WO2015056259A1

WO2015056259A1 - System and method for improved artificial pancreas management

Info

Publication number: WO2015056259A1
Application number: PCT/IL2014/050886
Authority: WO
Inventors: Revital Nimri; Eran Atlas; Shahar Miller; Ido MULLER; Moshe Phillip; Aviel FOGEL
Original assignee: Dreamed-Diabetes Ltd.
Priority date: 2013-10-14
Filing date: 2014-10-07
Publication date: 2015-04-23

Abstract

A method for delivering insulin, comprising using a processor for computing a level of insulin to be delivered to an individual patient using an artificial pancreas, as a bolus before a meal, including determining a total dose which would have been administered for a predetermined window of time following the meal; administering the bolus; and refraining from administering at least a portion of said total dose during said predetermined window of time and instead adding said at least a portion to said insulin level.

Description

SYSTEM AND METHOD FOR IMPROVED ARTIFICIAL PANCREAS

MANAGEMENT

REFERENCE TO CO-PENDING APPLICATIONS

Priority is claimed from US provisional application No. 61/890,351 , entitled "Insulin Bolus Calculator For Artificial Pancreas System" and filed 14 October 2013.

FIELD

The present invention relates generally to computerized health systems and in particular to computerized dosage administration.

BACKGROUND

Meal treatment is a challenging task in type 1 diabetes management. The use of different artificial pancreas systems could not completely eliminate the frequently found postprandial glucose excursions above the target range. This happens mainly due to a delay in subcutaneous insulin absorption and action. A more aggressive insulin treatment for meals together with the use of artificial pancreas system might reduce the glucose excursion, but may, on the other hand, cause postprandial hypoglycemia, due to the lack of counter-regulatory means to control low blood glucose.

Hence, most current closed-loop systems use the approach of "hybrid closed-loop control" which means giving manual pre-meal bolus of insulin to cover the meal (1-3). Various techniques have been developed aimed at helping the patient to compute the amount of insulin needed in order to cover the meal consumed. In most of them, the computation is based on a prior knowledge of the patient Carbohydrates Ratio (CR), the reported carbohydrates consumption and the current

glucose level before the meal.

Conventional technology includes:

1. Weinzimer S, Steil G, Swan K, Dziura J, Kurtz N, Tamborlane W: Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diabetes Care 2008;31:934-939

2. Hovorka R, Allen J, Elleri D, Chassin L, Harris J, Xing D, Kollman C, Hovorka T, Larsen A, Nodale M, De Palma A, Wilinska M, Acerini C, Dunger D: Manual closed- loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial. Lancet 2010;375:743-751

3. Breton M, Farret A, Bruttomesso D, Anderson S, Magni L, Patek S, Dalla Man C, Place J, Demartini S, Del Favero S, Toffanin C, Hughes-Karvetski C, Dassau E, Zisser H, Doyle FJ, De Nicolao G, Avogaro A, Cobelli C, Renard E, Kovatchev B, Group IAPS: Fully integrated artificial pancreas in type 1 diabetes: modular closed-loop glucose control maintains near normoglycemia. Diabetes 2012;61:2230-2237

The disclosures of all publications and patent documents mentioned in the specification, and of the publications and patent documents cited therein directly or indirectly, are hereby incorporated by reference.

SUMMARY

There are limitations in the design of conventional bolus calculators. First, the CR might change between days and even within the same day. Second, meals with different glycemic index may need a different way to compute the amount of insulin as well as the type of insulin bolus to cover the meal. Third, the calculators are based on a single glucose level measured by a glucometer. This input is lacking the time factor - data that can be provided by a continuous glucose sensor. Thus, the glucose trends before the meal and prediction of glucose post meal may also be taken into consideration when computing the meal bolus. Furthermore, the method of the insulin pump's bolus calculator is based on the assumption that the patient cannot continuously track his glucose level and give additional correction dosing if necessary. Such constrains may not be necessary if an automatic system monitors glucose levels continuously. Certain embodiments seek to provide a method to improve the post-prandial glycemic control while the patient is treated with an artificial pancreas system. This MD-Logic Bolus Calculator may automatically adjust the patient's carbohydrate ratio (CR- ratio that relates between amount of carbohydrates consumed to insulin and the patient basal insulin dosing) and/or correction factor (CF - amount of insulin injection affecting the glucose level) for the pre-meal bolus dosing computation and may also take into account some or all of: the glucose trends before the meal, glucose prediction, previous insulin delivery and amount of carbohydrate consumed.

Monitoring typically takes into account inputs entered to the calculator by the user such as but not limited to some or all of: the amount of carbohydrates consumed, other ingredients of the meal (e.g. percentage of fat and/or protein), specific event patient is participating in, function of a specific time of day (e.g. sleep, meal, exercise, disease). To determine the compounds of each meal the calculator may present the user with pre-programmed meals including details of ingredients.

