A method to avoid hypoglycemia by minimizing delayed postprandial insulin infusion in an AID system.

Abstract

Embodiments may relate to an insulin delivery controller that implements a processor configuration for efficiently achieving an insulin delivery target. The insulin delivery controller may include a processor and memory associated with the processor. The processor can process glucose data received from memory, including a data representation of a glucose disturbance (d(t)). The processor can determine the glucose change rate (G'(t)). The processor can generate command signals to dynamically reshape the glucose disturbance within the insulin delivery controller's predictive range according to G'(t). The processor can generate insulin command signals to the insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate.

Description

[Technical Field]

[0001] Cross-references to related applications This patent application relates to and claims priority to U.S. Provisional Patent Application No. 63 / 583,371, filed on 18 September 2023, and the entire content of this application is incorporated herein by reference.

[0002] The embodiment may relate to an insulin delivery controller that implements a processor configuration for efficiently achieving insulin delivery targets. [Background technology]

[0003] The performance of model-based automated insulin delivery (AID) systems can depend on how glycemic disturbances are modeled and expected over time. However, due to the high level of uncertainty surrounding glucose dynamics, there is still no clear solution to this problem. [Overview of the project]

[0004] Exemplary embodiments may relate to an insulin delivery controller that implements a processor configuration for efficiently achieving an insulin delivery target. The insulin delivery controller may include a processor and a memory associated with the processor. The memory may include instructions stored therein, which, when executed by the processor, cause the processor to perform one or more functions described herein. Instructions may cause the processor to process glucose data received from the memory, including a data representation of a glucose disturbance (d(t)). Instructions may cause the processor to determine a glucose change rate (G'(t)). Instructions may cause the processor to generate a command signal to dynamically reshape the glucose disturbance within the insulin delivery controller's predictive range according to G'(t). Instructions may cause the processor to generate an insulin command signal to an insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate.

[0005] An exemplary embodiment relates to a computer-readable medium containing instructions stored therein, which, when executed by a processor, causes the processor to efficiently achieve an insulin delivery target. The instructions can cause the processor to efficiently achieve an insulin delivery target by processing glucose data, including a data representation of a glucose disturbance (d(t)). The instructions can cause the processor to efficiently achieve an insulin delivery target by determining a glucose change rate (G'(t)). The instructions can cause the processor to efficiently achieve an insulin delivery target by generating command signals to dynamically reshape the glucose disturbance within the predictive range of the insulin delivery controller according to G'(t). The instructions can cause the processor to efficiently achieve an insulin delivery target by generating insulin command signals to an insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate.

[0006] An exemplary embodiment may be a method for managing a processor configuration to efficiently achieve an insulin delivery target. The method may involve processing glucose data, including a data representation of a glucose disturbance (d(t)). The method may involve determining a glucose change rate (G'(t)). The method may involve generating a command signal to dynamically reshape the glucose disturbance within the predictive range of an insulin delivery controller according to G'(t). The method may involve generating an insulin command signal to an insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate.

[0007] Other features and advantages of this disclosure will become clearer upon reading the following detailed description in conjunction with the attached drawings, and similar elements are indicated by similar numbers. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an exemplary insulin delivery controller architecture. [Figure 2] This is a diagram illustrating an exemplary insulin control system architecture. [Figure 3] This is an example of d-shaping aligned with a predicted range based on the glucose change rate. [Figure 4] This figure shows the timeline for the BPS comparison session and assignment days. [Figure 5] This figure shows the changes in glucose levels over 3 hours after a meal (top) and the percentage of insulin injected (bottom) for AID systems with and without a working BPS. [Figure 6] This figure shows the percentage of insulin injected over the first three hours after a meal for AID systems with and without a working BPS. [Figure 7A]This figure shows the closed-loop response for 100 in-silico individuals with and without disturbance shaping features. The solid line represents the median, the darkly shaded area represents the 25th–75th percentile, and the lightly shaded area represents the 5th–95th percentile. [Figure 7B] This figure shows the distribution of 15g hypoglycemic rescue carbohydrates used in-silico, with and without disturbance shaping. [Figure 8] This is an exemplary high-level functional block diagram of an embodiment of an insulin delivery controller. [Figure 9] This diagram shows an exemplary computing device configuration. [Figure 10] This is a block diagram illustrating an exemplary system, including a computer system and associated internet connectivity, in which an embodiment of an insulin delivery controller may be implemented. [Figure 11] This figure illustrates a system in which one or more embodiments of an insulin delivery controller may be implemented using a network, or part of a network, or a computer. [Figure 12] This is a block diagram illustrating an example of a machine in which one or more embodiments of an insulin delivery controller can be implemented. [Modes for carrying out the invention]

[0009] Embodiments disclosed herein improve the operation of one or more processors 102. This can be achieved by implementing a model or algorithm that improves the efficiency of the operation of the processors 102 by requiring fewer iterations and fewer computational resources. For example, a model or algorithm may enable the processor 102 to stabilize its operation more quickly to achieve insulin delivery targets more efficiently. Known systems and methods include overshooting or undershooting the target and then (in some cases) overshooting or undershooting again through overcorrection, etc. Through implementation of embodiments of the models or algorithms disclosed herein, the processor 102 can achieve delivery targets more quickly and efficiently, thereby providing higher accuracy (e.g., the selected dose is closer to the target) and requiring fewer computational resources (e.g., requiring fewer iterations, requiring a smaller processor size and components, etc.), etc.

[0010] Referring to Figures 1 and 2, the embodiment may relate to an insulin delivery controller 100 that can implement a processor 102 configuration for efficiently achieving an insulin delivery target. The insulin delivery controller 100 may include a processor 102. The insulin delivery controller 100 may include a memory 104. The memory 104 may be associated with the processor 102.

[0011] The processor 102 can be any of the processors 102 disclosed in the present specification. The processor 102 may be part of a machine (logic, one or more components, a circuit (e.g., a module), or a mechanism), or may be capable of communicating with the machine. The processor 102 may be hardware (e.g., a processor, an integrated circuit, a central processing unit, a microprocessor, a core processor, a computer device, etc.), firmware, software, etc., configured to perform operations by executing instructions embodied in algorithms, data processing program logic, artificial intelligence programming, automated reasoning programming, and the like. It should be noted that the use of the processor 102 in the present specification may include any one or a combination of a graphics processing unit (GPU), a field programmable gate array (FPGA), a central processing unit (CPU), and the like. The processor 102 may include one or more processing modules. The processing modules may be operational modules of software or firmware configured to perform any of the method steps disclosed in the present specification. The processing modules may be embodied as software and stored in a memory 104, which is operatively associated with the processor 102. The processing modules may be embodied as web applications, desktop applications, console applications, and the like. Exemplary embodiments of the processor 102 and the machine will be discussed later.

[0012] The processor 102 may include or be associated with a computer or machine-readable medium. As will be discussed in greater detail later, the computer or machine-readable medium may include a memory 104. The computer or machine-readable medium may be configured to store one or more instructions 106 thereon. The instructions 106 may be in the form of an algorithm, program logic, or the like that causes the processor 102 to construct and implement an embodiment of a model.

[0013] Any of the memories 104 discussed herein may be computer-readable memories configured to store data. Memory 104 may include volatile or non-volatile, temporary or non-temporary memory, and may be embodied as in-memory, active memory, cloud memory, etc. Embodiments of memory 104 may include processor modules and other circuits to enable data to and from memory 104, and data transfer to and from memory 104 may include data transfer to and from other components of the communication system. This transfer may be via wired or wireless transmission. The communication system may include transceivers that can be used in combination with switches, receivers, transmitters, routers, gateways, waveguides, etc., to facilitate communication via communication methods or protocols for controlled and regulated signal transmission and processing to any other component or combination of components of the communication system. Transmission may be via a communication link. The communication link may be electron-based, optical-based, optoelectronic-based, quantum-based, etc.

[0014] The processor 102 may communicate with other processors of other devices 108 (e.g., blood glucose state monitoring devices, glucose management systems, insulin recommendation systems, insulin delivery devices, non-portable computer devices, portable computer devices, handheld computer devices, wearable computer devices, smartphones, smart watches, etc.). Any of these other devices 108 may include any of the exemplary processors disclosed herein. Any of the processors may have a transceiver or other communication device / circuit for facilitating the transmission and reception of wireless signals. Any of the processors may include an application programming interface (API) as a software intermediary that allows two applications to interact. The use of the API may allow the software of the processor 102 of the system 100 to communicate with the software of the processor of another device 108 when the processor 102 of the system 100 is not the same processor as the processor of the device 108.

[0015] Any data transfer, such as between the processor 102 and the memory 104, between the processor 102 and a database, and between the processor 102 and the processor of another device 108, may be via a pull operation (e.g., the processor 102 may pull data) or a push operation (e.g., data may be pushed to the processor 102). The processor 102 may receive data in a streaming format, or may store it in the memory 104 before it is processed. Also, the embodiments of the models disclosed herein may be deployed as application software ("app") implemented on the processor of a device 108 (e.g., a wearable device). The app may be sent in a streaming format, or may be sent to and stored in a memory associated with the device 108 or accessible by the device 108.

