Classifications

• • 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
• • 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
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7221—Determining signal validity, reliability or quality
• • 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
• • 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
  • A61M2205/00—General characteristics of the apparatus
  • A61M2205/50—General characteristics of the apparatus with microprocessors or computers
• • 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

• TI TRE Automated blood sugar control system
• the present description relates to the field of automated systems for regulating blood sugar, also called artificial pancreas.
• An artificial pancreas is a system making it possible to automatically regulate the insulin intake of a diabetic subject or patient based on his blood sugar history (or blood glucose level), his meal history, and his insulin injection history.
• a blood sugar regulation system comprising a processing and control unit configured to implement a method for automated blood sugar regulation, wherein the regulation method takes into account at least one hyperparameter having a default value, the value of said at least one hyperparameter being able to be adjusted on the fly by the processing and control unit by means of a function of adjustment,
• the system further comprises:
• the glycemia regulation method implemented by the processing and control unit comprises the control of the insulin injection device taking into account measurements supplied by the glycemia sensor.
• the performance indicator is an indicator of the group comprising:
• the value of said at least one hyperparameter is kept constant by the processing and control unit during the regulation period.
• said at least one hyperparameter is used a plurality of times by the processing and control unit during the regulation period.
• said at least one hyperparameter is used at least twenty times by the processing and control unit during the regulation period.
• said at least one hyperparameter in a hyperparameter from the group comprising:
• said at least one hyperparameter is the coefficient used by the processing and control unit to determine the volume of doses of insulin to be injected to the user, and said at less one performance indicator is the percentage of time spent in hyperglycemia or the number of times in hyperglycemia during the regulation period,
• the processing and control unit increases the value of the coefficient if the value of the indicator is greater than a threshold.
• FIG. 1 schematically represents, in the form of blocks, an example of an automated system for regulating the blood sugar of a patient according to one embodiment
• FIG. 2 illustrates an example of an automated method for adjusting a method for regulating blood sugar which can be implemented by the system of FIG. 1.
• FIG. 1 schematically represents, in the form of blocks, an example of an embodiment of an automated system for regulating the glycemia of a diabetic subject or user.
• the system of Figure 1 comprises a sensor 101 (CG) suitable for measuring the subject's blood sugar.
• the sensor 101 can be permanently positioned on or in the subject's body, for example at the height of his abdomen.
• the sensor 101 is for example a sensor of the CGM type (standing for “Continuous Glucose Monitoring”), that is to say a sensor suitable for measuring continuously or at a relatively high frequency (eg. example at least once every twenty minutes and preferably at least once every five minutes) the subject's blood sugar.
• the sensor 101 is for example a subcutaneous glycemia sensor.
• the system of Figure 1 further comprises an insulin injection device 103 (PMP), for example a subcutaneous injection device.
• PMP insulin injection device
• the device 103 is for example an automatic injection device of the insulin pump type, comprising an insulin reservoir connected to an injection needle implanted under the subject's skin, the pump being electrically controllable to automatically inject doses of insulin determined at specific times.
• the injection device 103 can be permanently positioned in or on the subject's body, for example at the level of his abdomen.
• the system of Figure 1 further comprises a processing and control unit 105 (CTRL) connected on the one hand to the blood glucose sensor 101, for example by wired link or by radio link (wireless), and d 'on the other hand to the injection device 103, for example by wire or radio link.
• CTRL processing and control unit 105
• the processing and control unit 105 is adapted to receive the patient's blood glucose data measured by the sensor 101, and to electrically control the device 103 to inject the subject with determined doses of insulin at determined times.
• the processing and control unit 105 is further adapted to receive, via a non-detailed user interface, data cho (t) representative of the evolution, as a function of time, of the amount of glucose ingested by the patient.
• the processing and control unit 105 can also be adapted to receive any additional data, for example data relating to the physical activity and / or to the state of stress of the user, or even to his health.
