WO2023070252A1 - Système de commande de multi-médicaments d'insuline pour pancréas artificiel en boucle fermée - Google Patents

Système de commande de multi-médicaments d'insuline pour pancréas artificiel en boucle fermée Download PDF

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Publication number
WO2023070252A1
WO2023070252A1 PCT/CN2021/126032 CN2021126032W WO2023070252A1 WO 2023070252 A1 WO2023070252 A1 WO 2023070252A1 CN 2021126032 W CN2021126032 W CN 2021126032W WO 2023070252 A1 WO2023070252 A1 WO 2023070252A1
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algorithm
infusion
insulin
blood glucose
amount
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PCT/CN2021/126032
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English (en)
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Cuijun YANG
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Medtrum Technologies Inc.
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4836—Diagnosis combined with treatment in closed-loop systems or methods
    • A61B5/4839—Diagnosis combined with treatment in closed-loop systems or methods combined with drug delivery
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/168—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body
    • A61M5/172—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic
    • A61M5/1723—Means for controlling media flow to the body or for metering media to the body, e.g. drip meters, counters ; Monitoring media flow to the body electrical or electronic using feedback of body parameters, e.g. blood-sugar, pressure
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M5/00—Devices for bringing media into the body in a subcutaneous, intra-vascular or intramuscular way; Accessories therefor, e.g. filling or cleaning devices, arm-rests
    • A61M5/14—Infusion devices, e.g. infusing by gravity; Blood infusion; Accessories therefor
    • A61M5/142—Pressure infusion, e.g. using pumps
    • A61M2005/14208—Pressure infusion, e.g. using pumps with a programmable infusion control system, characterised by the infusion program
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
    • A61M2230/00—Measuring parameters of the user
    • A61M2230/20—Blood composition characteristics
    • A61M2230/201—Glucose concentration

Definitions

  • the present invention mainly relates to the field of medical device, and in particular, to a closed-loop artificial pancreas multi-drug infusion control system.
  • pancreas of healthy people can automatically secrete the required insulin/glucagon according to the glucose level in the human blood, thereby maintaining a reasonable range of blood glucose fluctuations.
  • diabetes mellitus is defined as a metabolic disease caused by abnormal pancreatic function, and it is also classified as one of the top three chronic conditions by the WHO.
  • the present medical advancement has not been able to find a cure for diabetes mellitus. Yet, the best the technology could do is control the onset symptoms and complications by stabilizing the blood glucose level for diabetes patients.
  • Diabetic patients on an insulin pump need to check their blood glucose before infusing insulin into their bodies.
  • Most detection methods can continuously detect blood glucose and send the blood glucose data to the remote device in real-time for the user to view.
  • This detection method is called Continuous Glucose Monitoring (CGM) , which requires the detection device to be attached to the surface of the patients’ skin, and the sensor carried by the device to be inserted into the interstitial fluid for testing.
  • CGM Continuous Glucose Monitoring
  • the infusion system mimics an artificial pancreas to fill the gaps of the required insulin amount via the closed-loop pathway or the semi-closed-loop pathway.
  • the proportional-integral-derivative (PID) algorithm and the model-predictive-control (MPC) algorithm have been widely studied, but due to the simple constitute of the PID algorithm, it is not suitable for more complex scenarios.
  • the MPC algorithm faces the dilemma of establishing an accurate model and dealing with large computations, which may lead to deviation for the predicted infusion.
  • the same problem also exists in the closed-loop artificial pancreas multi-drug infusion control system.
  • the embodiment of the present invention discloses a closed-loop artificial pancreas multi-drug infusion control system.
  • the system is preset with one or more of rMPC algorithm, rPID algorithm or compound artificial pancreas algorithm, making full use of the advantages of rPID algorithm and rMPC algorithm to face complex scenarios, so that the artificial pancreas can provide reliable drug categories and drug infusion amount to control blood glucose under various conditions, so that blood glucose can reach the ideal level at the expected time, realizing precise control for closed-loop artificial pancreas mlti-drug infusion system.
  • the invention discloses a closed-loop artificial pancreas insulin infusion control system, including: a detection module configured to the current blood glucose level G continuously; a program module, connected to the detection module, preset with one or more of rMPC algorithm, rPID algorithm or compound artificial pancreas algorithm; and an infusion module, connected to the program module, can perform anti-hypoglycemic drugs and/or hypoglycemic drugs infusion, the program module controls the infusion module to infuse corresponding drugs, according to the corresponding drug infusion instructions, calculated by the rMPC algorithm, rPID algorithm or compound artificial pancreas algorithm.
  • the blood glucose risk conversion method of the rMPC algorithm and the rPID algorithm includes one or more of a segmented weighting conversion, a relative value conversion, a blood glucose risk index conversion, and an improved control variability grid analysis conversion.
  • the blood glucose risk conversion method of the rMPC algorithm and the rPID algorithm further include one or more of the following processing methods:
  • the autoregressive method is used to compensate for the detecting delay of interstitial fluid
  • the corresponding output instructions includes a drug infusion category and a drug infusion amount, and the drug infusion category is obtained by comparing the predicted blood glucose concentration G P and the target blood glucose concentration G B .
  • the infusion module when G P ⁇ G B , the infusion module starts to infuse the hypoglycemic drug; when G P ⁇ G B , the infusion module starts to infuse the anti-hypoglycemic drug.
  • I b represents the hypoglycemic drugs amount that need to be infused to control blood glucose at the target blood glucose level G B
  • I t represents the required hypoglycemic drug infusion amount calculated by the algorithm, when G P ⁇ G B , 0 ⁇ I t ⁇ I b , the infusion module simultaneously infuses the hypoglycemic drugs and anti-hyperglycemic drugs.
  • the corresponding output instructions are obtained by comparing the required hypoglycemic drug amount I t and the target hypoglycemic drug amount I b.
  • the infusion module when I t ⁇ 0, the infusion module starts to infuse the hypoglycemic drug; when I t ⁇ 0, the infusion module starts to infuse the anti-hypoglycemic drug.
  • the infusion module when 0 ⁇ I t ⁇ I b , the infusion module simultaneously infuses the hypoglycemic drugs and anti-hyperglycemic drugs.
  • the compound artificial pancreas algorithm including a first algorithm and a second algorithm.
  • the first algorithm is used to calculate the first insulin infusion amount I 1
  • the second algorithm is used to calculate the second insulin infusion volume I 2
  • the compound artificial pancreas algorithm further optimizes I 1 and I 2 to obtain the final insulin infusion amount I 3 .
  • the final insulin infusion amount I 3 is optimized by the average value of the first insulin infusion amount I 1 and the second insulin infusion amount I 2 :
  • the final insulin infusion amount I 3 is optimized by the weighted value of the first insulin infusion amount I 1 and the second insulin infusion amount I 2 :
  • the first algorithm and the second algorithm are one of the classic PID algorithm, the classic MPC algorithm, the rMPC algorithm, or the rPID algorithm.