[0016] As described herein, the processor 102 may be configured to be a component of another device / system 100, to be used in combination with another device / system 100, or to communicate with another device / system 100, for example, this includes the processor 102 being part of a device / system 100, the device / system 100 being part of the processor 102, or the processor 102 communicating with a device / system 100. "Being part of" may include being on the same board or integrated circuit. For example, the processor 102 may be a component of a predictive modeling system (e.g., a system for predicting the risk of hypoglycemia or hyperglycemia), a decision support system (e.g., a system for assisting medical triage), or an automated control system (e.g., an artificial pancreas), and may be used in combination with or to communicate with these. The processor 102 may use models or algorithms, or may provide models or algorithms to these devices / systems to support or enhance the performance of these devices / systems. The output of the model or algorithm may be used by a device / system to help or enhance predicting or responding to aspects of blood glucose status, or to help or enhance determining or modifying insulin therapy.

[0017] As described herein, the processor 102 may be configured to be a component of, or used in combination with, or communicate with, a predictive modeling system, a decision support system, an automatic control system, a non-portable computer device, a portable computer device, a handheld computer device, a wearable computer device, an insulin monitoring device, or an insulin delivery device. For example, the processor 102 may be configured to be a component of, or used in combination with, an insulin delivery device 108, or communicate with, and instruction 106 may cause the processor 102 to generate a command signal configured to cause the insulin delivery device 108 to adjust the insulin delivery dose and / or insulin delivery rate.

[0018] As can be perceived from this disclosure, memory 104 may include instructions 106 stored therein, which, when executed by processor 102, cause processor 102 to perform one or more functions described herein. For example, instruction 106 may cause processor 102 to process glucose data received from memory 104. Glucose data may be the result of measurements such as blood glucose, interstitial fluid glucose, etc. Glucose data may be measured by glucose sensors, glucose meters, glucose monitors, etc. Glucose data may be transmitted to memory 104 in real time, periodically, on demand, continuously, in batches, sent directly to memory 104, sent to a different data store (e.g., a database) before being sent to memory 104, etc. Glucose data may include a data representation of glycemic disturbances (d(t)). A disturbance is something (e.g., an event such as a meal, exercise, or stress) that causes a subject's glycemic state to deviate from the target glycemic state. This may include deviations from the target glycemic state in a closed-loop implementation. Therefore, disturbances can be represented in glucose data as blood glucose measurement results (e.g., glucose data) that are deviations from predicted, expected, or desirable blood glucose measurement results. Disturbances can be represented in glucose data as multiple blood glucose measurement results that are trending from a predicted, expected, or desirable set of blood glucose measurement results. Disturbances can be represented in glucose data as predicted outputs of blood glucose measurement results that deviate from, are trending to, are expected to deviate from, or are expected to trend from, or are expected to trend from, or are expected to deviate a threshold.For example, a disturbance could be glucose measurement data as a function of time (e.g., a time series of multiple glucose measurement results) containing one or more deviation values ​​or standard deviations.

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[0020] Instruction 106 instructs processor 102 to determine the glucose change rate (G'(t)). This is the current

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[0024] As a non-restrictive example, (G'(t)) could be as follows: G'(t)>

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[0033] Instruction 106 causes processor 102 to generate a command signal to dynamically reshape blood glucose disturbances within the predictive range of the insulin delivery controller. This command signal can be based on G'(t), can be a function of G'(t), can follow G'(t), and so on. For example, G'(t) ≤

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[0039] The instruction can cause the processor 102 to generate an insulin command signal for the insulin delivery unit 108. This command signal may indicate, suggest, or recommend adjustment of the insulin delivery dose and / or insulin delivery rate of the insulin delivery unit 108. This adjustment may be based on adjustment, as a function of adjustment, or subject to adjustment, etc. It should be noted that the insulin command signal may be based on adjustment, and the adjustment may be non-adjusted, e.g., no adjustment. For example, a disturbance prediction may be "no disturbance," or there may be a disturbance, but it may be at an acceptable level that does not require adjustment. In this scenario, the insulin command signal may require, suggest, or recommend no adjustment.

[0040] Instruction 106 can cause processor 102 to generate an insulin command signal to insulin delivery unit 108, which adjusts the insulin delivery dose and / or insulin delivery rate to coordinate insulin delivery. Insulin delivery may be adjusted to minimize or prevent hypoglycemia, or to mitigate glycemic disturbances that tend towards hyperglycemia. This is in contrast to keeping disturbances constant along a predictable range. Keeping disturbances constant can lead to control actions that are too conservative at the beginning of the post-meal interval and too aggressive at the end of the post-meal interval where glucose may decrease. In a system that keeps disturbances constant, processor 102 does not efficiently or accurately achieve delivery targets. Instead, processor 102 has to administer inaccurate insulin doses, perform additional data processing, readjust the insulin dose for overly conservative or overly aggressive control actions, and administer another insulin dose to compensate, resulting in system inaccuracy, reduced system precision, unnecessary costs of computational resources, increased processing power, increased memory, etc. Furthermore, existing systems that maintain a constant level of disturbance may require additional safety modules to compensate for a lack of accuracy and precision.

[0041] Figure 2 shows an exemplary insulin control system architecture that may be implemented as closed-loop control ("CLC") in an insulin management system 200. The insulin management system 200 may be, or may include, an insulin monitoring unit and an insulin delivery unit 202, a blood glucose monitoring unit 204, etc. The CLC system includes one or more control algorithms for glucose adjustment, which may be model predictive control (MPC), proportional-integral-derivative (PID), and fuzzy logic (FL). The MPC algorithm can predict future glucose levels / concentrations to bring the current glucose levels / concentrations within a target range. The PID algorithm can analyze the deviation of measured glucose levels / concentrations from a target range to calculate the amount of insulin to deliver. The fuzzy algorithm can mimic insulin dosage calculations performed by clinical professionals based on monitoring data. One of the goals of such algorithms may be to maintain glucose levels, concentrations within a safe range (e.g., not reaching hypoglycemia or hyperglycemia, not causing hypoglycemia or hyperglycemia, or not having a tendency to cause them). The CLC system may integrate one or more algorithms for adjusting the control intensity of insulin infusion, for example. In addition to the disturbance techniques disclosed herein, CLC may rely on past control actions, glucose measurements, and any derivatives thereof.

[0042] Additionally or alternatively, embodiments may be used to optimize the CLC of an insulin management system. This includes optimization to automate or assist in the automation of insulin delivery. For example, an insulin command signal may be able to adjust the insulin delivery dose and / or insulin delivery rate to coordinate insulin delivery so that browsing is automated as needed. This may include, as needed, automatically adjusting insulin delivery to eliminate, attenuate, suppress, increase, or mitigate glycemic disturbances. This may be done, for example, to mitigate the effects of glycemic disturbances that tend to lead to hyperglycemia while preventing the possibility of hypoglycemia.

[0043] As a non-limiting example, the insulin management system 200 may include a processor configured to perform the MPC. The system may include or communicate with an embodiment of the insulin delivery controller 100. The insulin management system may be performed, for example, in a Diabetes Assistant (DiA) format provided by a smartphone. The glucose meter 204 may be, for example, a continuous glucose monitor (CGM). The DiA may define a general control paradigm that predicts future blood glucose levels and calculates the optimal insulin dose to maintain an individual's target glucose level / concentration. For example, the DiA may be configured to predict blood glucose levels and calculate the insulin dose based on any variation of the models disclosed herein. Based on any detected disturbance, the MPC (having its incorporated insulin delivery controller 100) may, for example, instruct the insulin pump 202 to deliver a basal insulin dose (e.g., a microbolus). The dose may be adjusted by adjusting the aggression and amount of such administration as a function of the rate of glucose change. Thus, the MPC may be optimized for a predetermined fixed target glucose level such that hypoglycemic and hyperglycemic events may be substantially avoided. More specifically, MPC may require a deviation from a fixed baseline rate to obtain the optimal dosage.

[0044] Embodiments of the insulin management system 200 may include additional operational modules to assist insulin control. These may include one or more of the following: a Kalman filter module 206 (storing and / or implementing one or more algorithms or models for optimally estimating one or more target variables); a safety module 208 (storing and / or implementing one or more redundant algorithms or model controls for avoiding insulin infusions that would create hypoglycemic and / or hyperglycemic states); a bolus pump system module 210 (storing and / or implementing one or more algorithms or models for generating basal infusions based on basal rates); an MPC module 212 (storing and / or implementing one or more MPC control algorithms or models, including one or more disturbance-forming algorithms or models disclosed herein); and a hyperglycemia mitigation system module 214 (storing and / or implementing algorithms or model controls for taking corrective action when a hyperglycemic state is detected). In an exemplary insulin management system 200, the glucose monitor 204 can supply glucose data to the Kalman filter module 206 in parallel with supplying glucose data to the bolus pump system module 210. Glucose data may be processed by the Kalman filter module 206 and transmitted to the MPC module 212. The output from the MPC module 212 may be coupled with the output from the bolus pump system module 210 via a first pathway. The output from the MPC module 212 may be transmitted to the hyperglycemia relief system module 214 via a second pathway. The output from the first pathway and the output from the hyperglycemia relief system module 214 may be coupled before being transmitted to the safety module 210. The safety module 210 may be fed back to the system 200 via the Kalman filter module 206 and can generate delivery commands that can be supplied in parallel to the insulin delivery device 202.

[0045] As can be recognized, the predicted rate of glucose change (G'(t)) is within the range of the predicted disturbance.

