A dynamic monitoring and early warning pad for intraoperative positional pressure points and a system

Abstract

This invention discloses a dynamic monitoring and early warning pad and system for intraoperative positional pressure points, comprising a pad body, a data processing unit, and an early warning unit. The pad body adopts a modular structure and embeds a pressure sensor array with a gradient density distribution. The data processing unit includes a dynamic threshold calculation module and a risk assessment module. The dynamic threshold calculation module calculates the pressure safety threshold in real time based on the surgical position type, patient weight, and surgical duration. The risk assessment module calculates a pressure injury risk index based on a pressure cumulative load model. The early warning unit triggers multi-level early warning signals based on the risk index. This invention solves the shortcomings of existing technologies, such as insufficient monitoring accuracy, rigid threshold settings, and incomplete risk assessment, and can significantly reduce the incidence of intraoperative pressure injuries.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a dynamic monitoring and early warning pad and system for intraoperative body position pressure points. Background Technology

[0002] Intraoperative pressure injury refers to skin and subcutaneous tissue damage caused by prolonged immobilization and continuous pressure on local tissues during surgery. Statistics show that the incidence of pressure injury in surgical patients is as high as 7.62%, and can reach as high as 27.4% in intensive care unit patients.

[0003] In the prior art, CN202422951000 discloses a device for measuring local skin pressure and temperature at pressure points in surgical patients, employing an adhesive flexible thin-film pressure sensor. This type of technology suffers from the problem that the sensor needs to be directly attached to the patient's skin, making it prone to detachment during surgical disinfection and changes in body position. CN202311156545 discloses an intraoperative continuous pressure monitoring bed and system, integrating the pressure sensor into the operating table. However, this type of system is large, complex, and expensive, making widespread deployment in operating rooms difficult. CN117695103B discloses a multi-point pressure detection and pressure ulcer early warning system based on a flexible pressure sensor, primarily for bedridden patients. It uses a neural network regression model to calculate thresholds, which is computationally complex and requires high-performance hardware. Summary of the Invention

[0004] The present invention aims to solve the technical problems of intraoperative pressure monitoring in the prior art, such as unreasonable sensor integration, unscientific threshold setting, and incomplete risk assessment.

[0005] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a dynamic monitoring and early warning pad for intraoperative body position pressure points, including a pad body. The pad body adopts a modular structure, including a detachably connected headrest module, trunk module, and limb module. The pad body is embedded with a pressure sensor array, which is distributed in a gradient density along the bony prominence area of ​​the patient. The data processing unit is electrically connected to the pressure sensor array. The data processing unit includes a dynamic threshold calculation module and a risk assessment module. The dynamic threshold calculation module is used to calculate the pressure safety threshold of each part in real time according to the surgical position type, patient weight and surgical duration. The risk assessment module is used to calculate the pressure injury risk index of each part based on the pressure cumulative load model. The early warning unit is electrically connected to the data processing unit and triggers multi-level early warning signals based on the pressure injury risk index.

[0006] As a preferred embodiment of the present invention, the gradient density distribution of the pressure sensor array is as follows: the sensor density in the central region of the bony prominence is the first density, the sensor density in the edge transition region is the second density, and the sensor density in the non-bony prominence region is the third density, wherein the first density is 3-4 times the third density, and the second density is 1.5-2 times the third density.

[0007] As a preferred embodiment of the present invention, the data processing unit further includes a temperature drift compensation module. The temperature drift compensation module receives ambient temperature data and raw data from the pressure sensor, corrects the raw data from the pressure sensor based on a temperature-drift model and an adaptive filtering algorithm, and outputs the compensated pressure data.

[0008] As a preferred embodiment of the present invention, the dynamic threshold calculation module receives patient weight parameters, body position type parameters, and surgery duration parameters, and calculates a dynamic threshold based on the coupling relationship between a baseline threshold, a weight correction factor, a body position type correction factor, and a time decay factor. The weight correction factor is determined based on the degree of deviation of the patient's body mass index from a reference body mass index. The body position type correction factor is preset with different values ​​based on the severity of pressure on bony prominences under different body positions. The time decay factor decays exponentially with the duration of surgery.

[0009] As a preferred embodiment of the present invention, the risk assessment module receives real-time pressure data and dynamic threshold data, calculates the pressure-to-threshold ratio over time to obtain the cumulative pressure load risk index, and triggers an early warning when the cumulative pressure load risk index exceeds a preset risk threshold.

