WO2014089282A1 - Method and system for tuning a closed-loop controller for an artificial pancreas - Google Patents
Method and system for tuning a closed-loop controller for an artificial pancreas Download PDFInfo
- Publication number
- WO2014089282A1 WO2014089282A1 PCT/US2013/073289 US2013073289W WO2014089282A1 WO 2014089282 A1 WO2014089282 A1 WO 2014089282A1 US 2013073289 W US2013073289 W US 2013073289W WO 2014089282 A1 WO2014089282 A1 WO 2014089282A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- glucose
- index
- controller
- insulin
- cal
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 26
- 210000000496 pancreas Anatomy 0.000 title description 9
- WQZGKKKJIJFFOK-GASJEMHNSA-N Glucose Natural products OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-GASJEMHNSA-N 0.000 claims abstract description 176
- 239000008103 glucose Substances 0.000 claims abstract description 176
- NOESYZHRGYRDHS-UHFFFAOYSA-N insulin Chemical compound N1C(=O)C(NC(=O)C(CCC(N)=O)NC(=O)C(CCC(O)=O)NC(=O)C(C(C)C)NC(=O)C(NC(=O)CN)C(C)CC)CSSCC(C(NC(CO)C(=O)NC(CC(C)C)C(=O)NC(CC=2C=CC(O)=CC=2)C(=O)NC(CCC(N)=O)C(=O)NC(CC(C)C)C(=O)NC(CCC(O)=O)C(=O)NC(CC(N)=O)C(=O)NC(CC=2C=CC(O)=CC=2)C(=O)NC(CSSCC(NC(=O)C(C(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC=2C=CC(O)=CC=2)NC(=O)C(CC(C)C)NC(=O)C(C)NC(=O)C(CCC(O)=O)NC(=O)C(C(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC=2NC=NC=2)NC(=O)C(CO)NC(=O)CNC2=O)C(=O)NCC(=O)NC(CCC(O)=O)C(=O)NC(CCCNC(N)=N)C(=O)NCC(=O)NC(CC=3C=CC=CC=3)C(=O)NC(CC=3C=CC=CC=3)C(=O)NC(CC=3C=CC(O)=CC=3)C(=O)NC(C(C)O)C(=O)N3C(CCC3)C(=O)NC(CCCCN)C(=O)NC(C)C(O)=O)C(=O)NC(CC(N)=O)C(O)=O)=O)NC(=O)C(C(C)CC)NC(=O)C(CO)NC(=O)C(C(C)O)NC(=O)C1CSSCC2NC(=O)C(CC(C)C)NC(=O)C(NC(=O)C(CCC(N)=O)NC(=O)C(CC(N)=O)NC(=O)C(NC(=O)C(N)CC=1C=CC=CC=1)C(C)C)CC1=CN=CN1 NOESYZHRGYRDHS-UHFFFAOYSA-N 0.000 claims abstract description 173
- 102000004877 Insulin Human genes 0.000 claims abstract description 88
- 108090001061 Insulin Proteins 0.000 claims abstract description 88
- 229940125396 insulin Drugs 0.000 claims abstract description 87
- 238000001802 infusion Methods 0.000 claims abstract description 29
- 206010012601 diabetes mellitus Diseases 0.000 claims abstract description 20
- 238000005259 measurement Methods 0.000 claims description 33
- 210000004369 blood Anatomy 0.000 claims description 25
- 239000008280 blood Substances 0.000 claims description 25
- 230000001667 episodic effect Effects 0.000 claims description 16
- 238000004891 communication Methods 0.000 claims description 9
- 230000002503 metabolic effect Effects 0.000 claims description 7
- 238000007726 management method Methods 0.000 claims description 5
- 235000012054 meals Nutrition 0.000 description 41
- 238000012377 drug delivery Methods 0.000 description 25
- 230000006870 function Effects 0.000 description 16
- 238000004422 calculation algorithm Methods 0.000 description 15
- 230000002641 glycemic effect Effects 0.000 description 14
- 239000003814 drug Substances 0.000 description 10
- 230000000694 effects Effects 0.000 description 9
- 238000005516 engineering process Methods 0.000 description 9
- 238000012544 monitoring process Methods 0.000 description 9
- 238000005457 optimization Methods 0.000 description 9
- 229940079593 drug Drugs 0.000 description 8
- 238000004088 simulation Methods 0.000 description 8
- 229940088597 hormone Drugs 0.000 description 7
- 239000005556 hormone Substances 0.000 description 7
- 239000007924 injection Substances 0.000 description 7
- 238000002347 injection Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 238000000126 in silico method Methods 0.000 description 6
- 210000002237 B-cell of pancreatic islet Anatomy 0.000 description 5
- 230000002218 hypoglycaemic effect Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 5
- 238000005070 sampling Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 4
- 239000012528 membrane Substances 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000007920 subcutaneous administration Methods 0.000 description 4
- 206010067584 Type 1 diabetes mellitus Diseases 0.000 description 3
- WQZGKKKJIJFFOK-VFUOTHLCSA-N beta-D-glucose Chemical compound OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O WQZGKKKJIJFFOK-VFUOTHLCSA-N 0.000 description 3
- 238000012937 correction Methods 0.000 description 3
- 230000037406 food intake Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 210000002381 plasma Anatomy 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 108090000790 Enzymes Proteins 0.000 description 2
- 102000004190 Enzymes Human genes 0.000 description 2
- 108010015776 Glucose oxidase Proteins 0.000 description 2
- 239000004366 Glucose oxidase Substances 0.000 description 2
- 208000013016 Hypoglycemia Diseases 0.000 description 2
- 206010022489 Insulin Resistance Diseases 0.000 description 2
- 150000001720 carbohydrates Chemical class 0.000 description 2
- 235000014633 carbohydrates Nutrition 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000009795 derivation Methods 0.000 description 2
- 239000003792 electrolyte Substances 0.000 description 2
- 229940088598 enzyme Drugs 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 229940116332 glucose oxidase Drugs 0.000 description 2
- 235000019420 glucose oxidase Nutrition 0.000 description 2
- 201000001421 hyperglycemia Diseases 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000002560 therapeutic procedure Methods 0.000 description 2
- BHELIUBJHYAEDK-OAIUPTLZSA-N Aspoxicillin Chemical compound C1([C@H](C(=O)N[C@@H]2C(N3[C@H](C(C)(C)S[C@@H]32)C(O)=O)=O)NC(=O)[C@H](N)CC(=O)NC)=CC=C(O)C=C1 BHELIUBJHYAEDK-OAIUPTLZSA-N 0.000 description 1
- 208000024172 Cardiovascular disease Diseases 0.000 description 1
- 208000005156 Dehydration Diseases 0.000 description 1
- 208000002230 Diabetic coma Diseases 0.000 description 1
- 102000051325 Glucagon Human genes 0.000 description 1
- 108060003199 Glucagon Proteins 0.000 description 1
- 206010070070 Hypoinsulinaemia Diseases 0.000 description 1
- 206010023379 Ketoacidosis Diseases 0.000 description 1
- 208000007976 Ketosis Diseases 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- 208000028389 Nerve injury Diseases 0.000 description 1
- 206010057430 Retinal injury Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 239000011149 active material Substances 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000002266 amputation Methods 0.000 description 1
- 230000003466 anti-cipated effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- GINJFDRNADDBIN-FXQIFTODSA-N bilanafos Chemical compound OC(=O)[C@H](C)NC(=O)[C@H](C)NC(=O)[C@@H](N)CCP(C)(O)=O GINJFDRNADDBIN-FXQIFTODSA-N 0.000 description 1
- 239000003181 biological factor Substances 0.000 description 1
- 230000008512 biological response Effects 0.000 description 1
- 235000021152 breakfast Nutrition 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 208000020832 chronic kidney disease Diseases 0.000 description 1
- 208000022831 chronic renal failure syndrome Diseases 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000018044 dehydration Effects 0.000 description 1
- 238000006297 dehydration reaction Methods 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- -1 e.g. Proteins 0.000 description 1
- 238000006911 enzymatic reaction Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- MASNOZXLGMXCHN-ZLPAWPGGSA-N glucagon Chemical compound C([C@@H](C(=O)N[C@H](C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CC=1C2=CC=CC=C2NC=1)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H]([C@@H](C)O)C(O)=O)C(C)C)NC(=O)[C@H](CC(O)=O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](C)NC(=O)[C@H](CCCNC(N)=N)NC(=O)[C@H](CCCNC(N)=N)NC(=O)[C@H](CO)NC(=O)[C@H](CC(O)=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC=1C=CC(O)=CC=1)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CO)NC(=O)[C@H](CC=1C=CC(O)=CC=1)NC(=O)[C@H](CC(O)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](NC(=O)[C@H](CC=1C=CC=CC=1)NC(=O)[C@@H](NC(=O)CNC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](CO)NC(=O)[C@@H](N)CC=1NC=NC=1)[C@@H](C)O)[C@@H](C)O)C1=CC=CC=C1 MASNOZXLGMXCHN-ZLPAWPGGSA-N 0.000 description 1
- 229960004666 glucagon Drugs 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000001794 hormone therapy Methods 0.000 description 1
- 230000003345 hyperglycaemic effect Effects 0.000 description 1
- 230000035860 hypoinsulinemia Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 0.000 description 1
