WO2017145119A1 - Estimation de la vitesse d'apparition du glucose à partir de cgs et administration sous-cutanée d'insuline dans le diabète de type i - Google Patents

Estimation de la vitesse d'apparition du glucose à partir de cgs et administration sous-cutanée d'insuline dans le diabète de type i Download PDF

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WO2017145119A1
WO2017145119A1 PCT/IB2017/051100 IB2017051100W WO2017145119A1 WO 2017145119 A1 WO2017145119 A1 WO 2017145119A1 IB 2017051100 W IB2017051100 W IB 2017051100W WO 2017145119 A1 WO2017145119 A1 WO 2017145119A1
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glucose
appearance
rate
subject
subcutaneous
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PCT/IB2017/051100
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Taous Meriem LALEG
Ali Ahmed AL-MATOUQ
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King Abdullah University Of Science And Technology
Prince Sultan University
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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
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7278—Artificial waveform generation or derivation, e.g. synthesising signals from measured signals
    • 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
    • G16H20/13—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 from dispensers
    • 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
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • 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
    • A61M2205/33—Controlling, regulating or measuring
    • A61M2205/3303—Using a biosensor
    • 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
    • A61M2205/35—Communication
    • A61M2205/3507—Communication with implanted devices, e.g. external control
    • A61M2205/3523—Communication with implanted devices, e.g. external control using telemetric means
    • 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
    • A61M2205/00—General characteristics of the apparatus
    • A61M2205/50—General characteristics of the apparatus with microprocessors or computers
    • A61M2205/52—General characteristics of the apparatus with microprocessors or computers with memories providing a history of measured variating parameters of apparatus or patient
    • 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
    • A61M2230/005—Parameter used as control input for the apparatus
    • 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
    • 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
    • A61M5/14244—Pressure infusion, e.g. using pumps adapted to be carried by the patient, e.g. portable on the body
    • 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
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • 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

Definitions

  • the present disclosure relates to medical systems, more specifically, to a system for estimating glucose rate of appearance from the intestine.
  • Continuous Glucose Sensors can provide continuous monitoring of subcutaneous glucose in real time using a small electrode sensor that is implanted on the patient's skin. It is well known that subcutaneous glucose concentration is indicative of plasma glucose concentration after correcting for the delay associated with the diffusion of glucose from the plasma to the interstitial fluid and after filtering out possible measurement noise from the sensor [Breton and Kovatchev, 2008].
  • Glucose Rate of Appearance from the intestine is a variable that can be measured while a patient is undergoing an oral glucose tolerance test (hereafter OGTT) or a meal tolerance test (hereafter MTT) in a clinical setting.
  • OGTT oral glucose tolerance test
  • MTT meal tolerance test
  • Measurement of GRA is commonly used for detecting abnormalities in glucose absorption in elderly and diabetic individuals.
  • the methods for measuring GRA are generally complex and require the use of tracers infused intravenously and also intravenous measurements of both glucose and insulin plasma concentrations. There is currently no sensing device that measures GRA continuously in a minimally invasive manner similar to measuring blood glucose using a CGS.
  • bolus insulin optimal delivery profiles depends on the type of meals consumed as shown in the recent study by [Srinivasan et al., 2014]. This is because of the inherent variations in glucose absorption rates with meal composition. Hence, it is important to determine the glucose rate of appearance for different type of meals so that it can be used in determining the optimal bolus insulin delivery profiles.
  • the present disclosure provides improved systems and methods for estimating GRA in real-time.
  • the estimated GRA can be applied for detecting abnormalities in glucose absorption and/or for insulin dosing.
  • the systems and methods use CGS noisy measurements and amount(s) of insulin delivered to the patient. Hence, they can make use of simple and accessible CGS measurements that can be taken from the patient easily using a small electrode implanted on the patient's skin and possibly an infusion device for insulin. This enables the estimation of GRA using minimally invasive sensors as compared to estimating GRA using tracers and intravenous measurements of blood glucose and insulin.
  • estimating GRA continuously as compared to estimating GRA during OGTT and/or MTT only, enables continuous monitoring of this variable which can be useful for diagnosis of metabolism abnormalities, detecting the time of occurrence and extent of certain meals and for developing control algorithms for maintaining euglycemia for people with type 1 diabetes. Specifically, it can enhance the predictability of Model Predictive Controllers and consequently enhance glucose control during meal disturbances.
