Integrated monitoring system for bladder pressure information

Abstract

The invention discloses an integrated monitoring system for bladder pressure information, which is used for monitoring bladder pressure changes of patients with acute pancreatitis and severe acute pancreatitis and predicting disease progression through dynamic analysis. The system comprises a bladder pressure monitoring device, a data transmission module, a data analysis and early warning system and an integrated monitoring platform. The bladder pressure monitoring device adopts a high-precision pressure sensor, measures the pressure in the bladder in real time through a catheter, and transmits data to the data analysis system through a wireless network. The data analysis system calculates a disease risk score through a regression model based on multi-dimensional data such as a bladder pressure value, a change rate and an acceleration, and provides early warning and treatment decision support for doctors. The integrated monitoring platform displays real-time bladder pressure data and risk assessment, and helps doctors to master patient condition changes. Compared with the prior art, the bladder pressure monitoring system has the advantages that the functions of continuous dynamic monitoring, accurate risk prediction and real-time early warning are provided, and the accuracy and timeliness of bladder pressure monitoring are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bladder pressure monitoring, and more particularly, to an integrated monitoring system for bladder pressure information. Background Art

[0002] Acute pancreatitis (AP) is a common digestive system disease, which can develop into severe acute pancreatitis (SAP) in severe cases. Among them, about 15%-20% of patients may develop into severe cases, manifested as persistent organ failure (POF) and multiple organ dysfunction syndrome (MODS). The mortality rate of severe acute pancreatitis is as high as 50%. Among them, organ failure is one of the main factors leading to death. Although some treatment measures have been taken clinically, such as anti-inflammatory drugs, nutritional support, fluid management, etc., due to the lack of specific therapeutic drugs at present, the treatment of acute pancreatitis, especially severe acute pancreatitis, is still full of challenges. Therefore, how to early predict the severe development and organ failure of acute pancreatitis remains an urgent clinical problem to be solved.

[0003] Intermittency and limitation of bladder pressure monitoring: In the clinical treatment of acute pancreatitis and severe acute pancreatitis, intra-abdominal pressure (IAP) and intra-bladder pressure (IBP) are important clinical monitoring indicators, which can indirectly reflect the impact of intra-abdominal pressure on organs, and then infer the risk of multiple organ failure. Existing studies have shown that the continuous increase of bladder pressure is closely related to the occurrence and mortality of severe acute pancreatitis. Most traditional bladder pressure monitoring methods rely on single or intermittent measurements. This monitoring method cannot reflect the dynamic changes of bladder pressure in real time and continuously, and is easy to miss some important pathological changes, resulting in inaccurate or delayed judgment of the condition. For example, the bladder pressure of some patients may fluctuate violently in a short period of time, while traditional intermittent measurements cannot capture these rapidly changing signals.

[0004] Accuracy problem of bladder pressure monitoring: At present, most clinical bladder pressure monitoring devices still have certain accuracy problems. Especially when using traditional manual measurement methods, due to factors such as the technical level of operators and the stability of equipment, the measurement error may be relatively large. In addition, existing bladder pressure monitoring devices usually do not take into account the individual differences and disease changes of patients, resulting in large fluctuations in bladder pressure measurement results, affecting the usability and reliability of data.

[0005] Lack of dynamic monitoring and early warning mechanism: Most traditional bladder pressure monitoring systems are limited to static and single measurements, lacking the ability to monitor the dynamic response of patients' condition changes in real time. This results in clinicians being able to intervene only when the bladder pressure reaches an extreme value, rather than being able to give an early warning at the initial stage of abnormal fluctuations in bladder pressure, missing the opportunity for early intervention. Existing technologies have not effectively combined dynamic factors such as the change rate and change acceleration of bladder pressure to predict the future trend of the condition, thus unable to provide sufficient decision-making support for clinicians.