- 239000004026 insulin derivative Substances 0.000 description 1
- 238000007912 intraperitoneal administration Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 208000030159 metabolic disease Diseases 0.000 description 1
- 230000008986 metabolic interaction Effects 0.000 description 1
- 230000003278 mimic effect Effects 0.000 description 1
- 230000008764 nerve damage Effects 0.000 description 1
- 230000000474 nursing effect Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000035790 physiological processes and functions Effects 0.000 description 1
- 230000000291 postprandial effect Effects 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
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
-
- 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
- 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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
-
- 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
- A61M2005/14288—Infusion or injection simulation
- A61M2005/14296—Pharmacokinetic models
-
- 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
- A61M2205/3546—Range
- A61M2205/3553—Range remote, e.g. between patient's home and doctor's office
-
- 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
- A61M2205/3546—Range
- A61M2205/3561—Range local, e.g. within room or hospital
-
- 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
- A61M2205/3576—Communication with non implanted data transmission devices, e.g. using external transmitter or receiver
- A61M2205/3592—Communication with non implanted data transmission devices, e.g. using external transmitter or receiver using telemetric means, e.g. radio or optical transmission
-
- 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
- A61M2205/70—General characteristics of the apparatus with testing or calibration facilities
- A61M2205/702—General characteristics of the apparatus with testing or calibration facilities automatically during use
-
- 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
- A61M2209/00—Ancillary equipment
- A61M2209/01—Remote controllers for specific 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
- A61M2230/005—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
Definitions
- Diabetes mellitus is a chronic metabolic disorder caused by an inability of the pancreas to produce sufficient amounts of the hormone insulin, resulting in the decreased ability of the body to metabolize glucose.
- This failure leads to hyperglycemia, i.e. the presence of an excessive amount of glucose in the blood plasma.
- Persistent hyperglycemia and/or hypoinsulinemia has been associated with a variety of serious symptoms and life threatening long term complications such as dehydration, ketoacidosis, diabetic coma, cardiovascular diseases, chronic renal failure, retinal damage and nerve damages with the risk of amputation of extremities.
- a permanent therapy is necessary which provides constant glycemic control in order to always maintain the level of blood glucose within normal limits.
- Such glycemic control is achieved by regularly supplying external insulin to the body of the patient to thereby reduce the elevated levels of blood glucose.
- Substantial improvements in diabetes therapy have been achieved by the development of the drug delivery device, relieving the patient of the need for syringes or drug pens and the administration of multiple daily injections.
- the drug delivery device allows for the delivery of drug in a manner that bears greater similarity to the naturally occurring physiological processes and can be controlled to follow standard or individually modified protocols to give the patient better glycemic control.
- Drug delivery devices can be constructed as an implantable device for subcutaneous arrangement or can be constructed as an external device with an infusion set for subcutaneous infusion to the patient via the transcutaneous insertion of a catheter, cannula or a transdermal drug transport such as through a patch.
- External drug delivery devices are mounted on clothing, hidden beneath or inside clothing, or mounted on the body and are generally controlled via a user interface built-in to the device or on a separate remote device.
- Blood or interstitial glucose monitoring is required to achieve acceptable glycemic control.
- delivery of suitable amounts of insulin by the drug delivery device requires that the patient frequently determines his or her blood glucose level and manually input this value into a user interface for the external pumps, which then calculates a suitable modification to the default or currently in-use insulin delivery protocol, i.e., dosage and timing, and subsequently communicates with the drug delivery device to adjust its operation accordingly.
- the determination of blood glucose concentration is typically performed by means of an episodic measuring device such as a hand-held electronic meter which receives blood samples via enzyme-based test strips and calculates the blood glucose value based on the enzymatic reaction.
- CGM Continuous glucose monitoring
- PID controllers have been utilized with mathematical model of the metabolic interactions between glucose and insulin in a person.
- the PID controllers can be tuned based on simple rules of the metabolic models. However, when the PID controllers are tuned or configured to aggressively regulate the blood glucose levels of a subject, overshooting of the set level can occur, which is often followed by oscillations, which is highly undesirable in the context of regulation of blood glucose.
- Alternative controllers were investigated.
- MPC model predictive controller
- MPC can be viewed therefore as a combination of feedback and feed forward control.
- MPC typically requires a metabolic model to mimic as closely as possible the interaction between insulin and glucose in a biological system.
- MPC models continue to be further refined and developed and details of the MPC controllers, variations on the MPC and mathematical models representing the complex interaction of glucose and insulin are shown and described in the following documents: [0009] US Patent No. 7,060,059;
- Applicants have discovered a technique that allows for the tuning of the model predictive control based on two variables relating to calibration data and missing or incomplete CGM data.
- a method is provided to control an infusion pump with a controller to control the pump and receive data from at least one glucose sensor.
- the method can be achieved by: measuring glucose level in the subject from the glucose sensor to provide at least one glucose measurement in each time interval in a series of discrete time interval index ("k"); obtaining a glucose calibration measurement during at least one time interval in the series of time interval index to provide for a calibration index; ascertaining whether one or more glucose measurements were not provided during a time interval of the series of time interval index to provide for an omission index; determining a tuning factor based on both the calibration index and omission index; calculating insulin amount for delivery by the controller based on a model predictive controller that utilizes: (a) the plurality of glucose measurements to predict a trend of the glucose level from estimates of a metabolic state of the subject and (b) the tuning factor so as to provide a calculated insulin amount to be delivered to the subject for each interval of the interval index; delivering an insulin amount determined from the calculating step.
- k discrete time interval index
- a system for management of diabetes includes an episodic glucose meter, continuous glucose meter, and an infusion pump coupled to a controller.
- the episodic glucose meter is configured to measure blood glucose of a subject at discrete non-uniform time intervals and provide such episodic blood glucose levels as a calibration index for each interval in a time interval index (k).
- the continuous glucose monitor that continuously measures glucose level of the subject at discrete generally uniform time intervals and provide the glucose level at each interval in the form of glucose measurement data, in which any omission of a glucose measurement in any interval is stored in an omission index.
- the insulin infusion pump is controlled by the controller to deliver insulin to the subject.