  • the final insulin infusion amount I 3 is optimized by comparing the first insulin infusion amount I 1 and the second insulin infusion amount I 2 with the current statistical analysis result I 4 :
  • the calculation algorithms for the infusion amount of the hypoglycemic drugs and the anti-hypoglycemic drugs are the same or different.
  • any two of the detection module, the program module and the infusion module are connected to each other configured to form a single part whose attached position on the skin is different from the third module.
  • the detection module, the program module and the infusion module are connected together configured to form a single part which is attached on only one position on the skin.
  • the hypoglycemic drug is insulin
  • the anti-hypoglycemic drug is glucagon
  • the system is preset with one or more of the rMPC algorithm the rPID algorithm or compound artificial pancreas algorithm, which converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, making full use of the advantages of rPID algorithm and rMPC algorithm to face complex scenarios, so that the artificial pancreas can provide reliable insulin infusion under various conditions, and blood glucose can reach the ideal level at the expected time, realizing precise control for closed-loop artificial pancreas multi-drug infusion system.
  • the rMPC algorithm the rPID algorithm or compound artificial pancreas algorithm
  • the drug infusion category is obtained by comparing the predicted blood glucose concentration G P and the target blood glucose concentration G B or by comparing the required hypoglycemic drug amount I t and the target hypoglycemic drug amount I b , which can be selected flexibly.
  • hypoglycemic drugs and anti-hyperglycemic can be infused at the same time, which can effectively prevent hypoglycemia.
  • hypoglycemic drug infusion data and the anti-hypoglycemic infusion data are the same, ensure the consistency of the basic conditions, which make the calculation results more accurate.
  • the final output of the compound artificial pancreas algorithm is the same result calculated by the rMPC algorithm the rPID algorithm, making the result more feasible and reliable.
  • the final output of the compound artificial pancreas algorithm is obtained by comparing the different results calculated by the rMPC algorithm the rPID algorithm with the statistical analysis results of the historical data, so as to ensure the reliability of the insulin infusion from another aspect.
  • the detection module, the program module and the infusion module are connected together configured to form a single part which is attached on only one position on the skin. If the three modules are connected as a whole and attached in the only one position, the number of the device on the user skin will be reduced, thereby reducing the interference of more attached devices on user activities. At the same time, it also effectively solves the problem of the poor wireless communication between separating devices, further enhancing the user experience.
  • FIG. 1 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to one embodiment of the present invention.
  • FIG. 2 is a comparison diagram of the blood glucose in the original physical space and the risk space which is obtained through the segmented weighting and the relative value conversion according to an embodiment of the present invention.
  • FIG. 3 is a comparison diagram of the blood glucose in the original physical space and the risk space which is obtained through BGRI and CVGA method according to an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of an insulin IOB curve according to an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of four types of mainstream clinical optimal basal rate settings according to an embodiment of the present invention
  • FIG. 6 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.
  • FIG. 7 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.
  • FIG. 8 is a schematic diagram of the module relationship of the closed-loop artificial pancreas multiple-drug infusion control system according to another embodiment of the present invention.
  • Fig. 9 is a schematic diagram of dual-drug switching according to an embodiment of the present invention.
  • FIG. 10 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.
  • the present invention provides a closed-loop artificial pancreas insulin infusion control system
  • the system is preset with one or more of the rMPC algorithm the rPID algorithm or compound artificial pancreas algorithm, which converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, making full use of the advantages of rPID algorithm and rMPC algorithm to face complex scenarios, so that the artificial pancreas can provide reliable insulin infusion under various conditions, and blood glucose can reach the ideal level at the expected time, realizing precise control for closed-loop artificial pancreas insulin infusion system.
  • FIG. 1 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to the embodiment of the present invention.
  • the closed-loop artificial pancreas insulin infusion control system disclosed in the embodiment of the present invention mainly includes a detection module 100, a program module 101, and an infusion module 102.
  • the detection module 100 is used to continuously detect the user's real-time blood glucose (BG) level.
  • detection module 100 is a Continuous Glucose Monitoring (CGM) for detecting real-time BG, monitoring BG changes, and sending them to the program module 101.
  • CGM Continuous Glucose Monitoring
  • Program module 101 is used to control the detection module 100 and the infusion module 102. Therefore, program module 101 is connected to detection module 100 and infusion module 102, respectively.
  • the connection refers to a conventional electrical connection or a wireless connection.
  • the infusion module 102 includes the essential mechanical assemblies used to infuse insulin and is controlled by program module 101. According to the current insulin infusion dose calculated by program module 101, infusion module 102 injects the current insulin dose required into the user's body. At the same time, the real-time infusion status of infusion module 102 can also be fed back to program module 101.
  • the embodiment of the present invention does not limit the specific positions and connection relationships of the detection module 100, the program module 101 and the infusion module 102, as long as the aforementioned functional conditions can be satisfied.
  • the three are electrically connected to form a single part. Therefore, the three modules can be attached on only one position of the user's skin. If the three modules are connected as a whole and attached in only one position, the number of the device on the user skin will be reduced, thereby reducing the interference of more attached devices on user activities. At the same time, it also effectively solves the problem of poor wireless communication between separating devices, further enhancing the user experience.
  • Another embodiment of the present invention is that the program module 101 and the infusion module 102 are electrically connected to form a single part, while the detection module 100 is separately provided in another part. At this time, the detection module 100 and the program module 101 transmit wireless signals to realise the mutual connection. Therefore, program module 101 and infusion module 102 can be attached to the user's skin position while the detection module 100 is attached to the other position.
  • Another embodiment of the present invention is that the program module 101 and the detection module 100 are electrically connected, forming a single part, while the infusion module 102 is separately provided in another part.
  • the infusion module 102 and the program module 101 transmit wireless signals to realise the mutual connection. Therefore, program module 101 and the detection module 100 can be attached to the same position of the user's skin while the infusion module 102 is attached to the other position.
  • Another embodiment of the present invention is that the three are provided in different parts, thus being attached to different positions. Simultaneously, program module 101, detection module 100, and infusion module 102 transmit wireless signals to realize the mutual connection.
  • program module 101 of the embodiment of the present invention also has functions such as storage, recording, and access to the database.
  • program module 101 can be reused. In this way, the user's physical condition data can be stored, but the production and consumption costs can be saved.
  • program module 101 can be separated from the detection module 100, the infusion module 102, or both the detection module 100 and the infusion module 102.
  • the service lives of the detection module 100, the program module 101, and the infusion module 102 are different. Therefore, when the three are electrically connected to form a single device, the three can also be separated in pairs. For example, if one module expires, the user can only replace this module and keep the other two modules continuously using.
  • the program module 101 of the embodiment of the present invention may also include multiple sub-modules. According to the functions of the sub-modules, different sub-modules can be respectively assembled in a different part, which is not a specific limitation herein, as long as the control conditions of the program module 101 can be satisfied.
