embodiment of this application. The system includes a deep vein catheter data collection module, a deep vein catheter data processing module, a bloodstream infection intelligent analysis module, a bloodstream infection intelligent diagnosis module, a risk assessment module, and an early warning module.

[0023] The deep vein catheter data collection module is used to collect patient data related to deep vein catheters, including but not limited to the patient's physiological parameters, catheter usage (such as insertion time, insertion site, catheter type and other usage), and clinical operation records. After collecting the patient data, the patient data is transmitted to the deep vein catheter data processing module for preprocessing, and then imported into the bloodstream infection intelligent analysis module for data calculation.

[0024] Specifically, the deep vein catheter data collection module includes a physiological parameter unit, a complication data unit, and a clinical data unit. Patient data includes the patient's physiological parameters, complication data, and daily clinical and catheter usage data. In the physiological parameter unit, physiological parameters such as body temperature, blood pressure, heart rate, respiratory rate, blood oxygen saturation, white blood cell count, C-reactive protein (CRP), and other indicators are collected through medical device interfaces and sensors (such as thermometers, blood pressure monitors, blood cell analyzers, and wearable measuring devices). In the complication data unit, catheter-related patient complication data are collected through the electronic medical record system and manual input by medical staff, including the diagnostic criteria for catheter-related bloodstream infections (CRBSI), infection site, infection time, infection severity, treatment measures, complication rate, and implementation status of infection control measures. In the clinical data unit, daily clinical and catheter usage data are collected through mobile clinical terminals and clinical information systems, including catheter insertion time, catheter maintenance frequency, clinical operation records, skin disinfection status, catheter fixation status, clinical staff operation standardization, clinical staff training records, and clinical quality assessment results.

[0025] The deep vein catheter data processing module is used to process the collected patient data for quality control, including data cleaning, format standardization, and preliminary classification and archiving. It also extracts key characteristic variables related to catheter use and infection risk to support subsequent analysis and diagnosis.

[0026] The bloodstream infection intelligent analysis module is used to calculate physiological abnormality scores based on processed patient data. Catheter-related infection risk index and puncture infection risk index .

[0027] Specifically, the bloodstream infection intelligent analysis module includes a physiological abnormality analysis unit, a complication analysis unit, and a puncture infection risk analysis unit. The physiological abnormality analysis unit calculates a physiological abnormality score based on the patient's physiological parameters, including body temperature, blood pressure, heart rate, respiratory rate, blood oxygen saturation, white blood cell count, C-reactive protein (CRP), and other indicators. The calculation formula is as follows: In the formula, Indicates the physiological abnormality score. This represents the measured value of the i-th physiological parameter (such as body temperature, blood pressure, heart rate, respiratory rate, blood oxygen saturation, white blood cell count, C-reactive protein, and other indicators). This represents the standard value of the i-th physiological parameter. This represents the weight of the i-th physiological parameter. This means that the portion of the measured physiological parameter value that exceeds the standard value will be included in the physiological abnormality score. This represents the total number of physiological parameters. It is calculated by setting a physiological abnormality score. It is then compared with a preset threshold range to serve as a directional standard for early warning of infection risk. The calculated physiological abnormality score... Within the low-risk threshold range, routine monitoring measures are implemented; when the calculated physiological abnormality score... When the risk threshold is high, enhanced monitoring and intervention measures should be taken so that the system can both detect potential infection risks early and reduce the incidence of infection.

[0028] The complication analysis unit calculates the catheter-related complication risk index based on patient complication data, including diagnostic criteria, infection site, infection time, infection severity, treatment measures, complication incidence, and implementation of infection control measures for catheter-related bloodstream infections (CRBSI). The calculation formula is as follows: In the formula, This indicates the risk index of infection complications caused by catheterization. The baseline risk for catheter type is derived by statistically analyzing the infection incidence of different catheter types in a large number of clinical cases and then standardizing the data. The patient risk correction coefficient is calculated by weighting factors such as the patient's immune status, underlying diseases, and history of infection in the patient's electronic medical record. The risk coefficient representing the duration of catheter indwelling is derived by fitting a model based on the actual number of days the catheter is indwelling and the trend of clinical infection rate over time. , , These represent the weights of the baseline risk of catheter type, the patient risk adjustment factor, and the catheter indwelling duration risk factor, respectively. The risk index of catheter-related complications and infections is calculated. The calculated catheter-related infection risk index is compared with a preset threshold range and used as a directional standard for assessing the risk of catheter-related infections. When the risk threshold is low, routine clinical procedures should be followed; when the calculated catheter-related infection risk index is... When the risk threshold is within the range, optimized catheter maintenance and enhanced infection control measures are taken to enable the system to accurately identify high-risk catheter usage, thereby reducing the occurrence of complications.