- the controller is in communication with the pump, glucose meter and the glucose monitor in which the controller determines a tuning factor based on: (a) a calibration index derived from episodic blood glucose measurement and (b) an omission index for a model- predictive-control such that controller determines an insulin delivery rate for each time interval in the time interval index (k) from the model predictive control based on: (1) desired glucose concentration, (2) glucose concentration measured by the monitor at each interval of the interval index (k), and (3) the tuning factor.
- the at least one glucose sensor may include both a continuous glucose sensor and an episodic glucose meter; and the tuning factor may be derived from an equation of the form
- R(k) R NOM CAL(k) + r 2 * MISSED(k)
- (k) may be a tuning factor at each time interval index k
- R/vofl/iiiay be a predetermined nominal tuning factor
- k may be a discrete time interval index
- CAL(k) may be a calibration index in which
- CAL(k) k-kc al2 -6 (for k- k cal ⁇ 6); kcai may be the sample index of the most recent calibration for the continuous glucose sensor; k ca i2 may be the sample index of the second most recent
- MISSED(k) may be an omission index in which one or more glucose values during the series of time intervals of the index k are missing or unreported to the controller.
- the value for ri can be any number from about 1 to about 50 and for r 2 can be any number from about 10 to about 500.
- Figure 1 illustrates the system in which a controller for the pump or glucose monitor(s) is separate from both the infusion pump and the glucose monitor(s) and in which a network can be coupled to the controller to provide near real-time monitoring.
- Figure 2A illustrates an exemplary embodiment of the diabetic management system in schematic form.
- Figure 3 illustrates the logic utilized in the controller of Figure 1 or Figure 2A.
- the terms “about” or “approximately” for any numerical values or ranges indicate a suitable dimensional tolerance that allows the part or collection of components to function for its intended purpose as described herein.
- the terms “patient,” “host,” “user,” and “subject” refer to any human or animal subject and are not intended to limit the systems or methods to human use, although use of the subject invention in a human patient represents a preferred embodiment.
- the term “user” includes not only the patient using a drug infusion device but also the caretakers (e.g., parent or guardian, nursing staff or home care employee).
- the term “drug” may include hormone, biologically active materials, pharmaceuticals or other chemicals that cause a biological response (e.g., glycemic response) in the body of a user or patient.
- Figure 1 illustrates a drug delivery system 100 according to an exemplary embodiment that utilizes the principles of the invention.
- Drug delivery system 100 includes a drug delivery device 102 and a remote controller 104.
- Drug delivery device 102 is connected to an infusion set 106 via flexible tubing 108.
- Drug delivery device 102 is configured to transmit and receive data to and from remote controller 104 by, for example, radio frequency communication 1 12.
- Drug delivery device 102 may also function as a stand-alone device with its own built in controller.
- drug delivery device 102 is an insulin infusion device and remote controller 104 is a hand-held portable controller.
- data transmitted from drug delivery device 102 to remote controller 104 may include information such as, for example, insulin delivery data, blood glucose information, basal, bolus, insulin to carbohydrates ratio or insulin sensitivity factor, to name a few.
- the controller 104 is configured to include an MPC controller 10 that has been programmed to receive continuous glucose readings from a CGM sensor 112.
- Data transmitted from remote controller 104 to insulin delivery device 102 may include glucose test results and a food database to allow the drug delivery device 102 to calculate the amount of insulin to be delivered by drug delivery device 102.
- the remote controller 104 may perform basal dosing or bolus calculation and send the results of such calculations to the drug delivery device.
- an episodic blood glucose meter 1 14 may be used alone or in conjunction with the CGM sensor 1 12 to provide data to either or both of the controller 104 and drug delivery device 102.
- the remote controller 104 may be combined with the meter 114 into either (a) an integrated monolithic device; or (b) two separable devices that are dockable with each other to form an integrated device.
- Each of the devices 102, 104, and 1 14 has a suitable micro-controller (not shown for brevity) programmed to carry out various functionalities.
- Drug delivery device 102 may also be configured for bi-directional wireless communication with a remote health monitoring station 1 16 through, for example, a wireless communication network 1 18.
- Remote controller 104 and remote monitoring station 116 may be configured for bi-directional wired communication through, for example, a telephone land based communication network.
- Remote monitoring station 1 16 may be used, for example, to download upgraded software to drug delivery device 102 and to process information from drug delivery device 102.
- Examples of remote monitoring station 116 may include, but are not limited to, a personal or networked computer 126, server 128 to a memory storage, a personal digital assistant, other mobile telephone, a hospital base monitoring station or a dedicated remote clinical monitoring station.
- Drug delivery device 102 includes electronic signal processing components including a central processing unit and memory elements for storing control programs and operation data, a radio frequency module 1 16 for sending and receiving communication signals (i.e., messages) to/from remote controller 104, a display for providing operational information to the user, a plurality of navigational buttons for the user to input information, a battery for providing power to the system, an alarm (e.g., visual, auditory or tactile) for providing feedback to the user, a vibrator for providing feedback to the user, a drug delivery mechanism (e.g. a drug pump and drive mechanism) for forcing a insulin from a insulin reservoir (e.g., a insulin cartridge) through a side port connected to an infusion set 108/106 and into the body of the user.
- a drug delivery mechanism e.g. a drug pump and drive mechanism for forcing a insulin from a insulin reservoir (e.g., a insulin cartridge) through a side port connected to an infusion set 108/106 and into the body of the user.
- Glucose levels or concentrations can be determined by the use of the CGM sensor 1 12.
- the CGM sensor 112 utilizes amperometric electrochemical sensor technology to measure glucose with three electrodes operably connected to the sensor electronics and are covered by a sensing membrane and a biointerface membrane, which are attached by a clip.
- the top ends of the electrodes are in contact with an electrolyte phase (not shown), which is a free-flowing fluid phase disposed between the sensing membrane and the electrodes.
- the sensing membrane may include an enzyme, e.g., glucose oxidase, which covers the electrolyte phase.
- the counter electrode is provided to balance the current generated by the species being measured at the working electrode.
- the species being measured at the working electrode is H2O2.
- the current that is produced at the working electrode (and flows through the circuitry to the counter electrode) is proportional to the diffusional flux of H2O2.
- a raw signal may be produced that is representative of the concentration of glucose in the user's body, and therefore may be utilized to estimate a meaningful glucose value. Details of the sensor and associated components are shown and described in US Patent No. 7,276,029, which is incorporated by reference herein as if fully set forth herein this application. In one embodiment, a continuous glucose sensor from the Dexcom Seven System® (manufactured by Dexcom Inc.) can also be utilized with the exemplary embodiments described herein.
- the following components can be utilized as a system for management of diabetes that is akin to an artificial pancreas: OneTouch Ping® Glucose Management System by Animas Corporation that includes at least an infusion pump and an episodic glucose sensor; and DexCom® SEVEN PLUS® CGM by DexCom Corporation with interface to connect these components and programmed in MATLAB®language and accessory hardware to connect the components together; and control algorithms in the form of an MPC that automatically regulates the rate of insulin delivery based on the glucose level of the patient, historical glucose measurement and anticipated future glucose trends, and patient specific information.
- OneTouch Ping® Glucose Management System by Animas Corporation that includes at least an infusion pump and an episodic glucose sensor
- DexCom® SEVEN PLUS® CGM by DexCom Corporation with interface to connect these components and programmed in MATLAB®language and accessory hardware to connect the components together
- control algorithms in the form of an MPC that automatically regulates the rate of insulin delivery based on the glucose level of the patient, historical glucose measurement
- Figure 2A illustrates a schematic diagram 200 of the system 100 in Figure 1 programmed with the solution devised by applicants to counteract a less than desirable effect of a closed-loop control system.
- Figure 2A provides for an MPC programmed into a control logic module 10 that is utilized in controller 104.
- MPC logic module 10 receives a desired glucose concentration or range of glucose concentration 12 (along with any modification from an update filter 28 so that it is able to maintain the output (i.e., glucose level) of the subject within the desired range of glucose levels.