The processor utility typically analyzes glucose history measurements to produce suitable parameter/s such as past trend of glucose levels and typically utilizes a suitable conventional prediction model for predicting glucose level in blood based on measured glucose level.

The bolus calculator output typically combines available inputs to determine appropriate insulin dosing for the meal.

Certain embodiments seek to provide a method for monitoring diabetes treatment and a system for use in monitoring diabetes treatment of a patient connected to an artificial pancreas.

Certain embodiments seek to provide a method for computing an insulin dose to cover a meal assuming a patient is under artificial pancreas treatment, such that for a patient on artificial pancreas treatment a more aggressive regimen of basal/bolus insulin delivery (Giving more insulin than conventional treatment guidelines might recommend for a specific data sets of glucose/history of insulin etc.) may be employed to compensate for the consumed carbohydrate and achieve tighter control without increasing hypoglycemia events e.g. by: a. considering the data from the glucose continuous sensor, the glucose level during the meal and the trends of the glucose in the past & future in order to tune the insulin dosing; and/or

b. loading basal insulin treatment on top of the computation of the meal bolus in order to increase the amount of insulin dosage and achieve lower post - meal glucose levels; and/or c. using other ingredients of the meal such as protein /fat, as input data to compute dosing (insulin infusion e.g.).

A particular advantage of certain embodiments is that glucose may be kept at all times within or close to a safe range of glucose levels e.g. 80-120 mg/dl.

In contrast, Conventional advisors for insulin dosing for meals, e.g. software features available in insulin pumps and related applications may use the discrete glucose level measured using a glucometer and may not use continuous glucose sensor data.

Certain embodiments seek to provide a method for computing meal bolus as function of (inter alia) glucose trends assuming the patient is under artificial pancreas treatment.

Because there is an algorithm that continuously "see" the glucose and can make "decisions" you may be more aggressive with the meal insulin dosing and achieve tighter control. Certain embodiments seek to achieve tighter glucose control without increasing hypoglycemia events by determination of meal insulin dosing which enhances glycemic control of the patient. It is appreciated that conventional artificial pancreas systems sometimes have difficulty handling meals e.g. due to delays in insulin absorptions and/or due to the fact that the conventional system is causal (e.g. the conventional system's output depends on past and current inputs but not future inputs)

The term "glucose trends" is intended to include glucose rate of change typically accompanied by data characterizing the duration for which a specific rate of change lasts. For example, perhaps in a first data point the rate of change of glucose is -2 mg/dl/min which rate of change lasts for 3 minutes. Then, in a second data point, the rate of change of glucose is -2 mg/dl/min which rate of change lasts for 3 hours. In this case, the trend of the first data point is similar vis a vis rate of change but the trend of the second data point is smaller than - 2mg/dl/min (for example -4mg/dl/min) since that rate of change endures for a long period of time.

Certain embodiments seek to provide a method for increasing insulin dosage relative to the amount of insulin conventional treatment guidelines might recommend for the same meal based on the same data sets of glucose/history of insulin. ) and for achieving lower post - meal glucose levels relative to conventionally recommended post meal glucose levels after delivering the amount of insulin conventionally recommended as a dose for a

meal.Certain embodiments seek to provide a method for computing an insulin dose (bolus) to cover a meal consumed by a patient on artificial pancreas treatment, the method comprising computing (and delivering) a level of insulin to be delivered, by Receiving continuous glucose level data series from a continuous glucose level sensor during the meal; Using a processor for Identifying the impact of glucose dynamics in said continuous glucose level data series on the amount of insulin needed for the meal; and determining said insulin level based on said dynamics. The method may also comprise additional insulin dosing which is equivalent to the basal insulin dosage for a certain period of time ahead

According to certain embodiments, trends are identified from the raw data of the glucose in the past & future in order to tune insulin dosing. Predictive glucose levels may be generated for several minutes ahead (e.g. 30-60 minutes ahead). The predictive glucose is an indicator of the past and current glucose levels and may be computed by taking the current glucose level and using a predictive trend to find the predictive glucose level at a certain time ahead. According to certain embodiments, basal insulin treatment is loaded on top of computation of the meal bolus in order to increase the amount of insulin dosage and achieve lower post - meal glucose levels. Typically, the Post meal period is approximately 2 hours post meal. Diabetes patients are conventionally given a physician-determined "basal" rate of insulin in order to compensate for the internal production of glucose by the liver. According to certain embodiments, patient takes the amount of overall insulin dosage of the basal rate the patient is to receive during the post meal period (e.g 2 hours post meal) and delivers the same amount as a bolus at meal time. A particular advantage is that subsequently, glucose levels post meal improve and typically reach a lower peak. For example, suppose the basal rate of a patient is 1 unit/hour and the meal was to be covered by 3 units of insulin (considering the B_dynamics and B_CHO). According to certain embodiments, the tool or system described herein may deliver the patient 5 units of insulin = 3 for the meal + 2 for 2 hours of basal and may set the basal rate delivery for the 2 hours period post meal to zero, to avoid over dosing.

