Classifications

• • 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
  • A61M31/00—Devices for introducing or retaining media, e.g. remedies, in cavities of the body
  • A61M31/002—Devices for releasing a drug at a continuous and controlled rate for a prolonged period of time
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
  • A61P3/00—Drugs for disorders of the metabolism
  • A61P3/08—Drugs for disorders of the metabolism for glucose homeostasis
  • A61P3/10—Drugs for disorders of the metabolism for glucose homeostasis for hyperglycaemia, e.g. antidiabetics
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/66—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving blood sugars, e.g. galactose
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/74—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving hormones or other non-cytokine intercellular protein regulatory factors such as growth factors, including receptors to hormones and growth factors
• • 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
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
  • G16Z99/00—Subject matter not provided for in other main groups of this subclass
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
  • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
  • G01N2333/575—Hormones
  • G01N2333/62—Insulins

Definitions

• the disclosure relates to maintaining multiple defined physiological zones using model predictive control.
• T1DM type 1 diabetes mellitus
• IIT intensive insulin therapy
• IIT hyperglycemia and can lead to a reduction in the prevalence of these complications. IIT also increases the risk of
• hypoglycemic events and increases the burden on the caregiver and/or patient administering the therapy.
• Hypoglycemia is any lower than normal BG; symptoms of hypoglycemia, such as tachycardia and nausea, occur at around 50-70 mg/dL.
• Multi-Zone-MPC multiple zone model predictive control
• a Multi Zone-MPC controller with embedded artificial insulin and meal memory is developed to regulate blood glucose to a predefined tunable zone with or without meal information.
• the controller uses an average model to regulate the
• the average model is obtained by a novel parametric fitting technique that uses data that is collected from several of subjects.
• controller tunings are predefined to be a function of the predicted blood glucose concentrations .
• Multi-Zone-MPC provides different tunings for the MPC weights based on four regional distribution of glycemia.
• the Multi-Zone-MPC comprises a hypoglycemia, normoglycemia, elevated glycemia, and hyperglycemia zones. Defining these four zones provides richer control tunings that result in safe and effective control.
• the controller predictions are based on an average ARX-model that is developed using data collected from a meal response of ten different in silico adult
• Multi-Zone-MPC with insulin memory is an alternative control strategy for the artificial pancreas.
• Multi-Zone-MPC is superior to standard set-point control due to the minimization of pump activity and more concentrated control effort.
• the use of four control zones in the Multi-Zone-MPC has successfully proven to be a better control strategy that reduces the postprandial peaks and at the same time avoids any hypoglycemia due to efficient insulin administration, hence, providing effective and safe glucose regulation to individuals with T1DM.
• the subject algorithm and controller are generally applicable to delivering alternative drugs and physiological interventions, particularly drugs and interventions associated with titratable physiological zones .
• a protocol for controlling drug delivery comprising a multi-zone model predictive control (MPC) algorithm having a plurality of defined physiological zones, each with a different predefined control tuning.
• MPC multi-zone model predictive control
• the algorithm provides: (a) a relatively conservative control action for a large deviation from a normal or desired zone, and (b) a relative aggressive control action for within the normal or desired zone .
• the protocol comprises a logic scheme as shown in Fig. 5, wherein the algorithm
• the physiological zones are zones of blood glucose concentration, anesthesia,
• analgesia blood pressure
• heart rate blood pH
• temperature blood pH
• temperature blood pH
• in vivo drug concentration in vivo drug concentration
• nitoprusside heart rate control using in intravenous beta blockers or calcium channel blockers
• pH control using ventilator modulation
• temperature control e.g. during open heart surgery
• heating and cooling elements etc.
• the protocols can be used to deliver modulatable physiological interventions, such as ventilation, electrical stimulation, heat, etc.
• the drug is a hormone, a blood glucose regulator (such as insulin, glucagon or amylin), an anesthetic (such as Barbiturates, e.g. Amobarbital,
• Methohexital, Thiamylal and Thiopental Methohexital, Thiamylal and Thiopental, Benzodiazepines, e.g. Diazepam, Lorazepam and Midazolam, Etomidate, Ketamine and Propofol), an analgesic (such as Alfentanil, Fentanyl,
• Cisatracurium Rocuronium, Vecuronium, Alcuronium, Doxacurium, Gallamine, Metocurine, Pancuronium, Pipecuronium and
• Tubocurarine a chemotherapeutic (such as alkylating agents, antimetabolites, anthracyclines , plant alkaloids,
• topoisomerase inhibitors or a antihypertensive (such as diuretics, ACE inhibitors, angiotensin II receptor antagonists, beta blockers, calcium channel blockers, renin inhibitors and glyceryl trinitrates) .