• the processing and control unit 105 is suitable for determining the doses of insulin to be injected into the patient, taking into account in particular the measured blood glucose history by the sensor 101, the history of insulin injected by the device 103, and the history of glucose ingestion by the patient, as well as any additional data, for example data relating to physical activity and / or the patient's state of stress.
• the processing and control unit 105 comprises a digital calculation circuit (not detailed), comprising for example a microprocessor.
• the processing and control unit 105 is, for example, a mobile device carried by the patient throughout the day and / or night.
• the processing and control unit 105 is mounted integral with the insulin injection device 103, or with the sensor 101.
• the processing and control unit 105 is a device. independent of the injection device 103 and of the sensor 101, for example a device of the smartphone type.
• the processing and control unit 105 is configured to implement an automated regulation process which may include several distinct regulation bricks or modules corresponding respectively to distinct regulation modes.
• the regulation method implemented by the processing and control unit 105 may comprise a brick implementing a regulation method of MPC type (standing for "Model-based Predictive Control") , also called a method of regulation with predictive control, in which the regulation of the dose of insulin administered takes into account a prediction of the future evolution of the patient's glycemia as a function of time made from a mathematical model, for example a physiological model describing the assimilation of insulin by the patient's body and its impact on the patient's blood sugar.
• a mathematical model for example a physiological model describing the assimilation of insulin by the patient's body and its impact on the patient's blood sugar.
• the actual blood glucose data measured by the sensor 101 are used mainly for calibration of the mathematical model.
• the regulation method implemented by the processing and control unit 105 may further include a brick implementing a safety hat algorithm, called hypominimizer (HM), or hypoglycemia minimization algorithm, having for the function of anticipating and preventing imminent hypoglycemia by interrupting the flow of insulin administered by the device 103 and / or by offering the patient a re-sugaring, that is to say an ingestion of glucose.
• HM hypominimizer
• hypoglycemia minimization algorithm having for the function of anticipating and preventing imminent hypoglycemia by interrupting the flow of insulin administered by the device 103 and / or by offering the patient a re-sugaring, that is to say an ingestion of glucose.
• hypoglycemia minimization hat algorithm makes it possible to predict an imminent risk of hypoglycemia, and, when such a risk is detected, to reduce or interrupt the flow of insulin injected to the patient, or even to offer the patient a re-sugaring of way to try to avoid hypoglycemia.
• the regulation method implemented by the processing and control unit 105 can further comprise a brick implementing a decisional matrix (MD) type regulation method, which can be used as a substitute for the algorithm. predictive control regulation of the MPC brick, for example when it is determined that the predictions made by the mathematical model of the MPC brick are not sufficiently reliable
• the regulation method implemented by the processing and control unit 105 can further include a post-meal management brick (PMM - from the English "Post Prandial Management ”) implementing a specific regulation method during the phases following the meals declared by the user.
• PMM post-meal management brick
• the regulation method implemented by the processing and control unit 105 may further comprise a brick implementing a bolus adjustment algorithm and various sensitivity parameters, for example by using a decision tree .
• the regulation method implemented by the processing and control unit 105 uses a large number of parameters. Some of these parameters are fixed, that is to say that they cannot be modified without completely recompiling the control software, which implies an interruption of the control to carry out their update. Other parameters, called hyperparameters, can be modified on the fly, or hot, that is to say without interrupting the regulation. Each hyperparameter has a default value, and can be adjusted by the processing and control unit 105 by means of an adjustment function, between a minimum value and a maximum value.
• the regulation method can use a hyperparameter, which will be called hereinafter PATIENT_HYPO_LIMIT, corresponding to a blood sugar threshold below which the user will be considered to be in hypoglycemia by the hypominimizer (HM) security brick.
• PATIENT_HYPO_LIMIT a hyperparameter
• This parameter can in particular be used by the hat algorithm implemented by the HM brick to decide to interrupt the flow of insulin administered by the device 103 and / or to offer re-sugaring to the user.