[0006] The system lacks integrated data analysis: Currently, many bladder pressure monitoring devices can only provide single bladder pressure data and lack the function of in-depth data analysis. Even if some systems have multiple data recording functions, they usually lack real-time data integration and interaction functions and cannot analyze multi-dimensional data such as bladder pressure, change rate, and acceleration simultaneously. Therefore, the overall disease risk of patients is difficult to be comprehensively evaluated, and doctors face the problem of insufficient information when judging the condition of patients, often relying on traditional clinical experience for decision-making, increasing the difficulty and risk of diagnosis and treatment.

[0007] There is no unified standard and optimization algorithm: Although the importance of bladder pressure in acute pancreatitis has been recognized clinically, there is currently no complete set of standardized and personalized bladder pressure monitoring algorithms. In existing technologies, the processing of bladder pressure data mostly relies on manual experience or simple formula calculations, lacking accurate algorithms based on big data analysis. Multi-dimensional information such as the change rate and acceleration of bladder pressure changes has not been effectively applied in actual clinical practice for risk assessment and disease prediction.

[0008] Therefore, we urgently need to design an integrated monitoring system for bladder pressure information to solve the above problems. Summary of the Invention

[0009] The purpose of the present invention is to solve the existing technical problems proposed in the above background technology and provide an integrated monitoring system for bladder pressure information.

[0010] The purpose of the present invention is achieved through the following technical solutions:

[0011] On the one hand, the present invention proposes an integrated monitoring system for bladder pressure information, and the system includes: a bladder pressure monitoring device, a data transmission module, a data analysis and early warning system, and an integrated monitoring platform; wherein, the bladder pressure monitoring device measures the intra-bladder pressure IBP in real time through the trans-bladder technology and transmits the bladder pressure data IBP ( t ) to the data transmission module every 6 hours or at a set time interval.

[0012] The data analysis and early warning system calculates the change trend, change rate and acceleration of the bladder pressure based on the received bladder pressure data, and determines whether the patient has a continuously increasing bladder pressure;

[0013] The integrated monitoring platform is used to display the real-time data and historical data of the intravesical pressure (IBP), and generate an early warning report according to the dynamic monitoring results.

[0014] As a preferred technical solution of the present invention, the bladder pressure monitoring device inserts a catheter into the patient's bladder, and uses a bladder pressure sensor to accurately measure the intravesical pressure (IBP) ( t ) , and transmits the data to the data analysis and early warning system in real time through a wireless data transmission module, where the bladder pressure data (IBP ( t ) is the real-time pressure value recorded every 6 hours, and the data recording is maintained for at least 72 hours to ensure a sufficient time span for analyzing the change trend of the bladder pressure.

[0015] As a preferred technical solution of the present invention, the data transmission module uses wireless network technology to ensure accurate data transmission between the bladder pressure monitoring device and the data analysis and early warning system. Among them, the bladder pressure data (IBP ( t ) ) is transmitted through data packets, and each data packet includes information such as the current bladder pressure value, change rate, and acceleration, which is used to monitor the dynamic changes of the patient's condition in real time.

[0016] As a preferred technical solution of the present invention, the data analysis and early warning system calculates the change rate (R ( t ) ) of the bladder pressure according to the received bladder pressure data (IBP ( t ) ), and its calculation formula is as follows:

[0017]

[0018] Among them, R(t) represents the change rate of the bladder pressure at time t, IBP(t) is the current bladder pressure value, IBP(t - 1) is the bladder pressure value at the previous moment, t is the current time, and t - 1 is the time at the previous moment. R(t) reflects the change amount of the bladder pressure per unit time.

[0019] As a preferred technical solution of the present invention, the data analysis and early warning system further calculates the change acceleration (A(t)) of the bladder pressure, and its formula is as follows:

[0020]

[0021] Among them, A(t) represents the acceleration of the change in bladder pressure, R(t) is the rate of change of bladder pressure at the current moment, R(t - 1) is the rate of change of bladder pressure at the previous moment, and A(t) reflects the rate of change of the rate of change of bladder pressure, which is used to analyze the accelerating trend of the change in bladder pressure.