[0010] As a preferred embodiment of the present invention, the data processing unit further includes a shear force analysis module. The shear force analysis module receives pressure distribution data from the pressure sensor array, calculates the shear force distribution based on the pressure gradient of adjacent sensor units, and triggers an early warning when the shear force exceeds a preset shear force threshold.

[0011] As a preferred embodiment of the present invention, the data processing unit further includes a pressure-temperature coupling analysis module. The pressure-temperature coupling analysis module receives pressure data, dynamic threshold data, and temperature data, calculates the integral value of the product of the ratio of pressure to threshold and the ratio of temperature difference to critical value over the duration of the operation, wherein the temperature difference is the difference between the surface temperature of the pad and the normal body temperature, and triggers an early warning when the integral value exceeds a preset thermodynamic risk critical value.

[0012] This invention also provides a dynamic monitoring and early warning system for intraoperative positional pressure points, comprising: The above-mentioned intraoperative positional pressure point dynamic monitoring and early warning pad; The monitoring terminal is wirelessly connected to the early warning unit and is used to display pressure distribution heatmaps, risk area markings, and early warning information; A mobile terminal is used to receive warning signals, which include at least one of the following: monitoring screen prompts, audible and visual alarms, nurse wristband vibrations, and mobile terminal push notifications.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention employs a gradient density sensor array, where the sensor density in bony prominence areas is many times higher than in non-bony prominence areas, improving monitoring accuracy in key areas while reducing the overall number of sensors. A dynamic threshold calculation module adjusts the pressure safety threshold in real time based on patient weight, body position, and surgical duration, providing personalized assessments for patients of different body types, positions, and surgical stages, avoiding missed or false alarms caused by fixed thresholds. Furthermore, by introducing a multi-parameter coupled analysis model of pressure cumulative load, shear force, and temperature, it can identify high-risk situations where a single factor may not exceed the limit but the synergistic effect leads to a comprehensive risk assessment, overcoming the shortcomings of traditional methods in terms of incomplete risk assessment. Attached Figure Description

[0014] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a diagram of the overall system architecture of the present invention; Figure 2 This is a layout diagram of the sensor array of the present invention; Figure 3 This is a flowchart of the dynamic threshold calculation of the present invention; Figure 4 This is a flowchart of the risk assessment process of this invention; Detailed Implementation

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0016] The intraoperative pressure monitoring and early warning positioning pad provided in this embodiment is referenced. Figure 1 It includes a cushion, a data processing unit, and an early warning unit.

[0017] The cushion adopts a modular structure, including a headrest module, a torso module, and limb modules. These modules are detachably connected via Velcro and clips, allowing for configuration and combination to meet different surgical positioning needs. (Reference) Figure 2An array of pressure sensors is embedded inside the pad to detect pressure values ​​at various points on the patient's body. The pressure sensor array is distributed in a gradient density along the bony prominences of the patient, with the sensor density in bony prominences being 2-3 times that in non-bony prominences.

[0018] The data processing unit is electrically connected to the pressure sensor array and is used to receive and process the pressure data collected by the array. The data processing unit includes a dynamic threshold calculation module, a risk assessment module, a temperature drift compensation module, a shear force analysis module, and a pressure-temperature coupling analysis module. The dynamic threshold calculation module calculates the pressure safety threshold for each site in real time based on the surgical position, patient weight, and surgical duration. The risk assessment module calculates the pressure injury risk index for each site based on a pressure cumulative load model.

[0019] The early warning unit is electrically connected to the data processing unit and triggers multi-level early warning signals based on the pressure injury risk index, including various forms such as monitoring screen prompts, audible and visual alarms, nurse wristband vibrations, and mobile terminal push notifications.

[0020] Furthermore, the pressure sensor array adopts a gradient density layout, with high-density sensing units in bony prominence areas such as the sacrum, coccyx, and heel, and low-density units in non-bony prominence areas. The gradient density distribution includes: the sensor density in the central area of ​​the bony prominence is the first density, the sensor density in the edge transition area is the second density, and the sensor density in the non-bony prominence area is the third density, with the first density being 3-4 times the third density and the second density being 1.5-2 times the third density.

[0021] By employing a gradient density layout, both monitoring accuracy in key areas is ensured and costs are reduced. Monitoring accuracy in bony prominence areas is improved by more than 2 times, while the overall number of sensors can be reduced by 30%-50%. Further reference Figure 3 The dynamic threshold calculation module receives patient weight parameters, body position type parameters, and operation duration parameters, and calculates the dynamic threshold based on the coupling relationship between the baseline threshold, weight correction factor, body position type correction factor, and time decay factor.