  • the program module 101 is preset with an rPID (risk-proportional-integral-derivative) algorithm that converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose in the risk space.
  • the rPID algorithm is obtained by converting the classic PID (proportional-integral-derivative) algorithm. The specific converting method will be detailed below.
  • module 101 controls the infusion Module 102 infuses insulin.
  • K P is the gain coefficient of the proportional part
  • K I is the gain coefficient of the integral part
  • K D is the gain coefficient of the differential part
  • G represents the current blood glucose level
  • G B represents the target blood glucose level
  • PID (t) represents the infusion instruction sent to the insulin infusion system.
  • the normal blood glucose range is 80-140 mg/dL, and it can also be widened to 70-180 mg/dL.
  • General hypoglycemia can reach 20-40 mg/dL, while high blood glucose can reach 400-600 mg/dL.
  • the distribution of high/low blood glucose (original physical space) has significant asymmetry.
  • the risk of high blood glucose and low blood glucose corresponding to the same degree of blood glucose deviation from the normal range will be significantly different, such as a decrease of 70 mg/dL, from 120mg/dL to 50mg/dL will be considered severe hypoglycemia, with high clinical risk, and emergency measures such as supplementing carbohydrates need to be taken.
  • the increase of 70 mg/dL, from 120mg/dL to 190mg/dL is just beyond the normal range.
  • the degree of high blood glucose is not serious, and it is often reached in daily situations, and there is no need to take treatment measures.
  • the asymmetric blood glucose in the original physical space is converted to the approximately symmetric blood glucose in risk space, making the PID algorithm more robust.
  • rPID (t) represents the infusion instruction sent to the insulin infusion system after risk conversion
  • r blood glucose risk
  • a blood glucose value greater than the target blood glucose G B is converted by the relative value, as follows:
  • Fig. 2 is a comparison diagram of the blood glucose in the original physical space and the risk space obtained through the segmented weighting and the relative value conversion according to an embodiment of the present invention.
  • the blood glucose risk (ie Ge) on both sides of the target blood glucose value presents a severe asymmetry consisting of the original physical space.
  • the blood glucose risk on both sides of the target blood glucose value is approximately symmetric. In this way, the integral term can be kept stable, making the rPID algorithm more robust.
  • BGRI blood glucose risk index
  • the conversion function f (G) is as follows:
  • the blood glucose concentration at zero risk point is 112mg/dL.
  • the blood glucose concentration at the zero-risk point can also be adjusted in conjunction with clinical practice risks and data trends; there is no specific limitation here.
  • the specific fitting method is not specifically limited.
  • an improved Control Variability Grid Analysis (CVGA) method is used.
  • the blood glucose concentration at zero risk point is defined as 110 mg/dL in the original CVGA, and the following equal-risk blood glucose concentration data pairs are assumed (90 mg/dL, 180mg/dL; 70mg/dL, 300mg/dL; 50mg/dL, 400mg/dL) .
  • the risk data of (70mg/dL, 300mg/dL) was revised to (70mg/dL, 250mg/dL)
  • blood glucose concentration at zero risk point is defined as G B .
  • a polynomial model is fitted to it, and the following risk functions for the two sides of the zero-risk point are obtained:
  • n is from 0 to 80mg/dL, preferably, the value of n is 60mg/dL.
  • the blood glucose concentration at the zero-risk point and equal risk data pairs can also be adjusted in conjunction with clinical practice risks and data trends, and there is no specific limitation here.
  • the specific fitting method is not specifically limited.
  • the data used to limit the maximum is also not specifically limited here.
  • Fig. 3 is a comparison diagram of the blood glucose in the original physical space and the risk space, which has been obtained through the BGRI and CVGA method according to an embodiment of the present invention.
  • Zone-MPC Similar to the treatment of Zone-MPC, within the normal range of blood glucose, the blood glucose risk after conversion by BGRI and CVGA methods is quite flat, especially within 80-140mg/dL. Unlike Zone-MPC, where the blood glucose risk is completely zero in this range, it loses the ability to adjust further. Although the blood glucose risk in rPID is smooth within this range, it still has a stable and slow adjustment ability, making blood glucose further adjust to close the target value to achieve more precise blood glucose control.
  • a unified processing method can be used for data deviating from both sides of the zero-risk point.
  • the BGRI or CVGA method can deal with the data deviating from both sides of the zero-risk point;
  • Different treatment methods can also be used, such as combining the BGRI and CVGA methods at the same time.
  • the glucose concentration at zero risk point blood is the same, such as G B .
  • the BGRI method is used, and the blood glucose concentration is greater than G B , the CVGA method is used. At this time:
  • the conversion function f (G) is as follows:
  • the BGRI method is used, and the blood glucose concentration is less than G B , the CVGA method is used. At this time:
  • the conversion function f (G) is as follows:
  • n is from 0 to 80mg/dL, preferably, the value of n is 60mg/dL.
  • the blood glucose level at the zero risk point can also be set as the target blood glucose value G B , when the blood glucose concentration is less than G B , the BGRI method is used, when the blood glucose concentration is great than G B , such as segmented weighting or relative value converting.
  • the conversion function f (G) is as follows:
  • the conversion function f (G) is as follows:
  • the blood glucose value at the zero risk point is the target blood glucose value G B
  • the segmented weighting converting, relative value converting, and CVGA method are used, the functions are the same. Therefore, when the blood glucose concentration is great than G B , the BGRI method is used, when the blood glucose concentration is less than G B , such as segmented weighting or relative value converting, the result is equivalent to the result that when the blood glucose value is less than the target blood glucose value G B , the CVGA method is used when the blood glucose level is greater than the target blood glucose value G B , the BGRI method is used, and the calculation formula is not repeated here.
  • the target blood glucose value G B is 80-140 mg/dL; preferably, the target blood glucose value G B is 110-120 mg/dL.
  • the asymmetric blood glucose in the original physical space can be converted to the approximately symmetric blood glucose in risk space in the rPID algorithm to retain the simplicity and robustness of the PID algorithm and control blood glucose risk with clinical value, to achieve precise control of the closed-loop artificial pancreatic insulin infusion system.
  • insulin absorption delay about 20 minutes from subcutaneous to blood circulation tissue, and about 100 minutes to liver
  • insulin onset delay about 30-100 minutes
  • interstitial fluid glucose concentration about 5-150 minutes
  • blood glucose detecting delay approximately 5-15 minutes
  • an insulin feedback compensation mechanism is introduced.
  • the amount of insulin that has not been absorbed in the body is subtracted from the output, which is a component that is proportional to the estimated plasma insulin concentration (the plasma insulin concentration also regulates the actual human insulin secretion as a negative feedback Signal) .
  • the formula is as follows:
  • PID (t) represents the infusion instruction sent to the insulin infusion system
  • PIDc (t) represents the infusion instruction with compensation sent to the insulin infusion system
  • ⁇ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output. If the coefficient increases, the algorithm will be relatively conservative, and if the coefficient decreases, the algorithm will be relatively aggressive. Therefore, in the embodiment of the present invention, the range of ⁇ is 0.4-0.6. Preferably, ⁇ is 0.5.