Inputs may be entered by the user such as ingredients of the meal e.g. percentage of fat and protein, which may for example be obtainable by looking at the table of ingredients in the food's packaging, or by using available tables which allow the patient to see ingredients in different types of food.

Pre-meal bolus dosing computation may take into account a specific event that the patient is in or is about to be in, e.g. as a function of a specific time of day (as sleep, meal, exercise, disease). For example, different CR and CF values may be set as a function of the specific time of day or event. For example, if the patient is doing exercise, the insulin sensitivity of the patient may change resulting in different CF and CR values being used in computations described herein.

There are thus provided, at least the following embodiments:

Embodiment 1 : A method, system or computer program product for delivering insulin, the method comprising Using a processor for computing a level of insulin to be delivered to an individual patient using an artificial pancreas, as a bolus before a meal, including determining a total dose which would have been administered for a predetermined window of time following the meal; administering the bolus; and refraining from administering at least a portion of said total dose during said predetermined window of time and instead adding said at least a portion to said level of insulin delivered as a bolus in said administering step.

Embodiment 2. A method according to Embodiment 1 and also comprising: receiving continuous glucose level data for the individual patient from a continuous glucose level sensor prior to said window of time and using a processor to generate a predicted glucose level during said window of time irrespective of the meal based on said glucose level data; and

determining said level of insulin delivered as a bolus in said administering step by combining said at least a portion of said total dose with an insulin level suitable for said predicted glucose level.

Embodiment 3. A method according to Embodiment 1 and also comprising taking at least one meal ingredient into account for said determining.

Embodiment 4. A method according to Embodiment 1 wherein said refraining comprises entirely refraining from administering said total dose (zero basal rate), during said predetermined window of time and wherein said at least a portion of said total dose comprises the entirety of said total dose.

Embodiment 5. A method according to Embodiment 1 wherein said bolus is administered before the meal.

Embodiment 6. A method according to Embodiment 1 wherein said window of time comprises a period of time whose length is within a range of 1.25 - 3.5 hours.

Embodiment 7. A method according to Embodiment 1 wherein said window of time comprises a period of time whose length is within a range of 1.5 - 3 hours.

Embodiment 8. A method according to Embodiment 1 wherein said window of time comprises a period of time whose length is within a range of 1.75 - 2.5 hours.

Embodiment 9. A method according to Embodiment 1 wherein said window of time comprises a period of time whose length is 2 hours.

Embodiment 10. A method according to Embodiment 1 wherein the total dose which would have been administered for a predetermined window of time following the meal is determined based on a basal plan currently followed by said individual patient's artificial pancreas.

Embodiment 11. A method according to Embodiment 2 wherein said predicted glucose level is generated using linear interpolation.

Embodiment 12. A method according to Embodiment 2 wherein said predicted glucose level is generated using an autoregression model. Embodiment 13. A method according to Embodiment 2 wherein said insulin level suitable for said predicted glucose level is negative and said combining comprises subtracting said insulin level from said at least a portion of said total dose.

The autoregression model used to generate the predicted glucose level may for example be as described in: J Diabetes Sci Technol. Sep 2007; 1(5): 645-651. Published online Sep 2007. doi: 10.1901/iaba.2007.1-645. Sensors & Algorithms for Continuous Glucose Monitoring - Glucose Prediction Algorithms from Continuous Monitoring Data:

Assessment of Accuracy via Continuous Glucose Error-Grid Analysis, Francesca Zanderigo, Ph.D., Giovanni Sparacino, Ph.D., Boris Kovatchev, Ph.D.,² and Claudio Cobelli, Ph.D.

Also provided is a computer program product, comprising a computer usable medium or computer readable storage medium, typically tangible, having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement any or all of the methods shown and described herein. It is appreciated that any or all of the computational steps shown and described herein may be computer-implemented. The operations in accordance with the teachings herein may be performed by a computer specially constructed for the desired purposes or by a general purpose computer specially configured for the desired purpose by a computer program stored in a computer readable storage medium.

Any suitable processor, display and input means may be used to process, display e.g. on a computer screen or other computer output device, store, and accept information such as information used by or generated by any of the methods and apparatus shown and described herein; the above processor, display and input means including computer programs, in accordance with some or all of the embodiments of the present invention. Any or all functionalities of the invention shown and described herein may be performed by a conventional personal computer processor, workstation or other programmable device or computer or electronic computing device, either general-purpose or specifically constructed, used for processing; a computer display screen and/or printer and/or speaker for displaying; machine-readable memory such as optical disks, CDROMs, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs, EEPROMs, magnetic or optical or other cards, for storing, and keyboard or mouse for accepting. The term "process" as used above is intended to include any type of computation or manipulation or transformation of data represented as physical, e.g. electronic, phenomena which may occur or reside e.g. within registers and /or memories of a computer. Also provided, excluding signals, is a computer program comprising computer program code means for performing any of the methods shown and described herein when said program is run on at least one computer; and a computer program product, comprising a typically non- transitory computer-usable or -readable medium e.g. non-transitory computer -usable or - readable storage medium, typically tangible, having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement any or all of the methods shown and described herein. The operations in accordance with the teachings herein may be performed by at least one computer specially constructed for the desired purposes or general purpose computer specially configured for the desired purpose by at least one computer program stored in a typically non-transitory computer readable storage medium. The term "non-transitory" is used herein to exclude transitory, propagating signals or waves, but to otherwise include any volatile or non-volatile computer memory technology suitable to the application.