• This parameter has a default value, for example of the order of 70 mg / dl, but can be modified without interrupting the regulation between a minimum value, for example of the order of 60 mg / dl, and a maximum value, through example of the order of 85 mg / dl.
• the adjustment of this parameter ultimately makes it possible to control the number of re-sugarings offered to the patient and / or the number of interruptions in the flow of insulin administered to the patient.
• a hyperparameter that can be used by the regulation method is a coefficient which will be called hereinafter MD_BOLUS_FACTOR, used by the decision matrix brick (MD) to determine the size of the boluses (doses of insulin) to be injected into the patient at the end of a decision phase.
• MD_BOLUS_FACTOR used by the decision matrix brick (MD) to determine the size of the boluses (doses of insulin) to be injected into the patient at the end of a decision phase.
• This parameter has a default value, for example of the order of 0.7, and can be modified without interrupting the regulation between a minimum value, for example of the order of 0.3, and a maximum value, for example of the order of 1.3. The adjustment of this parameter ultimately makes it possible to control, for a given patient, the quantity of insulin injected when the regulation is ensured by the decision-making matrix brick.
• a hyperparameter that can be used by the regulation method is an error threshold, which will be called hereinafter MODEL_MISMATCH, used to estimate the reliability of the forecasts made by the mathematical model of the MPC brick , and decide whether or not to switch from the MPC brick (predictive control regulation on the basis of a mathematical model) to the MD brick (regulation by decision matrix).
• the processing and control unit 105 can be configured to, during a phase of estimating the reliability of the mathematical model of the MPC brick, calculate a digital indicator representative of the error between the blood glucose estimated at from the model and the actual blood glucose measured by sensor 101, and compare this indicator to the MODEL_MISMATCH threshold.
• the regulation continues to be implemented by the predictive control module MPC. If the calculated error is greater than the threshold, the MPC brick temporarily ceases to be used and the regulation is implemented by the regulation brick by decision matrix MD.
• the MODEL_MISMATCH parameter has a default value, and can be modified without interrupting the regulation between a minimum value and a maximum value. The adjustment of this parameter ultimately makes it possible to control, for a given patient, the ratio between the time spent in MPC regulation and the time spent in MD regulation.
• hyperparameter that can be used by the regulation method is an inhibition period between two resucrage proposals, which will be called SNACK_INHIB_DURATION hereinafter.
• This is a minimum period following a re-sugaring declared by the user, during which the HM brick is not authorized to propose a new re-sugaring.
• This parameter has a default value, and can be modified without interrupting the regulation, between a minimum value and a maximum value. The adjustment of this parameter ultimately makes it possible to control, for a given user, the number of sugar replenishers offered to the patient during a given time interval.
• hyperparameter that can be used by the regulation method is an increase in the target glycemic target, applied before a future physical activity declared by the user so as to take into account the physical activity. coming into regulation. This parameter, which will be called hereafter
• BEFORE_PA_TARGET_MAJORATION presents a default value, and can be modified without interrupting the regulation, between a minimum value and a maximum value. The adjustment of this parameter ultimately makes it possible to control, for a given user, the risk of hypoglycemia linked to physical activity. More generally, many other hyperparameters are likely to be used in the regulation method implemented by the processing and control unit 105.
• provision is made to automatically adjust the values of one or more hyperparameters, for example one or more hyperparameters of the group comprising the parameters PATIENT_HYPO_LIMIT, MD_BOLUS_FACTOR, MODEL_MISMATCH, SNACK_INHIB_DURATION and
• the processing and control unit is configured for, at the end of a regulation phase implementing the hyperparameter (s) to be adjusted, calculating one or more indicators representative of the performance of the monitoring system. regulation during said regulation phase, then adjusting on the fly (that is to say without interrupting regulation) the value of the hyperparameter (s) considered as a function of the calculated performance indicator (s).
• FIG. 2 illustrates an example of an automated method for adjusting a method for regulating blood glucose which can be implemented by the system of FIG. 1.
• the method of Figure 2 comprises a step 201 during which the system automatically regulates the user's blood sugar.
• the processing and control unit 105 implements a regulation method, based for example on one or more of the regulation bricks described above.
• the processing and control unit determines the doses of insulin to be injected into the patient, taking into account in particular the blood glucose history measured by the sensor 101, the history of insulin injected by the patient. device 103, the history of glucose ingestion by the patient, as well as any additional data, for example data relating to the patient's physical activity and / or the state of stress.
• the processing and control unit 105 further controls the insulin injection unit 103 to deliver the determined insulin doses to the user.
• each of the hyperparameters that one seeks to adjust is kept constant.
• the duration of the regulation phase 201 is chosen such that each of the hyperparameters that one seeks to adjust is used at least once, and preferably several times, during the regulation phase 201.
• the duration of the regulation phase 201 is chosen so as to include, for each of the hyperparameters which one seeks to adjust, at least 20 occurrences of an event implementing the hyperparameter considered.
• the method of FIG. 2 further comprises, at the end of the regulation step 201, a step 202 for evaluating the performance of the regulation implemented in step 201.
• the processing and control unit 105 calculates or determines one or more indicators representative of the performance of the regulation implemented during phase 201.
• the performance indicators are for example one or more of the indicators of the group comprising:
• hypoglycemia a condition in which the patient's blood sugar is below a predetermined low threshold
• hyperglycemia a condition in which the patient's blood sugar is above a predetermined high threshold
• - normoglycemia a condition in which the patient's blood sugar is between the low threshold of hypoglycemia and the high threshold of hyperglycemia.
• the method of Figure 2 further comprises, at the end of step 202, a step 203 of adjusting the values of the hyperparameter (s) considered, taking into account the performance indicator (s) determined at the step 202.
• the processing and control unit 105 modifies the values of the hyperparameter (s) considered according to predetermined rules, in order to try to improve the performance of the regulation system. For example, in the case where the performance indicator includes the percentage of time spent in hypoglycemia or the percentage of time spent in hyperglycemia, the adjustment of the hyperparameter (s) may be aimed at trying to reduce this percentage. where the performance indicator includes the percentage of time spent in normoglycemia, the adjustment of the hyperparameter (s) may be aimed at trying to increase this percentage.
• the adjustment of the hyperparameter (s) may be aimed at trying to reduce this variability.
• the adjustment of the hyperparameter (s) may be aimed at trying to reduce this number.
• the PATIENT_HYPO_LIMIT parameter if it is judged at step 203 that the number of passages in hypoglycemia following recommendations or decisions based on the use of this parameter is too high, it is possible to plan to increase the value of the PATIENT_HYPO_LIMIT threshold, for example to pass it from 70 mg / dl to 75 mg / dl for further regulation.
• the MD_BOLUS_FACTOR parameter if it is judged in step 203 that the number of hyperglycemia following bolus injections or insulin doses calculated on the basis of this parameter is too high, we may plan to increase the value of the MD_BOLUS_FACTOR parameter, for example by 10%, for the rest of the regulation.
• step 203 the person skilled in the art will know, as a function of the hyperparameters and of the performance indicators considered, to determine the automatic adjustment rules to be applied in step 203 in order to improve the performance of the regulation system.
• the adjustment of the hyperparameter (s) in step 203 is carried out on the fly, that is to say without interrupting the regulation, by means of an adjustment function implemented by the control unit. treatment and control 105.
• Steps 201 to 203 can then be repeated, it being understood that the adjustment rules implemented in step 203 can take into account the evolution of the performance indicator (s) between successive iterations, in particular to determine if the performance of the system evolves in the right direction.

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• 2019
  • 2019-07-25 FR FR1908457A patent/FR3099043B1/fr active Active
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