[0022] The formula for calculating the dynamic threshold is: in, : The dynamic warning threshold for the i-th site during the operation duration t; Baseline pressure threshold, ranging from 32 to 42 mmHg; Weight correction factor; Postural type correction factor; Time decay function; The weight correction factor is determined based on the degree of deviation of the patient's body mass index from the reference body mass index: Where: BMI: Body Mass Index, BMI = weight (kg) / height 2 (m 2 ); Reference Body Mass Index (BMI), valued at 22 kg / m². 2 ; Weight sensitivity coefficient, with a value range of 0.3-0.7; The body position type correction factor is preset with different values ​​based on the severity of pressure on bony prominences under different body positions. For example, in the supine position, the sacrococcygeal region... =1.2, chest in prone position =1.3, hip position in lateral decubitus position =1.4.

[0023] The time decay factor decreases exponentially with the duration of the operation: Where: t: duration of surgery, in hours; μ: Time decay coefficient, with a value range of 0.15-0.25; Through dynamic threshold calculation, patients of different body types, positions, and surgical stages can obtain personalized pressure safety thresholds. Further reference Figure 4 The risk assessment module receives real-time pressure data and dynamic threshold data, and calculates the cumulative pressure load risk index by integrating the pressure-to-threshold ratio over time.

[0024] The formula for calculating the cumulative pressure load risk index (RPI) is as follows: in: : The cumulative pressure load risk index of the i-th part at time t; : The real-time pressure value of the i-th part at time τ; : The dynamic early warning threshold of the i-th part at time τ; n: Non-linear exponent, with a value range of 2-4; The role of the nonlinear exponent n is: when the pressure exceeds the threshold, The accumulation rate increases rapidly with increasing n; when the pressure is below the threshold, The rate decreases rapidly as n increases, and the accumulation rate slows down.

[0025] when An early warning is triggered when the risk threshold Rcritical is exceeded. The value of Rcritical ranges from 1.5 to 2.5. Through the cumulative pressure load model, risk situations that slightly exceed the standard but persist for a long time can be identified.

[0026] Furthermore, shear force is one of the important factors leading to pressure damage. The shear force analysis module receives pressure distribution data from the pressure sensor array and calculates the shear force distribution based on the pressure gradient between adjacent sensing units.

[0027] The formula for calculating shear force is: in: : Shear force at position (i,j) at time t; Friction coefficient, ranging from 0.3 to 0.6; , : The pressure gradient in the x and y directions; The pressure gradient is obtained by calculating the ratio of the pressure difference between adjacent sensor units to their spacing using the differential method. When the shear force exceeds a preset shear force threshold, a higher-level warning is triggered in conjunction with the pressure warning.

[0028] Furthermore, temperature is a crucial factor influencing pressure injuries. The pressure-temperature coupling analysis module receives pressure data, dynamic threshold data, and temperature data, and calculates the integral value of the product of the ratio of pressure to threshold and the ratio of temperature difference to critical value over the duration of the surgery.

[0029] The formula for calculating the Thermodynamic Risk Index (TRI) is as follows: in: Thermodynamic risk index at time t :time temperature difference, =|Tsurface - Tnormal|, where Tnormal is 37℃ Critical temperature difference, ranging from 2 to 5℃. when Temperature-related warnings are triggered when the thermodynamic risk threshold (TRIcritical) is exceeded. The TRIcritical value ranges from 1.8 to 3.0. Through pressure-temperature coupling analysis, high-risk situations can be identified where individual factors do not exceed limits but their synergistic effects lead to high risks.

[0030] Furthermore, the early warning unit triggers different levels of early warning signals based on the risk level and urgency.

[0031] Level 1 Warning (Monitoring Screen Prompt): Triggered when the local pressure at the surgical site exceeds the dynamic safety threshold by more than 20% for a duration exceeding 30-90 seconds. The warning is displayed as a pop-up window on the monitoring screen, showing the abnormal location, current pressure value, and the percentage exceeding the threshold.

[0032] Level 2 Warning (Audio-Visual Alarm): Triggered when the local pressure at the surgical site exceeds the dynamic safety threshold by more than 50% for a duration exceeding 15-45 seconds; or when the pressure injury risk index reaches more than 80% of the critical value. The warning method is an audio-visual alarm on the monitoring screen plus an alarm light at the nurse's station.