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[0056] As described herein, instruction 106 can cause processor 102 to generate command signals for processing glucose data, determining (G'(t)), and dynamically reshaping blood glucose disturbances via a closed-loop control (CLC) process. For example, processor 102 can implement model predictive control (MPC) algorithms, proportional-integral-derivative (PID) algorithms, fuzzy logic (FL) algorithms, etc., as part of the CLC process.

[0057] In some embodiments, instruction 106 causes processor 102 to generate an insulin command signal to insulin delivery unit 108, which then adjusts the insulin delivery dose and / or insulin delivery rate to coordinate insulin delivery. Insulin delivery may be adjusted to approach a target glucose level.

[0058] Some embodiments may include an insulin delivery controller 100 in combination with an insulin delivery unit 108. The insulin delivery unit 108 may include a processor and a memory having instructions stored therein that, when executed by the insulin delivery processor, cause the insulin delivery processor to generate an insulin delivery dose based on the insulin delivery dose and / or insulin delivery rate.

[0059] An exemplary embodiment may relate to a computer-readable medium containing instructions 106 stored therein, which, when executed by the processor 102, can cause the processor 102 to efficiently achieve an insulin delivery target. Instructions 106 can cause the processor 102 to efficiently achieve an insulin delivery target by processing glucose data, including a data representation of a glucose disturbance (d(t)). Instructions 106 can cause the processor 102 to efficiently achieve an insulin delivery target by determining a glucose change rate (G'(t)). Instructions 106 can cause the processor 102 to efficiently achieve an insulin delivery target by generating a command signal to dynamically reshape the glucose disturbance within the predicted range of the insulin delivery controller 100 according to G'(t). Instructions 106 can cause the processor 102 to efficiently achieve an insulin delivery target by generating an insulin command signal to the insulin delivery unit 108 to adjust the insulin delivery dose and / or insulin delivery rate.

[0060] An exemplary embodiment may be a method for managing a processor configuration to efficiently achieve an insulin delivery target. The method may involve processing glucose data, including a data representation of a glucose disturbance (d(t)). The method may involve determining a glucose change rate (G'(t)). The method may involve generating a command signal to dynamically reshape the glucose disturbance within the predictive range of the insulin delivery controller 100 according to G'(t). The method may involve generating an insulin command signal to the insulin delivery unit 108 to adjust the insulin delivery dose and / or insulin delivery rate. [Examples]

[0061] The following are exemplary systems, methods, and implementations of embodiments disclosed herein. While these examples may focus on one implementation, it should be understood that these are illustrative and the embodiments disclosed herein are not limited thereto.

[0062] (Example 1) As described herein, the performance of a model-based automated insulin delivery (AID) system depends heavily on how glycemic disturbances are modeled and expected over time. It should be noted that how disturbances are expected over time directly affects glucose prediction and how aggressively the controller controls insulin infusion. This is particularly critical during the postprandial period, because overestimating disturbances can lead to delayed hypoglycemia. For example, maintaining disturbances constant along the predicted range can lead to overly conservative control actions at the beginning of the postprandial period and overly aggressive control actions at the end of the postprandial period, when glucose may be decreasing. In this regard, embodiments disclosed herein can dynamically reshape glycemic disturbances within the controller's predicted range according to the rate of glucose change.

[0063] For example, consider a glucose disturbance, denoted as (d(t)), estimated every 5 minutes using a Kalman filter. And the current estimate of the disturbance...

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[0073] G'(t)≦

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[0075] G'(t) Please note that it will be adjusted as a function of .

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[0080] Case study In the context of our MPC-based AID system, consider the following mathematical model:

[0081] [Equation] Here, IR a =k1l sc1 +k2l sc2 ΔX=X cl -X ol

[0082] X cl and X ol respectively represent the filtered (after Kalman filter) insulin concentration and the unfiltered (open-loop) insulin concentration in the remote compartment. The model parameters and variables are reported in Table 1 and Table 2.

[0083] To maintain a manageable computational load, the controller is designed in a linear framework, which means linearization is performed around a subject-specific operating point, for example, we set G p =120mg / dL, Jctrl=u b , and u b =TDI / 48 is the basal insulin, TDI is calculated using the patient-specific insulin records, then the steady-state solution of the model Error! Reference source not found is sought. Afterwards, discretization is performed over the linearized system using a 5-minute sampling time and a zero-order hold for the inputs.

[0084] As described above, since the uncertainty is high before and after d(t), it is modeled as an integrator, and its value is estimated every 5 minutes using a Kalman filter. Thereafter, an example of d-shaping throughout the entire prediction horizon is presented in Figure 3,

[0085] [Equation] = 0.05 mg / dL / min, at time t

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[0088] As shown in Figure 3, d(t) tracks the change in G'(t) to inform the controller whether a positive perturbation is expected to affect glucose levels within the predicted range. In this way, the controller's aggressiveness can be better adjusted, and insulin infusion can be aggressively reduced when glucose begins to decrease.

[0089] [Table 1]

[0090] [Table 2]

[0091] (Example 2) Automated insulin delivery devices (AIDs) are widely available to people with type 1 diabetes (T1D) and offer superior glycemic control compared to conventional methods. Next-generation AID devices focus particularly on minimizing user / device interaction before and after meals ("fully closed-loop," FCL). Our goal was to evaluate the impact of bolus priming systems (BPS), algorithms that deliver a fixed insulin dose based on the likelihood of an accumulated meal, on postprandial blood glucose in combination with UVA's latest AIDs.

[0092] Eleven adults with T1D participated in a supervised, randomized, crossover trial evaluating two 24-hour sessions with identical meals and activities, with and without BPS. On the days between study sessions, participants underwent food and activity challenges to test the safety and robustness of BPS. Continuous glucose monitoring (CGM) outcomes and total insulin doses were assessed overall and post-meal with regard to the likelihood of BPS requiring additional insulin (CGM >90 mg / dL 1 hour prior).

[0093] Daytime CGM outcomes were similar with and without BPS, with time-in-range (TIR) ​​of 70–180 mg / dL being 70.6% [62.2–76.5] vs. 65.7% [58.6–80.6%], and time-below-range (TBR) of less than 70 mg / dL being 0% [0–2.1] vs. 0% [0–1.3]. Insulin delivery during the 3 hours postprandial was nearly identical, at 33.5 units [26.4–47.0] vs. 35.7 units [28.7–44.9]. Among 43 of the 66 meals potentially inducing BPS (24 with BPS, 19 without BPS), the area under the curve (iAUC) for postprandial blood glucose elevation was lower in the group with BPS compared to the group without BPS (2530±1934 vs. 3228±2029, p<0.047). However, the CGM outcome was not conclusive, with 4-hour TIR being 51.2% [19.8~83.3] vs. 40.2% [20.8~56.3] (p=0.24). There were no serious adverse events.

[0094] When active (e.g., not experiencing hypoglucose), BPS improved postprandial control of FCL through earlier insulin infusion.

[0095] Prologue Blood glucose control in type 1 diabetes (T1D) has been significantly improved with the use of automated insulin delivery (AID) systems, in which blood glucose data from a continuous glucose monitor (CGM) is fed into a computer algorithm to guide insulin infusion. AID systems improve blood glucose time-in-range by 70-180 mg / dL by 9-15% compared to previous care. Currently available AID systems are designed primarily for use in hybrid closed-loop control (HCL), which still requires users to input data on carbohydrate intake, for example. However, individuals with T1D generally often skip boluses between food intakes, and research has focused on the feasibility of a modality of use that minimizes (or completely eliminates) the need for meal announcements, also known as full closed-loop or FCL.

[0096] In most cases, carbohydrates are absorbed faster than insulin, so maintaining sufficient insulin delivery before and after meals is the most challenging aspect of maintaining glycemic control for AID systems used as FCLs. We previously reported on a model predictive controller (MPC) AID system that included a feature called a "bolus priming system" (BPS), which instructs the controller to deliver a rapid insulin dose after detecting a glucose level disturbance that the system interprets as likely representing recent meal intake. When used as an FCL, this system provided superior TIR compared to commercially available systems. However, we have not previously determined whether the BPS feature itself provided improved glycemic control in the absence of meal announcements compared to an MPC system with the BPS module silent. Also, because these BPS doses were triggered by glucose disturbances not necessarily related to food intake, we recognized that such systems may be at risk if a rapid rise in glucose is triggered, for example, by intense anaerobic activity such as high-intensity interval training (HIIT) or by the intake of a small amount of rapidly absorbed glucose. We also had concerns about the safety of such systems during delayed gastric emptying, such as after consuming high-carbohydrate, high-fat meals like pizza.

[0097] Our objectives for this project are (i) to compare this system as a fasting blocker (FCL) with and without BPS, both during the daytime and after meals, and (ii) to evaluate the safety of such a system in a series of glycemic challenges. We hypothesized that BPS would result in improved glycemic control compared to BPS without. These data are important for the incorporation of bolus priming in the development of AID systems used as FCLs.

[0098] Study design and methods The University of Virginia Institutional Review Board (IRB-HSR#22026) and the U.S. Food and Drug Administration (IDE#G220204) approved this randomized controlled clinical trial (ClinicalTrials.gov NCT05528770). Each participant submitted written informed consent. Inclusion criteria included age 18–65 years, a diagnosis of T1D, and insulin pump therapy for at least 3 months. Exclusion criteria included diabetic ketoacidosis (DKA) or severe hypoglycemic events (defined as seizures or loss of consciousness) in the past 6 months, use of oral glucose-lowering agents including metformin, pregnancy, and any medical condition deemed high-risk by the principal investigator.