  • PID c (n-1) represents the output with compensation at the previous moment
  • K 0 represents the coefficient of the output part with compensation at the previous moment
  • K 1 represents the coefficient of the estimated part of the plasma insulin concentration at the previous moment
  • K 2 represents the coefficient of the estimated part of the plasma insulin concentration at the previous time
  • the time interval can be selected according to actual needs.
  • rPID c (t) represents the infusion instruction with compensation sent to the insulin infusion system after risk conversion
  • IOB insulin on board
  • Fig. 4 is an insulin IOB curve according to an embodiment of the present invention.
  • the cumulative residual amount of insulin previously infused can be calculated, and the selection of the specific curve can be determined based on the actual insulin action time of the user.
  • PID′ (t) PID (t) -IOB (t)
  • PID' (t) represents the infusion instruction sent to the insulin infusion system after deducting IOB
  • PID (t) represents the infusion instruction sent to the insulin infusion system
  • IOB (t) represents the amount of insulin that has not yet worked in the body at time t.
  • the output formula after deducting the amount of insulin that has not yet worked in the body after risk conversion through the aforementioned method is as follows:
  • rPID′ (t) represents the infusion instruction sent to the insulin infusion system after risk conversion, deducting the amount of insulin that has not yet worked in the body;
  • IOB (t) is divided into meal insulin IOBm and non-meal insulin IOBo.
  • the formula is as follows:
  • IOB (t) IOB m, t +IOB o, t
  • IOB m, t represents the amount of meal insulin that has not yet worked in the body at time t;
  • IOB o, t represents the amount of non-meal insulin that has not yet worked in the body at time t;
  • I m, t represents the amount of meal insulin
  • I o, t represents the amount of non-meal insulin
  • IOB (t) represents the amount of insulin that has not yet worked in the body at time t.
  • Dividing the IOB into meal and non-meal insulin can make insulin cleared faster when meals ingesting or blood sugar are too high and can obtain greater insulin output and regulate blood glucose more quickly.
  • a longer insulin action time curve is used to make insulin clear more slowly, and blood sugar regulation is more conservative and stable.
  • an autoregressive method is used to compensate for detecting delay of interstitial fluid glucose concentration and blood glucose concentration.
  • the formula is as follows:
  • G SC (n) represents the glucose concentration in the interstitial fluid at the current moment, that is, the measured value of the detecting system
  • G SC (n-1) and G SC (n-2) represent the glucose concentration in the interstitial fluid at the first previous time and the second previous time, respectively;
  • K 0 represents the coefficient of the estimated concentration of blood glucose at the previous moment
  • K 01 and K 2 respectively represent the coefficient of glucose concentration in the interstitial fluid at the first previous time and the second previous time, respectively.
  • the blood glucose concentration is estimated by the interstitial fluid glucose concentration, which compensates for the detecting delay of the interstitial fluid glucose concentration and blood glucose, making the PID algorithm more accurate.
  • the rPID algorithm can also more accurately calculate the actual insulin demand for the human body.
  • the insulin absorption delay, the insulin onset delay, the detecting delay of interstitial fluid glucose concentration and blood glucose can be partially compensated or fully compensated.
  • all delay factors are considered fully compensated for making the rPID algorithm more accurate.
  • the program module 101 is preset with an rMPC (risk-model-predict-control) algorithm that converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose in the risk space.
  • the rMPC algorithm is obtained by converting the classic MPC (risk-model-predict-control) algorithm.
  • program module 101 controls infusion Module 102 infuses insulin.
  • the classic MPC algorithm consists of three elements, the prediction model, the value function and the constraints.
  • the classic MPC prediction model is as follows:
  • I t represents the amount of insulin infusion at the current moment
  • G t represents the blood glucose concentration at the current moment.
  • the parameter matrix is as follows:
  • b1, b2, b3, Ki are initial values.
  • the value function of the MPC algorithm is composed of the sum of squared deviations of the output G (blood glucose level) and the sum of squared changes of the input I (insulin amount) .
  • the MPC algorithm needs to obtain the minimum solution of the value function.
  • I′ t+j represents the change of insulin infusion after step j;
  • N and P are the number of steps in the control time window and the predictive time window, respectively;
  • R is the weighting coefficient of the insulin component.
  • the amount of insulin infusion at step j isI t +I′ t+j .
  • control time window Tc 30min
  • prediction time window Tp 60min
  • weighting coefficient R of the amount of insulin is 11000. It should be noted that although the control time window used in the calculation is 30min, only the first step calculation result of insulin output is used in the actual operation. After the operation, the minimum solution of the above value function is recalculated according to the latest blood glucose data obtained.
  • the infusion time step in the control time window is j n , and the range of j n is 0-30 min, preferably 2 min.
  • the number of steps N T c /j n , and the range of j is 0 to N.
  • the weighting coefficients of the amount of insulin, the control time window and the predicted time window can also be selected as other values, which are not specifically limited here.
  • the distribution of high/low blood glucose (original physical space) has significant asymmetry.
  • the risk of high blood glucose and low blood glucose corresponding to the same degree of blood glucose deviation from the normal range will be significantly different in clinical practice.
  • the asymmetric blood glucose in the original physical space is converted to the approximately symmetric blood glucose in risk space, making the MPC algorithm more accurate and flexible.
  • r t+j represents the blood glucose risk after step j
  • I′ t+j represents the change of insulin infusion after step j.
  • the deviation of blood glucose value is converted to the corresponding blood glucose risk.
  • the specific conversion method is the same as that in the aforementioned rPID algorithm, such as segmented weighting and relative value converting; it also includes setting a fixed zero risk point in the risk space.
  • the blood glucose concentration at the zero risk point can be set as the target blood glucose value.
  • Data on both sides deviating from the zero risk point are processed, such as using BGRI and the improved CVGA method; it also includes different methods for processing data that deviates from the target blood glucose value.
  • n is from 0 to 80mg/dL, preferably, the value of n is 60mg/dL.
  • step j G t+j If the detected blood glucose concentration in step j G t+j is less than G B , the BGRI method will be used. If the detected blood glucose concentration in step j G t+j is greater than G B , the CVGA method will be used:
  • step j G t+j If the detected blood glucose concentration in step j G t+j is great than G B , the BGRI method will be used. If the detected blood glucose concentration in step j G t+j is less than G B , the CVGA method will be used:
  • n is from 0 to 80mg/dL, preferably, the value of n is 60mg/dL.