  • the present disclosure provides for estimating GRA signal in real-time by using (1) a single or a plurality of noisy CGS measurements; (2) a simple glucose-insulin dynamic model of the person; (3) information indicating the time and amount of bolus and basal insulin units injected and (4) a recursive algorithm that calculates an estimated value for glucose rate of appearance GRAe(t) that is indicative of the true GRA.
  • This calculated estimate for GRA using the embodiments of the present disclosure is robust to both model uncertainties and measurement noise.
  • the embodiments herein can be readily programmed on a simple micro-controller or digital signal processor for implementation and the recursive solution that it provides allows real time estimates of GRA to be found. The accuracy of these estimates can be enhanced by using a plurality of CGS measurements as exemplified in the disclosed embodiments of the present disclosure.
  • this disclosure makes use of simple and accessible CGS measurements enabling the estimation of GRA using minimally invasive sensors.
  • it employs a simple dynamic model that models the rate of change in glucose when the diabetes patient is subject to glucose disturbances due to meals and injections of subcutaneous bolus insulin.
  • This model can be constructed using standard data available from OGTT and/or MTT.
  • a model available from the literature may be used; i.e. the Bergman model [Herrero et al., 2012b] or the Delia Man model [Dalla Man et al., 2007].
  • a continuous glucose rate of appearance measurement system can comprise a receiver configured to receive subcutaneous glucose concentration data from a continuous glucose sensor that is coupled to a subject, the receiver further configured to receive insulin injection data; processing logic configured to determine a blood glucose concentration in the subject in response to a glucose disturbance and a rate of glucose appearance from the intestine of the subject; and processing logic configured to determine an estimated glucose rate of appearance in response to received subcutaneous glucose concentration data and a determined blood glucose concentration in the subject in response to the glucose disturbance and the a rate of glucose appearance from the intestine of the subject.
  • a method of estimating a glucose rate of appearance can comprise receiving subcutaneous glucose concentration data from a continuous glucose sensor that is coupled to a subject; receiving insulin injection data; determining a blood glucose concentration in the subject in response to a glucose disturbance and a rate of glucose appearance from the intestine of the subject; and determining an estimated glucose rate of appearance in response to received subcutaneous glucose concentration data and a determined blood glucose concentration in the subject in response to the glucose disturbance and the a rate of glucose appearance from the intestine of the subject.
  • a non-transitory computer-readable medium can embody a program executable in at least one computing device, wherein when executed the program causes the at least one computing device to at least: receive insulin injection data of a subject; determine a blood glucose concentration in the subject in response to a glucose disturbance and a rate of glucose appearance from the intestine of the subject; and determine an estimated glucose rate of appearance in response to received subcutaneous glucose concentration data and a determined blood glucose concentration in the subject in response to the glucose disturbance and the a rate of glucose appearance from the intestine of the subject.
  • a system comprising: at least one computing device; and at least one program executable in the at least one computing device, wherein when executed the at least one program causes the at least one computing device to at least: receive insulin injection data of a subject; determine a blood glucose concentration in the subject in response to a glucose disturbance and a rate of glucose appearance from the intestine of the subject; and determine an estimated glucose rate of appearance in response to received subcutaneous glucose concentration data and a determined blood glucose concentration in the subject in response to the glucose disturbance and the a rate of glucose appearance from the intestine of the subject.
  • subcutaneous glucose concentration data can be noisy subcutaneous glucose concentration data. Determining an estimated glucose rate of appearance further comprises determining a filtered subcutaneous glucose concentration.
  • the glucose disturbance can be a meal, an injection of insulin, or a combination thereof. Controlling insulin delivery to the subject can be in response at least to a determined estimated glucose rate of appearance.
  • FIG. 1 is a block diagram for a GRA state estimator of the present disclosure.
  • FIG. 2 is a diagram showing blood glucose concentration recorded data of the person G(t) and corresponding noisy measurements CGSn(t) obtained from a simulation experiment using the UV-Padova model [Dalla Man et al., 2006], [Dalla Man et al., 2014].