[0099] Enrollment and screening visits were conducted via telephone or secure internet video connection, during which medical history and insulin use parameters were obtained, and health checkup materials from the past year were reviewed. Female participants of potential pregnancy were provided with a urine pregnancy test. Once enrolled and screened, participants were trained to use the Dexcom G6 CGM system (Dexcom, Inc., San Diego, California) and collected sensor data and insulin records for at least 14 days of home use. Once baseline data collection was complete (see below), participants were randomized into two sessions, one with and one without BPS. For participants already using the Dexcom G6, retrospective data from before enrollment could be used. Participants not using the Dexcom G6 CGM were trained before study entry and sent the device for at least 14 days of data. Baseline data was used to initialize the controller with the participant's TDI as an indicator of insulin requirement.

[0100] Participants then traveled to the University of Virginia for a sequential evaluation of closed-loop control patterns in a supervised hotel environment. The timeline of hotel entry is shown in Figure 4. The two 24-hour study sessions were designed to be as similar as possible in terms of meal and activity timing and content, with meals pre-selected by participants and repeated every 24-hour period. Breakfast was at 7:00 a.m., lunch at noon, and dinner at 7:00 p.m. At 10:30 a.m. each study period, participants took a light 25-minute walk together, covering approximately 1.5 miles. Participants were otherwise asked not to engage in strenuous exercise during the 24-hour study sessions, and if they did engage in physical activity, they were asked to repeat the same routine at the same time during the next study session.

[0101] Assignment Session Between the two 24-hour study sessions, there was an additional 24-hour period to further test the safety of the BPS system in three different task situations: (i) a supervised exercise session with HIIT activity, (ii) a high-carbohydrate, high-fat meal (pizza), and (iii) a low-carbohydrate, fast-carbohydrate breakfast. The tasks were performed with and without BPS, depending on the randomization (parallel RCT design).

[0102] The task exercise sessions were conducted to attempt to induce a rapid increase in blood glucose associated with the catecholamine response to intense anaerobic activity. The sessions were supervised by a trainer, who encouraged participants through short (maximum 1 minute) intervals of intense activity, interspersed with 2-minute rest periods. Participants were encouraged to complete up to six of these interval intervals. During each 1-minute activity, participants were asked to rate their perceived exertion (RPE) every 20 seconds using a 6-20 Borg scale, with the goal of achieving an RPE > 17 ("maximum exertion"). The types of activity were selected by the participants and included running intervals, stair climbing, burpees, and stationary cycling.

[0103] In the pizza task (which occurred 1-2 hours after the HIIT activity), participants selected the number of pizza slices without declaring the amount of carbohydrates. Each slice contained 38g of carbohydrates.

[0104] A low-carbohydrate and fast-carbohydrate breakfast consists of drinking a juice containing 15g of carbohydrates without declaring the carbohydrates in the system.

[0105] Research devices Participants were asked to swap their Dexcom G6 sensors 24–48 hours prior to the first study session. On the morning of the first 24-hour study session, participants switched their therapy to a Tandem t:AP insulin pump (connected to a t:slim pump from Tandem Diabetes Care, San Diego, California) using their home insulin parameters. Approximately one hour before the study session, both devices were connected to a DiAs system (University of Virginia, Charlottesville, Virginia) using a study-provided Android smartphone (UVa DWM system) that enabled remote monitoring. A study blood glucose meter (ContourNext Link, Ascencia Diabetes Care, Parsippany, New Jersey) and a study blood ketone meter (Precision Xtra, Abbott, Alameda, California) were provided to all participants for use as needed to ensure adherence to blood glucose guidelines. Upon entering the hotel, participants were also fitted with physical activity trackers (Fitbit® Charge 3, manufactured by Fitbit, Inc., San Francisco, California; data is not used by the AID system).

[0106] Research AID system This study compared two versions of the latest UVA AID system used in FCL, namely with and without an activated BPS module. The control algorithm was previously described. Briefly, it is an MPC system that continuously predicts future blood glucose and calculates the optimal insulin dose to maintain the user's desired glucose target, using the participant's insulin requirement, informed by the current total daily insulin (TDI) usage. Analysis of baseline blood glucose control and total daily insulin (TDI) usage required a minimum of 7 days of CGM and pump information. The system includes a module that activates the BPS, an algorithm that determines the probability of glucose deviations, such as meals occurring in the past 30 minutes, exceeding a pre-specified threshold, and delivers a fixed priming dose based on the user / physician's pre-determined daily profile and the user's total daily insulin requirement. In this study, the profile was fixed at 0% from 11 p.m. to 6 a.m. and at 6 a.m. to 11 p.m. This module can be deactivated to evaluate the AID system function in the absence of the BPS.

[0107] Remote monitoring and blood glucose treatment guidelines Members of the research team remotely monitored participants' real-time CGM, insulin, and device connectivity data via the UVA DiAs web-based monitoring system (DWM) throughout the entire hotel portion of the study, and notified the study's nurses, physicians, and technicians of any blood glucose concerns or device connectivity issues. Treatment decisions were based on CGM data, and SMBG was performed only at the recommendation of the medical team. Hypoglycemia treatment was administered at the presence of at least one CGM value below 0 mg / dL or at the request of participants experiencing hypoglycemia-related symptoms.

[0108] Outcomes and statistical analysis All blood glucose outcomes were calculated based on CGM recordings. CGM data related to recorded device issues or protocol deviations unrelated to the studied algorithm (e.g., pump occlusion, site failure, CGM disconnection, or prolonged DiAs-pump disconnection) were excluded from the analysis. The primary outcome was the percentage TIR of 70–180 mg / dL during the daytime / nighttime period (6 a.m. to 12 a.m.). In accordance with consensus guidelines, secondary glycemic outcomes included the number of hypoglycemic events (defined as CGM < 70 mg / dL for 15 minutes or longer, or the presence of hypoglycemic treatment), percentage times below 70 mg / dL, percentage times in the narrow range of 80–140, percentage times in the range of 70–180, percentage times above the range (>180, >250), area under the combined curve (iAUC) considering the baseline BG, low blood glucose index (LBGI), high blood glucose index (HBGI), CGM coefficient of variation, and mean CGM glucose value. Secondary outcomes were calculated over a 24-hour study period, not only during the day (6 a.m. to midnight) and night, but also within 4 hours postprandial.

[0109] Since the AID system protects against large insulin infusions while or after the CGM value is less than 90 mg / dL, we conducted a secondary analysis of the post-meal period excluding meals in which (i) the lowest glucose value one hour prior to the meal was less than 90 mg / dL and (ii) there was a BPS triggered within four hours prior to the meal.

[0110] Linear mixed-effects regression models were used for both primary and secondary outcomes, with pre-randomization variables (e.g., participant characteristics, study stage, etc.) and sex as covariates (for normally distributed data). For non-normally distributed samples, a non-parametric Wilcoxon signed-rank test was used. The significance level was defined as p-value < 0.05 (all p-values ​​were two-tailed tests). Data were reported as mean ± standard deviation (SD) for normally distributed data and as median [IQR] for non-normally distributed data. Statistical analyses were performed using SPSS version 28 software. Given the safety and feasibility nature of the pilot study, output analyses were not calculated for clinical trial designs.

[0111] result Between October 20, 2022 and January 15, 2023, 15 participants signed informed consent, 2 failed screening, and 2 dropped out before randomization. Therefore, 11 participants were randomized, and of the randomized participants, 11 (100%) completed the study. Demographic characteristics are shown in Table 3. The carbohydrate content of the meals on study days, selected by the participants according to their preferences, varied from an average of 74.0g (absolute range 38–152g) at dinner to 31.6g (range 15–65g) at breakfast and 43.0g (range 16–60g) at lunch. During the pizza task, the carbohydrate content was 96.7g (range 38–152g).

[0112] In the comparison between BPS-enabled and BPS-unenabled patients, daytime CGM outcomes were similar in both groups, with TIR at 67.4% [15.1%] versus 65.1% (18.45%) (p=0.43), and time-below-range (TBR) <70 mg / dL at 0% [0~2.1] versus 0% [0~1.3], which were similar over the overall and nocturnal periods (Table 4). Insulin delivery over the first three hours after a meal was nearly identical, at 33.5 U [26.4~47.0] versus 35.7 U [28.7~44.9].

[0113] [Table 3]

[0114] [Table 4] JPEG2026533935000051.jpg104155

[0115] To avoid controller BPS attenuation, which would eliminate system bias and / or the possibility of BPS firing due to hypoglycemia treatment, we subsequently selected meals in which the CGM value exceeded 90 mg / dL one hour prior. This included 43 out of a total of 66 meals (24 with BPS and 19 without). Postprandial control differed between BPS-enabled and BPS-unenabled meals, with incremental AUC 2530±1934 vs. 3228±2029, p<0.047. However, CGM outcomes were not conclusive, with 4-hour TIR being 51.2% (33.2%) vs. 40.2% (25.5%) (p=0.24) and 4-hour TBR being 0[0~00] vs. 0[0~0] (p=0.2). Differences in blood glucose levels over the postprandial period are further illustrated in Figure 5. These differences in postprandial control may be related to the increased insulin infusion during the first hour postprandial compared to the system with BPS compared to the system without BPS, as shown in Figure 6. With BPS, 57.24% [50.9%–69.16%] of insulin was infused during the first hour, compared to 34.71% [11.68%–45.02%] without BPS.