- the first output 14 of the MPC-enabled control logic 10 can be a control signal to an insulin pump 16 to deliver a desired quantity of insulin 18 into a subject 20 at predetermined time intervals, which can be indexed every 5 minutes using time interval index k.
- a second output in the form of a predicted glucose value 15 can be utilized in control junction B.
- a glucose sensor 22 (or 112 in Fig. 1) measures the glucose levels in the subject 20 in order to provide signals 24 representative of the actual or measured glucose levels to control junction B, which takes the difference between measured glucose concentration 24 and the MPC predictions of that measured glucose concentration. This difference provides input for the update filter 26 of state variables of the model.
- the difference 26 is provided to an estimator (also known as an update filter 28) that provides for estimate of state variables of the model that cannot be measured directly.
- the update filter 28 is preferably a recursive filter in the form of a Kalman filter with tuning parameters for the model.
- the output of the update or recursive filter 28 is provided to control junction A whose output is utilized by the MPC in the control logic 10 to further refine the control signal 14 to the pump 16 (or 102 in Fig. 1).
- a tuning factor 34 is used with the MPC controller 10 to "tune" the controller in its delivery of the insulin. To accomplish this, applicants have devised the use of a calibration index module 30 and data omission module 32 to adjust the tuning factor.
- Calibration index module 30 is configured to track the number of glucose measurement calibration, which is typically accomplished by an episodic glucose monitor, such as, for example, a blood glucose test strip and meter system.
- Data omission index module 32 is configured to track the number of missing measurements or data from the continuous glucose monitor 22.
- the MPC logic is formulated to control a subject glucose level to a safe glucose zone, with the lower blood glucose limit of the zone varying between 80-100 mg/dL and the upper blood glucose limit varying between about 140-180 mg/dL; the algorithm will henceforth be referred to as the "zone MPC".
- Controlling to a target zone is, in general, applied to controlled systems that lack a specific set point with the controller's goal being to keep the controlled variable, (CV), for example the glucose values, in a predefined zone.
- CV controlled variable
- Control to zone i.e., a normaglycemic zone
- zone i.e., a normaglycemic zone
- an inherent benefit of control to zone is the ability to limit pump actuation/activity in a way that if glucose levels are within the zone then no extra correction shall be suggested.
- the insulin delivery rate ID from the zone MPC law is calculated by an online optimization, which evaluates at each sampling time the next insulin delivery rate.
- the optimization at each sampling time is based on the estimated metabolic state (plasma glucose, subcutaneous insulin) obtained from the dynamic model stored in module 10.
- the MPC of control logic 10 incorporates an explicit model of human T1DM glucose- insulin dynamics.
- the model is used to predict future glucose values and to calculate future controller moves that will bring the glucose profile to the desired range.
- Software constraints ensure that insulin delivery rates are constrained between minimum (i.e., zero) and maximum values.
- the first insulin infusion (out of N steps) is then implemented. At the next time step, k + l based on the new measured glucose value and the last insulin rate, the process is repeated.
- G' is the measured glucose concentration
- IM is the "mapped insulin" which is not a measured quantity
- I'D is the delivered insulin or a manipulated variable and coefficients a x ⁇ 2.993; a 2 ⁇ (-3.775); a 3 ⁇ 2.568; a 4 ⁇ (-0.886); a 5 ⁇ 0.09776; b ⁇ (-1.5); ci-1.665; c 2 ⁇ (-0.693); d ⁇ O.01476; d 2 ⁇ 0.01306.
- Equation (2) Using the FDA accepted metabolic simulator known to those skilled in the art, Eq. (1) can be reduced to the following linear difference model in Equation (2):
- Meal M (k) 1.50 ⁇ Meal M (k-Y) + 0.5427 Meal M (k - 2)
- G' is the glucose concentration output (G) deviation variable (mg/dL), i.e.,
- ID' is the insulin infusion rate input (I D ) deviation variable (U/h), i.e., I D ' ⁇ I D - basal U/h,
- Meal is the CHO ingestion input (gram- CHO)
- IM is the mapped subcutaneous insulin infusion rates (U/h)
- Mealu is the mapped CHO ingestion input (gram- CHO).
- the dynamic model in Eq. (2) relates the effects of insulin infusion rate (ID), and CHO ingestion input (Meal) on plasma glucose.
- ID insulin infusion rate
- Meal CHO ingestion input
- the model represents a single average model for the total population of subjects. The model and its parameters are fixed.
- the second-order input transfer functions described by parts (b) and (c) in Eq. (2) are used to generate an artificial input memory in the zone MPC schema to prevent insulin overdosing, and consequently prevent hypoglycemia.
- the evaluation of any sequential insulin delivery must take into consideration the past administered insulin against the length of the insulin action.
- a one-state linear difference model with a relatively low order uses the output (glycemia) as the main source of past administered input (insulin) "memory.”
- IM and Mealu two additional states for the mapped insulin and meal inputs that carry a longer insulin memory.
- Zone MPC is applied when the specific set point value of a controlled variable (CV) is of low relevance compared to a zone that is defined by upper and lower boundaries. Moreover, in the presence of noise and model mismatch there is no practical value using a fixed set point. Zone MPC was developed through research by the University of California at Santa Barbara and the Sansum Diabetes Research Institute. Other details of the derivation for the Zone MPC technique are shown and described in Benyamin Grosman, Ph.D., Eyal Dassau, Ph.D., Howard C. Zisser, M.D., Lois Jovanovic, M.D., and Francis J. Doyle III, Ph.D.
- Zone MPC Zone MPC
- zone MPC A related derivation of zone MPC was presented in Maciejowski JM., "PREDICTIVE CONTROL WITH CONSTRAINTS " Harlow, UK: Prentice-Hall, Pearson Education Limited, 2002.
- the zone MPC is implemented by defining fixed upper and lower bounds as soft constraints by letting the optimization weights switch between zero and some final values when the predicted CVs are in or out of the desired zone, respectively.
- the predicted residuals are generally defined as the difference between the CV that is out of the desired zone and the nearest bound.
- Zone MPC is typically divided into three different zones. The permitted range is the control target and it is defined by upper and lower bounds. The upper zone represents undesirable high predicted glycemic values.
- the lower zone represents undesirable low predicted glycemic values that represent hypoglycemic zone or a pre- hypoglycemic protective area that is a low alarm zone.
- the zone MPC optimizes the predicted glycemia by manipulating the near-future insulin control moves to stay in the permitted zone under specified constrains.
- Zone MPC lies in its cost function formulation that holds the zone formulation.
- Zone MPC like any other forms of MPC, predicts the future output by an explicit model using past input/output records and future input moves that need to be optimized. However, instead of driving to a specific fixed set point, the optimization attempts to keep or move the predicted outputs into a zone that is defined by upper and lower bounds. Using a linear difference model, the glycemic dynamics are predicted and the optimization reduces future glycemic excursions from the zone under constraints and weights defined in its cost function.
- zone MPC cost function J used in the presented work is defined as follows:
- Q is a weighting factor on the predicted glucose term
- R is a tuning factor on the future proposed inputs in the cost function
- vector ID contains the set of proposed near-future insulin infusion amounts. It is the "manipulated variable” because it is manipulated in order to find the minimum in J.
- G ZONE is a variable quantifying the deviation of future model-predicted CGM values G outside a specified glycemic zone, and is determined by making the following comparisons:
- Max_i 400, which is fixed
- Termination on the cost function values Term_cost le-6, which is fixed, o Termination tolerance Term ol on the manipulated variables ID is le-6.
- basal is the subject's basal rate as set by the subject or his/her physician
- control horizontal parameter M and prediction horizon parameter P have significant effects on the controller performance, and are normally used to tune an MPC based controller, they can be heuristically tuned based on knowledge of the system. Tuning rules are known to those skilled in the field. According to these rules M and P may vary between:
- the ratio of the output error weighting factor Q and the input change weighting matrix or tuning factor R may vary between:
- the tuning parameter (designated here as "R") can have a significant effect on the quality of the glucose control.