  • step j G t+j If the detected blood glucose concentration in step j G t+j is less than G B , the BGRI method will be used. If the detected blood glucose concentration in step j G t+j is great than G B , the segmented weighting converting will be used:
  • the BGRI method When the detected blood glucose concentration in step j G t+j is less than G B , the BGRI method is used, when the detected blood glucose concentration in step j G t+j is great than G B , the relative value converting is used:
  • the functions are the same when the segmented weighting converting, relative value converting, and CVGA method is used. Therefore, when the blood glucose concentration is great than G B , the BGRI method is used, when the blood glucose concentration is less than G B , such as segmented weighting or relative value converting, the result is equivalent to the result that when the blood glucose value is less than the target blood glucose value G B , the CVGA method is used when the blood glucose level is greater than the target blood glucose value G B , the BGRI method is used, and the calculation formula is not repeated here.
  • r t+j represents the blood glucose risk at step j
  • G t+j represents the blood glucose level detected in step j.
  • the target blood glucose value G B is 80-140 mg/dL, preferably, the target blood glucose value G B is 110-120 mg/dL.
  • the insulin feedback compensation mechanism can be used; in order to compensate for the delay of insulin onset, IOB can be used; in order to compensate for detecting delay of interstitial fluid glucose concentration and blood glucose concentration, the autoregressive method can be used.
  • the specific compensation method is also consistent with the rPID algorithm, specifically:
  • I t+j represents the infusion instruction sent to the insulin infusion system after step j;
  • rI c (t+j) represents the infusion instruction with compensation sent to the insulin infusion system after step j;
  • ⁇ represents the compensation coefficient of the estimated plasma insulin concentration to the algorithm output. If the coefficient increases, the algorithm will be relatively conservative, and if the coefficient decreases, the algorithm will be relatively aggressive. Therefore, in the embodiment of the present invention, the range of ⁇ is 0.4-0.6. Preferably, ⁇ is 0.5.
  • rI′ t+j represents the infusion instruction sent to the insulin infusion system after deducting IOB at step j after risk conversion
  • rI t+j represents the infusion instruction sent to the insulin infusion system at step j after risk conversion
  • IOB (t+j) represents the amount of insulin that has not yet worked in the body at time t+j.
  • IOB (t+j) can be divided into meal insulin and non-meal insulin.
  • the formula is as follows:
  • IOB (t+j) IOB m, t+j +IOB o, t+j
  • IOB m, t+j represents the amount of meal insulin that has not yet worked in the body at time t+j;
  • IOB o, t +j represents the amount of non-meal insulin that has not yet worked in the body at time t+j;
  • I m, t+j represents the amount of meal insulin at time t+j
  • I o, t+j represents the amount of non-meal insulin at time t+j
  • IOB (t+j) represents the amount of insulin that has not yet worked in the body at time t+j.
  • the final insulin infusion amount is rI′ t+j ;
  • the autoregressive method is used to detect the delay of interstitial fluid glucose concentration and blood glucose concentration.
  • G SC (t+j) represents the glucose concentration in the interstitial fluid at the time t+j, that is, the measured value of the detecting system
  • G SC (t+j-1) and G SC (t+j-2) represent the glucose concentration in the interstitial fluid at the time t+j-1 and t+j-2, respectively;
  • K 0 represents the coefficient of the estimated concentration of blood glucose at the time t+j-1;
  • K 01 and K 2 respectively represent the coefficient of glucose concentration in the interstitial fluid at the time t+j-1 and t+j-2, respectively.
  • the compound artificial pancreas algorithm is preset in program module 101.
  • the compound artificial pancreas algorithm includes a first algorithm and a second algorithm.
  • the detection module 100 detects the current blood glucose level and sends the current blood glucose level to the program module 101
  • the first algorithm calculates the first insulin infusion amount I 1
  • the second algorithm calculates the second insulin infusion amount I 2
  • the compound artificial pancreas algorithm optimises the first insulin infusion amount I 1 and the second insulin infusion amount I 2 to obtain the final insulin infusion, and send the final insulin infusion amount I 3 to the infusion module 102
  • the infusion module 102 performs insulin infusion according to the final infusion amount I 3 .
  • the first and second algorithms are classic PID algorithms, the classic MPC algorithm, the rMPC algorithm, or the rPID algorithm.
  • the rMPC algorithm or rPID algorithm is an algorithm that converts blood glucose that is asymmetric in the original physical space to a blood glucose risk that is approximately symmetric in the risk space.
  • the conversion method of blood glucose risk in rMPC algorithm and rPID algorithm is as described above.
  • the algorithm parameter is K P
  • K D T D /K P
  • T D 60min-90 min
  • K I T I *K P
  • T I can be 150min-450 min.
  • the algorithm parameter is K.
  • the algorithm parameter is K P
  • K D T D /K P
  • T D 60min-90 min
  • K I T I *K P
  • T I can be 150min-450 min.
  • the algorithm parameter is K.
  • ⁇ and ⁇ can be adjusted according to the first insulin infusion amount I 1 and the second insulin infusion amount I 2 .
  • I 1 ⁇ I 2 , ⁇ ; when I 1 ⁇ I 2 , ⁇ ; preferably, ⁇ + ⁇ 1.
  • ⁇ and ⁇ may also be other value ranges, which are not specifically limited here.
  • the algorithms are mutually referenced.
  • the first algorithm and the second algorithm are the rMPC algorithm and the rPID algorithm, which are mutually referenced to improve the accuracy of the output further and make the result more feasible and reliable.
  • the program module 101 also provides a memory that stores the user's historical physical state, blood glucose level, insulin infusion, and other information. Statistical analysis can be performed based on the information in the memory to obtain the current statistical analysis result I 4 , when I 1 ⁇ I 2 , compare I 1 , I 2 and I 4 to calculate the final insulin infusion amount I 3 , the one that is closer to the statistical analysis result I 4 is selected as a result of the compound artificial pancreas algorithm, that is the final insulin infusion amount I 3 , and the program module 101 sends the final insulin infusion amount I 3 to the infusion module 102 to infuse;
  • the blood glucose risk space conversion method in the rMPC algorithm and/or rPID algorithm and/or the compensation method regarding the delay effect can also be changed to adjust and make them more closely, and then finally determine the output result of the compound artificial pancreas algorithm through the above arithmetic average, weighting processing, or comparison with the statistical analysis result.
  • the closed-loop artificial pancreas control system further includes a meal recognition module and/or a motion recognition module, used to identify whether the user is eating or exercising.
  • a meal recognition module and/or a motion recognition module, used to identify whether the user is eating or exercising.
  • Commonly used meal identification can be determined based on the rate of blood glucose change and compared with a specific threshold.
  • the rate of blood glucose change can be calculated from two moments or obtained by linear regression at multiple moments within a period of time. Specifically, when the rate of change at the two moments is used for calculation, the calculation formula is:
  • G t represents the blood glucose level at the current moment
  • G t-1 represents the blood glucose level at the previous moment
  • ⁇ t represents the time interval between the current moment and the last moment.
  • G t represents the blood glucose level at the current moment
  • G t-1 represents the blood glucose level at the previous moment
  • G t-2 represents the blood glucose level at the second previous moment
  • ⁇ t represents the time interval between the current moment and the last moment.
  • the original continuous glucose data can also be filtered or smoothed.
  • the threshold can be set to 1.8mg/mL-3mg/mL or personalised.
  • exercise recognition can also be detected based on the rate of blood glucose change and a specific threshold.
  • the rate of blood glucose change can also be calculated as described above, and the threshold can be personalised.
  • the closed-loop artificial pancreas insulin infusion control system further includes a movement sensor (not shown) .
  • the motion sensor automatically detects the user's physical activity, and the program module 101 can receive physical activity status information.
  • the motion sensor can automatically and accurately sense the user's physical activity state and send the activity state parameters to the program module 101 to improve the output reliability of the compound artificial pancreas algorithm in exercise scenarios.
  • the motion sensor is provided in detection module 100, the program module 101 or the infusion module 102.
  • the motion sensor is provided in the program module 101.
  • the embodiment of the present invention does not limit the number of motion sensors and the installation positions of these multiple motion sensors, as long as the conditions for the motion sensor to sense the user's activity status can be satisfied.
  • the motion sensor includes a three-axis acceleration sensor or a gyroscope.
  • the three-axis acceleration sensor or gyroscope can more accurately sense the body's activity intensity, activity mode or body posture.
  • the motion sensor combines a three-axis acceleration sensor and a gyroscope.
  • the blood glucose risk conversion methods used by the rMPC algorithm and the rPID algorithm can be the same or different, and the compensation methods for the delay effect can also be the same or different.
  • the calculation process can also be adjusted based on actual conditions.
  • the program module 101 provides an adaptive unit that adjusts the algorithm gain coefficient according to the user's weight.
  • the infusion module 102 or the program module 101 can indicate the user's daily insulin requirement DIR.
  • the weight adjustment coefficient e can be set as the population mean value, 0.53U/kg, and it can also be customised according to their exercise habits. For example, a lower weight adjustment coefficient can be used for professional sports patients, such as 0.4U/kg; for patients less involved in the exercise, a higher weight adjustment factor can be used, such as 0.6 U/kg.
  • a personalised weight adjustment factor can be selected in a larger range based on their pancreatic secretion function and insulin resistance, such as 0.1-1.5 U/kg, and the more commonly used range is 0.6-1.1 U/kg.
  • the different coefficients can be set during daytime and night. For example, a smaller time parameter can be selected at night.
  • the algorithm preset in the program module 101 is the classic MPC algorithm or rMPC algorithm, and its gain coefficient K is related to weight BW,
  • c is the safety factor
  • s is the clinical experience coefficient
  • e is the weight adjustment coefficient
  • the safety factor c is set as 1.25 -3; the clinical experience coefficient s can be 1500, 1700, 1800, 2000, 2200, 2500, etc., which can be adjusted according to the clinical results, and there is no specific limitation here.
  • the clinical experience coefficient s is 1700; the range of the weight adjustment coefficient e is described above.
  • the gain coefficient Kp of the PID algorithm or rPID algorithm and the gain coefficient K of the MPC algorithm or rMPC algorithm can also be adjusted by introducing the coefficient Sb (t) related to the basal insulin requirement, correspondingly:
  • Ba y*DIR/24
  • y is the basal insulin compensation coefficient, which takes a value of 0.1 to 5.
  • the average population value of this coefficient is 0.47, and the data for children is slightly smaller, for example, 0.3-0.4.
  • the daily basal insulin quantity Ba average can be calculated according to the user's actual basal rate setting.
  • the basal insulin requirement B (t) at time t can be set according to the four mainstream clinical optimal basal rate settings.
  • FIG. 5 shows the four types of mainstream clinical optimal basal rate settings from the reference Holterhus, PM, J. Bokelmann, et al. (2013) . "Predicting the Optimal Basal Insulin Infusion Pattern in Children and Adolescents on Insulin Pumps. " Diabetes Care 36 (6) : 1507-1511, where the horizontal axis is time, 24 hours a day, and the vertical axis is the relative deviation between the basal insulin requirement and the average of the daily basal insulin quantity Ba at the corresponding time. Most of them are within [0.5, 1.5] .
  • B(t) can also be set refer to the basic rate segmentation settings commonly used in clinical practice, such as three-stage settings, as follows:
  • B (t) can also be calculated according to the user-known and appropriate base rate setting.
  • the range of Sb (t) is 0.2-2, preferably 0.5-1.5.
  • the conversion method of rPID algorithm and the rMPC algorithm which converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in risk space, and the processing method for the calculation result, and the beneficial effects are as described above, which will not be repeated here.
  • FIG. 6 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.
  • the closed-loop artificial pancreas insulin infusion control system mainly includes a detection module 100, an infusion module 102, and an electronic module 103.
  • the detection module 100 is used to detect the user's real-time blood glucose level continuously.
  • the detection module 100 is a continuous glucose monitor (Continuous Glucose Monitoring, CGM) , which can detect blood glucose levels in real-time, monitor blood glucose changes, and send the current blood glucose levels to the infusion module 102 and the electronic module 103.
  • CGM Continuous Glucose Monitoring
  • the infusion module 102 includes the mechanical assembly necessary for insulin infusion and other components capable of executing the first algorithm, such as an infusion processor 1021, controlled by the electronic module 103.
  • the infusion module 102 receives the current blood glucose level sent by the detection module 100, calculates the first insulin infusion amount I 1 currently required through the first algorithm and sends the calculated first insulin infusion amount I 1 to the electronic module 103.
  • the electronic module 103 is used to control the operation of detection module 100 and the infusion module 102. Therefore, the electronic module 103 is connected to the detection module 100 and the infusion module 102, respectively.
  • the electronic module 103 is an external electronic device such as a mobile phone or a handset, and the connection refers to a wireless connection.
  • the electronic module 103 includes a second processor. In the embodiment of the present invention, the second processor is capable of executing the second algorithm and the third algorithm, such as an electronic processor 1031. After the electronic module 103 receives the current blood sugar level, the current required second insulin infusion amount I 2 is calculated through the second algorithm.
  • the first and second algorithms used by the electronic module 103 and the infusion module 102 to calculate the amount of insulin currently required are different.
  • the electronic module 103 After the electronic module 103 receives the first insulin infusion amount I 1 sent by the infusion module 102, it further optimises the first insulin infusion amount I 1 and the second insulin infusion amount I 2 through the third algorithm to obtain the final insulin infusion amount I 3 , and sends final insulin infusion amount I 3 to the infusion module 102, the infusion module 102 injects the currently needed insulin amount I 3 into the user's body. At the same time, the infusion status of the infusion module 102 can also be fed back to the electronic module 103 in real-time.
  • the specific optimisation method is as described above. which is:
  • the electronic module 103 can also compare I 1 , I 2 and I 4 , which is a statistical analysis result at the current time by analysing the historical information based on the user's body state, blood sugar level and insulin infusion at each time in the past. The one that is closer to the statistical analysis result I 4 is selected as the final insulin infusion amount I 3 , and the electronic module 103 sends the final insulin infusion amount I 3 to the infusion module 102 to infuse;
  • the user's historical information may be stored in the electronic module 103 or a cloud management system (not shown) , and the cloud management system and the electronic module 103 are connected wirelessly.