[0116] BPS Safety Issues Test Tables 5 and 6 provide data on pre- and post-task blood glucose levels for individual tasks on the BPS system administered between two 24-hour comparison sessions. During the HIIT session and in the 1-2 hours prior to dinner, TIR was 85.6% (21.2%) for participants with an active BPS and 76.3% (33.4%) for participants without an active BPS. Hypoglycemia was observed in 3 out of 11 participants (all in the group with an active BPS). Among participants with an active BPS, the overall TBR below 70 was 0% [0%-11.1%], and the TBR below 54 was 0% [0%-0%]. In the 90-minute interval between exercise and dinner, one participant experienced a TBR of less than 70 as measured by CGM in 61.1% of participants and a TBR of less than 54 in 27.78% of participants. However, at the lowest CGM reading of 45 mg / dL, finger-prick blood glucose was 64 mg / dL, and finger-prick blood glucose returned to above 70 mg / dL at least 10 minutes earlier than the CGM reading. This participant belonged to the BPS group, but no BPS bolus was triggered during the HIIT session. In the group with active BPS, a BPS bolus was triggered in 3 out of 7 participants, and two of these participants experienced hypoglycemia within 2 hours. In these two participants during the time before dinner, TBRs of less than 70 were 13.3% and 8.8%, and TBRs of less than 54 were 0% and 0%.

[0117] [Table 5]

[0118] [Table 6]

[0119] During the pizza task and for 8 hours thereafter, the TIR among participants with an active BPS was 54.6% (5.8%), the TAR was 42.7% [35.4%~45.3%], the TBR <70 was 0% [0%~5.73%], and the TBR <54 was 0% [0%~0%]. All seven participants with an active BPS received a BPS bolus, and one experienced hypoglycemia (minimum BG of 49 mg / dL).

[0120] During the orange juice task and for the four hours following, the TIR was 89% (14.2%) when the BPS was activated. The TAR was 11% (14.2%), and TBR below 70 was 0% [0%~0%]. Among participants without an activated BPS, the TIR was 63.5% [46.9%~62.1%]%, the TAR was 36.5% [23.4%~53.1%], and TBR below 70 was 0% [0%~0%]. Of the seven participants who had an activated BPS throughout this task, five received a BPS bolus, and none of them experienced hypoglycemia in the four hours following the bolus.

[0121] Essay Deviations of blood glucose during meals were particularly problematic for AID systems used as FCLs, due to a mismatch between rapid carbohydrate absorption and slower insulin infusion and absorption. While systems designed to deliver rapid insulin boluses after glucose disturbances such as meals did not increase overall TIR (primarily due to large variability among participants), they were found to result in a reduction of glycemic iAUC during episodes where BPS administration was possible given the current glycemic status. This provides proof of concept that early, rapid insulin administration after carbohydrate intake can mitigate some of the glucose elevation before standard controller-based insulin delivery is given. In this current test over two 24-hour periods, only 65% ​​of meals were eligible to receive BPS based on pre-meal blood glucose and IOB. If these results are maintained over longer timeframes, it would be expected that BPS would result in an increase in TIR.

[0122] Because of legitimate concerns about the safety of such a system in real-world situations, we additionally tested three situations that could increase the risk of hypoglycemia if the system delivered an excessive amount of insulin outside of meal intake. These included when only small amounts of monosaccharides were consumed (e.g., orange juice for breakfast), when glucose levels increased due to strenuous physical activity (HIIT), and after a high-fat, high-carbohydrate meal (e.g., pizza). Our concern in testing these was that the insulin delivered by BPS would significantly exceed physiological needs, resulting in dangerous hypoglycemia. While we noticed hypoglycemia in the time following these episodes, only one subject had a TBR below 54, and none required two treatments, including during the episode in which a BPS bolus was delivered during strenuous exercise. This suggests that the system's response to the drop in glucose levels after BPS administration (e.g., reduced insulin delivery after a drop in glucose levels) was sufficient to avoid severe hypoglycemia. Nevertheless, this system still has many additional problematic challenges and needs further testing in real-world environments.

[0123] This system, despite being used in FCL without carbohydrate announcements, resulted in an overall TIR exceeding 70% of the recommended ADA threshold. This contrasts with the 58-78% TIR of other insulin-only AID systems used in FCL. While the current goal is to enable adequate management without extensive user interaction, we previously found that this AID system maintained better glycemic control with an additional 9% TIR when using carbohydrate announcements. Nevertheless, this type of system could be particularly beneficial for users who regularly omit meal announcements in certain environments. Currently available AID systems are not able to adjust quickly enough to accommodate deviations in postprandial blood glucose, highlighting the need for the development of such novel systems.

[0124] This study leveraged the advantages of a randomized crossover design, where each participant acted as their own control. A disadvantage is that while we tested three distinct tasks on the system, additional challenges such as stressors and meal and exercise timing could introduce further problems. Furthermore, the highly supervised nature of this study differed from real-world situations where BPS firing episodes could occur without user awareness, increasing the risk of delayed response and hypoglycemia.

[0125] Referring to Figures 7 and 8, the conclusion is that when comparing meals in which the BPS system was likely to act, the BPS system resulted in a reduction in iAUC blood glucose levels. These data increase the potential to incorporate such a system into AID controllers to reduce postprandial hyperglycemia without announcement, potentially moving away from a complete hybrid closed-loop system and enabling additional flexibility for people with type 1 diabetes.

[0126] Figures 8 to 12 are exemplary system architectures, functional block diagrams, etc., that may be used in embodiments of the system 100, processor 102, or device 108 disclosed herein.

[0127] As shown in Figure 8, the processor 804 or controller communicates with a glucose monitor or data source 812, and optionally with an insulin delivery device (e.g., another device 810). The glucose monitor or device communicates with the subject 800 to monitor the subject's glucose levels. The processor 804 or controller is configured to perform the necessary calculations. Optionally, the insulin delivery device communicates with the subject 800 to deliver insulin to the subject 800. The processor 804 or controller is configured to perform the necessary calculations. The glucose monitor and insulin delivery device may be implemented as separate devices or as a single device. The processor 804 may be implemented locally with the glucose monitor, insulin delivery device, or standalone device (or any combination of two or more of the glucose monitor, insulin device, or standalone device). The processor 804 or a portion of the system may be installed remotely so that the device operates as a remote medical device.

[0128] Referring to Figure 9, in its most basic configuration, the computing device 900 typically includes at least one processor 904 and memory 906. Depending on the exact configuration and type of the computing device, the memory 906 may be volatile (such as RAM), non-volatile (such as ROM, flash memory), or some combination of the two.

[0129] Furthermore, the computing device 900 may have other features and / or functions. For example, the computing device 900 may include, but is not limited to, writable electrical storage media, including, but not limited to, magnetic or optical disks or tapes. Such additional storage is shown in the figure by removable storage 902a and non-removable storage 902b. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data. Memory, removable storage, and non-removable storage are all examples of computer storage media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory, or other memory technologies, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage, or other magnetic storage devices, or any other media that can be used to store desired information and are accessible by the device. Any such computer storage medium may be part of a device or used in conjunction with a device.

[0130] The computing device 900 may also include one or more communication connections 908 that enable the device to communicate with other devices (e.g., other computing devices). The communication connections carry information on a communication medium. The communication medium typically includes any information distribution medium, which embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transmission mechanism. The term “modulated data signal” means a signal in which one or more of its characteristics are set or modified in such a way that it encodes, executes, or processes information within the signal. For example, non-limitingly, the communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as wireless, RF, infrared, and other wireless media. As discussed above, the term computer-readable medium, when used herein, includes both storage media and communication media.

[0131] In addition to standalone computing machines, embodiments of the present invention may also be implemented on a network system comprising multiple computing devices communicating with network means such as an infrastructure network or an ad-hoc network. The network connection may be a wired connection or a wireless connection. As an example, Figure 9 illustrates a network system in which embodiments of the present invention may be implemented. In this example, the network system includes a computer 910 (e.g., a network server), network connection means 912 (e.g., wired and / or wireless connection), a computer terminal 914, and a PDA (e.g., a smartphone) 916 (or other handheld or portable devices such as a mobile phone, laptop computer, tablet computer, GPS receiver, MP3 player, handheld video player, pocket projector, or a combination of such features, or a handheld device (or non-portable device)). It should be noted that in one embodiment, module 914 may be a glucose monitoring device. It should be noted that in one embodiment, the module enumerated as 914 may be a glucose monitoring device, an artificial pancreas, and / or an insulin device (or other intervention or diagnostic device). There may be multiple components of any one of these. Embodiments of the present invention may be implemented on any one of the devices in the system. For example, the execution of an instruction or other desired processing may be performed on the same computing device 900. Alternatively, embodiments of the present invention may be implemented on different computing devices in the network system. For example, certain desired or required processing or execution may be performed on one of the computing devices in the network (e.g., server 910 and / or glucose monitor device), while other processing and execution of an instruction may be performed on another computing device in the network system (e.g., terminal 914), and vice versa.In fact, certain processing or execution may be performed on one computing device (e.g., server 910 and / or insulin device, artificial pancreas, or glucose monitoring device (or other intervention or diagnostic device)), while other processing or execution of instructions may be performed on different computing devices, which may or may not be connected to the network. For example, certain processing may be performed on terminal 914, while other processing or instructions are passed to computing device 900, where the instructions are executed. This scenario can be particularly valuable when a PDA device accesses the network, for example, through computer terminal 914 (or an access point in an ad-hoc network). As another example, protected software may be executed, encoded, or processed using one or more embodiments of the present invention. The processed, encoded, or executed software may then be distributed to the customer. Distribution may be in the form of a storage medium (e.g., disk) or an electronic copy.