- the parameter known as the aggressiveness factor, gain, and other names - determines the speed of response of the algorithm to changes in glucose concentration.
- a relatively conservative value of R results in a controller that is slow to adjust insulin infusion amounts (relative to basal) in response to changes in glucose; on the other hand, a relatively aggressive value of R results in a controller that is quick to respond to changes in glucose.
- an aggressive controller would result in the best glucose control if 1) the available glucose measurements are accurate, and moreover 2) the model predictions of future glucose trends are accurate. If these conditions are not true, then it may be safer to use a conservative controller.
- R the aggressiveness factor
- the aggressiveness factor to be used in the MPC calculation for the current sampling instant, indexed by k, is R(k), where R(k) is automatically updated at each sample based on a nominal R value, RNOM, which could in general be different for each user:
- CAL(k) is the calibration index.
- RNOM can be a value from about zero to about 1000 (e.g., 0 ⁇ R NOM ⁇ 1000; although it should be noted that this value and range are dependent on the specific cost function in the particular model being used and one skilled in the art would be able to configure the appropriate range for RNOM, depending on the model being used.
- ri can be any number from about 1 to about 50 and r 2 can be any number from about 10 to about 500.
- the current sample index is k where k CAL is the sample index of the most recent CGM calibration with a suitable referential glucose analyzer or glucose meter 1 14, and k CAL2 is the sample index of the second-most recent CGM calibration with a referential glucose meter 114.
- k CAL is the sample index of the most recent CGM calibration with a suitable referential glucose analyzer or glucose meter 1 14
- k CAL2 is the sample index of the second-most recent CGM calibration with a referential glucose meter 114.
- the R factor is varied based on two indices (i.e., calibration "CAL” and omission “MISSED”) to ensure that the blood glucose of the subject is within the upper limit of approximately 200 mg/dL (Fig. 2B)
- the system of Figure 2 A is provided to manage diabetes of a subject.
- the following components are utilized: an episodic glucose meter 29, continuous glucose sensor 22, pump 16, and controller 10.
- the meter 29 measures blood glucose of a subject at discrete non-uniform time intervals (e.g., every 4 hours) and provide such episodic blood glucose level as a calibration index 30 for each interval in a time interval index (k where a time interval between k and k+1 is about 5 minutes).
- the continuous glucose monitor continuously measure glucose level of the subject at discrete generally uniform time intervals (e.g., approximately every 30 seconds or every minute) and provide the glucose level at each interval in the form of glucose measurement data, in which any omission of a glucose measurement in any interval is stored in an omission index 32.
- the insulin infusion pump is controlled by the controller 10 to deliver insulin to the subject 20.
- the controller 10 is programmed with the appropriate MPC program to control the pump and communicate with the glucose meter and the glucose monitor.
- the controller determines a tuning factor 34 based on (a) a calibration index 30 derived from episodic blood glucose measurements and (b) an omission index 32 for the MPC 10 such that controller determines an insulin delivery rate for each time interval in the time interval index (k) from the model predictive control based on (1) desired glucose concentration 12, (2) glucose concentration 24 measured by the monitor 22 at each interval of the interval index (k), and (3) the tuning factor 34.
- a method to control an infusion pump to control an infusion pump with a controller to control the pump and receive data from at least one glucose sensor With reference to Figure 3, exemplary method will now be described.
- the method starts by measuring in step 302 a glucose level in the subject from the glucose sensor (22 or 29) to provide at least one glucose measurements in each time interval in a series of discrete time interval index ("k").
- the method obtains a glucose calibration measurement during at least one time interval in the series of time interval index k to provide for a calibration index that will be used in the tuning factor calculation of step 308.
- the system ascertain whether one or more glucose measurements were not provided during a time interval of the series of time interval index k to provide for an omission index, which will also be used to determine the tuning factor R.
- the method determines a tuning factor based on both the calibration index and omission index obtained previously.
- the method calculates an insulin amount for delivery by the controller based on a model predictive controller that utilizes (a) the plurality of glucose measurements to predict a trend of the glucose level from estimates of a metabolic state of the subject and (b) the tuning factor so as to provide a calculated insulin amount to be delivered to the subject for each interval of the interval index.
- the pump is controlled to deliver an insulin amount determined from the calculating step 310.
- the routine returns to step 302 or another routine.
- Meal-related insulin boluses administered according the patient's insulin-to-CHO ratio, but they did not include a correction component.
- Pre-meal meal-related boluses were administered 20 min prior to the start of the meal if the current CGM value was 100 mg/dL or higher. If the current CGM value was less than 100 mg/dL, the bolus was administered at the start of the meal.
- Results Table 3 summarizes the results of each scenario in the Stage 5 simulations in terms of CGM values. Irrespective of the scenario, none of the patients experienced appreciable amounts of CGM values below 70 mg/dL. For the smaller, 30-g CHO meals, less than 1% of CGM values were above 180 mg/dL. The larger, 70-g CHO meals resulted in more time spent at CGM values above 180 mg/dL, but even in the "worst" case scenario - 8B, using mealtime boluses and a conservative aggressiveness factor - only 5.5% of the closed-loop time was spent at these elevated glucose levels.
- Table 3 also indicates that the effects of both the aggressiveness factor and the meal bolus timing on the resulting CGM levels were very subtle. For the 30-g CHO meals and either of the meal bolus timing paradigms, adjusting the aggressiveness factor from a conservative value to an aggressive value affected the frequency of CGM values within 70-180 mg/dL by only 0.1%. For the 70-g CHO meals, this value was just 0.7%.
- Tables 4a and 4b present the data in Table 3 in arrays more conducive to assessing the effect of the aggressiveness factor or the bolus timing, while holding the other value constant.
- Table 4a indicates that for the 30-g CHO meals, virtually no difference (in terms of the frequency of CGM values in the 70-180 mg/dL range) is realized by varying either the aggressiveness factor or the bolus timing. Similarly, Table 4b indicates only modest differences in this value.
- Table 5 quantifies the difference in insulin infusion across the spectrum of algorithm aggressiveness, relative to the corresponding basal rates.
- the values are average algorithm- determined insulin delivery rates, as a difference relative to the corresponding basal rates (for example, a value of 0% implies that the algorithm delivered exactly the basal amount).
- the zone was defined throughout the simulations as 90-140 mg/dl. An entry of "n/a” indicates that there were no below-zone excursions for any of the 100 patients for that combination of aggressiveness factor and simulated protocol. All applicable values are negative (an entry of "n/a” implies that there were no below-zone excursions for any subject), indicating that even for the above-zone excursions, the algorithm was delivering less insulin than the corresponding basal rates.
- the closed-loop controller need not be an MPC controller but can be, with appropriate modifications by those skilled in the art, a PID controller, a PID controller with internal model control (IMC), a model-algorithmic-control (MAC) that are discussed by Percival et al, in "Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic ⁇ Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers' ' ' Journal of Diabetes Science and Technology, Vol. 2, Issue 4, July 2008.