Any suitable processor/s, display and input means may be used to process, display e.g. on a computer screen or other computer output device, store, and accept information such as information used by or generated by any of the methods and apparatus shown and described herein; the above processor/s, display and input means including computer programs, in accordance with some or all of the embodiments of the present invention. Any or all functionalities of the invention shown and described herein, such as but not limited to steps of flowcharts, may be performed by at least one conventional personal computer processor, workstation or other programmable device or computer or electronic computing device or processor, either general-purpose or specifically constructed, used for processing; a computer display screen and/or printer and/or speaker for displaying; machine-readable memory such as optical disks, CDROMs, DVDs, BluRays, magnetic-optical discs or other discs; RAMs, ROMs, EPROMs, EEPROMs, magnetic or optical or other cards, for storing, and keyboard or mouse for accepting. The term "process" as used above is intended to include any type of computation or manipulation or transformation of data represented as physical, e.g. electronic, phenomena which may occur or reside e.g. within registers and /or memories of at least one computer or processor. The term processor includes a single processing unit or a plurality of distributed or remote such units.

The above devices may communicate via any conventional wired or wireless digital communication means, e.g. via a wired or cellular telephone network or a computer network such as the Internet. The apparatus of the present invention may include, according to certain embodiments of the invention, machine readable memory containing or otherwise storing a program of instructions which, when executed by the machine, implements some or all of the apparatus, methods, features and functionalities of the invention shown and described herein. Alternatively or in addition, the apparatus of the present invention may include, according to certain embodiments of the invention, a program as above which may be written in any conventional programming language, and optionally a machine for executing the program such as but not limited to a general purpose computer which may optionally be configured or activated in accordance with the teachings of the present invention. Any of the teachings incorporated herein may wherever suitable operate on signals representative of physical objects or substances.

BRIEF DESCRIPTION OF THE DRAWINGS

In the accompanying drawings, the following embodiments are illustrated:

Fig. 1 is a schematic diagram of a treatment system utilizing an artificial pancreas system; Fig. 2 is a schematic diagram of a treatment system utilizing an artificial pancreas system together with a bolus calculator;

Fig. 3 is a simplified flow diagram of a method for monitoring a diabetes treatment of a patient.

Flowchart steps typically comprises some or all of the steps illustrated, suitably ordered e.g. as shown.

DETAILED DESCRIPTION OF EMBODIMENTS

Fig. 1 is a simplified block diagram of an artificial pancreas system 10 for carrying out diabetes treatment including controllable delivery of insulin and glucagon). The artificial pancreas system 10 may be associated with a glucose measurement device 12 (e.g. continuous glucose sensor), and a drug delivery device 14 (e.g. insulin/glucagon delivery pumps). Measured blood glucose (BG) level from measurement device 12, whether directly measured or predicted from measured tissue glucose level, enters , e.g. through a communication dongle 16, a data processing and analyzing utility 20 of the artificial pancreas. Measured data may also include activity diary, such as meals, physical activity, sleep time etc. A treatment recommendation may be sent, e.g. through a communication dongle 16, to a delivery device 14. The measured data, decision of the artificial pancreas and other parameters if any, may be sent to a cloud by a remote monitoring module 18, to allow remote monitoring capabilities. Fig. 2 is a simplified block diagram illustration of an artificial pancreas system 10 for carrying out diabetes treatment e.g. controllable delivery of insulin and glucagon, utilizing a monitoring system 20 and a bolus calculator 30 as per embodiments of the present invention. The monitoring system 20 may be associated with a glucose measurement device 12 e.g. continuous glucose sensor and a drug delivery device 14 e.g. insulin/glucagon delivery pumps. Measured blood glucose (BG) level, BG_aorent , from measurement device 12 may be either directly measured or predicted from measured tissue glucose level and enters a data processing and analyzing utility 23 of the monitoring system (aka the control algorithm) and also, typically, a memory utility 22 for storage and/or update of reference data, e.g. patient's profile related data and possibly also Zog-utility relating to treatment history data. Additional measured data such as but not limited to: time of day, meals description e.g. amount of carbohydrates (CHO), percentage of protein (P), percentage of fat (F), human selection of a specific meal description from a list, physical activity (PA) whether accomplished or planned, sleep time, and presence of illness may be entered into the memory utility 22 by the user using a suitable user interface 24. If the user enters input data related to meals, the bolus calculator 30 may, e.g. as shown herein, recommend a required amount of insulin in order to compensate for a consumed meal. The bolus calculator, which may comprise a processor utility, may operate data available in the memory utility 22 and may analyze the glucose history measurement to produce several parameters such as glucose rate of change. The bolus calculator 30 may also utilize a prediction model such as but not limited to an auto-regressive model for prediction of glucose levels for predicting the glucose level based on the measured history of glucose level. The bolus calculator 30 may also use the history of insulin delivery to compute the amount of insulin on board I_active .