[0029] In another feasible embodiment, to further improve the predictive accuracy and sensitivity of the catheter-related complication infection risk index, a novel calculation formula based on nonlinear interactive modeling is proposed. While keeping the original three key variables unchanged, the mathematical structure of the risk assessment model is redesigned to better reflect the synergistic effect of multiple risk factors in actual clinical scenarios. Catheter-induced complication infection risk index The calculation formula is as follows: In the formula, This represents the risk cross-penalty coefficient, used to limit the fluctuation of the risk value within a reasonable range, ensuring that the final output risk index is still limited to the probability range between 0 and 1. , , This is an exponential factor used to adjust the weights of various interactive risk factors. The core of this formula lies in using the pairwise cross-products of the three basic variables as the core expression of risk, and strengthening its synergistic effect modeling ability through an exponential function. This structural improvement reflects a high degree of alignment with actual clinical practice. In clinical practice, catheter type, patient condition, and indwelling time do not independently affect risk, but rather exhibit a high degree of dependence and amplification. For example, a standard catheter in a healthy patient may have almost no risk of infection after 5 days of indwelling, but if a central venous catheter is used in an immunosuppressed patient and left in place for more than 7 days, the risk of infection increases rapidly. Traditional linear models cannot accurately express this "risk multiplication" phenomenon, easily leading to ambiguous risk assessment results and unclear grading boundaries, affecting clinical judgment and intervention timing.

[0030] Compared to existing linear weighted formulas, this improved formula features structural modifications. It introduces pairwise cross-product terms between R, P, and K, weighted by a power function, which more realistically reflects the enhanced coupling effect between different risk factors, thereby improving the model's responsiveness in multi-factor concurrency scenarios. Secondly, the inclusion of a normalization suppression factor in the denominator dynamically suppresses the risk index when multiple high-risk values ​​occur simultaneously, preventing abnormal inflation of risk estimates and enhancing model stability. Through these designs, the model not only improves the accuracy of identifying high-risk catheter use scenarios but also enhances its specificity in identifying low-risk patient groups. This nonlinear interactive modeling strategy can more effectively distinguish between low, medium, and high-risk patients, providing a more reliable quantitative basis for subsequent risk stratification and intervention recommendations. More importantly, the new Qz calculation model maintains consistency with the original system's data structure and risk output interface, facilitating seamless integration with subsequent intelligent bloodstream infection diagnosis and risk assessment modules, further enhancing the intelligence and precision of the infection control system.

[0031] It should be noted that the basis for the above formula improvement lies in, Indicates the basic risk of catheter type. This represents the patient's risk correction factor. The risk coefficient representing the duration of catheter placement not only has independence in infection formation but also exhibits a high degree of interaction dependence. For example, a certain type of catheter inherently carries a certain risk of infection (high R value), but in young, healthy patients (low P) and with short-term use (low K), the actual likelihood of infection is low. Conversely, if the catheter is of medium risk (medium R), but the patient is in a postoperative immunosuppressive state (high P) and the placement time exceeds 7 days (high K), even if the individual factor risk is not extreme, the overall risk may increase sharply. Clinical evidence also shows that when multiple medium-risk factors act together, the infection rate is often higher than the sum of their individual risks, demonstrating a strong nonlinear enhancement effect. Therefore, while traditional linear weighted models are intuitive and easy to calculate, they cannot truly characterize the aforementioned multi-factor amplification phenomenon. The formula proposed in this embodiment, by introducing three sets of pairwise cross-product terms, explicitly models these three typical clinical risk coupling paths and uses a power exponent to adjust their amplification degree, giving the model higher sensitivity and expressive power. Because the R, P, and K parameters respectively carry clinical risk information from the dimensions of device, individual, and time, this nonlinear interactive model can fully leverage its structural advantages to more accurately simulate the formation path of catheter-related infections under the influence of multiple factors. This design not only demonstrates significant innovation at the mathematical modeling level but also exhibits high rationality and engineering adaptability in medical semantics, effectively improving the system's specificity in identifying infection risks and its efficiency in early warning response for different patient groups and operational scenarios.

[0032] The puncture infection risk analysis unit calculates the puncture infection risk index. The calculation formula is as follows: In the formula, This indicates the risk index of infection from puncture. This indicates the basic risk value of the puncture procedure itself. This represents the compliance rate of the i-th puncture procedure (such as the compliance rate of hand hygiene, skin disinfection, sterile barrier usage, and puncture site selection). The range value is between 0 and 1. This represents the weight corresponding to the compliance rate of the i-th puncture operation. , , These represent the medical staff's theoretical examination score, simulated puncture operation assessment score, and experience coefficient, respectively. , , These represent the theoretical examination score, simulated puncture operation assessment score, and experience coefficient of the medical staff performing the procedure, respectively. The range value is between 0 and 1. This represents the patient-related risk factor correction coefficient. It is calculated using the puncture infection risk index. The calculated puncture infection risk index is compared with a preset threshold range to serve as a directional standard for assessing the infection risk of puncture procedures. When within the low-risk threshold range, standard precautions should be taken; when the calculated puncture infection risk index... When the risk threshold is high, strengthening operational training and strictly adhering to aseptic techniques will ensure that the system meets the standardized puncture procedure, thereby reducing the risk of puncture-related infections.