[0022] As a preferred technical solution of the present invention, the data analysis and warning system determines whether there is a continuously increasing bladder pressure based on the real-time data of bladder pressure (IBP) and the acceleration of the change in bladder pressure (A(t)). If the bladder pressure (IBP) is greater than 15 mmHg, the acceleration A(t) is greater than 0, and it lasts for more than 48 hours, it is considered that the patient has a continuously increasing bladder pressure, and a warning is triggered to prompt the doctor to pay attention to the risk of the patient's condition.

[0023] As a preferred technical solution of the present invention, the integrated monitoring platform displays the maximum value IBP of bladder pressure max , the trend chart of the rate of change R(t) and acceleration A(t) of bladder pressure. Doctors can view the trend of the change in the patient's bladder pressure in real time on the platform and generate a dynamic analysis report to make accurate treatment decisions.

[0024] As a preferred technical solution of the present invention, the data analysis and warning system generates a risk assessment of the patient's condition based on the maximum value IBP of bladder pressure max and the acceleration of the change in bladder pressure A(t), and uses a regression model (P risk ) to predict the risk of the patient. The formula is as follows:

[0025] P risk = α·IBP max + β·A(t)+ γ

[0026] Among them, P risk is the risk score of the patient, α, β, and γ are regression coefficients, IBP max is the maximum value of bladder pressure, A(t) is the acceleration of the change in bladder pressure, and P risk represents the probability of the patient getting sick and reflects the severity of the condition.

[0027] As a preferred technical solution of the present invention, the data analysis and warning system generates a risk level based on the calculated disease risk P risk and displays it on the integrated monitoring platform, which is divided into three levels: low risk, medium risk, and high risk. Doctors can further adjust the treatment plan and intervention measures according to the risk level.

[0028] As a preferred technical solution of the present invention, the system continuously stores the bladder pressure data IBP(t), the rate of change R(t), the acceleration A(t), and the disease risk P riskGenerate historical data records of the patient based on the above data, and display the patient's disease development trend on the integrated monitoring platform for doctors to track and evaluate during subsequent treatment to ensure timely intervention and adjustment of the patient's condition.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. By adopting a high-precision pressure sensor and a standardized measurement process, the present invention can achieve real-time and continuous monitoring of bladder pressure and ensure the accuracy of each measurement result. In addition, by combining the method of injecting 25 ml of sterile normal saline into the bladder, the measurement of bladder pressure is made more stable and comparable, avoiding the measurement errors and inaccuracies of traditional bladder pressure monitoring devices.

[0031] 2. The present invention not only monitors the value of bladder pressure, but also analyzes multi-dimensional data such as the rate of change of bladder pressure (R ( t ) ) and the acceleration of change (A ( t ) ). Through these dynamic data, the system can comprehensively evaluate the patient's condition and generate a risk score (P risk ) based on the regression analysis algorithm, realizing accurate prediction of the condition. This provides a more scientific and comprehensive treatment basis for clinicians.

[0032] 3. Through real-time data monitoring and dynamic analysis, the present invention can promptly identify abnormal fluctuations in bladder pressure, trigger early warnings, and help doctors quickly take corresponding intervention measures when the patient's condition changes. This early intervention mechanism significantly improves the timeliness and effectiveness of treatment, helps reduce the patient's mortality rate, and improves the patient's prognosis. The integrated data platform of the system also provides comprehensive and intuitive data support for doctors, optimizing the process of making treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a system block diagram of an integrated monitoring system for bladder pressure information. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions and advantages of the present invention more clear, the following will, in conjunction with the accompanying drawings in the present invention, clearly and completely describe the technical solutions in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0036] The following will, in conjunction with the attached Figure 1 , describe in detail the specific embodiments of the present invention.

[0037] The embodiment of the present invention relates to an integrated monitoring system for bladder pressure information, and the specific implementation includes the following:

[0038] 1. Bladder pressure monitoring device: The bladder pressure monitoring device in this system is mainly composed of the following components: Urinary catheter: Used to insert into the patient's bladder and continuously monitor the pressure in the bladder (IBP ( t ) ). The urinary catheter is made of high-quality materials and has an anti-infection function, which can ensure the comfort and safety of the patient.