The bolus calculator 30 output may be produced after considering all possible inputs to determine the appropriate insulin dosing for the meal based e.g. on the caring physician's approach to decision making vis a vis a specific patient under treatment and may control further treatment accordingly. The meal bolus recommendation may for example be a combination of:

Bmeal ~ [¾G .Dynamics + ^BCHO,P + basal] \ ^.immediate)

Where:

• BCHO,P ~ ^me amount of insulin to cover consumed carbohydrates and other meal

ingredients such as protein and fat. B_CHOi is may be computed as a function of one or more of: the amount of carbohydrates that the patient consumed (CHO), the patient specific carbohydrate ratio (CR), the amount of the percentage of protein (P),

percentage of fat (F) and the patient specific sensitivity for these ingredients (a), e.g.:

BCHO,P = f(CHO, CR, P, F, a)

B_CHOip may for example be:

CHO

BCHO,P — k ^x

Whereas k is an empirically determined scaling factor to conventional insulin dosage computation to cover for CHO consumption. A; may for example be equal to 0.8 . ¾G Dynamics ⁼ the amount of insulin computed to fine tune a given bolus according to glucose dynamics and projected patient events (if entered) at the time of the meal. Glucose dynamics may be characterized by one or more of: current glucose level ( BG_MMNT), glucose rate of change computed based on the history of measured glucose

(aka "past trend") and predicted glucose level. For example, if the glucose levels are stable, B_BGJ)ynarnics may be equal to zero. If glucose levels are rapidly rising

BBG .Dynamics may be positive. B_{BG Dynamics} may be a function of the current glucose level ( BG^_M ), a multiplier corresponding to the glucose dynamics ( P_BOLUS ), the glucose target (7), the patient's correction factor (CF) and the history of insulin delivery to compute the amount of insulin on board I_ACTJVE , e.g. as follows:

BBG_Dynamics ⁼ f(S> ^bolus' ^ · ^ > ^active)

For example:

_ Pbolus * BG current ~ ^ _ »

"BG_Dynamics ~ Qp ^active

In some embodiments of the present invention:

"bolus ~ np

" ^Current

To ensure safety and dependence on the accuracy of the prediction model used, P_¾0j_us may also be limited to a certain range of acceptable ratios between BG_Predicted and BGcurrent■ A reasonable range to limit P_boi_us may be between 0.6 to 1.2. ^BBG_Dynamics ^may have positive or negative values. Thus, the value of B_{BG Dynamics} may be limited to a certain percentage of B_{CH0 P} , to avoid cases where B_{BG Dyna}mics has too much effect on the amount of insulin required for the consumed carbohydrates. A reasonable limit of B_{BG Dynamtcs} may for example be +/-20% of B_{CH0 P} .

• B_basal : is the amount of basal insulin the patient is to receive for a period of 1.5-3.5 hours ahead, which according to certain embodiments is administered as a bolus instead whereas during the above period the basal treatment for the patient may be zero, if all of the basal dosing was delivered as bolus or may be reduced, if some of the basal dosing was delivered as bolus.

As per the inputs the bolus calculator 30 may also recommend a suitable pre-programmed dosing strategy of B_meal , e.g. as immediate bolus, square bolus or dual bolus, generated using conventional guidelines for selecting dosing strategy typically as a function of ingredients of the meal.

The B_meal and the output of the control algorithm 23 may be processed by a treatment jury module 31 such as that described in co-pending US 2012/0123234 entitled "method and system for automatic monitoring of diabetes related treatments". The control algorithm recommendation may be related to the correction of the patient's current glucose level e.g. as described in co-pending US 2012/0123234 entitled "method and system for automatic monitoring of diabetes related treatments". Safety considerations may be used to provide optimal insulin dosing without risking the patient. The final decision relating data from the treatment jury module 31 may be used for updating reference data (treatment history) e.g. in a Zog-utility and may be sent to the delivery device 14 and/or presented in the user interface 24.