[0033] The intelligent diagnostic module for bloodstream infection is used to calculate physiological abnormality scores. Catheter-related infection risk index and puncture infection risk index Combining clinical diagnostic criteria and a medical knowledge base, the system intelligently diagnoses whether a patient has a bloodstream infection. Specifically, the intelligent bloodstream infection diagnosis module first assesses physiological abnormalities... By combining clinical diagnostic criteria and physiological abnormality threshold ranges from the medical knowledge base, a preliminary judgment is made as to whether a bloodstream infection has occurred due to a physiological abnormality. Secondly, the risk index of catheter-related complications is used. The determination of whether catheter-related infection has occurred is made by combining clinical diagnostic criteria and the risk threshold range of catheter-related complications in the medical knowledge base; finally, the puncture infection risk index is used. The determination of whether a puncture-related infection has occurred is made by combining clinical diagnostic criteria and the puncture infection risk threshold range in the medical knowledge base.

[0034] The risk assessment module is used to perform tiered risk assessments based on the direction of intelligent diagnosis, classifying patients into low-risk, medium-risk, and high-risk categories; specifically, when physiological abnormality scores... A score below 20, and a risk index for catheter-related complications and infections. <0.5%, with a risk index of infection during puncture. A score <0.05% is considered low-risk, indicating a low likelihood of the patient currently developing a deep vein catheter-related bloodstream infection. When the physiological abnormality score... When the score is between 20 and 60, the risk index for catheter-related complications and infections is considered high. Between 0.5% and 5%, or the risk index for puncture infection. A score between 0.05% and 1% is considered a medium-risk status, indicating a potential risk of bloodstream infection and requiring close monitoring. When the physiological abnormality score... A score of ≥60, or an infection risk index for catheter-related complications. ≥5%, or the risk index of puncture infection A rate of ≥1% indicates a high-risk status, meaning the patient is highly likely to develop a deep vein catheter-related bloodstream infection and requires immediate intervention.

[0035] The early warning module is used to issue warnings based on the risk level. Specifically, for low-risk patients, the system sends routine clinical reminders via mobile clinical terminals or the clinical information system, recommending standard catheter maintenance and monitoring of vital signs, and adding the patient's information to the daily observation list. For medium-risk patients, the system sends warning information to the responsible nurse and attending physician via pop-ups and SMS, suggesting increased frequency of vital sign monitoring (e.g., every 1.5 hours), enhanced local clinical care of the catheter, and, if necessary, blood cultures and other examinations, and developing a personalized clinical plan. High-risk patients trigger the system's highest level warning. In addition to sending an emergency alert to medical staff, the system automatically generates a list of infection management recommendations, including preferred antibiotic types, catheter replacement or removal suggestions, and simultaneously initiates a multidisciplinary consultation process to ensure the patient receives timely and effective treatment.

[0036] In summary, by establishing modules for deep vein catheter (DVC) data collection, processing, intelligent bloodstream infection analysis, intelligent diagnosis, risk assessment, and early warning, and through the collaborative efforts of these modules, the system achieves comprehensive monitoring and real-time early warning of DVC-related bloodstream infections. This improves the accuracy of the system's risk assessment for DVC infections, elevating the system to a level of intelligent, precise, and automated infection control. This is further enhanced by constructing a physiological abnormality scoring system. Catheter-related infection risk index and puncture infection risk index The system integrates three major quantitative models, including patient physiological parameters, catheter usage, and clinical operation records, to achieve standardized data processing and dynamic analysis. This enables the system to identify early abnormal signals of infection in real time, accurately classify low, medium, and high risk levels, and take corresponding measures such as routine monitoring, enhanced clinical and emergency interventions. This effectively solves the problems of difficulty in early warning of bloodstream infections, delayed risk assessment, and lack of targeted intervention measures, thereby reducing the incidence of deep vein catheter-related bloodstream infections and improving the timeliness of clinical infection control.