[0039] Pressure sensor: Connected to the urinary catheter and used to measure the pressure in the bladder in real time. This pressure sensor uses a high-precision sensor and can stably and accurately collect the pressure data IBP(t) in the bladder. The pressure sensor can be connected to the inner cavity of the urinary catheter to ensure direct measurement of the pressure value in the bladder.

[0040] Injection device: Used to inject 25 ml of sterile normal saline into the bladder to ensure the accuracy and reliability of bladder pressure measurement. This device controls the injection volume of normal saline to ensure the comparability and accuracy of each measurement data. The measurement method of bladder pressure IBP(t) follows the standards of the World Society of Abdominal Compartment Syndrome (WSACS). The bladder pressure data obtained in this way is transmitted to the data transmission module.

[0041] 2. Data transmission module: The data transmission module uses wireless network technology (such as Wi-Fi or Bluetooth) to transmit bladder pressure data. Through this module, the bladder pressure monitoring device can send real-time data to the data analysis and warning system. The bladder pressure data is sent in the form of data packets, and each data packet contains information such as the bladder pressure value IBP(t), change rate R(t), acceleration A(t), etc. at the current moment. These data packets will be sent regularly and stored on the integrated monitoring platform for subsequent viewing and analysis.

[0042] 3. Data analysis and warning system: The data analysis and warning system is mainly responsible for analyzing the transmitted bladder pressure data. Its algorithm is based on the following formula: Calculation of the bladder pressure change rate R(t):

[0043]

[0044] Among them, R(t) is the change rate of bladder pressure, reflecting the degree of change in bladder pressure per unit time. If the change rate is large, it indicates that the bladder pressure fluctuates violently, which may suggest a change in the patient's condition. Calculation of the acceleration of bladder pressure change (A(t)):

[0045]

[0046] This formula is used to calculate the rate of change of the change rate of bladder pressure. The acceleration reflects the accelerating trend of bladder pressure change. If the acceleration continues to increase, it may mean that the patient's condition is deteriorating rapidly.

[0047] Disease risk assessment (P risk ): Through the following regression model, combined with the maximum value of bladder pressure and the acceleration of change, calculate the disease risk of the patient:

[0048] P risk = α·IBP max + β·A(t)+ γ

[0049] Among them, P risk is the disease risk score of the patient, α, β, γ are regression coefficients, IBP max is the maximum value of bladder pressure, and A(t) is the acceleration of bladder pressure change.

[0050] By calculating these values, the system can predict whether the patient has a continuously increasing bladder pressure and issue a warning. When the bladder pressure (IBP) exceeds 15 mmHg and the acceleration (A(t)) is greater than 0, and this state persists for more than 48 hours, the system will trigger an alarm, indicating the potential risk of the patient's condition to medical staff.

[0051] 4. Integrated monitoring platform: The integrated monitoring platform is used to display the real-time data and historical data of the patient's bladder pressure, including: Real-time display of bladder pressure (IBP): The platform displays the bladder pressure value of each measurement through a graphical interface.

[0052] Trend chart of the change rate R(t) and acceleration A(t) of bladder pressure: Displays the change trend, fluctuation situation of bladder pressure, and the accelerating trend of bladder pressure change.

[0053] Risk level prompt: According to the calculated disease risk score (P risk ), the platform displays the disease risk level of the patient, which is divided into low risk, medium risk, and high risk levels.

[0054] 5. Implementation process of bladder pressure monitoring: When the patient is monitored by this system in the hospital, first, the doctor inserts a urinary catheter and injects 25 ml of sterile normal saline. Then, the pressure sensor measures the bladder pressure IBP(t) in real time and transmits it to the data analysis and early warning system through a wireless network. Every 6 hours, the system automatically records the bladder pressure value and transmits it to the integrated monitoring platform for the doctor to monitor in real time.

[0055] The following is a specific description in combination with several embodiments;

[0056] Embodiment 1: Monitoring of patients with severe acute pancreatitis;

[0057] Patient information: Patient A, male, 65 years old, diagnosed with severe acute pancreatitis (SAP). At admission, the bladder pressure IBP(t) was 12 mmHg. Subsequently, after drug treatment, the bladder pressure gradually increased.

[0058] Implementation process:

[0059] Device setting: The doctor inserts a urinary catheter for Patient A and injects 25 mI of sterile normal saline. The pressure sensor starts to measure the bladder pressure IBP(t) in real time and transmits the data to the data analysis and early warning system through a wireless network.

[0060] Data transmission and processing: Every 6 hours, the system automatically records the bladder pressure data IBP(t) and performs calculations.

[0061] At the 24th hour, the bladder pressure data reached 16 mmHg, the change rate R(t) was 0.5 mmHg / h, and the acceleration A(t) was 0.2 mmHg / h 2 .

[0062] Early warning and risk assessment: The system detects that the bladder pressure continues to rise and the acceleration A(t) is greater than 0, triggering an early warning.

[0063] Disease risk score (P risk ) is calculated as follows:

[0064] P risk = 0.3·16 + 0.5·0.2 + 2.5 = 5.55

[0065] According to the calculation result, the disease risk of Patient A is rated as "high risk".

[0066] Doctor intervention: The doctor decides to strengthen the treatment based on the early warning information and risk score, including adjusting the drugs and increasing the monitoring frequency.

[0067] Result tracking: After further treatment of Patient A, the bladder pressure drops to the normal range (12 mmHg), the change rate and acceleration gradually decrease, and the disease risk score (P risk) also returned to normal levels.

[0068] Example 2: Monitoring of patients with moderate acute pancreatitis;

[0069] Patient information: Patient B, female, 50 years old, was diagnosed with moderate acute pancreatitis (MAP). When admitted to the hospital, the bladder pressure was 13 mmHg. After monitoring, the bladder pressure was elevated for a time, but returned to the normal range within 48 hours.

[0070] Implementation process: Equipment setup: The doctor inserted a urinary catheter into patient B and injected 25 ml of sterile saline.

[0071] The pressure sensor starts to monitor the bladder pressure IBP(t) in real time.

[0072] Data transmission and processing: At the 12th hour, the bladder pressure of patient B rose to 16 mmHg, the rate of change R(t) was 0.3 mmHg / h, and the acceleration (A(t)) was 0.1 mmHg / h 2 .

[0073] At the 24th hour, the bladder pressure reached a maximum of 18 mmHg, but subsequent monitoring data showed that the bladder pressure gradually dropped to 12 mmHg.

[0074] Data analysis and warning: The data analysis system calculates the rate of change of bladder pressure (R(t)) and acceleration (A(t)). After the bladder pressure dropped back to the normal range, the system did not trigger a warning.

[0075] Disease risk score (P risk ) is calculated as follows:

[0076] P risk =0.3·18+0.5·0.1+2.5=5.73

[0077] Although the bladder pressure once exceeded 15 mmHg, the system considered that patient B belonged to the low-risk group because the bladder pressure eventually dropped.

[0078] Doctor's treatment: The system did not trigger any warning. The doctor continued to observe according to the routine treatment plan and strengthened monitoring.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An integrated monitoring system for bladder pressure information, characterized in that: The system includes: a bladder pressure monitoring device, a data transmission module, a data analysis and early warning system, and an integrated monitoring platform; wherein the bladder pressure monitoring device measures the intra-bladder pressure IBP in real time through transvesical technology, and transmits the bladder pressure data IBP(t) to the data transmission module every 6 hours or at a set time interval; The data analysis and early warning system calculates the change trend, change rate and acceleration of bladder pressure based on the received bladder pressure data to determine whether the patient has a continuously elevated bladder pressure; The integrated monitoring platform is used to display the real-time and historical data of bladder pressure IBP and generate early warning reports based on dynamic monitoring results.