- IMC internal model control
- MAC model-algorithmic-control
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Veterinary Medicine (AREA)
- Heart & Thoracic Surgery (AREA)
- Life Sciences & Earth Sciences (AREA)
- Animal Behavior & Ethology (AREA)
- Anesthesiology (AREA)
- Hematology (AREA)
- Vascular Medicine (AREA)
- Medical Informatics (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Diabetes (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Chemical & Material Sciences (AREA)
- Medicinal Chemistry (AREA)
- Infusion, Injection, And Reservoir Apparatuses (AREA)
- External Artificial Organs (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Cardiology (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Transplantation (AREA)
Abstract
Description
Claims
Priority Applications (9)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
KR1020157018039A KR20150093213A (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning a closed-loop controller for an artificial pancreas |
BR112015013332A BR112015013332A2 (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning a closed loop controller to an artificial pancreas |
AU2013355206A AU2013355206B2 (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning a closed-loop controller for an artificial pancreas |
EP13860173.7A EP2928524A4 (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning a closed-loop controller for an artificial pancreas |
RU2015127092A RU2652059C2 (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning closed-loop controller for artificial pancreas |
JP2015545836A JP6312696B2 (en) | 2012-12-07 | 2013-12-05 | Method and system for closed loop controller adjustment of an artificial pancreas |
CA2892785A CA2892785A1 (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning a closed-loop controller for an artificial pancreas |
CN201380063636.4A CN104837517B (en) | 2012-12-07 | 2013-12-05 | Method and system for the closed loop controller for tuning artificial pancreas |
HK16102958.8A HK1214983A1 (en) | 2012-12-07 | 2016-03-15 | Method and system for tuning a closed-loop controller for an artificial pancreas |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US13/708,032 | 2012-12-07 | ||
US13/708,032 US9486578B2 (en) | 2012-12-07 | 2012-12-07 | Method and system for tuning a closed-loop controller for an artificial pancreas |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2014089282A1 true WO2014089282A1 (en) | 2014-06-12 |
Family
ID=50881750
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2013/073289 WO2014089282A1 (en) | 2012-12-07 | 2013-12-05 | Method and system for tuning a closed-loop controller for an artificial pancreas |
Country Status (12)
Country | Link |
---|---|
US (1) | US9486578B2 (en) |
EP (1) | EP2928524A4 (en) |
JP (1) | JP6312696B2 (en) |
KR (1) | KR20150093213A (en) |
CN (1) | CN104837517B (en) |
AU (1) | AU2013355206B2 (en) |
BR (1) | BR112015013332A2 (en) |
CA (1) | CA2892785A1 (en) |
HK (1) | HK1214983A1 (en) |
RU (1) | RU2652059C2 (en) |
TW (1) | TWI619481B (en) |
WO (1) | WO2014089282A1 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015050699A1 (en) | 2013-10-04 | 2015-04-09 | Animas Corporation | Method and system for controlling a tuning factor due to sensor replacement for closed-loop controller in an artificial pancreas |
Families Citing this family (66)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8065161B2 (en) | 2003-11-13 | 2011-11-22 | Hospira, Inc. | System for maintaining drug information and communicating with medication delivery devices |
JP2010507176A (en) | 2006-10-16 | 2010-03-04 | ホスピラ・インコーポレイテツド | System and method for comparing and utilizing dynamic information and configuration information from multiple device management systems |
US7751907B2 (en) | 2007-05-24 | 2010-07-06 | Smiths Medical Asd, Inc. | Expert system for insulin pump therapy |
US8221345B2 (en) | 2007-05-30 | 2012-07-17 | Smiths Medical Asd, Inc. | Insulin pump based expert system |
US7959598B2 (en) | 2008-08-20 | 2011-06-14 | Asante Solutions, Inc. | Infusion pump systems and methods |
US8271106B2 (en) | 2009-04-17 | 2012-09-18 | Hospira, Inc. | System and method for configuring a rule set for medical event management and responses |
US8882701B2 (en) | 2009-12-04 | 2014-11-11 | Smiths Medical Asd, Inc. | Advanced step therapy delivery for an ambulatory infusion pump and system |
CA2816314C (en) | 2010-10-31 | 2018-03-06 | Trustees Of Boston University | Blood glucose control system |
US9594875B2 (en) | 2011-10-21 | 2017-03-14 | Hospira, Inc. | Medical device update system |
CA2897925C (en) * | 2013-01-14 | 2017-05-30 | The Regents Of The University Of California | Daily periodic target-zone modulation in the model predictive control problem for artificial pancreas for type 1 diabetes applications |
WO2014138446A1 (en) | 2013-03-06 | 2014-09-12 | Hospira,Inc. | Medical device communication method |
US10357606B2 (en) | 2013-03-13 | 2019-07-23 | Tandem Diabetes Care, Inc. | System and method for integration of insulin pumps and continuous glucose monitoring |
US9561324B2 (en) | 2013-07-19 | 2017-02-07 | Bigfoot Biomedical, Inc. | Infusion pump system and method |
JP6621748B2 (en) | 2013-08-30 | 2019-12-18 | アイシーユー・メディカル・インコーポレーテッド | System and method for monitoring and managing a remote infusion regimen |
US9662436B2 (en) | 2013-09-20 | 2017-05-30 | Icu Medical, Inc. | Fail-safe drug infusion therapy system |
US10311972B2 (en) | 2013-11-11 | 2019-06-04 | Icu Medical, Inc. | Medical device system performance index |
EP3071253B1 (en) | 2013-11-19 | 2019-05-22 | ICU Medical, Inc. | Infusion pump automation system and method |
US10569015B2 (en) | 2013-12-02 | 2020-02-25 | Bigfoot Biomedical, Inc. | Infusion pump system and method |
CN112515665B (en) | 2014-01-31 | 2024-03-29 | 波士顿大学董事会 | Blood glucose level control system |
JP6853669B2 (en) | 2014-04-30 | 2021-03-31 | アイシーユー・メディカル・インコーポレーテッド | Patient treatment system with conditional alert forwarding |
CN104027867A (en) * | 2014-06-13 | 2014-09-10 | 杨力 | Portable intelligent dual-function diabetes treatment pump |
US9724470B2 (en) | 2014-06-16 | 2017-08-08 | Icu Medical, Inc. | System for monitoring and delivering medication to a patient and method of using the same to minimize the risks associated with automated therapy |
US9539383B2 (en) | 2014-09-15 | 2017-01-10 | Hospira, Inc. | System and method that matches delayed infusion auto-programs with manually entered infusion programs and analyzes differences therein |
CN104667379B (en) * | 2015-03-06 | 2017-08-15 | 上海交通大学 | The insulin pump of Dynamic Closed Loop Control |
US9878097B2 (en) | 2015-04-29 | 2018-01-30 | Bigfoot Biomedical, Inc. | Operating an infusion pump system |
US20160339175A1 (en) * | 2015-05-20 | 2016-11-24 | Medtronic Minimed, Inc. | Infusion devices and related methods for therapy recommendations |
WO2016189417A1 (en) | 2015-05-26 | 2016-12-01 | Hospira, Inc. | Infusion pump system and method with multiple drug library editor source capability |
CA2991047C (en) * | 2015-06-28 | 2020-04-14 | The Regents Of The University Of California | Velocity-weighting model predictive control of an artificial pancreas for type 1 diabetes applications |
WO2017027459A1 (en) | 2015-08-07 | 2017-02-16 | Trustees Of Boston University | Glucose control system with automatic adaptation of glucose target |
CA3005206A1 (en) * | 2015-11-12 | 2017-05-18 | Avent, Inc. | Patient outcome tracking platform |