Certain embodiments seek to provide a bolus calculator comprising a computerized advisor whose processor is operative for providing individualized insulin dosing to cover meals, and comprises inter alia some or all of memory utility, data processing and analyzing utility, and a processor utility. The bolus calculator is typically operable as a feature within an artificial pancreas system, e.g. as described in co-pending US 2012/0123234 entitled "method and system for automatic monitoring of diabetes related treatments". Therefore the bolus calculator may be operative in conjunction with any or all of the following:

a. The monitoring system of claim 1 in co-pending US 2012/0123234 for use in monitoring diabetes treatment of a patient, the system comprising: a control unit comprising

a first processor module for processing measured data indicative of blood glucose level and generating first processed data indicative thereof,

a second processor module (which may serve as the treatment jury module 31) comprising at least one fuzzy logic module; said fuzzy logic module receives input parameters corresponding to the measured data, the first processed data and a reference data including individualized patient's profile related data, individualized patient's treatment history related data, processes the received parameters to produce at least one qualitative output parameter indicative of patient's treatment parameters; such that said second processor module determines whether any of the treatment parameters is to be modified.

b. The method of claim 25 in co-pending US 2012/0123234 for automatic monitoring of diabetes-related treatment, the method comprises:

obtaining a reference data including individualized patient's profile related data, individualized patient's treatment history related data;

analyzing measured data generated by at least one of drug delivery devices and glucose measurement devices; and

deciding about treatment modification in accordance with said reference data by controlling the operation of the drug injection devices to enable real-time automatic individualized monitoring of the treatment procedure.

c. The method of claim 43 in co-pending US 2012/0123234 for determining insulin basal plan from a series of basal treatment rates for a patient in need thereof, comprises:

obtaining a series of basal treatment rates as a function of time;

obtaining measured data of glucose level in the patient as a function of time;

determining a series of changes in glucose levels as a function of time;

determining the personal time delay of the patient estimated from the series of basal treatment rates and the series of changes in glucose levels, thereby obtaining a series of basal treatment rates and corresponding changes of glucose level in the patient; and

selecting a basal plan which incorporates the basal rates that minimizes a change in the glucose level.

d. The method of claim 44 in co-pending US 2012/0123234 for determining a insulin sensitivity for use in close-loop treatment of a patient's need thereof, comprising: obtaining a first glucose sensor reading and a second glucose sensor reading defining a time window; obtaining the difference between the first and second glucose sensor readings; adjusting the difference between the first and second glucose sensor readings by estimating glucose derived from the consumed carbohydrate within the time window; thereby obtaining an adjusted glucose amount; and determining the insulin sensitivity in accordance to the relation between the adjusted glucose amount and insulin bolus provided during the time window,

e. A monitoring system of co-pending US 2012/0123234 for use in monitoring diabetes treatment of a patient, the system comprising: a control unit comprising

a first processor module for processing measured data indicative of blood glucose level and generating first processed data indicative thereof, a second processor module comprising at least one fuzzy logic module; said fuzzy logic module receives input parameters corresponding to the measured data, the first processed data and a reference data including individualized patient's profile related data, individualized patient's treatment history related data and a structure of rules setting, applies at least one fuzzy logic model to quantitative input parameters corresponding to the measured data, the first processed data by using the structure of rules setting to produce at least one qualitative output parameter indicative of patient's treatment parameters;

a third processor module (which may serve as the treatment jury module 31) for determining a current amount of glucagon and/or insulin active section in the blood according to the patient's profile, and determining the amounts of insulin and/or glucagon to be delivered based on the at least one qualitative output parameter received from the second processor module, the patient's treatment history, the insulin/glucagon sensitivity from the patient profile and the current amount of glucagon and/or insulin active section in the blood.

The bolus calculator may be am input to a memory utility of an artificial pancreas and/or may communicate with other sensing devices. The bolus calculator may be used: for processing measured data from any known suitable measurement device for measuring blood/tissue glucose levels and/or for computing amount of insulin dosing only.

Certain embodiments seek to provide a bolus calculator which utilizes a specific bolus calculator to be used considering a closed-loop analysis of measured data. Analysis may be based on the physician approach for decision making with respect to a specific patient under treatment and controlling the further treatment accordingly.

Processing of the measured data typically utilizes inputs measured automatically together with inputs provided to the calculator by the user. Possible automatic inputs may include some or all of: the history of glucose measurements, the history of treatment for the specific patient, the time of the day (e.g. morning, lunch or evening) and the patient's profile (e.g. some or all of: sensitivity to insulin injection, the ratio that relates between amount of carbohydrates consumed to insulin and the patient basal insulin dosing).

The treatment history related data may include for example insulin basal rate given to the patient at different hours of the day and insulin bolus dosing.

The patient's profile related data may include a set of previously computed (and calibratable or updatable during the treatment) parameters about the patient's condition with respect to a treatment, such as at least one of a response time to insulin absorption, sensitivity to insulin, CF, and the CR) all typically being a function of time and patient's current condition depending on his/her activity. The system typically utilizes the patient's profile, which includes a set of calibratable/updatable parameters and typically applies a self-learning approach for updated the patient's profile based on the executed treatment e.g. using any suitable learning process such as those described in co-pending US 2012/0245106 entitled "monitoring device for management of insulin delivery". The system herein may therefore be used in conjunction with any or all of the following:

i. The monitoring system of claim 1 in co-pending US 2012/0245106 for use with diabetic treatment management, the system comprising:

a communication interface configured and operable to permit access to stored raw log data obtained over a certain time and being time spaced data points of glucose measurements, meals consumed and insulin delivery;

a control unit configured for receiving and processing said raw log data, the control unit comprising:

a sectioning module configured sectioning the raw log data within a time window; the sectioned time window having a starting point and an end point being at least one of Basal data Section (BaS); Meals data Section (MS) and Bolus data Section (BS), the BaS being selected outside an effect window of either meal or bolus, the MS being selected at a predetermined time ahead a meal data point, and the starting point of the BS being selected as one of the following: the end point of the MS or the BaS, and an insulin bolus data point which is outside the MS; the end point of the BS being selected as one of the following, the starting point of the MS or BaS and a predetermined time ahead of insulin bolus data point without any bolus insulin;

an unsupervised learning controller configured and operable to determine an informative data piece from residual log data portion of said raw log data, analyzing said informative data piece and selecting a sectioned time window for calculation of individualized patient's profile related data comprising at least one global insulin pump setting of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, wherein the BaS enables to calculate basal rate, the MS enables to calculate at least one of insulin activity curve parameters, correction factor (CF) and carbohydrate ratio (CR) and the BS enables to calculate correction factor (CF) or insulin activity curve parameters.

ii. The method in claim 22 of co-pending US 2012/0245106 for use in determination of insulin pump settings, the method comprising:

performing unsupervised learning of the insulin pump settings, said unsupervised learning comprising:

(a) obtaining raw log data input accumulated on one or more glucose monitoring units

recording glucose levels of a single treated patient along a certain time;

(b) sectioning the raw log data to predetermined data sections; wherein the data section is one of Basal Section (Bas), Bolus Section (BS), or Meal Section (MS);

(c) determining informative data piece from raw log data input being sectioned to data

sections, the informative data piece being determined from said data section; and

(d) calculating global insulin pump settings from the informative data piece, wherein said settings include at least one parameter of basal plan, Carbohydrate Ratio (CR), Correction Factor (CF) or Active Insulin Function (AIF) wherein different insulin pump settings' parameters are acquired at different selected time windows in said certain time.

iii. The method in claim 43 of co-pending US 2012/0245106 , for determining an Active Insulin Function (AIF) for use in insulin treatment of a patient, the method comprising:

(a) obtaining raw log data obtained over a certain time and being indicative of glucose

measurements of the patient, the raw log data being indicative of glucose measurements, meals events and insulin delivery of the patient; the raw log data being sectioned, containing data obtained at a time section;

(b) obtaining a set of glucose measurements and paired time stamps for the raw log data in the time section;

(c) normalizing each glucose measurement of the set thereby obtaining a series of normalized glucose measurements and paired time stamp.

(d) Processing said normalized glucose measurements and paired time stamp into a

substantially monotonic non-increasing series; thereby obtaining the Active Insulin Function (AIF). iv. The control unit of claim 44 of co-pending US 2012/0245106 for use with diabetic treatment management, the control unit comprising: a data processor utility configured and operable as an unsupervised learning controller preprogrammed for processing raw log data input obtained over a certain time and being indicative of glucose measurements, meals events and insulin delivery, said processing comprising determining an informative data piece from residual log data portion of said raw log data and selecting said informative data piece for further processing to determine at least one of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, and sectioning the raw log data within a predetermined time window; the predetermined time window being at least one of Basal data Section (BaS); Meals data Section (MS) and Bolus data Section (BS) and generating global insulin pump settings wherein different insulin pump settings' are acquired at different selected time windows in said certain time.

v. the computer program of claim 45 of co-pending US 2012/0245106, recordable on a storage medium and comprising a machine readable format, the computer program being configured and operable to, when being accesses, carry out the following: identifying raw log data input corresponding to a certain time period and comprising glucose measurements, meals events and insulin delivery; determining an informative data piece and residual log data portion of said raw log data; sectioning the raw log data within a predetermined time window; the predetermined time window being at least one of Basal data Section (BaS); Meals data Section (MS) and Bolus data Section (BS) selecting said informative data piece and calculating therefrom at least one of basal rate, correction factor (CF), carbohydrate ratio (CR) and insulin activity curve parameters, and generating output data comprising values for global insulin pump settings wherein different insulin pump settings' are acquired at different selected time windows in said certain time.