[0132] Figure 10 is a block diagram illustrating a system 1000 including a computer system 1002 and an associated Internet 1004 connection, in which an embodiment may be implemented. Such a configuration is typically used for a computer (host) that is connected to the Internet 1004 and runs server or client (or a combination thereof) software. Any computer or processor described herein, as well as source computers such as laptops, final destination computers, and relay servers, may use the computer system configuration and Internet connection shown in Figure 10. System 1004 may be used as a portable electronic device such as a notebook / laptop computer, a media player (e.g., an MP3-based or video player), a mobile phone, a personal digital assistant (PDA), a glucose monitoring device, an artificial pancreas, an insulin delivery device (or other intervention or diagnostic device), an image processing device (e.g., a digital camera or video recorder), and / or other handheld computing devices, or any combination of these devices. While Figure 10 illustrates various components of a computer system, it is not intended to represent any particular architecture or method of interconnecting the components, and therefore the details are not closely related to the present invention. It will also be recognized that network computers, handheld computers, mobile phones, and other data processing systems with few or, in some cases, more components may also be used. The computer system in Figure 10 may be, for example, an Apple Macintosh computer or PowerBook, or an IBM-compatible PC. The computer system 1000 includes a bus 1006, interconnects, or other communication mechanisms for communicating information, and a processor, usually in the form of an integrated circuit, coupled to the bus 1006 for processing information and executing computer executable instructions.The computer system 1000 also includes main memory 1008, such as random access memory (RAM) or other dynamic storage devices, coupled to bus 1006 for storing information and instructions executed by processor 1010.

[0133] Main memory 1008 is also used to store temporary variables or other intermediate information during the execution of instructions performed by processor 1010. Computer system 1000 further includes read-only memory (ROM) 1008 (or other non-volatile memory) or other static storage devices coupled to bus 1006 for storing static information and instructions for processor 1010. Storage devices 1012 such as magnetic disks or optical disks, hard disk drives for reading from and writing to hard disks, magnetic disk drives for reading from and writing to magnetic disks, and / or optical disk drives (such as DVDs) for reading from and writing to removable optical disks are coupled to bus 1006 for storing information and instructions. Hard disk drives, magnetic disk drives, and optical disk drives may be connected to the system bus by hard disk drive interfaces, magnetic disk drive interfaces, and optical disk drive interfaces, respectively. The drives and their associated computer-readable media provide non-volatile storage for computer-readable instructions, data structures, program modules, and other data for general-purpose computing devices. Typically, a computer system 1000 includes an operating system (OS) stored in non-volatile storage for managing computer resources, and provides applications and programs with access to computer resources and interfaces. The operating system typically processes system data and user input, and responds by allocating and managing tasks and internal system resources, such as controlling and allocating memory, prioritizing system requests, controlling input and output devices, facilitating networking, and managing files. Non-exclusive examples of operating systems include Microsoft Windows, Mac OS X, and Linux.

[0134] The term “processor” includes, but is not limited to, reduced instruction set core (RISC) processors, CISC microprocessors, microcontroller units (MCUs), CISC-based central processing units (CPUs), and digital signal processors (DSPs), and any integrated circuit or other electronic device (or collection of devices) capable of performing operations for at least one instruction. The hardware of such a device may be integrated on a single substrate (e.g., a silicon “die”) or distributed across two or more substrates. Furthermore, various functional aspects of a processor may simply be implemented as software or firmware associated with the processor.

[0135] The computer system 1000 may be coupled via bus 1006 to a display 1014, such as a cathode ray tube (CRT), liquid crystal display (LCD), flat-screen monitor, touchscreen monitor, or similar means for displaying text and graphic data to a user. The display may be connected via a video adapter to support the display. The display allows the user to view, input, and / or edit information related to the operation of the system. An input device 1016, including alphanumeric keys and other keys, is coupled to bus 1006 to transmit information and command selections to the processor 1010. Another type of user input device is a cursor control 1018, such as a mouse, trackball, or cursor directional keys, for transmitting directional information and command selections to the processor 1010, and for controlling the movement of a cursor on the display 1014. This input device typically has two degrees of freedom, a first axis (e.g., x) and a second axis (e.g., y), allowing the device to specify a position in a plane.

[0136] Computer system 1002 may be used to carry out the methods and techniques described herein. According to one embodiment, these methods and techniques are carried out by computer system 1002 in response to processor 1010 executing one or more sequences of one or more instructions contained in main memory 1020. Such instructions may be read into main memory 1022 from another computer-readable medium, such as storage device 1012. The execution of the sequence of instructions contained in main memory 1022 causes processor 1010 to carry out the processing steps described herein. In alternative embodiments, hardwired circuits may be used instead of or in combination with software instructions to carry out the arrangement. Thus, embodiments of the present invention are not limited to any particular combination of hardware circuits and software.

[0137] The term “computer-readable medium” (or “machine-readable medium” as used herein is an expandable term referring to any medium or memory, or any mechanism for storing or transmitting information in a machine-readable format, that is involved in providing execution instructions to a processor (such as processor 1010). Such a medium may store computer-executable instructions executed by processing elements and / or control logic, and data manipulated by processing elements and / or control logic, and may take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Transmission media include coaxial cables, copper wires, and optical fibers, which constitute the wiring of bus 1006. Transmission media may also take the form of sound waves or optical waves, such as sound waves or optical waves generated during radio and infrared data communications, or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Common forms of computer-readable media include, for example, floppy disks, flexible disks, hard disks, magnetic tapes, or any other magnetic media; CD-ROMs, any other optical media; punch cards, paper tapes, any other physical media having a pattern of holes; RAM, PROMs, EPROMs, FLASH-EPROMs, any other memory chips or cartridges; carriers as described below; or any other media that a computer can read.

[0138] When transporting one or more sequences of one or more instructions to processor 1010 for execution, various forms of computer-readable media may be involved. For example, the instructions may first be transported onto a magnetic disk of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 1000 may receive data over the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector may receive the data transported by the infrared signal, and appropriate circuitry may place the data on bus 1006. Bus 1006 transports the data to main memory 1022, from which processor 1010 retrieves and executes the instructions. The instructions received by main memory 1022 may optionally be stored in storage device 1012 either before or after execution by processor 1010.

[0139] The computer system 1000 also includes a communication interface 1024 coupled to bus 1006. The communication interface 1024 provides bidirectional data communication coupling to a network link 1026 coupled to local network 1028. For example, the communication interface 1024 may be an Integrated Services Digital Network (ISDN) card or modem for providing data communication connectivity to a corresponding type of telephone line. In another non-limiting example, the communication interface 1024 may be a local area network (LAN) card for providing data communication connectivity to a compatible LAN. For example, Ethernet-based connections based on IEEE 802.3 standards, such as 10 / 100BaseT, 1000BaseT (Gigabit Ethernet), 10 Gigabit Ethernet (10GE or 10GbE or 10GigE as standard IEEE standard 802.3ae-2002), 40 Gigabit Ethernet (40GbE), or 100 Gigabit Ethernet (100 GbE as standard IEEE P802.3ba), may be used, as described in Cisco Systems, Inc. Publication number 1-587005-001-3 (6 / 99), "Internetworking Technologies Handbook", Chapter 7: "Ethernet Technologies", pages 7-1 to 7-38, where the whole thing is incorporated for all purposes, as fully described in this book.In such cases, the communication interface 1818 typically includes a LAN transceiver or modem, such as the Standard Microsystems Corporation (SMSC) LAN91C111 10 / 100 Ethernet transceiver described in the Standard Microsystems Corporation (SMSC) data-sheet "LAN91C111 10 / 100 Non-PCI Ethernet Single Chip MAC+PHY" Data-Sheet, Rev. 15 (02-20-04), which is incorporated as a whole for all purposes, as fully described in this document.

[0140] Wireless links may also be implemented. In any such implementation, the communication interface 1024 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

[0141] Network link 1026 typically provides data communication to other data devices through one or more networks. For example, network link 1026 provides connection to a host computer through local network 1028 or to data equipment operated by an Internet service provider (ISP) 1025. ISP 1025 then provides data communication services through the Internet 1004, which is a worldwide packet data communication network. Both local network 1028 and the Internet 1004 use electrical, electromagnetic, or optical signals to carry digital data streams. Signals across various networks, as well as signals on network link 1026 and through communication interface 1024, that carry digital data to and from computer system 1000 are exemplary forms of carrier waves for transmitting information.

[0142] The received code may be executed by the processor 1010 upon receipt, or it may be stored in the storage device 1012 or other non-volatile storage for later execution. In this manner, the computer system 1000 may acquire the application code in carrier form.