[0037] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0038] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A smart early warning system for deep vein catheter-related bloodstream infections, characterized in that, The system includes a deep vein catheter data collection module, a deep vein catheter data processing module, a bloodstream infection intelligent analysis module, a bloodstream infection intelligent diagnosis module, a risk assessment module, and an early warning module. The deep vein catheter data collection module is used to collect patient data related to deep vein catheters; The deep vein catheter data processing module is used to process the collected patient data for quality control. The bloodstream infection intelligent analysis module is used to calculate physiological abnormality scores based on processed patient data. Catheter-related infection risk index and puncture infection risk index ; The intelligent bloodstream infection diagnosis module is used to calculate physiological abnormality scores. Catheter-related infection risk index and puncture infection risk index By combining clinical diagnostic criteria and a medical knowledge base, the system can intelligently diagnose whether a patient has developed a bloodstream infection. The risk assessment module is used to perform graded risk assessment based on the direction of intelligent diagnosis, classifying patients into low-risk, medium-risk, and high-risk categories. The early warning module is used to perform corresponding early warning operations based on the risk level.

2. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 1, characterized in that, The patient data includes the patient's physiological parameters, complication data, daily clinical data, and catheter usage data. The deep vein catheter data collection module includes a physiological parameter unit, a complication data unit, and a clinical data unit. After collecting the patient data, the patient data is transmitted to the deep vein catheter data processing module for preprocessing, and then imported into the bloodstream infection intelligent analysis module for data calculation.

3. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 2, characterized in that, The physiological parameter unit collects the patient's physiological parameters, including body temperature, blood pressure, heart rate, respiratory rate, blood oxygen saturation, white blood cell count, C-reactive protein, and other indicators, through medical device interfaces and sensors.

4. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 2, characterized in that, The complication data unit collects patient complication data related to catheters through electronic medical record systems and manual input by medical staff. This includes the diagnostic criteria for catheter-related bloodstream infections, infection site, infection time, infection severity, treatment measures, complication rate, and implementation status of infection control measures.

5. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 2, characterized in that, The clinical data unit collects patients' daily clinical data and catheter usage data through mobile clinical terminals and clinical information systems, including catheter insertion time, catheter maintenance frequency, clinical operation records, skin disinfection status, catheter fixation status, clinical staff operation standards, clinical staff training records, and clinical quality assessment results.

6. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 1, characterized in that, The physiological abnormality score The calculation formula is: In the formula, Indicates the physiological abnormality score. This represents the measured value of the i-th physiological parameter. This represents the standard value of the i-th physiological parameter. This represents the weight of the i-th physiological parameter. This represents the total number of physiological parameters.

7. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 1, characterized in that, The catheter may cause infection and complications. The calculation formula is: In the formula, This indicates the risk index of infection complications caused by catheterization. Indicates the basic risk of catheter type. This represents the patient's risk correction factor. This indicates the risk factor for the duration of catheter placement. , , These represent the weights of the baseline risk of catheter type, the patient risk correction factor, and the risk factor of catheter indwelling time, respectively.

8. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 1, characterized in that, The risk index of puncture infection The calculation formula is: In the formula, This indicates the risk index of infection from puncture. This indicates the basic risk value of the puncture procedure itself. This represents the compliance rate of the i-th puncture operation. This represents the weight corresponding to the compliance rate of the i-th puncture operation. , , These represent the medical staff's theoretical examination score, simulated puncture operation assessment score, and experience coefficient, respectively. , , These represent the medical staff's theoretical examination score, simulated puncture operation assessment score, and experience coefficient, respectively. This represents the correction factor for patient-related risk factors.

9. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 1, characterized in that, In the risk assessment module, when physiological abnormality score A score below 20, and a risk index for catheter-related complications and infections. <0.5%, with a risk index of infection during puncture. A value of <0.05% is considered low-risk, indicating that the patient is unlikely to have a current deep vein catheter-related bloodstream infection. When physiological abnormality score When the score is between 20 and 60, the risk index for catheter-related complications and infections is considered high. Between 0.5% and 5%, or the risk index for puncture infection. A rate between 0.05% and 1% is considered a medium-risk status, indicating a potential risk of bloodstream infection in the patient, requiring enhanced monitoring. When physiological abnormality score A score of ≥60, or an infection risk index for catheter-related complications. ≥5%, or puncture infection risk index A rate of ≥1% indicates a high-risk status, meaning the patient is highly likely to develop a deep vein catheter-related bloodstream infection and requires immediate intervention.

10. The intelligent early warning system for deep vein catheter-related bloodstream infections according to claim 1, characterized in that, In the aforementioned early warning module, for low-risk patients, routine clinical reminders are sent via mobile clinical terminals or clinical information systems, and the patient's information is included in the daily observation list. For medium-risk patients, early warning information is sent to the responsible nurse and attending physician via pop-ups and text messages, prompting them to increase the frequency of vital sign monitoring, strengthen local catheter clinical care, and develop personalized clinical plans. For high-risk patients, the highest level of early warning is triggered, which, in addition to pushing emergency alerts to medical staff, automatically generates a list of infection management recommendations and simultaneously initiates a multidisciplinary consultation process.

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