• a antihypertensive such as diuretics, ACE inhibitors, angiotensin II receptor antagonists, beta blockers, calcium channel blockers, renin inhibitors and glyceryl trinitrates.
• the physiological zones are blood glucose concentration zones
• the drug is insulin
• the algorithm provides: (a) a relative conservative control action for a elevated "hyperglycemia” zone; and (b) a relatively aggressive control action for a normal or near normal "euglycemia” zone.
• the physiological zones are blood glucose concentration zones
• the drug is insulin
• the algorithm provides: (a) a relative conservative control action for a elevated "hyperglycemia” zone; (b) a relatively aggressive control action for a normal or near normal “euglycemia” zone; (c) control inaction for a
• normoglycemia zone (d) a control action that results in suspending insulin delivery for a hypoglycemia zone .
• the algorithm provides glycemia zones and control weights ratios (Q k and R k ) according to the following schedule:
• zone 1 G > 180 mg/dL
• control actions are restrained to prevent over-dosing
• zone 2 140 ⁇ G ' t ⁇ 180
• zone 3 80 ⁇ G ⁇ ⁇ 140
• the controller is quiescent to deviation in glucose
• the controller is allowed to respond relatively fast to potential hypoglycemia.
• physiological zones are blood glucose concentration zones
• the drug is insulin
• the algorithm provides: (a) a relative conservative control action for a elevated "hyperglycemia” zone; and (b) a relatively aggressive control action for a normal or near normal "euglycemia” zone.
• the controller may further comprise the physiologic sensor and/or the drug infuser, such as a glucose sensor and an insulin infuser or pump.
• the drug infuser such as a glucose sensor and an insulin infuser or pump.
• the invention provides a method of delivering a drug comprising: (a) sensing a physiological metric with a sensor and receiving the resultant signal at the input contact of a subject controller; (b) processing the input signal to form the output signal; (c) transmitting the output signal from the output contact to an drug infuser; and (d) delivering the drug from the infuser pursuant to the output signal.
• the invention provides a method of delivering insulin comprising: (a) sensing blood glucose concentration with a glucose sensor and receiving the
• the invention provides a method of delivering a drug, comprising: (a) obtaining drug and event data for a subject using an open loop protocol to obtain time values, drug values and physiological metric values; (b) defining a plurality of physiological zones as a function of physiological metric values; and (c) programming an automated drug pump comprising a controller having a multi-zone-model predictive control (MPC) algorithm, wherein the controller obtains physiological metric measurements from the subject, compares the physiological metrics to the plurality of physiological zones and causes delivery of the drug to the subject pursuant to the MPC algorithm.
• MPC multi-zone-model predictive control
• the invention provides a method of delivering insulin, comprising: (a) obtaining insulin and meal data for a subject using an open loop protocol to obtain time values, insulin values and glucose values; (b) defining a plurality of glycemia zones as a function of blood glucose concentration; and (c) programming an automated insulin pump comprising a controller having a multi-zone-model predictive control algorithm, wherein the controller obtains glucose measurements from the blood, compares the glucose values to the plurality of glycemia zones and causes delivery of insulin to the subject, particularly wherein the plurality of zones is set forth in Table 1.
• the invention provides a method of delivering a drug comprising: (a) determining physiological metric values from the blood of a subject; (b) comparing the physiological metric values to a plurality of defined
• the invention provides a method of delivering insulin comprising: (a) determining glucose values from the blood of a subject; (b) comparing the glucose values to a plurality of defined glycemia zones defined as a function of blood glucose concentration; and (c) automatically
• an insulin pump comprising a controller having a multi-zone-model predictive control algorithm comprising the glycemia zones .
• the invention provides a method of continuous monitoring and delivery of a drug to a subject.
• the method comprises obtaining values associated with blood levels of a drug for a subject; mapping the data using a transfer function; generating a linear difference model comprising a plurality of states; obtaining a plurality of defined value zones for the drug or biological agent in the subject;
• the recited drug may be drug metabolite or byproduct or induced factor .
• the value can be the blood concentration of the drug or a physiological metric or symptom (e.g., temperature).
• FIGURE 1 shows prediction trajectories of the average model (grey lines) compared to the 10 in silico subjects (black lines) .
• Panels (a) and (b) depict the initial guess for the optimization and the final optimization result, respectively.
• the initial guess of model (a) is generated by averaging the ARX individualized models population.
• the optimized average model (b) is obtained by a nonlinear optimization that was conducted on the population response.