[0143] Figure 11 illustrates a system in which one or more embodiments of the present invention may be implemented using a network, or part of a network, or a computer. The glucose monitor, artificial pancreas, or insulin device (or other intervention or diagnostic device) of the present invention may be implemented without a network. Figure 11 schematically illustrates an exemplary system in which examples of the present invention may be implemented. In one embodiment, the glucose monitor, artificial pancreas, or insulin device (or other intervention or diagnostic device) may be implemented locally by a subject (or patient) at home or other desired location. However, in an alternative embodiment, it may be implemented in a clinic setting or an assisted setting. For example, a clinic setting 1100 provides a physician (e.g., 1102) or clinician / assistant with a place to diagnose glucose-related diseases and patients (e.g., 1104) with related diseases and conditions. The glucose monitoring device 1106 may be used as a standalone device to monitor and / or test a patient's glucose levels. Although only the glucose monitoring device 1106 is shown in the figure, it should be recognized that the system of the present invention and any of its components may be used in the manner depicted by Figure 11. The system or its components may be fitted to the patient or communicate with the patient as desired or as needed. For example, a system or combination of its components, including the glucose monitoring device 1106 (or controller, and / or other related devices or systems such as an artificial pancreas, insulin pump (or other intervention or diagnostic device), or any other desired or required device or component), may be in contact with, communicate with, or fitted to the patient via tape or tubing (or other medical device or component), or communicate via wired or wireless connections. Such monitoring and / or testing may be short-term (e.g., clinical visit) or long-term (e.g., clinical stay or family).The output of the glucose monitoring device may be used by a physician (clinician or assistant) for appropriate action, such as insulin infusion or food delivery to the patient, or for other appropriate action or modeling. Alternatively, the output of the glucose monitoring device may be delivered to a computer terminal 1112 for immediate or future analysis. Delivery may be via cable, wireless, or any other suitable medium. The output of the glucose monitoring device from the patient may also be delivered to a portable device such as a PDA 1110. The output of the glucose monitoring device with improved accuracy may be delivered to a glucose monitoring center 1112 for processing and / or analysis. Such delivery may be achieved by various means, such as a network connection unit 1114, which may be wired or wireless.

[0144] In addition to the output of the glucose monitoring device, errors, parameters for accuracy improvement, and any accuracy-related information may be sent to a computer and / or glucose monitoring center 1112, etc., for error analysis. This can provide centralized accuracy monitoring, modeling, and / or accuracy improvement to the glucose center (or other intervention or diagnostic center) due to the importance of the glucose sensor (or other intervention or diagnostic sensor or device).

[0145] Examples of the present invention may also be implemented in a glucose monitoring device, artificial pancreas, and / or insulin device (or a standalone computing device associated with other interventional or diagnostic devices).

[0146] Referring to Figure 12, an embodiment of one invention includes, but is not limited to, a system, a method, and a computer-readable medium, and describes a block diagram of an exemplary machine 1200 in which one or more embodiments (e.g., the methodologies discussed) may be implemented (e.g., put into operation).

[0147] Figure 12 illustrates a block diagram of an exemplary machine 1200 in which one or more embodiments (e.g., the methodologies discussed) may be implemented (e.g., put into operation).

[0148] Examples of machine 1200 may include logic, one or more components, circuits (e.g., modules), or mechanisms. A circuit is a tangible entity configured to perform a certain operation. In one example, a circuit may be arranged in a specified manner (e.g., internally or relative to external entities such as other circuits). In one example, one or more computer systems (e.g., standalone, client, or server computer systems) or one or more hardware processors (processors) may be configured as circuits that operate by software (e.g., instructions, application parts, or applications) to perform a certain operation as described herein. In one example, the software may reside (1) on a non-temporary machine-readable medium, or (2) in a transmitted signal. In one example, the software, when executed by the underlying hardware of the circuit, causes the circuit to perform a certain operation.

[0149] In one example, a circuit may be implemented mechanically or electronically. For example, a circuit may comprise a dedicated circuit or logic specifically configured to implement one or more technologies, such as those discussed above, including a dedicated processor, a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). In one example, a circuit may comprise programmable logic (e.g., a circuit contained within a general-purpose processor or other programmable processor) that can be temporarily configured (e.g., by software) to perform certain operations. It will be recognized that the decision to implement a circuit mechanically (e.g., with a dedicated, permanently configured circuit) or with a temporarily configured (e.g., with a software-configured circuit) circuit may be made based on cost and time considerations.

[0150] Therefore, the term “circuit” is understood to encompass tangible entities that are physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., transiently) (e.g., programmed) to operate in a specified manner or perform a specified operation. For example, given several temporarily configured circuits, each of the circuits does not need to be configured or instantiated at any given time. For example, if a circuit comprises a general-purpose processor configured via software, the general-purpose processor can be configured as each of two different circuits at different points in time. The software can therefore configure the processor to, for example, configure a particular circuit at one point in time and a different circuit at a different point in time.

[0151] In one example, a circuit can provide information to other circuits and receive information from other circuits. In this example, a circuit can be considered to be communicatively coupled with one or more other circuits. If multiple such circuits exist simultaneously, communication can be achieved through signal transmission connecting the circuits (e.g., via appropriate circuits and buses). In embodiments where multiple circuits are configured or instantiated at different points in time, communication between such circuits can be achieved, for example, through the storage and retrieval of information in a memory structure that multiple circuits have access to. For example, one circuit can perform an operation and store the output of that operation in a memory device to which it is communicatively coupled. Further circuits can then access the memory device to retrieve and process the stored output. In one example, a circuit can be configured to initiate or receive communication with an input or output device and operate on a resource (e.g., a collection of information).

[0152] Various operations of the method examples described herein may be at least partially performed by one or more processors that are temporarily or permanently configured (e.g., by software) to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute a processor implementation circuit that operates to perform one or more operations or functions. In one example, the circuit referred to herein may include a processor implementation circuit.

[0153] Similarly, the methods described herein can be implemented at least partially by processors. For example, at least some of the operations of the methods can be implemented by one or more processors or circuits of processor implementation. Certain operations can be distributed among one or more processors and deployed across several machines, rather than residing within a single machine. In one example, one or more processors may be located in a single location (e.g., a home environment, an office environment, or a server farm), while in other examples, processors may be distributed across multiple locations.

[0154] One or more processors can also operate within a “cloud computing” environment or as “Software as a Service” (SaaS) to support the execution of related operations. For example, at least some of these operations may be performed by a group of computers (as an example of machines containing processors), and these operations may be accessible over a network (e.g., the Internet) and through one or more appropriate interfaces (e.g., application programming interfaces (APIs)).

[0155] Exemplary embodiments (e.g., apparatus, system, or method) may be implemented in digital electronic circuits, computer hardware, firmware, software, or any combination thereof. Exemplary embodiments may be implemented using computer program products (e.g., computer programs tangibly embodied in information carriers or machine-readable media for execution by or control of the operation of data processing devices such as programmable processors, computers, or multiple computers).

[0156] Computer programs can be written in any form of programming language, including compiled or interpreted languages, and can be deployed in any form, including as standalone programs or as software modules, subroutines, or other units suitable for use in a computing environment. Computer programs can be deployed to run on one computer, on multiple computers at one site, or distributed across multiple sites and interconnected by a communication network.

[0157] In one example, the operation may be carried out by one or more programmable processors that execute a computer program to perform a function by acting on input data and producing an output. Alternatively, examples of method operation may also be carried out by dedicated logic circuits (e.g., field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs)), and the exemplary device may be implemented as a dedicated logic circuit.

[0158] A computing system can include clients and servers. Clients and servers are typically remote to each other and usually interact through a communication network. The relationship between the client and server arises from computer programs running on each computer that have a client-server relationship with each other. In embodiments of deploying a programmable computing system, it will be understood that both hardware and software architectures must be considered. Specifically, it will be recognized that the choice of whether to perform certain functions with permanently configured hardware (e.g., ASICs), with temporarily configured hardware (e.g., a combination of software and a programmable processor), or with a combination of permanently and temporarily configured hardware may be a design choice. The following describes the hardware (e.g., machine 1200) and software architectures that may be deployed in an exemplary embodiment.

[0159] For example, machine 1200 can operate as a standalone device, or machine 1200 can be connected to other machines (e.g., networked).

[0160] In a networked deployment, machine 1200 can operate as either a server or a client machine in a server-client network environment. For example, machine 1200 can function as a peer machine in a peer-to-peer (or other distributed) network environment. Machine 1200 may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, web appliance, network router, switch, or bridge, or any machine capable of executing (sequential or otherwise) instructions that specify the actions taken (e.g., performed) by machine 1200. Furthermore, although only a single machine 1200 is described, the term “machine” may also be understood to include any set of machines that individually or collectively execute one or more sets of instructions in order to implement any one or more of the methodologies discussed herein.

[0161] An exemplary machine (e.g., a computer system) 1200 may include a processor 1250 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), main memory 1252a, and static memory 1252b, some or all of which can communicate with each other via a bus 1220. Machine 1200 may further include a display unit 1202, an alphanumeric input device 1204 (e.g., a keyboard), and a user interface (UI) navigation device 1206 (e.g., a mouse). In one example, the display unit 1202, the input device 1204, and the UI navigation device 1206 may be touchscreen displays. Machine 1200 may further include a storage device (e.g., a drive unit) 1208, a signal generating device 1210 (e.g., a speaker), a network interface device 1212, and one or more sensors 1214 such as a Global Positioning System (GPS) sensor, a compass, an accelerometer, or other sensors.

[0162] The storage device 1208 may include a machine-readable medium 1216 containing one or more sets of data structures or instructions 1254 (e.g., software) that embody or are utilized by any one or more of the methodologies or functions described herein. The instructions 1254 may also reside, fully or at least partially, in main memory 1252a, static memory 1252b, or processor 804 during their execution by machine 1200. In one example, one or any combination of processor 804, main memory 1252a, static memory 1252b, or storage device 1208 may constitute the machine-readable medium.