2. The integrated monitoring system for bladder pressure information according to claim 1, characterized in that: The bladder pressure monitoring device uses a catheter to be inserted into the patient's bladder, uses a bladder pressure sensor to accurately measure the bladder pressure IBP(t), and transmits the data in real time to the data analysis and early warning system through a wireless data transmission module, wherein the bladder pressure data IBP(t) is a real-time pressure value recorded every 6 hours, and the data record is kept for at least 72 hours.

3. The integrated monitoring system for bladder pressure information according to claim 1, characterized in that: The data transmission module uses wireless network technology, wherein the bladder pressure data IBP(t) is transmitted through data packets, each of which includes the current bladder pressure value, change rate, and acceleration information, for real-time monitoring of the dynamic changes of the patient's condition.

4. The integrated monitoring system for bladder pressure information according to claim 1, characterized in that: The data analysis and early warning system calculates the bladder pressure change rate (R(t)) according to the received bladder pressure data IBP(t), and the calculation formula is as follows: Among them, R(t) represents the rate of change of bladder pressure at time t, IBP(t) is the bladder pressure value at the current moment, IBP(t-1) is the bladder pressure value at the previous moment, t is the current time, t-1 is the time at the previous moment, and R(t) reflects the change in bladder pressure per unit time.

5. The integrated monitoring system for bladder pressure information according to claim 4, characterized in that: The data analysis and early warning system further calculates the bladder pressure change acceleration (A(t)), and the formula is as follows: Among them, A(t) represents the acceleration of bladder pressure change, R(t) is the bladder pressure change rate at the current moment, R(t-1) is the bladder pressure change rate at the previous moment, and A(t) reflects the rate of change of the bladder pressure change rate, which is used to analyze the acceleration trend of bladder pressure change.

6. The integrated monitoring system for bladder pressure information according to claim 5, characterized in that: The data analysis and early warning system determines whether there is a persistently elevated bladder pressure based on real-time data of bladder pressure IBP and bladder pressure change acceleration A(t). If the bladder pressure IBP is greater than 15 mmHg and the acceleration A(t) is greater than 0 and lasts for more than 48 hours, it is considered that the patient has a persistently elevated bladder pressure and a warning is triggered to alert the doctor to the risk of the patient's condition.

7. The integrated monitoring system for bladder pressure information according to claim 6, characterized in that: The integrated monitoring platform displays the maximum bladder pressure IBP through a graphical interface max , bladder pressure change rate R(t) and acceleration A(t) trend graphs. Doctors can view the patient's bladder pressure change trend in real time on the platform and generate dynamic analysis reports to make accurate treatment decisions.

8. The integrated monitoring system for bladder pressure information according to claim 6, characterized in that: The data analysis and early warning system is based on the maximum bladder pressure IBP max and bladder pressure change acceleration A(t) to generate the risk assessment of the patient's condition, and the regression model P risk To predict the patient's risk, the formula is as follows: P risk =α·IBP max +β·A(t)+γ Among them, P risk is the patient's risk score, α, β, γ are regression coefficients, IBP max is the maximum value of bladder pressure, A(t) is the acceleration of bladder pressure change, P risk It indicates the probability that the patient is ill and reflects the severity of the disease.

9. The integrated monitoring system for bladder pressure information according to claim 8, characterized in that: The data analysis and early warning system is based on the calculated disease risk P risk Risk levels are generated and displayed on the integrated monitoring platform, divided into three levels: low risk, medium risk and high risk. Doctors further adjust treatment plans and intervention measures based on the risk level.

10. The integrated monitoring system for bladder pressure information according to claim 1, characterized in that: The system continuously stores bladder pressure data IBP(t), rate of change R(t), acceleration A(t) and disease risk P risk Data is collected and used to generate historical data records of patients, and the development trend of patients' conditions is displayed on the integrated monitoring platform for doctors to track and evaluate during subsequent treatment.

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