  • FIG. 3 is a diagram showing the training data used to develop a simple
  • Top figure blood glucose concentration signal G(t).
  • Middle figure glucose rate of appearance from the intestine signal GRA(t).
  • Bottom figure bolus insulin injection units INS(t) as explained in the detailed description of the preferred embodiments of the present invention.
  • FIG. 4 is a diagram showing the step response of the simple glucose/insulin dynamic model obtained in the detailed description of the preferred embodiments of the
  • Top figure step response of G(t) with respect to INS(t).
  • Bottom figure step response of G(t) with respect to GRA(t).
  • FIG. 5 is a diagram showing a comparison plot between simulated data for G(t) from the UV-Padova model and a predicted signal Gp(t) that is calculated using a simple glucose/insulin model as explained in the detailed description below.
  • FIG. 6 is a diagram showing an example of a signal G(t), noisy measurements of subcutaneous glucose CGSn(t) and the corresponding filtered signal CGSf(t) that is calculated using a descriptor state estimator as explained in the detailed description below.
  • FIG. 7 is a diagram showing GRA(t) and an estimated signal GRAe(t) that is calculated using a descriptor state estimator as explained in the detailed description below.
  • FIG. 8 is a diagram showing an example of a signal G(t), noisy measurements of subcutaneous glucose CGSn(t) and the corresponding filtered signal CGSf(t) that is calculated using a descriptor state estimator as further explained herein.
  • FIG. 9 is a diagram showing GRA(t) and an estimated signal GRAe(t) that is calculated using a descriptor state estimator as further explained herein.
  • FIG. 10 is a diagram showing an example of a signal G(t), noisy measurements of subcutaneous glucose CGSn(t) and the corresponding filtered signal CGSf(t) that is calculated using a descriptor moving horizon state estimator as further explained herein.
  • FIG. 11 is a diagram showing GRA(t) and an estimated signal GRAe(t) that is calculated using a descriptor moving horizon state estimator as further explained herein.
  • FIG. 12 is a schematic diagram of an apparatus in which embodiments for providing estimation of glucose rate of appearance (GRA) disclosed herein can be implemented.
  • GAA glucose rate of appearance
  • FIG. 1 depicts a block diagram showing a CGS device 12 which can be used to continuously measure the subcutaneous glucose concentration of the person according to an example of the invention.
  • the CGS can be coupled to a person having diabetes (not shown).
  • the CGS 12 can transmit noisy (unfiltered) measurements of subcutaneous glucose concentration CGSn(t) 28 to a state estimator module 14.
  • the noisy measurements CGSn(t) are interfaced to the state estimator system 14 via a wired or wireless communication link.
  • the state estimator 14 can also take a noisy measurement INS(t) 22 that measures the time and amount of bolus insulin units injected to the person subcutaneously (for example, in units of pmole) which can come from an insulin pump device 16 or directly from the person as user input data to the state estimator.
  • the state estimator can include a dynamic model 20 that can capture the dynamic behaviour of glucose concentration in the blood G(t) subject to meal and subcutaneous bolus insulin disturbances.
  • FIG. 2 depicts an example of a two-dimensional graph of a plasma glucose concentration signal G(t) and noisy subcutaneous glucose measurement signal CGSn(t) of the person in units oimg/dL obtained from a CGS device coupled to a person for three consecutive days.
  • the data was obtained from a simulation experiment conducted using the UVPadova simulator for type 1 diabetes (release 3.2) described in [Dalla Man et al., 2014] which incorporates a first principle model of the glucose/insulin system of a human with parameters based on specific patient data for normal and diabetic individuals.
  • the simulation experiment used a sample rate of 1 minute and included three meals and 1 snack per day for three consecutive days as input to the model.
  • Bolus subcutaneous insulin injections before 10 minutes of each meal were also used as input to the model with amounts in pmole calculated based on patient specific carbohydrate/insulin ratio and a correction factor.
  • the endogenous glucose production rate from the liver was assumed constant at a rate of 2.63 mg/kg:min while the balas insulin concentration rate was assumed constant at a rate of 0.8 Units /hr.
  • Other specific patient parameters for this simulation experiment can be found in reference to the average adult parameters given in the UVPadova model [Dalla Man et al., 2014].
  • the noise associated with CGS is a sample rate of 1 minute and included three meals and 1 snack per day for three consecutive days as input to the model.
  • the differences between the actual and measured glucose by the CGS is known to be attributed mostly to diffusion delays of glucose from plasma to the interstitial fluid and measurement sensor calibration errors. Other possible random errors could originate, for example, from sensor vibration or change in position, accumulation of fat on the electrode sensor and/or exposure to surrounding electromagnetic interferences.
  • the present invention can estimate GRA(t) in real time using (1) a single or a plurality of noisy CGS measurements; (2) a signal INS(t) indicating the amount of subcutaneous bolus insulin injected (e.g., in pmole) and (3) a simple model that captures the dynamic behaviour between blood glucose concentration G(t), glucose rate of appearance from the intestine GRA(t) and the signal INS(t).
  • This model can be developed using first principles combined with parameter identification as done for example in the UVPadova model given in [Da Ha Man et al., 2006] or using the Bergman model as described in [Herrero et al., 2012b], or by using system identification techniques to develop black box models from experimental data obtained, for example, from a standard meal tolerance test, both of which are incorporated by reference as if fully described herein. In an aspect, this will be exemplified herein by using data collected from simulation experiments from the UV-Padova simulator.
  • the system 14 for estimating GRA of a person having diabetes can comprise a recursive unknown input state estimator which can be used to estimate the GRA of the person and filter CGS measurement noise simultaneously.
  • recursive unknown input state estimators which can be used include Descriptor State Estimator (hereafter DSE) and Descriptor Moving Horizon State Estimator (hereafter DMHE). Identification of a simple glucose/insulin model
  • a simple dynamic model can be developed and employed for glucose concentration in plasma that captures the effect of glucose rate of appearance and subcutaneous insulin only.
  • the inputs to the model are GRA(t) and INS(t) and the single output of the model is G(t).
  • the influences of other important physiological variables i.e. endogenous glucose production from the liver and glucose utilization (both insulin dependent and insulin independent), will not be directly considered in the model rather their effect will be captured using black box system identification techniques and experimental data collected from either a simulated first principle model or data obtained from a standard OGTT and/or MTT that includes intravenous measurements of glucose and insulin concentrations and estimation of glucose rate of appearance.
  • This simple model can be a linear time invariant discrete state space model.
  • the model can be expressed as follows:
  • sequence of length are the input vector sequences measuring discrete samples of the signals INS(t) and GRA(t) respectively;
  • Gk is the single output sequence measuring discrete samples of the signal G(t).
  • R lxn are the model parameters to be identified.
  • the parameter ⁇ gives the number of discrete time samples reflect the time delay between the onset of injecting subcutaneous bolus insulin and the onset of its effect on plasma glucose concentration.
  • the diagram shows simulation data for G(t) (top), GRA(t) (middle) and INS(t) (bottom) generated using the UV-Padova model.
  • the simulation experiment was configured to have three meals and 1 snack per day for seven days using parameters for an average adult with type 1 diabetes as explained earlier in this disclosure. Both the carbohydrate content of meals and the amount of bolus insulin units injected sub- cutaneously were modified using randomly selected amounts for the purpose of collecting sufficiently excited data for system identification. Also, the timings of the insulin bolus injections relative to the time of meal occurrences has been selected so that some injections occur before the start of a meal and some after. Note that the simulation experiment was conducted within unhealthy glucose ranges only to help capture the dynamics of the system.
  • the input delay ⁇ between the onset of subcutaneous bolus insulin injections and the onset of its effect on plasma glucose concentration was identified to be 11 minutes using the "delayesf function available in Matlab also [MATLAB, 2012].
  • the step response of the identified model is shown in FIG. 4.
  • the model step responses reflect that a step change in subcutaneous bolus insulin will have a delayed effect in reducing blood glucose concentration while a step change in GRA will have an immediate effect in increasing blood glucose concentration as expected.
  • Insulin has the dual effect of suppressing endogenous glucose production and promoting glucose absorption by muscle and fat tissues.
  • the slopes of these step responses can depend on specific patient parameters including insulin sensitivity, rate constant of intestinal absorption, body weight etc. [Dalla Man and Cobelli, 2007].
  • the identified state space model can capture the effect of these specific patient parameters and influences indirectly through the identification of state space model parameters.
  • FIG. 5 shows a validation plot comparing glucose concentration data from the simulation of the UV-Padova model and a plot of predicted glucose concentration Gp(t) obtained by simulating the identified state space model given by (1) and (3).
  • the validation plot shows that the predicted glucose concentration Gp(t) obtained by simulating the identified state space model (using the same input signals GRA(t) and INS(t) in both models) matches closely with the simulated glucose signal G(t) with some deviation at low glucose concentrations.
  • a simple auto- regressive model can be identified from the data shown in FIG. 3 for GRA(t) as follows: where e k is a random sequence with a normal distribution of mean 0 and variance 1.
  • the RMSE for the identified model is 0.006 with a fit to estimation data of 96%.
  • the state estimator can estimate the GRA given CGS noisy measurements.
  • the unknown input GRA the unknown input GRA
  • DMHE Descriptor Moving Horizon Estimation
  • Measurement noise can also be modelled as a random sequence with zero mean and covariance matrix R denoted by The process noise
  • the measurement vector y k are noisy sample measurements of subcutaneous glucose CGSn(t).
  • ⁇ is used for element wise ⁇ comparison.
  • positivity constraints can be incorporated for estimating GRA since this variable can not be negative.
  • the estimation problem is to estimate by solving the following minimization problem:
  • /V is the horizon length that specifies the size of the sliding window in the past
  • This matrix can be used to approximate the second derivative of GRA k using three points in time. However, other second derivative approximations can be used.
  • the weighting function called the arrival cost function. This cost
  • a suitable arrival cost function can be found based on the arrival cost of the unconstrained minimization problem given by (7):
  • Algorithm II summarizes the DMHE method that is used as an example for this invention for estimating GRA.
  • the minimization problem can be solved using the convex program solver CVX [Grant and Boyd. 2012] in Matlab [MATLAB, 2012].
  • the first state estimation experiment used quantized and noisy glucose sensor measurements CGMn k shown in FIG. 2 obtained from the UV-Padova simulation model
  • FIG. 7 shows a diagram for GRA(t) and the estimated signal GRAe(t) that was found from this simulation experiment.
  • the calculated RMSE for estimating GRA is 74.4 and the condition number of the error covariance matrix is in the order of 10*.
  • the estimation errors can be attributed to unfiltered measurement noise as evident from FIG. 6 where the filtered signal CGSf(t) matches the noisy signal CGSn(t). Also, model uncertainties are evident in FIG. 7 when estimates are calculated as negative numbers.
  • FIG. 8 depicts one of the noisy measurements
  • FIG. 9 shows a diagram for GRA(t) and the estimated signal GRAe(t) for this experiment.
  • the calculated RMSE for GRA is 59 and the condition number of the error covariance matrix is reduced by approximately 30% signifying improvement over the previous experiment. This experiment shows that estimation accuracy is enhanced using a plurality of CGS measurements.
  • a third simulation experiment can be conducted using MHE with trend filtering as
  • FIG. 10 and FIG. 11 show the results of this simulation experiment using disciplined convex programming software CVX [Grant and Boyd, 2012].
  • the t x trend filtering penalty helped to suppress the effect of quantization noise while positivity constraints helped to ensure positive values of GRAe k .
  • the calculated RMSE value is 57 which shows improvement over DSE methods.
  • a processor system 700 that includes a processor 703 and a memory 706, both of which are coupled to a local interface 709.
  • the local interface 709 may be, for example, a data bus with an
  • the processor system 700 may comprise, for example, a computer system such as a server, desktop computer, laptop, mobile device (e.g., smart phone, tablet, personal digital assistant, etc.) or other system with like capability.
  • a computer system such as a server, desktop computer, laptop, mobile device (e.g., smart phone, tablet, personal digital assistant, etc.) or other system with like capability.
  • Coupled to the processor system 700 are various peripheral devices such as, for example, a continuous glucose sensor (CGS) 713, an insulin pump system (IPS) 716, and/or other devices as can be appreciated.
  • CGS 713 and insulin pump system 716 can communicate with the processor system 700 via a transceiver 719 (or transmitter and/or receiver).
  • the communications can be wired or wireless (e.g., Bluetooth, WiFi, etc.).
  • Stored in the memory 706 and executed by the processor 703 are various components that provide various functionality according to the various embodiments of the present invention.
  • stored in the memory 706 is an operating system 723, a state estimator application 726, various dynamic models 729, and potentially other information associated with the glucose rate of appearance.
  • the state estimator application 726 and dynamic models 129 can be executed by the processor 703 in order to determine the glucose rate of appearance as previously described.
  • a number of software components are stored in the memory 706 and are executable by the processor 703.
  • the term "executable" means a program file that is in a form that can ultimately be run by the processor 703.
  • Examples of executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory 706 and run by the processor 703, or source code that may be expressed in proper format such as object code that is capable of being loaded into a of random access portion of the memory 706 and executed by the processor 703, etc.
  • An executable program may be stored in any portion or component of the memory 506 including, for example, random access memory, read-only memory, a hard drive, compact disk (CD), floppy disk, or other memory components.
  • the memory 706 is defined herein as both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power.
  • the memory 706 may comprise, for example, random access memory (RAM), read- only memory (ROM), hard disk drives, floppy disks accessed via an associated floppy disk drive, compact discs accessed via a compact disc drive, magnetic tapes accessed via an appropriate tape drive, and/or other memory components, or a combination of any two or more of these memory components.
  • the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices.
  • the ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device.
  • the processor 703 may represent multiple processors and the memory 706 may represent multiple memories that operate in parallel.
  • the local interface 709 may be an appropriate network that facilitates communication between any two of the multiple processors, between any processor and any one of the memories, or between any two of the memories etc.
  • the processor 703 may be of electrical, optical, or molecular construction, or of some other construction as can be appreciated by those with ordinary skill in the art.
  • the operating system 723 is executed to control the allocation and usage of hardware resources such as the memory, processing time and peripheral devices in the processor system 700. In this manner, the operating system 723 serves as the foundation on which applications depend as is generally known by those with ordinary skill in the art.
  • the state estimation application 726 and dynamic models are described as being embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software/general purpose hardware and dedicated hardware. If embodied in dedicated hardware, each of the state estimation application 726 and dynamic models can be implemented as a circuit or state machine that employs any one of or a combination of a number of technologies.
  • These technologies may include, but are not limited to, discrete logic circuits having logic gates for implementing various logic functions upon an application of one or more data signals, application specific integrated circuits having appropriate logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), or other components, eft;.
  • PGA programmable gate arrays
  • FPGA field programmable gate arrays
  • Such technologies are generally well known by those skilled in the art and, consequently, are not described in detail herein.
  • the state estimation application 726 and dynamic models may comprise software or code
  • each can be embodied in any computer-readable medium for use by or in connection with an instruction execution system such as, for example, a processor in a computer system or other system.
  • the logic may comprise, for example, statements including instructions and declarations that can be fetched from the computer- readable medium and executed by the instruction execution system.
  • a "computer-readable medium" can be any medium that can contain, store, or maintain the state estimation application 726 and dynamic models for use by or in connection with the instruction execution system.
  • the computer readable medium can comprise any one of many physical media such as, for example, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor media.
  • the computer-readable medium may be a random access memory (RAM) including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM).
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • MRAM magnetic random access memory
  • the computer-readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device.
  • ROM read-only memory
  • PROM programmable read-only memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory

Abstract

La présente invention concerne un procédé et un système permettant de fournir des estimations de la vitesse d'apparition du glucose (GRA, Glucose Rate of Appearance) à partir de l'intestin à l'aide d'un capteur de glucose en continu (CGS, Continuous Glucose Sensor) effectuant des mesures sous-cutanées chez un patient diabétique, ainsi que la quantité d'insuline administrée au patient.
PCT/IB2017/051100 2016-02-26 2017-02-24 Estimation de la vitesse d'apparition du glucose à partir de cgs et administration sous-cutanée d'insuline dans le diabète de type i WO2017145119A1 (fr)

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US20100295686A1 (en) * 2009-05-22 2010-11-25 Abbott Diabetes Care Inc. Usability features for integrated insulin delivery system

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