US10569016B2 (en) | 2015-12-29 | 2020-02-25 | Tandem Diabetes Care, Inc. | System and method for switching between closed loop and open loop control of an ambulatory infusion pump |
EP3374900A1 (en) | 2016-01-05 | 2018-09-19 | Bigfoot Biomedical, Inc. | Operating multi-modal medicine delivery systems |
US10449294B1 (en) | 2016-01-05 | 2019-10-22 | Bigfoot Biomedical, Inc. | Operating an infusion pump system |
EP3374004B1 (en) | 2016-01-14 | 2023-06-28 | Bigfoot Biomedical, Inc. | Adjusting insulin delivery rates |
US10332632B2 (en) * | 2016-06-01 | 2019-06-25 | Roche Diabetes Care, Inc. | Control-to-range failsafes |
NZ750032A (en) | 2016-07-14 | 2020-05-29 | Icu Medical Inc | Multi-communication path selection and security system for a medical device |
CN108261591B (en) * | 2016-12-30 | 2021-10-08 | 上海移宇科技股份有限公司 | Closed-loop control algorithm of artificial pancreas |
US10500334B2 (en) | 2017-01-13 | 2019-12-10 | Bigfoot Biomedical, Inc. | System and method for adjusting insulin delivery |
US10583250B2 (en) | 2017-01-13 | 2020-03-10 | Bigfoot Biomedical, Inc. | System and method for adjusting insulin delivery |
EP3568859A1 (en) | 2017-01-13 | 2019-11-20 | Bigfoot Biomedical, Inc. | Insulin delivery methods, systems and devices |
US10758675B2 (en) | 2017-01-13 | 2020-09-01 | Bigfoot Biomedical, Inc. | System and method for adjusting insulin delivery |
WO2018132754A1 (en) | 2017-01-13 | 2018-07-19 | Mazlish Bryan | System and method for adjusting insulin delivery |
WO2018132723A1 (en) | 2017-01-13 | 2018-07-19 | Mazlish Bryan | Insulin delivery methods, systems and devices |
USD853583S1 (en) | 2017-03-29 | 2019-07-09 | Becton, Dickinson And Company | Hand-held device housing |
US10729849B2 (en) * | 2017-04-07 | 2020-08-04 | LifeSpan IP Holdings, LLC | Insulin-on-board accounting in an artificial pancreas system |
EP3618713B1 (en) * | 2017-05-05 | 2021-12-15 | Eli Lilly and Company | Closed loop control of physiological glucose |
EP3438858A1 (en) | 2017-08-02 | 2019-02-06 | Diabeloop | Closed-loop blood glucose control systems and methods |
EP3729446A1 (en) | 2017-12-21 | 2020-10-28 | Eli Lilly and Company | Closed loop control of physiological glucose |
EP3811373A1 (en) | 2018-06-22 | 2021-04-28 | Eli Lilly and Company | Insulin and pramlintide delivery systems, methods, and devices |
US11139058B2 (en) | 2018-07-17 | 2021-10-05 | Icu Medical, Inc. | Reducing file transfer between cloud environment and infusion pumps |
WO2020018388A1 (en) | 2018-07-17 | 2020-01-23 | Icu Medical, Inc. | Updating infusion pump drug libraries and operational software in a networked environment |
US11483403B2 (en) | 2018-07-17 | 2022-10-25 | Icu Medical, Inc. | Maintaining clinical messaging during network instability |
NZ772135A (en) | 2018-07-17 | 2022-11-25 | Icu Medical Inc | Systems and methods for facilitating clinical messaging in a network environment |
EP3827337A4 (en) | 2018-07-26 | 2022-04-13 | ICU Medical, Inc. | Drug library management system |
US10692595B2 (en) | 2018-07-26 | 2020-06-23 | Icu Medical, Inc. | Drug library dynamic version management |
EP4000075A4 (en) | 2019-07-16 | 2023-10-04 | Beta Bionics, Inc. | Blood glucose control system |
US11957876B2 (en) | 2019-07-16 | 2024-04-16 | Beta Bionics, Inc. | Glucose control system with automated backup therapy protocol generation |
CN114760918A (en) | 2019-07-16 | 2022-07-15 | 贝塔仿生公司 | Blood sugar control system |
US11883208B2 (en) | 2019-08-06 | 2024-01-30 | Medtronic Minimed, Inc. | Machine learning-based system for estimating glucose values based on blood glucose measurements and contextual activity data |
WO2021026399A1 (en) * | 2019-08-06 | 2021-02-11 | Medtronic Minimed, Inc. | Machine learning-based system for estimating glucose values |
US20220039755A1 (en) | 2020-08-06 | 2022-02-10 | Medtronic Minimed, Inc. | Machine learning-based system for estimating glucose values |
WO2021146902A1 (en) * | 2020-01-21 | 2021-07-29 | Medtrum Technologies Inc. | Medical device with safety verification and safety verification method thereof |
US11324889B2 (en) * | 2020-02-14 | 2022-05-10 | Insulet Corporation | Compensation for missing readings from a glucose monitor in an automated insulin delivery system |
CN112402731B (en) * | 2020-10-10 | 2023-05-26 | 广东食品药品职业学院 | Closed-loop insulin infusion system for preventing hypoglycemia |
WO2022081788A1 (en) * | 2020-10-14 | 2022-04-21 | University Of Virginia Patent Foundation | Method and system of closed loop control improving glycemic response following an unannounced source of glycemic fluctuation |
WO2023070250A1 (en) * | 2021-10-25 | 2023-05-04 | Medtrum Technologies Inc. | Closed-loop artificial pancreas insulin infusion personalized control system |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020106709A1 (en) * | 2000-08-18 | 2002-08-08 | Potts Russell O. | Methods and devices for prediction of hypoglycemic events |
US20080287761A1 (en) * | 2007-05-14 | 2008-11-20 | Abbott Diabetes Care, Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US20100305545A1 (en) * | 1999-06-03 | 2010-12-02 | Medtronic Minimed, Inc. | Apparatus and method for controlling insulin infusion with state variable feedback |
US20110196213A1 (en) * | 2009-06-26 | 2011-08-11 | Roche Diagnostics Operations, Inc. | Display For Biological Values |
US20110257627A1 (en) * | 2008-12-09 | 2011-10-20 | Cambridge Enterprise Limited | Substance monitoring and control in human or animal bodies |
WO2012051344A2 (en) * | 2010-10-12 | 2012-04-19 | Regents Of The University Of California, The | Maintaining multiple defined physiological zones using model predictive control |
US20120286953A1 (en) * | 2011-05-13 | 2012-11-15 | Roche Diagnostics Operations, Inc. | Dynamic data collection |
Family Cites Families (28)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4055175A (en) | 1976-05-07 | 1977-10-25 | Miles Laboratories, Inc. | Blood glucose control apparatus |
US4280494A (en) | 1979-06-26 | 1981-07-28 | Cosgrove Robert J Jun | System for automatic feedback-controlled administration of drugs |
US5298022A (en) | 1989-05-29 | 1994-03-29 | Amplifon Spa | Wearable artificial pancreas |
EP0524317A4 (en) | 1991-02-08 | 1995-02-15 | Tokyo Shibaura Electric Co | Model forecasting controller |
US5283729A (en) | 1991-08-30 | 1994-02-01 | Fisher-Rosemount Systems, Inc. | Tuning arrangement for turning the control parameters of a controller |
CN1859943B (en) | 2002-10-11 | 2010-09-29 | 贝克顿·迪金森公司 | System for controlling concentration of glucose in a patient |
US8060173B2 (en) | 2003-08-01 | 2011-11-15 | Dexcom, Inc. | System and methods for processing analyte sensor data |
US7591801B2 (en) * | 2004-02-26 | 2009-09-22 | Dexcom, Inc. | Integrated delivery device for continuous glucose sensor |
US7451004B2 (en) | 2005-09-30 | 2008-11-11 | Fisher-Rosemount Systems, Inc. | On-line adaptive model predictive control in a process control system |
RU2447905C2 (en) * | 2006-02-09 | 2012-04-20 | Дека Продактс Лимитед Партнершип | Pump systems for fluid delivery and methods for using stress application devices |
US7577483B2 (en) | 2006-05-25 | 2009-08-18 | Honeywell Asca Inc. | Automatic tuning method for multivariable model predictive controllers |
WO2008067284A2 (en) | 2006-11-27 | 2008-06-05 | University Of Virginia Patent Foundation | Method, system, and computer program for detection of conditions of a patient having diabetes |
US10154804B2 (en) * | 2007-01-31 | 2018-12-18 | Medtronic Minimed, Inc. | Model predictive method and system for controlling and supervising insulin infusion |
US8930203B2 (en) * | 2007-02-18 | 2015-01-06 | Abbott Diabetes Care Inc. | Multi-function analyte test device and methods therefor |
US10546659B2 (en) | 2007-06-21 | 2020-01-28 | University Of Virginia Patent Foundation | Method, system and computer simulation environment for testing of monitoring and control strategies in diabetes |
US9839395B2 (en) | 2007-12-17 | 2017-12-12 | Dexcom, Inc. | Systems and methods for processing sensor data |
TWI394580B (en) * | 2008-04-28 | 2013-05-01 | Halozyme Inc | Super fast-acting insulin compositions |
US8046089B2 (en) | 2008-06-20 | 2011-10-25 | Honeywell International Inc. | Apparatus and method for model predictive control (MPC) of a nonlinear process |
CA2753650A1 (en) | 2008-11-26 | 2010-06-03 | University Of Virginia Patent Foundation | Method, system, and computer program product for tracking of blood glucose variability in diabetes |
EP4243032A3 (en) | 2009-02-25 | 2023-12-06 | University Of Virginia Patent Foundation | Method, system and computer program product for cgm-based prevention of hypoglycemia via hypoglycemia risk assessment and smooth reduction insulin delivery |
WO2010097796A1 (en) * | 2009-02-26 | 2010-09-02 | Mor Research Applications Ltd. | Method and system for automatic monitoring of diabetes related treatments |
CN102576375B (en) | 2009-05-29 | 2016-05-18 | 弗吉尼亚大学专利基金会 | Be used for system coordination device and the modular architecture of the Open loop and closed loop control of diabetes |
US11238990B2 (en) | 2009-06-26 | 2022-02-01 | University Of Virginia Patent Foundation | System, method and computer simulation environment for in silico trials in pre-diabetes and type 2 diabetes |
WO2011028731A1 (en) | 2009-09-01 | 2011-03-10 | University Of Virginia Patent Foundation | System, method and computer program product for adjustment of insulin delivery (aid) in diabetes using nominal open-loop profiles |
CA2772663A1 (en) | 2009-09-02 | 2011-03-10 | University Of Virginia Patent Foundation | Tracking the probability for imminent hypoglycemia in diabetes from self-monitoring blood glucose (smbg) data |
US8762070B2 (en) | 2010-02-11 | 2014-06-24 | Regents Of The University Of California | Systems, devices and methods to deliver biological factors or drugs to a subject |
US20110313680A1 (en) | 2010-06-22 | 2011-12-22 | Doyle Iii Francis J | Health Monitoring System |
US8657746B2 (en) | 2010-10-28 | 2014-02-25 | Medtronic Minimed, Inc. | Glucose sensor signal purity analysis |
-
2012
- 2012-12-07 US US13/708,032 patent/US9486578B2/en active Active
-
2013
- 2013-12-05 BR BR112015013332A patent/BR112015013332A2/en not_active Application Discontinuation
- 2013-12-05 CN CN201380063636.4A patent/CN104837517B/en not_active Expired - Fee Related
- 2013-12-05 KR KR1020157018039A patent/KR20150093213A/en not_active Application Discontinuation
- 2013-12-05 EP EP13860173.7A patent/EP2928524A4/en not_active Withdrawn
- 2013-12-05 RU RU2015127092A patent/RU2652059C2/en active
- 2013-12-05 WO PCT/US2013/073289 patent/WO2014089282A1/en active Application Filing
- 2013-12-05 CA CA2892785A patent/CA2892785A1/en not_active Abandoned
- 2013-12-05 AU AU2013355206A patent/AU2013355206B2/en not_active Ceased
- 2013-12-05 JP JP2015545836A patent/JP6312696B2/en active Active
- 2013-12-06 TW TW102144747A patent/TWI619481B/en not_active IP Right Cessation
-
2016
- 2016-03-15 HK HK16102958.8A patent/HK1214983A1/en unknown
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100305545A1 (en) * | 1999-06-03 | 2010-12-02 | Medtronic Minimed, Inc. | Apparatus and method for controlling insulin infusion with state variable feedback |
US20020106709A1 (en) * | 2000-08-18 | 2002-08-08 | Potts Russell O. | Methods and devices for prediction of hypoglycemic events |
US20080287761A1 (en) * | 2007-05-14 | 2008-11-20 | Abbott Diabetes Care, Inc. | Method and apparatus for providing data processing and control in a medical communication system |
US20110257627A1 (en) * | 2008-12-09 | 2011-10-20 | Cambridge Enterprise Limited | Substance monitoring and control in human or animal bodies |
US20110196213A1 (en) * | 2009-06-26 | 2011-08-11 | Roche Diagnostics Operations, Inc. | Display For Biological Values |
WO2012051344A2 (en) * | 2010-10-12 | 2012-04-19 | Regents Of The University Of California, The | Maintaining multiple defined physiological zones using model predictive control |
US20120286953A1 (en) * | 2011-05-13 | 2012-11-15 | Roche Diagnostics Operations, Inc. | Dynamic data collection |
Non-Patent Citations (1)
Title |
---|
See also references of EP2928524A4 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2015050699A1 (en) | 2013-10-04 | 2015-04-09 | Animas Corporation | Method and system for controlling a tuning factor due to sensor replacement for closed-loop controller in an artificial pancreas |
EP3052161A4 (en) * | 2013-10-04 | 2017-10-25 | Animas Corporation | Method and system for controlling a tuning factor due to sensor replacement for closed-loop controller in an artificial pancreas |
Also Published As
Publication number | Publication date |
---|---|
EP2928524A1 (en) | 2015-10-14 |
TW201446227A (en) | 2014-12-16 |
RU2015127092A (en) | 2017-01-12 |
JP6312696B2 (en) | 2018-04-18 |
JP2016502869A (en) | 2016-02-01 |
RU2652059C2 (en) | 2018-04-24 |
KR20150093213A (en) | 2015-08-17 |
CN104837517B (en) | 2018-11-06 |
US9486578B2 (en) | 2016-11-08 |
BR112015013332A2 (en) | 2017-07-11 |
CN104837517A (en) | 2015-08-12 |
AU2013355206B2 (en) | 2018-04-26 |
AU2013355206A1 (en) | 2015-07-16 |
CA2892785A1 (en) | 2014-06-12 |
HK1214983A1 (en) | 2016-08-12 |
TWI619481B (en) | 2018-04-01 |
US20140163517A1 (en) | 2014-06-12 |
EP2928524A4 (en) | 2016-04-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US9486578B2 (en) | Method and system for tuning a closed-loop controller for an artificial pancreas | |
CA2926047C (en) | Method and system for controlling a tuning factor due to sensor replacement for closed-loop controller in an artificial pancreas | |
US20180147349A1 (en) | Method and system for a hybrid control-to-target and control-to-range model predictive control of an artificial pancreas | |
AU2013280898B2 (en) | Method and system to handle manual boluses or meal events for closed-loop controllers | |
US20180043095A1 (en) | Method and system for closed-loop control of an artificial pancreas |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 13860173 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2892785 Country of ref document: CA |
|
ENP | Entry into the national phase |
Ref document number: 2015545836 Country of ref document: JP Kind code of ref document: A |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
REG | Reference to national code |
Ref country code: BR Ref legal event code: B01A Ref document number: 112015013332 Country of ref document: BR |
|
WWE | Wipo information: entry into national phase |
Ref document number: 2013860173 Country of ref document: EP |
|
ENP | Entry into the national phase |
Ref document number: 20157018039 Country of ref document: KR Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 2015127092 Country of ref document: RU Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 2013355206 Country of ref document: AU Date of ref document: 20131205 Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 112015013332 Country of ref document: BR Kind code of ref document: A2 Effective date: 20150608 |