  • FIG. 7 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.
  • the closed-loop artificial pancreas insulin infusion control system mainly includes a detection module 100, an infusion module 102, and an electronic module 103.
  • the detection module 100 is used to detect the user's real-time blood glucose level continuously.
  • the detection module 100 is a continuous glucose monitor (Continuous Glucose Monitoring, CGM) , which can detect blood glucose levels in real-time, monitor blood glucose changes, and the current blood glucose levels have only been sent to the infusion module 102.
  • the detection module 100 further includes a second processor.
  • the second processor is capable of executing the second algorithm, such as a detection processor 1001. After detecting the real-time blood glucose level, detection module 100 directly calculates the second insulin infusion amount I 2 through the second algorithm and sends the calculated second insulin infusion amount I 2 to the electronic module 103.
  • infusion module 102 after receiving the current blood glucose level sent by the detection module 100, calculates the first insulin infusion amount I 1 currently required through the first algorithm and sends the calculated first insulin infusion amount I 1 to the electronic module 103.
  • the first and second algorithms used by the electronic module 103 and the infusion module 102 to calculate the amount of insulin currently required are different.
  • the electronic module 103 After the electronic module 103 receives the first insulin infusion amount I 1 sent by the infusion module 102 and the second insulin infusion amount I 2 sent by the detection module 103, it further optimises the first insulin infusion amount I 1 and the second insulin infusion amount I 2 through the third algorithm to obtain the final insulin infusion amount I 3 . It sends the final insulin infusion amount I 3 to the infusion module 102.
  • the infusion module 102 injects the currently needed insulin amount I 3 into the user's body. At the same time, the infusion status of the infusion module 102 can also be fed back to the electronic module 103 in real-time.
  • the specific optimisation method is as described above.
  • the infusion processor 1021 preliminarily calculates the first insulin infusion amount I 1 .
  • the second processor (such as the electronic processor 1031 and the detection processor 1001) preliminarily calculate the second insulin infusion amount I2, and I1 and I2 being sent to the electronic module 103.
  • the electronic module 103 performs further optimisation and then sends the optimised final insulin infusion amount I 3 to the infusion module 102 to infuse insulin, improving the accuracy of infusion instructions.
  • the first algorithm and the second algorithm are one of the classic PID algorithms, the classic MPC algorithm, the rMPC algorithm, or the rPID algorithm.
  • the advantages of using the rPID or rMPC algorithm to calculate are as described above, and the beneficial effects of other optimisation methods are also as described above and will not be repeated here.
  • the embodiment of the present invention does not limit the specific position and connection relationship of the detection module 100 and the infusion module 102, as long as the aforementioned functional conditions can be met.
  • the two modules are electrically connected to form an integral assembly and are pasted in the same place on the user's skin. If the two modules are connected as a whole and pasted in the same position, the number of user skin pasting devices will be reduced, thereby reducing the interference of more pasted devices on user activities; at the same time, it also effectively solves the problem of poor wireless communication between separate devices, which further enhance the user experience.
  • the two modules are arranged in different components and are passed on different positions of the user's skin.
  • the detection module 100 and the infusion module 102 transmit wireless signals to realise the mutual connection.
  • FIG. 8 is a schematic diagram of the module relationship of the closed-loop artificial pancreas multi-drug infusion control system according to another embodiment of the present invention.
  • the closed-loop artificial pancreas insulin infusion control system mainly includes a detection module 100, a program module 101, and an infusion module 102.
  • the infusion module 102 can perform multi-drug infusion, and the drugs can be a combination for regulating blood glucose for diabetic patients.
  • hypoglycemic drugs such as insulin and its analogue
  • other combination drugs are anti-hypoglycemic drugs, which has opposite effects with hypoglycemic drugs, such as pancreatic hypertension Glucagon and its analogs, cortisol and its analogs, growth hormone and its analogs, epinephrine and its analogs, glucose, etc., dextrins with similar effects Analogs (such as pramlintide) , etc.
  • the infusion module 102 can infuse the hypoglycemic drug and/or the anti-hypoglycemic drug into the user according to the hypoglycemic drug infusion instruction and/or the anti-hypoglycemic drug infusion instruction issued by the program module 101.
  • the hypoglycemic and blood sugar raising drugs can be infused separately through different drug paths or through the same drug path at different times.
  • the specific drug path design is not limited here.
  • FIG. 9 is a schematic diagram of dual-drug infusion switching according to two embodiments of the present invention.
  • the hypoglycemic drug infusion instruction and/or the current anti-hypoglycemic drug infusion instruction are obtained by comparing the predicted blood glucose concentration estimated G P with the target blood glucose value G B , and the predicted blood glucose concentration G P may be predicted based on the prediction model of rMPC or other suitable blood glucose prediction algorithms; the hypoglycemic drug infusion data and/or the anti-hypoglycemic drug infusion data can be calculated by the aforementioned rMPC algorithm or rPID algorithm or compound artificial pancreas algorithm. Specifically:
  • the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I t , which is calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm;
  • the infusion module 102 starts to infuse the anti-hypoglycemic drug infusion according to the anti-hypoglycemic drug infusion data D t , which is calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm;
  • I b represents the amount of hypoglycemic drugs that need to be infused to control blood glucose at the target blood glucose level G B without interference.
  • the infusion module 102 When the infusion module 102 has only one set of drug infusion paths, when G P ⁇ G B , that is, I t ⁇ I b , the infusion module 102 starts to infuse anti-hypoglycemic drugs, and the anti-hypoglycemic drug infusion data D t can be calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm, and the infusion of hypoglycemic drugs is stopped at the same time to prevent the hypoglycemic drugs and the anti-hyperglycemic drugs from affecting each other due to their antagonistic effects.
  • the hypoglycemic drugs and anti-hyperglycemic can be infused simultaneously, which can effectively prevent hypoglycemia.
  • I t ⁇ 0 the infusion of hyperglycemic drugs is stopped and only infuse anti-hyperglycemic drugs.
  • the hypoglycemic drug infusion instruction and/or the current anti-hypoglycemic drug infusion instruction may be directly performed by comparing the required amount of the hypoglycemic drug It with the target hypoglycemic drug amount I b , and the hypoglycemic drug required amount I t and the target hypoglycemic drug amount I b can be calculated by the aforementioned rMPC algorithm, rPID algorithm, or compound artificial pancreas algorithm. Specifically: when the infusion module 102 has at least two sets of drug infusion paths:
  • the infusion module 102 starts to infuse the hypoglycemic drug according to the hypoglycemic drug infusion data I t , which is calculated by the rMPC algorithm or the rPID algorithm or the compound artificial pancreas algorithm;
  • hypoglycemic drugs and anti-hypoglycemic can be infused at the same time, which can effectively prevent the occurrence of hypoglycemia.