[0033] Level 3 Early Warning (Emergency Push): Triggering conditions are: local pressure at the surgical site exceeds 100% of the dynamic safety threshold for 5-15 seconds; or the pressure injury risk index reaches or exceeds the critical value; or the thermodynamic risk index reaches or exceeds the critical value. The warning method includes audible and visual alarms on the monitoring screen, alarm lights at the nurse's station, vibration on the nurse's wristband, and push notifications from mobile devices.

[0034] The multi-level early warning mechanism adopts different response strategies according to the risk level, with different early warning intensities for mild and severe risks, and nursing resources are allocated rationally.

[0035] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic monitoring and early warning pad for intraoperative positional pressure points, characterized in that, The device includes a pad with a modular structure, comprising a detachably connected headrest module, a torso module, and a limb module. The pad contains an array of pressure sensors, which are distributed in a gradient density along the bony prominences of the patient. The data processing unit is electrically connected to the pressure sensor array. The data processing unit includes a dynamic threshold calculation module and a risk assessment module. The dynamic threshold calculation module is used to calculate the pressure safety threshold of each part in real time according to the surgical position type, patient weight and surgical duration. The risk assessment module is used to calculate the pressure injury risk index of each part based on the pressure cumulative load model. The early warning unit is electrically connected to the data processing unit and triggers multi-level early warning signals based on the pressure injury risk index.

2. The intraoperative positional pressure point dynamic monitoring and early warning pad according to claim 1, characterized in that, The gradient density distribution of the pressure sensor array is as follows: the sensor density in the central region of the bony prominence is the first density, the sensor density in the edge transition region is the second density, and the sensor density in the non-bony prominence region is the third density. The first density is 3-4 times the third density, and the second density is 1.5-2 times the third density.

3. The intraoperative positional pressure point dynamic monitoring and early warning pad according to claim 1, characterized in that, The data processing unit also includes a temperature drift compensation module, which receives ambient temperature data and raw data from the pressure sensor, corrects the raw data from the pressure sensor based on a temperature-drift model and an adaptive filtering algorithm, and outputs the compensated pressure data.

4. The intraoperative positional pressure point dynamic monitoring and early warning pad according to claim 1, characterized in that, The dynamic threshold calculation module receives patient weight parameters, body position type parameters, and surgery duration parameters. It calculates the dynamic threshold based on the coupling relationship between the baseline threshold, weight correction factor, body position type correction factor, and time decay factor. The weight correction factor is determined based on the degree of deviation of the patient's body mass index from the reference body mass index. The body position type correction factor is preset with different values ​​based on the severity of pressure on bony prominences under different body positions. The time decay factor decays exponentially with the duration of surgery.

5. The intraoperative positional pressure point dynamic monitoring and early warning pad according to claim 1, characterized in that, The risk assessment module receives real-time pressure data and dynamic threshold data, calculates the pressure-to-threshold ratio over time to obtain the cumulative pressure load risk index, and triggers an early warning when the cumulative pressure load risk index exceeds a preset risk threshold.

6. The intraoperative positional pressure point dynamic monitoring and early warning pad according to claim 1, characterized in that, The data processing unit also includes a shear force analysis module, which receives pressure distribution data from the pressure sensor array, calculates the shear force distribution based on the pressure gradient of adjacent sensor units, and triggers an early warning when the shear force exceeds a preset shear force threshold.

7. The intraoperative positional pressure point dynamic monitoring and early warning pad according to claim 1, characterized in that, The data processing unit also includes a pressure-temperature coupling analysis module. The pressure-temperature coupling analysis module receives pressure data, dynamic threshold data, and temperature data, and calculates the integral value of the product of the ratio of pressure to threshold and the ratio of temperature difference to critical value over the duration of the operation. The temperature difference is the difference between the surface temperature of the pad and the normal body temperature. When the integral value exceeds the preset thermodynamic risk critical value, an early warning is triggered.

8. A dynamic monitoring and early warning system for intraoperative positional pressure points, characterized in that, include: Intraoperative positional pressure point dynamic monitoring and early warning pad as described in any one of claims 1-7; The monitoring terminal is wirelessly connected to the early warning unit and is used to display pressure distribution heatmaps, risk area markings, and early warning information; A mobile terminal is used to receive warning signals, which include at least one of the following: monitoring screen prompts, audible and visual alarms, nurse wristband vibrations, and mobile terminal push notifications.

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