• the population responses were obtained for the scenario of one 75 g meal scenario given at 1 hour of simulation time
• FIGURE 2 shows a comparison between control
• CVGA variability grid analysis
• FIGURE 3 shows a population responses of the Zone- MPC (a), Multi-Zone-MPC (b) , and the histogram of the
• FIGURE 4 shows A Subject response to one 75 g meal scenario given at 8 hours from the beginning of the
• FIGURE 5 shows a generic flow diagram of the Multi- Zone-MPC and the Zone-MPC, respectively.
• Panels (a) and (b) are the glucose trajectories and the insulin control actions, respectively .
• FIGURE 5 shows a generic flow diagram of the Multi- Zone-MPC
• FIGURE 6 shows a four zone example of the Multi
• CGM cardiac monitoring
• CSII continuous subcutaneous insulin infusion
• CGM sensors and CSII pumps use the subcutaneous (SC) route for glucose measurement and insulin delivery,
• lag time associated with SC insulin infusion is an obstacle for a control algorithm: absorption of glucose into the blood from carbohydrate (CHO) raises BG faster than simultaneously injected SC insulin can lower it. Insulin delivery rates may also be limited by the physical limitations of the CSII pump and safety constraints driven by clinical parameters .
• a controller framework known to be suitable for systems with large lag times and constraints is model predictive control (MPC) . Central to each of these MPC implementations has been a dynamic model of the effects of subcutaneous insulin on glycemia.
• absorption variability is less than insulin action
• Basal - bolus insulin treatment or intensive insulin treatment can be administered as multiple daily injections (MDI) or via an insulin pump (Skyler, 2005) .
• MDI daily injections
• Skyler, 2005 Different insulin schedules are suggested for MDI therapy based on the insulin type and duration of action, the daily schedule of the patient, and other medical conditions.
• Initial doses are calculated based on body weight and are divided into basal and bolus partitions. However, since insulin requirements differ throughout the day, and from day to day, this initial setting needs to be fine tuned to prevent insulin overdose that will result in hypoglycemia or underdose that will result in hyperglycemia (Skyler, 2005) .
• Insulin therapy using continuous subcutaneous insulin infusion (CSII) has become common practice since its introduction in 1978 (Pickup et al . , 1978) .
• CSII allows a continuous administration of rapid-acting insulin, with patient-activated boluses at mealtimes. This feature introduces a more physiological insulin
• CSII treatment depends on patient decisions and on pre-estimated basal therapy that can result in suboptimal treatment, and therefore a closed-loop algorithm becomes an appealing alternative.
• the "correction factor” is the lowering effect of BG from administering one unit of rapid-acting insulin;
• ICR insulin-to-carbohydrate ratio
• Constrained MPC can necessitate the on-line solution of a quadratic program.
• This on-line optimization can be replaced with a single set of a priori optimizations via multi-parametric programming; the on-line problem is reduced to the evaluation of an affine function obtained from a lookup table. This reformulation is valuable in any application where on-line computation should be minimized, due to low
• Model predictive control is a computer control algorithm that uses an explicit process model to optimize future process response by manipulating future control moves
• CM dynamic matrix control
• MPC optimizes every control cycle with a cost function that includes P future process instants, known as predict ion horizon, and M future CM, the control horizon .
• P future process instants known as predict ion horizon
• M future CM the control horizon .
• the optimization is repeated using updated process data.
• only the first CM of each optimized sequence is sent to the process.
• Process inputs and outputs constraints are included directly such that the optimum solution prevents future constraint violation.
• the different MPC algorithms can be classified into four approaches to specify future process response: fixed set point, zone, reference trajectory, and funnel. Using a fixed set point for the future process response can lead to large input adjustments unless the controller is detuned.
• a zone control is designed to keep the controlled variable (CV) in a zone defined by upper and lower boundaries that are usually defined as soft constraints .
• Some MPC algorithms define a desired response path for the CVs, called reference
• the reference trajectory usually describes a define path from current CV state to a desired set point.
• the reference trajectory control returns to a fixed set-point control when the CV approaches the defined set point.
• the Robust Multivariable Predictive Control Technology attempts to keep the CV in a defined zone; however, when the CV is out of the zone, a funnel is defined to bring the CV back into the zone.
• the disclosure utilizes a lookup table defining zones of glycemia.
• the lookup table comprises two zones (e.g., a hypoglycemia zone and a
• the table comprises 3 or 4 zones.
• the zones are defined by blood glucose values (see Table 1, below) .
• physiological measurements of a subject to define a lookup table using carbohydrate consumption and insulin delivery e.g., delivery subcutaneously
• the look up table can take in to account risk constraints such as insulin-onboard measurements and the like. Once a set of measurements are made a look up table is defined including parameters.
• the invention is embodied in a closed loop infusion system for regulating the rate of fluid infusion into a body of a user based on feedback from an analyte concentration measurement taken from the body.
• the disclosure is embodied in a control system for regulating the rate of insulin infusion into the body of a user based on a glucose concentration measurement taken from the body.
• a method of the disclosure includes first making measurements of a body system in response to food/glucose/carbohydrate adsorption and insulin delivery and insulin-on-board measurements. These measurements are then modeled to the subject's metabolic system and a controller is ultimately programmed to include a look up table that relates insulin and glucose measurements as well as accepted or calculated glycemic zones (e.g., zone defining a low and high threshold for blood glucose
• hypoglycemia corresponding to, for example, hypoglycemia, hyperglycemia and the like .
• a typical insulin delivery device of the disclosure comprises a sensor system for measuring glucose and/or insulin, a controller and an insulin delivery system.
• the glucose sensor system generates a sensor signal representative of blood glucose levels in the body, and provides the sensor signal to the controller.
• the controller receives the sensor signal and generates commands that are communicated to the insulin delivery system.
• the insulin delivery system receives the commands and infuses insulin into the body in response to the commands .
• the glucose sensor system includes a glucose sensor, sensor electrical components to provide power to the sensor and generate the sensor signal, a sensor communication system to carry the sensor signal to the controller, and a sensor system housing for the electrical components and the sensor communication system.
• the controller includes controller electrical components and software to generate commands for the insulin delivery system based on the sensor signal, and a controller communication system to receive the sensor signal and carry commands to the insulin delivery system.
• the insulin delivery system includes an infusion device and an infusion tube to infuse insulin into the body.
• the infusion device includes infusion electrical components to activate an infusion motor according to the commands, an infusion
• the controller is housed in the infusion device housing and the infusion communication system is an electrical trace or a wire that carries the commands from the controller to the infusion device.
• the controller is housed in the sensor system housing and the sensor communication system is an electrical trace or a wire that carries the sensor signal from the sensor electrical components to the controller electrical components.
• the controller has its own housing or is included in a supplemental device.
• the controller is located with the infusion device and the sensor system all within one housing.
• the sensor, controller, and/or infusion communication systems may utilize a cable, a wire, fiber optic lines, RF, IR, or ultrasonic transmitters and receivers, or the like instead of the electrical traces.
• MPC model predictive control
• MPC was originally implemented in petroleum refineries and power plants, it can be found these days in wide variety of application areas including aerospace food, automotive and chemical applications (Qin and Badgwell, 2003) .
• reasons for the popularity of MPC are its handling of constraints, it accommodation of nonlinearities , and its ability to formulate unique performance criteria.
• Zone-MPC that is described in Grosman et al . (2010) contains a zone of normoglycemia in which the control is not responding to variations in glycemia, and regions surrounding this zone that are controlled with a fixed setting. Zone-MPC showed significant advantages over the "optimal" open-loop therapy, and it has shown the reduction of control moves variability with minimal loss of performance compared to set- point control.
• I M U/h is the mapped subcutaneous insulin infusion rate
• t3 ⁇ 4 g is the mapped carbohydrate (CHO) ingestion input
• M g is the CHO ingestion input.
• ⁇ is the regression vector
• a, ⁇ , and ⁇ are the glucose, the insulin, and meal regressors, respectively.
• An initial guess for the average model is obtained by averaging the ten different vectors ⁇ of (1) that are obtained from the identification of part (a) of (1) for each patient separately. Accuracy of the initial average model is illustrated in Fig. 1 (a) and shows limited predictability for some subjects.
• nonlinear optimization (2) is conducted to reduce the sum square of errors (SSE) between the average model's prediction for a period of eight hours and twenty minutes and the raw data collected from each subject.
• SSE sum square of errors
• PH is the prediction horizon of eight hours and twenty minutes.
• the optimization is carried out under the following constraints :
• Otis, ⁇ - ⁇ are the glucose and insulin regressors, respectively as described in (1) .
• oi l s, Xi-ii are the glucose and meal regressors, respectively as described in (1) .
• MPC optimizes every control sample with a cost function that includes P future process instants, known as the prediction horizon, and M future CM, the control horizon. In each sample, the optimization is repeated using updated process data. However, only the first CM of each optimized sequence is implemented on the process.
• constraints are included such that the optimum solution prevents future constraint violation.
• the blood glucose concentration can be divided into four zones: zone 1,
• BG hyperglycemia
• zone 2 near normal glycemia, 140 ⁇ BG ⁇ 180 mg/dL
• zone 3 normoglycemia, 80 ⁇ BG ⁇ 140 mg/dL
• zone 4 danger of imminent hypoglycemia, BG ⁇ 80 mg/dL.
• G[ is a binary function that yields the values of the upper bound of the normoglycemia zone (140 mg/dL) when
• G' j >140mg/dL, and yield the values of the lower bound of the normoglycemia zone (80 mg/dL) when G ⁇ 80mg/dL .
• Q k and R k are predicted blood glucose concentration dependent optimization weights as listed in Table 1.
• P and M are the output
• the Multi-Zone-MPC predicts P steps in every control sample.
• Table 1 describes the various glycemic zones and the control weights ratio (Q k and R k ) used for the Multi-Zone-MPC.
• zone 1 G > 180 mg/dL
• control actions are restrained to prevent over-dosing.
• zone 2 140 ⁇ G ⁇ 180
• most of the control action are implemented.
• zone 3 80 ⁇ G ⁇ 140
• the controller is quiescent to deviation in glucose measurements.
• zone 4 G' j ⁇ 80
• the controller is allowed to respond fast to potential hypoglycemia.
• the control saneness of the four glycemia zones is described by the following:
• control actions are proportional to the deviation of the predicted blood glucose concentration from the bounds of the normoglycemia zone ( 80 ⁇ G ⁇ 140 ) , and therefore large control moves are anticipated when the prediction are far from these bounds. This can lead to insulin overdosing in the present of noise or model mismatch.
• This zone represents the normoglycemia and it is assumed that a subject glycemia can vary between the bounds of this zone without need for regulation.
• CVGA variability grid analysis
• FIG. 2 depicts the CVGA for the Multi-Zone-MPC and the Zone-MPC.
• the Multi-Zone-MPC significantly reduces the risk of hypoglycemia and at the same time lowers the hyperglycemia levels.
• Figure 3 describes the population response to the tested scenario on all 100 UVa ⁇ Padova metabolic simulator subjects. As can be seen, Multi-Zone-MPC outperforms the Zone- MPC with extended time in the near normal glucose range without any severe hypoglycemic events .
• Table 2 summarizes a number of average indices of performance. First two rows present the low and the high blood glucose indices (LBGI, and HBGI) (Kovatchev et al . , 2002), respectively. The LBGI and the HBGI are non-negative
• the percentage of time the blood glucose concentration is between 80 to 180 mg/dL, and above 180 mg/dL are shown in the third and the fourth row, respectively.
• the fifth row presents the number of in silico subjects that experienced at least one hypoglycemic event. It can be seen that using the Multi-Zone- MPC approach reduces significantly the number hypoglycemic events from 7 to 1, and reduces the LBGI from 0.5 to 0.1, while introducing a lower HBGI and higher glycemic percentage of time between 80 and 180 mg/dL.
• Figure 4 shows a comparison of a subject's response to the Multi-Zone-MPC and Zone-MPC.
• Multi-Zone-MPC significantly reduces the hyperglycemia values while keeping the glucose levels away from
• hypoglycemia A good illustration of the algorithm features is depicted in Fig. 4.
• the initial simulation values that are between 140 ⁇ G ⁇ 180 are regulated by rapid control action around the second hour of the evaluation as closed-loop is engaged.
• Around hour 8 a meal is given and the values of the predicted glycemia enter the 140 ⁇ G' t ⁇ 180 mg/dL zone. This deviation is been handled fast by two relative large boluses .
• controller continues to regulate glycemia in a more
• Zone-MPC is less efficient in both reducing the hyperglycemia peak and avoiding hypoglycemia with nadir glucose of ⁇ 65 mg/dL.
• the CVGA results (Fig. 2) and the population results (Fig. 3) show that the Multi-Zone-MPC moves the extremum glycemia values to a lower hyperglycemia without reaching hypoglycemia.
• Table 2 also emphasizes that the Multi-Zone-MPC reduces significantly the number of hypoglycemic events from 7 to 1, and the LBGI from 0.5 to 0.1, while still manifesting better lower HBGI and higher percentage of time between 80 to 180 mg/dL. This indicates that the Multi-Zone-MPC can be safe and efficient at the same time.
• Multi-Zone-MPC was evaluated on the FDA-accepted UVa /Padova metabolic simulator. The control was based on an average ARX-model that was identified in a novel approach by applying a nonlinear optimization with an initial guess based on ARX-models of different subjects. Multi-Zone-MPC showed significant advantages over Zone-MPC that was presented in previous work and showed to be superior to MPC with fixed setpoint. The separation of the control tuning into four zones allows at the same time an efficient and safe glycemia regulation. Since fixed tuning was used for the population it is expected that individualized tuning may improve the results even more .

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