  • the hypoglycemic drug required amount I t and the target hypoglycemic drug amount I b can be calculated by the aforementioned rMPC algorithm, rPID algorithm, or compound artificial pancreas algorithm.
  • the anti-hypoglycemic drug infusion data D t can be calculated by the rMPC algorithm, rPID, compound artificial pancreas algorithm.
  • the hypoglycemic is insulin
  • the anti-hypoglycemic is glucagon
  • the calculation methods of the hypoglycemic drug infusion data and the anti-hypoglycemic infusion data at each stage may be the same or different.
  • the same algorithm architecture ensures the basic conditions' consistency, which makes the calculation results more accurate.
  • the compound artificial pancreas algorithm is used for calculation, and the advantages of the rPID algorithm and the rMPC algorithm are fully utilised to face complex scenarios to make the blood glucose control ideally.
  • FIG. 10 is a schematic diagram of the module relationship of the closed-loop artificial pancreas insulin infusion control system according to another embodiment of the present invention.
  • the closed-loop artificial pancreas insulin infusion control system disclosed in the embodiment of the present invention mainly includes a detection module 200 and an infusion module 202.
  • the detection module 100 is used to continuously detect the user's current blood glucose (BG) level.
  • detection module 100 is a Continuous Glucose Monitoring (CGM) for detecting real-time BG and monitoring BG changes.
  • the detection module 200 also includes a detection processing unit 2001.
  • the detection processing unit 2001 is preset with an algorithm for calculating insulin amount for infusion. When the user's current blood glucose level is detected by the detection module 200, the detection processing unit 2001 calculates the insulin amount required by the user through the preset algorithm. The insulin amount required by the user is sent to infusion module 202.
  • the infusion module 202 includes the essential mechanical assemblies for insulin infusion and an electronic transceiver that receives the user's insulin amount information from the detection module 200. According to the current insulin infusion amount sent by the detection module 200, infusion module 202 infuses the currently required insulin into the user's body. At the same time, the infusion status of infusion module 202 can also be fed back to detection module 200 in real-time.
  • the algorithm for calculating the insulin infusion amount, preset in the detection processing unit 2001 is one of the classic PID algorithms, the classic MPC algorithm, the rMPC rPID algorithm or the compound artificial pancreas algorithm.
  • the algorithm or the compound artificial pancreas algorithm is described above and will not be repeated here.
  • the embodiment of the present invention does not limit the specific position and connection relationship of the detection module 200 and the infusion module 202, as long as the aforementioned functional conditions can be met.
  • the two are electrically connected to form an integral assembly and are pasted in the same place on the user's skin. If the two modules are connected as a whole and pasted in the same position, the number of user skin pasting devices will be reduced, thereby reducing the interference of more pasted devices on user activities; at the same time, it also effectively solves the problem of poor wireless communication between separate devices, which further enhance the user experience.
  • the two modules are arranged in different components and are passed on different positions of the user's skin.
  • the detection module 100 and the infusion module 102 transmit wireless signals to realize the mutual connection.
  • the present invention discloses a closed-loop artificial pancreas insulin infusion control system, the system is preset with one or more of the rMPC algorithm the rPID algorithm or compound artificial pancreas algorithm, which converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose risk in the risk space, making full use of the advantages of rPID algorithm and rMPC algorithm to face complex scenarios, so that the artificial pancreas can provide reliable insulin infusion under various conditions, and blood glucose can reach the ideal level at the expected time, realizing precise control for closed-loop artificial pancreas insulin infusion system.

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Abstract

L'invention concerne un système de commande de perfusion d'insuline pour un pancréas artificiel en boucle fermée, comprenant : un module de détection (100) configuré au niveau de glycémie actuel ; un module de programme (101) relié au module de détection (100), préréglé avec un ou plusieurs des algorithmes rMPC, des algorithmes rPID ou des algorithmes du pancréas artificiel composé ; et un module de perfusion (102) connecté au module de programme (101), pouvant réaliser une perfusion de médicaments anti-hypoglycémiants et/ou de médicaments hypoglycémiants, le module de programme (101) commandant le module de perfusion (102) pour perfuser des médicaments correspondants, selon les instructions de perfusion de médicament correspondantes, qui sont calculées par l'algorithme rMPC, l'algorithme rPID ou l'algorithme du pancréas artificiel composé. Le système est préréglé avec un ou plusieurs de l'algorithme rMPC, de l'algorithme rPID ou de l'algorithme du pancréas artificiel composé, qui convertit la glycémie asymétrique dans l'espace physique d'origine en le risque de glycémie approximativement symétrique dans l'espace de risque, ce qui permet d'utiliser pleinement les avantages de l'algorithme rPID et de l'algorithme rMPC pour faire face à des scénarios complexes, de telle sorte que le pancréas artificiel peut fournir une perfusion d'insuline fiable dans diverses conditions, et la glycémie peut atteindre le niveau idéal au moment attendu, réalisant ainsi une commande précise pour le système de perfusion d'insuline du pancréas artificiel en boucle fermée.
PCT/CN2021/126032 2021-10-25 2021-10-25 Système de commande de multi-médicaments d'insuline pour pancréas artificiel en boucle fermée WO2023070252A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104667379A (zh) * 2015-03-06 2015-06-03 上海交通大学 动态闭环控制的胰岛素泵
CN104958077A (zh) * 2015-07-24 2015-10-07 珠海福尼亚医疗设备有限公司 一种智能控制的闭环式人工胰腺系统
CN106326651A (zh) * 2011-08-26 2017-01-11 弗吉尼亚大学专利基金会 用于糖尿病的自适应咨询控制的方法和系统
CN106860955A (zh) * 2017-01-21 2017-06-20 广东食品药品职业学院 基于模糊自适应比例微积分控制胰岛素泵闭环输注的方法
WO2020232232A1 (fr) * 2019-05-14 2020-11-19 University Of Virginia Patent Foundation Système et procédé pour pancréas artificiel avec commande prédictive de modèle à étages multiples

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106326651A (zh) * 2011-08-26 2017-01-11 弗吉尼亚大学专利基金会 用于糖尿病的自适应咨询控制的方法和系统
CN104667379A (zh) * 2015-03-06 2015-06-03 上海交通大学 动态闭环控制的胰岛素泵
CN104958077A (zh) * 2015-07-24 2015-10-07 珠海福尼亚医疗设备有限公司 一种智能控制的闭环式人工胰腺系统
CN106860955A (zh) * 2017-01-21 2017-06-20 广东食品药品职业学院 基于模糊自适应比例微积分控制胰岛素泵闭环输注的方法
WO2020232232A1 (fr) * 2019-05-14 2020-11-19 University Of Virginia Patent Foundation Système et procédé pour pancréas artificiel avec commande prédictive de modèle à étages multiples

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