[0163] Although the machine-readable medium 1216 is described as a single medium, the term “machine-readable medium” may include a single or multiple mediums configured to store one or more instructions 1254 (e.g., a centralized or distributed database, and / or associated caches and servers). The term “machine-readable medium” may also be understood as including any tangible medium capable of storing, encoding, or carrying instructions for machine execution, causing a machine to execute any one or more of the methodologies of this disclosure, or storing, encoding, or carrying data structures used by or associated with such instructions. The term “machine-readable medium” may therefore be understood as including, but not limited to, solid memory, as well as optical and magnetic media. Specific examples of machine-readable media may include, for example, semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and non-volatile memory including CD-ROMs and DVD-ROM disks.

[0164] Instruction 1254 may further be transmitted and received via a communication network 1218 using a transmission medium, via a network interface device utilizing any one of several transport protocols (e.g., Frame Relay, IP, TCP, UDP, HTTP, etc.). Illustrative communication networks may include, among others, local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile phone networks (e.g., cellular networks), basic telephone (POTS) networks, and wireless data networks (e.g., the IEEE 802.11 series of standards known as Wi-Fi®, the IEEE 802.16 series of standards known as WiMax®), and peer-to-peer (P2P) networks. The term “transmission medium” is understood to include any intangible medium capable of storing, encoding, or carrying instructions for execution by a machine, including digital or analog communication signals or other intangible mediums to facilitate the communication of such software.

[0165] While exemplary embodiments of this disclosure are described in detail in several examples herein, it should be understood that other embodiments are also to be considered. Therefore, this disclosure is not intended to be limited in scope to the structural and arrangement details of the components described in the following description or illustrated in the drawings. Other embodiments of this disclosure are possible and can be practiced or implemented in a variety of ways.

[0166] Any element, part, section, subsection, or component described with reference to any particular embodiment described above may be incorporated, integrated, or otherwise adapted for use in any other embodiment described herein, unless otherwise specified or unless it would render the device of the embodiment inoperable. Similarly, any step described with reference to a particular method or process may be integrated, incorporated, or otherwise combined with other methods or processes described herein, unless otherwise specified or unless it would render the method of the embodiment inoperable. Furthermore, devices or methods of multiple embodiments may be combined, incorporated, or otherwise integrated with one another to construct or develop further embodiments of the invention described herein.

[0167] It should be recognized that any of the components or modules mentioned in relation to any of the embodiments of the invention discussed herein may be formed integrally with or separately from one another. Furthermore, redundant functions or structures of components or modules may be implemented. Furthermore, various components may communicate locally and / or remotely with any user / clinician / patient or machine / system / computer / processor. Furthermore, various components may communicate wirelessly and / or via wired or other desired available means of communication, systems, and hardware. Furthermore, various components and modules may be replaced by other modules or components that provide similar functions.

[0168] It should be recognized that the apparatus and related components discussed herein may take on all shapes along the entire continuous geometric spectrum of operations in the x, y, and z planes in order to provide and satisfy anatomical, environmental, structural, and operational requirements. Furthermore, the location and arrangement of the various components may be modified as desired or as needed.

[0169] It should be recognized that the various sizes, dimensions, contours, stiffness, shapes, flexibility, and materials of any component or part of a component in the various embodiments discussed throughout this text may be modified and used as desired or necessary.

[0170] While some dimensions are provided in the aforementioned diagram, it should be noted that the device may be configured with various sizes, dimensions, contours, stiffness, shapes, flexibility, and materials to suit the components or parts of the device, and therefore may be modified and used as desired or necessary.

[0171] It should also be noted that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include the plural form unless the context explicitly specifies otherwise. Ranges may be expressed herein as “about” or “approximately” from a certain value and / or “about” or “approximately” to another specific value. Where such ranges are expressed, other exemplary embodiments include a range from a certain value and / or another specific value.

[0172] The terms "comprising," "containing," or "including" mean that at least the named compound, element, particle, or method step is present in the composition, article, or method, but do not exclude the presence of other compounds, materials, particles, or method steps, even if those compounds, materials, particles, or method steps have the same function as the named one.

[0173] In the exemplary embodiments described, technical terms will be used for clarity. Each term is intended to include all technical equivalents that operate in a similar manner to achieve a similar purpose, taking into account its broadest meaning as understood by those skilled in the art. It should also be understood that reference to one or more steps of a method does not preclude the existence of additional or intervening method steps between those explicitly identified steps. The steps of a method may be performed in an order different from that described herein without departing from the scope of this disclosure. Similarly, reference to one or more components in a device or system should also be understood that does not preclude the existence of additional or intervening components between those explicitly identified components.

[0174] Several references, which may include various patents, patent applications, and publications, are listed in the bibliography and discussed in the disclosure provided herein. Citations and / or discussions of such references are provided solely to clarify the descriptions in this disclosure and do not constitute an acknowledgment that any such reference is “prior art” to any aspect of this disclosure described herein. In terms of notation, "[n]" corresponds to the nth reference in the list. All references cited and discussed herein are incorporated herein by reference in whole, to the same extent as if each reference had been incorporated individually.

[0175] It should be recognized that, as discussed herein, subjects may be human or any animal. Animals may be any applicable type, including but not limited to mammals, veterinary animals, livestock, or pet-type animals. For example, animals may be experimental animals specifically selected to possess certain characteristics similar to humans (e.g., rats, dogs, pigs, monkeys). It should be recognized that subjects may be, for example, any applicable human patient.

[0176] The term "about" as used herein means approximately, within a range, roughly, or around. When the term "about" is used in conjunction with a numerical range, it modifies that range by expanding the upper and lower boundaries of the given number. Generally, the term "about" is used herein to modify the numbers above and below a given value by a 10% difference. In one aspect, the term "about" means plus or minus 10% of the number in which it is used. Thus, about 50% means the range of 45% to 55%. Numerical ranges cited herein by endpoints include all numbers and fractions within that range (for example, 1 to 5 includes 1, 1.5, 2, 2.75, 3, 3.90, 4, 4.24, and 5). Similarly, numerical ranges cited herein by endpoints include subranges within that range (for example, 1–5 includes 1–1.5, 1.5–2, 2–2.75, 2.75–3, 3–3.90, 3.90–4, 4–4.24, 4.24–5, 2–5, 3–5, 1–4, and 2–4). It should also be understood that all numerical values ​​and their fractions are presumed to be modified by the term “approximately”.

[0177] References The following patents, applications, and publications, as listed below and throughout this document, are incorporated herein by reference in their entirety and are not considered prior art relating to the present invention by including them in this section. 1. Brown SA, Kovatchev BP, Raghinaru D, et al. Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes. N Engl J Med 2019; 381(18): 1707-17. 2. Breton MD, Kanapka LG, Beck RW, et al. A Randomized Trial of Closed-Loop Control in Children with Type 1 Diabetes. N Engl J Med 2020; 383(9): 836-45. 3. Wadwa RP, Reed ZW, Buckingham BA, et al. Trial of Hybrid Closed-Loop Control in Young Children with Type 1 Diabetes. N Engl J Med 2023; 388(11): 991-1001. 4. Russell SJ, Beck RW, Damiano ER, et al. Multicenter, Randomized Trial of a Bionic Pancreas in Type 1 Diabetes. N Engl J Med 2022; 387(13): 1161-72. 5. Bally L, Thabit H, Kojzar H, et al. Day-and-night glycaemic control with closed-loop insulin delivery versus conventional insulin pump therapy in free-living adults with well controlled type 1 diabetes: an open-label, randomised, crossover study. Lancet Diabetes Endocrinol 2017; 5(4): 261-70. 6. Ware J, Allen JM, Boughton CK, et al. Randomized Trial of Closed-Loop Control in Very Young Children with Type 1 Diabetes. N Engl J Med 2022; 386(3): 209-19. 7. Ware J, Boughton CK, Allen JM, et al. Cambridge hybrid closed-loop algorithm in children and adolescents with type 1 diabetes: a multicentre 6-month randomised controlled trial. Lancet Digit Health 2022; 4(4): e245-e55. 8. Templer S. Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions. Front Endocrinol (Lausanne) 2022; 13: 919942. 9. Cameron FM, Ly TT, Buckingham BA, et al. Closed-Loop Control Without Meal Announcement in Type 1 Diabetes. Diabetes Technol Ther 2017; 19(9): 527-32. 10. Garcia-Tirado J, Diaz JL, Esquivel-Zuniga R, et al. Advanced Closed-Loop Control System Improves Postprandial Glycemic Control Compared With a Hybrid Closed-Loop System Following Unannounced Meal. Diabetes Care 2021. 11. Dovc K, Piona C, Yesiltepe Mutlu G, et al. Faster Compared With Standard Insulin Aspart During Day-and-Night Fully Closed-Loop Insulin Therapy in Type 1 Diabetes: A Double-Blind Randomized Crossover Trial. Diabetes Care 2020; 43(1): 29-36. 12. Tsoukas MA, Majdpour D, Yale JF, et al. A fully artificial pancreas versus a hybrid artificial pancreas for type 1 diabetes: a single-centre, open-label, randomised controlled, crossover, non-inferiority trial. Lancet Digit Health 2021; 3(11): e723-e32. 13. Chernavvsky DR, DeBoer MD, Keith-Hynes P, et al. