WO2023071012A1 - Système de commande de perfusion de médicament de pancréas artificiel à boucle complètement fermée - Google Patents

Système de commande de perfusion de médicament de pancréas artificiel à boucle complètement fermée Download PDF

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WO2023071012A1
WO2023071012A1 PCT/CN2022/079485 CN2022079485W WO2023071012A1 WO 2023071012 A1 WO2023071012 A1 WO 2023071012A1 CN 2022079485 W CN2022079485 W CN 2022079485W WO 2023071012 A1 WO2023071012 A1 WO 2023071012A1
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drug infusion
module
algorithm
blood glucose
infusion
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PCT/CN2022/079485
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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
    • A61B5/48—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
    • 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
    • A61B5/42—Detecting, measuring or recording for evaluating the gastrointestinal, the endocrine or the exocrine systems
    • A61B5/4222—Evaluating particular parts, e.g. particular organs
    • A61B5/425—Evaluating particular parts, e.g. particular organs pancreas
    • 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

Definitions

  • the present invention mainly relates to the field of medical device, and in particular, to a fully closed-loop artificial pancreas 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’s kin, 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.
  • a fully closed-loop control can be only realized in the closed-loop artificial pancreas system when the patient's blood glucose is high and insulin infusion is required. It can’ t be realized in anti-hypoglycemia drug infusion for patients with hypoglycemia. Similarly, for a patient, hypoglycemia or anti-hyperglycemia may occur during different periods of time, and fully closed-loop control in the current artificial pancreas has not yet achieved for multi-drug infusion.
  • the embodiment of the present invention discloses a fully closed-loop artificial pancreas drug infusion control system.
  • the system includes a program module, at least one detection module, a hypoglycemic drug infusion module and an anti-hypoglycemic drug infusion module.
  • the program module is preset with an algorithm.
  • the detection module detects the blood glucose level
  • the hypoglycemic drug infusion module performs hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm
  • the anti-hypoglycemic drug infusion module performs anti-hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm, realizing full closed-loop control of drug infusion in artificial pancreas system.
  • the invention discloses a closed-loop artificial pancreas insulin infusion control system, including: a program module, preset with an algorithm; at least a detection module, configured to continuously detect the current blood glucose level; a hypoglycemic drug infusion module, configured to perform hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm; and an anti-hypoglycemic drug infusion module, configured to perform anti-hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm.
  • any two modules of the detection module, the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module are connected or integrated to from a single part.
  • the detection module, the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module are separated configurations.
  • the three modules of the detection module, the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module are connected or integrated to from a single part.
  • the system includes two detection modules, one is connected or integrated with the hypoglycemic drug infusion module to from a single part, and the other one is connected or integrated with the anti-hypoglycemic drug infusion module to from another single part.
  • the preset algorithm is one or combination of classic PID algorithm, classic MPC algorithm, 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 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 program module is arranged in the external electronic device.
  • the program module is arranged in any one of the detection module, the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module.
  • the program module is a separate configuration.
  • the program module is connected to one or more of the detection module, the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module to form a single part.
  • the other detection module when one of the detection modules fails or is warming up, the other detection module performs detection.
  • the program module processes the blood glucose values after receiving the blood glucose values detected by the two detection modules.
  • hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module are integrated to form a single part, and the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module share the same fluid path.
  • hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module are integrated to form a single part, and the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module use different fluid paths.
  • the hypoglycemic drug infused by the hypoglycemic drug infusion module is insulin
  • the anti-hypoglycemic drug infused by the anti-hypoglycemic drug infusion module is glucagon
  • the system includes a program module, at least one detection module, a hypoglycemic drug infusion module and an anti-hypoglycemic drug infusion module.
  • the program module is preset with an algorithm.
  • the detection module detects the blood glucose level
  • the hypoglycemic drug infusion module performs hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm
  • the anti-hypoglycemic drug infusion module performs anti-hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm, realizing full closed-loop control of drug infusion in artificial pancreas system.
  • the detection module, the hypoglycemic drug infusion module and the anti-hypoglycemic drug infusion module can be arranged in various ways. Three modules can be arranged in one configuration at the same time, or any two can be arranged in the same configuration, or three modules can be arranged separately. It can be arranged according to actual needs to improve user experience.
  • the system includes two detection modules, one is connected or integrated with the hypoglycemic drug infusion module to from a single part, and the other one is connected or integrated with the anti-hypoglycemic drug infusion module to from another single part.
  • the program module can appropriately process the two received blood glucose values, based on the processed blood glucose values to calculate the drug infusion amount with the preset algorithm, making the drug infusion amount required by the user more accurate.
  • the other detection module can perform detection to ensure the continuity of blood glucose detection.
  • the program module can be set in an external electronic device such as a mobile phones, handhelds or electronic watches, and can also be connected or integrated with other modules to form a single part.
  • the program module is preset with one or more rMPC algorithm, rPID algorithm or compound artificial pancreas algorithm that converts the asymmetric blood glucose in the original physical space to the approximately symmetric blood glucose in the risk space, making full use of the advantages of the rPID algorithm and rMPC algorithm to face complex scenarios, so that the artificial pancreas can provide reliable drug types and drug infusion volumes to make the blood glucose reach an ideal level and realize precise control of the full closed-loop artificial pancreas drug infusion systems.
  • the final output of the compound artificial pancreas algorithm is obtained by comparing and optimizing the results calculated by different algorithms, making the result more feasible and reliable.
  • 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 the module relationship of the fully closed-loop artificial pancreas drug infusion control system according to another embodiment of the present invention.
  • Fig. 6 is a schematic diagram of dual-drug switching according to an embodiment of the present invention.
  • the fully closed-loop control hasn’ t been realized in the current artificial pancreas for anti-hypoglycemia drug infusion, and it has not yet been realized for multi-drug infusion such as hypoglycemia or anti-hyperglycemia infusion.
  • the present invention provides a fully closed-loop artificial pancreas insulin infusion control system
  • the system includes a program module, at least one detection module, a hypoglycemic drug infusion module and an anti-hypoglycemic drug infusion module.
  • the program module is preset with an algorithm.
  • the detection module detects the blood glucose level
  • the hypoglycemic drug infusion module performs hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm
  • the anti-hypoglycemic drug infusion module performs anti-hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm, realizing full closed-loop control of drug infusion in artificial pancreas 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, which also can be called as controller, including components such as memory and processor required to implement control functions. 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, such as, a reservoir, is used to store the drug; a fluid path, including the infusion needle, is used to infuse the drug into the user's body; a driving unit, is used to transfer the drug from the reservoir to the user's body through the fluid path. Specifically, it can be the insulin patch pump of Medtrum company.
  • the infusion module 102 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 or integrated 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 or integrated 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 realize 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 or integrated, 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 realize 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
  • t represents the current moment.
  • 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 3 represents the coefficient of the estimated concentration of blood glucose at the previous moment
  • K 4 and K 5 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.
  • the value function is as follows:
  • 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 is I 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 6 represents the coefficient of the estimated concentration of blood glucose at the time t+j-1;
  • K 7 and K 8 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.
  • FIG. 6 is a schematic diagram of the module relationship of the fully closed-loop artificial pancreas 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, a hypoglycemic drug infusion module 104 and an anti-hypoglycemic drug infusion module 103.
  • Hypoglycemic drugs include insulin and its analogues, and anti-hypoglycemic drugs, has opposite effects with hypoglycemic drugs, includes glucagon and its analogues, cortisol and its analogues substances, growth hormone and its analogs, epinephrine and its analogs, glucose, etc., and dextrin analogs (such as pramlintide) with similar effects.
  • the hypoglycemic drug infusion module 104 and the anti-hypoglycemic drug infusion module 103 can perform hypoglycemic drug and/or anti-hypoglycemic drug infusion into the body of the user according to the drug infusion instruction issued by the program module 101.
  • An algorithm is preset in the program module 101, and the algorithm is one or combination of above mentioned classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or compound artificial pancreas algorithm are preset in the infusion module 103, and according to the blood glucose value G detected by the detection module 100, the preset algorithm calculates the amount of hypoglycemic drug or the anti-hypoglycemic drug required by the user.
  • detection module 100 is a Continuous Glucose Monitoring (CGM) .
  • Program module 101 which also can be called as controller, is used to control the detection module 100, hypoglycemic drug infusion module 104 and the anti-hypoglycemic drug infusion module 103, including components such as memory and processor required to implement control functions.
  • the hypoglycemic drug infusion module 104 includes the essential mechanical assemblies used to infuse hypoglycemic drug
  • the anti-hypoglycemic drug infusion module 103 includes the essential mechanical assemblies used to infuse anti-hypoglycemic drug, such as, a reservoir, is used to store the drug; a fluid path, including the infusion needle, is used to infuse the drug into the user's body; a driving unit, is used to transfer the drug from the reservoir to the user's body through the fluid path.
  • a reservoir is used to store the drug
  • a fluid path including the infusion needle
  • a driving unit is used to transfer the drug from the reservoir to the user's body through the fluid path.
  • it can be the insulin patch pump of Medtrum company or other pumps with corresponding functions.
  • the detection module 100, the hypoglycemic drug infusion module 104 and the anti-hypoglycemic drug infusion module 103 are respectively arranged in three different configurations, therefore, the three are respectively pasted on different positions of the user's skin.
  • any two modules in the detection module 100, the hypoglycemic drug infusion module 104 and the anti-hypoglycemic drug infusion module 103 can be connected or integrated to form a single part, and pasted on a certain part of the skin, while the third module is another separately configuration and is attached to another position on the skin.
  • hypoglycemic drug infusion module 104 and the anti-hypoglycemic drug infusion module 103 are integrated in the same configuration, the hypoglycemic drug and the anti-hypoglycemic drug can be infused through different fluid paths respectively, or can be infused through the same fluid path but infused at the different time, the specific fluid path design is not limited here.
  • the detection module 100, the hypoglycemic drug infusion module 104 and the anti-hypoglycemic drug infusion module 103 are connected or integrated to form a single part, and adhered to one position on the skin.
  • the mentioned connected to form a single part means that two separate components are directly connected or connected through an intermediate component to form a single part
  • the mentioned integrated into a single part means that one component is arranged in another component, and become a part of another component, thus making the two be a single part.
  • the fully closed-loop artificial pancreas drug infusion control system includes two detection modules, one is connected to or integrated with the hypoglycemic drug infusion module 104 to form in a single part, and pasted on a certain position of the skin, and the other detection module is connected to or integrated with the anti-hypoglycemic drug infusion module 103 to form in a single part, and pasted on another position of the skin.
  • the detection module is a continuous glucose monitor (CGM) . Due to reasons such as process or calibration, there will be deviations in the blood glucose value detected by different CGM.
  • CGM continuous glucose monitor
  • the two detection modules detect blood glucose and send the corresponding blood glucose value to the program module 101
  • the program module 101 can appropriately process the two received blood glucose values, for example, comparing with historical data and then select an appropriate blood glucose value to calculate the drug infusion amount, or based on the weighted average of the two blood glucose values to calculate the drug infusion amount with the preset algorithm, making the drug infusion amount required by the user more accurate.
  • the other detection module can perform detection and send the blood glucose value to the program module 101 to ensure the continuity of blood glucose detection.
  • the position and connection relationship between the program module 101 and other modules are not specifically limited, as long as the detection module and the infusion module can be controlled.
  • the fully closed-loop artificial pancreas drug infusion control system further includes an external electronic device, such as a handheld, mobile phone or electronic watch, the program module 101 is set in the external electronic device, and communicated with other modules wirelessly in short range.
  • the program module 101 is provided in any module of the detection module 100, the hypoglycemic drug infusion module 104 or the anti-hypoglycemic drug infusion module 103, such as, analog and/or digital circuits are provided in the detection module 100, the hypoglycemic drug infusion module 104 or the anti-hypoglycemic drug infusion module 103, which may be implemented as program modules.
  • the program modules 101 is a single configuration, attached to a certain part of the skin, and connected with other modules wirelessly.
  • the program module 101 is provided in single configuration, but can be connected to one or more of the detection module 100, the hypoglycemic drug infusion module 104 or the anti-hypoglycemic drug infusion module 103 to form a single part, attached to one position on the skin with one or more of modules, and connected with other modules by wire or wirelessly.
  • the program module 100 can be connected with any one of them to form a single part.
  • the fully closed-loop artificial pancreas drug infusion system includes 3 separate configurations, attached to three different positions on the skin;
  • the program module 100 can be connected to any one of two parts.
  • the fully closed-loop artificial pancreas drug infusion system includes two separate configurations, attached to two different positions on the skin;
  • the program module 100 is connected with it again, so the fully closed-loop artificial pancreas drug infusion system only includes a single configuration, attached to one position on the skin.
  • FIG. 7 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 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 classic PID algorithm, classic MPC algorithm, rMPC algorithm, 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 classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or 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 classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or 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 classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or 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 classic PID algorithm, classic MPC algorithm, 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 classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or 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 classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or compound artificial pancreas algorithm.
  • the anti-hypoglycemic drug infusion data D t can be calculated by the classic PID algorithm, classic MPC algorithm, rMPC algorithm, rPID algorithm or 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.
  • the present invention discloses a closed-loop artificial pancreas insulin infusion control system
  • the system includes a program module, at least one detection module, a hypoglycemic drug infusion module and an anti-hypoglycemic drug infusion module.
  • the program module is preset with an algorithm.
  • the detection module detects the blood glucose level
  • the hypoglycemic drug infusion module performs hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm
  • the anti-hypoglycemic drug infusion module performs anti-hypoglycemic drug infusion according to the infusion instruction calculated by the algorithm, realizing full closed-loop control of drug infusion in artificial pancreas system.

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Abstract

L'invention concerne un système de commande de perfusion de médicament de pancréas artificiel à boucle complètement fermée, comprenant un module de programme (101), au moins un module de détection (100), un module de perfusion de médicament hypoglycémiant (104) et un module de perfusion de médicament anti-hypoglycémie (103). Le module de programme (101) est préréglé avec un algorithme. Le module de détection (100) détecte le taux de glucose dans le sang, le module de perfusion de médicament hypoglycémiant (104) effectue une perfusion de médicament hypoglycémiant conformément à l'instruction de perfusion calculée par l'algorithme, et le module de perfusion de médicament anti-hypoglycémie (103) réalise une perfusion de médicament anti-hypoglycémie conformément à l'instruction de perfusion calculée par l'algorithme, réalisant une commande en boucle complètement fermée de la perfusion de médicament dans un système de pancréas artificiel.
PCT/CN2022/079485 2021-10-25 2022-03-07 Système de commande de perfusion de médicament de pancréas artificiel à boucle complètement fermée WO2023071012A1 (fr)

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