It is appreciated that software components of the present invention including programs and data may, if desired, be implemented in ROM (read only memory) form including CD- ROMs, EPROMs and EEPROMs, or may be stored in any other suitable computer-readable medium such as but not limited to disks of various kinds, cards of various kinds and RAMs. Components described herein as software may, alternatively, be implemented wholly or partly in hardware, if desired, using conventional techniques. Conversely, components described herein as hardware may, alternatively, be implemented wholly or partly in software, if desired, using conventional techniques. Included in the scope of the present invention, inter alia, are electromagnetic signals carrying computer-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; machine-readable instructions for performing any or all of the steps of any of the methods shown and described herein, in any suitable order; program storage devices readable by machine, tangibly embodying a program of instructions executable by the machine to perform any or all of the steps of any of the methods shown and described herein, in any suitable order; a computer program product comprising a computer useable medium having computer readable program code, such as executable code, having embodied therein, and/or including computer readable program code for performing, any or all of the steps of any of the methods shown and described herein, in any suitable order; any technical effects brought about by any or all of the steps of any of the methods shown and described herein, when performed in any suitable order; any suitable apparatus or device or combination of such, programmed to perform, alone or in combination, any or all of the steps of any of the methods shown and described herein, in any suitable order; electronic devices each including a processor and a cooperating input device and/or output device and operative to perform in software any steps shown and described herein; information storage devices or physical records, such as disks or hard drives, causing a computer or other device to be configured so as to carry out any or all of the steps of any of the methods shown and described herein, in any suitable order; a program pre-stored e.g. in memory or on an information network such as the Internet, before or after being downloaded, which embodies any or all of the steps of any of the methods shown and described herein, in any suitable order, and the method of uploading or downloading such, and a system including server/s and/or client/s for using such; and hardware which performs any or all of the steps of any of the methods shown and described herein, in any suitable order, either alone or in conjunction with software.

Any computations or other forms of analysis described herein may be performed by a suitable computerized method. Any step described herein may be computer-implemented.

Features of the present invention which are described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, features of the invention, including method steps, which are described for brevity in the context of a single embodiment or in a certain order may be provided separately or in any suitable subcombination or in a different order, "e.g." is used herein in the sense of a specific example which is not intended to be limiting. Devices, apparatus or systems shown coupled in any of the drawings may in fact be integrated into a single platform in certain embodiments or may be coupled via any appropriate wired or wireless coupling such as but not limited to optical fiber, Ethernet, Wireless LAN, HomePNA, power line communication, cell phone, PDA, Blackberry GPRS, Satellite including GPS, or other mobile delivery. It is appreciated that in the description and drawings shown and described herein, functionalities described or illustrated as systems and sub-units thereof can also be provided as methods and steps therewithin, and functionalities described or illustrated as methods and steps therewithin can also be provided as systems and sub-units thereof. The scale used to illustrate various elements in the drawings is merely exemplary and/or appropriate for clarity of presentation and is not intended to be limiting.

Claims

1. A method for delivering insulin, the method comprising:

Using a processor for computing a level of insulin to be delivered to an individual patient using an artificial pancreas, as a bolus before a meal, including determining a total dose which would have been administered for a predetermined window of time following the meal;

administering the bolus; and

refraining from administering at least a portion of said total dose during said predetermined window of time and instead adding said at least a portion to said level of insulin delivered as a bolus in said administering step.

2. A method according to claim 1 and also comprising:

receiving continuous glucose level data for the individual patient from a continuous glucose level sensor prior to said window of time and using a processor to generate a predicted glucose level during said window of time irrespective of the meal based on said glucose level data; and

3. A method according to claim 1 and also comprising taking at least one meal ingredient into account for said determining.

4. A method according to claim 1 wherein said refraining comprises entirely refraining from administering said total dose (zero basal rate), during said predetermined window of time and wherein said at least a portion of said total dose comprises the entirety of said total dose.

5. A method according to claim 1 wherein said bolus is administered before the meal.

6. A method according to claim 1 wherein said window of time comprises a period of time whose length is within a range of 1.25 - 3.5 hours.

7. A method according to claim 1 wherein said window of time comprises a period of time whose length is within a range of 1.5 - 3 hours.

8. A method according to claim 1 wherein said window of time comprises a period of time whose length is within a range of 1.75 - 2.5 hours.

9. A method according to claim 1 wherein said window of time comprises a period of time whose length is 2 hours.

10. A method according to claim 1 wherein the total dose which would have been administered for a predetermined window of time following the meal is determined based on a basal plan currently followed by said individual patient's artificial pancreas.

11. A method according to claim 2 wherein said predicted glucose level is generated using linear interpolation.

12. A method according to claim 2 wherein said predicted glucose level is generated using an autoregression model.

13. A method according to claim 2 wherein said insulin level suitable for said predicted glucose level is negative and said combining comprises subtracting said insulin level from said at least a portion of said total dose.

14. A computer program product, comprising a non-transitory tangible computer readable medium having computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for delivering insulin, the method comprising:

administering the bolus; and

refraining from administering at least a portion of said total dose during said predetermined window of time and instead adding said at least a portion to said insulin level.

15. A system for delivering insulin, the system comprising:

a processor for computing a level of insulin to be delivered to an individual patient using an artificial pancreas, as a bolus before a meal, including determining a total dose which would have been administered for a predetermined window of time following the meal,

thereby to enable the bolus to be administered, and said at least a portion to be added to said level of insulin delivered as a bolus while refraining from administering at least a portion of said total dose during said predetermined window of time.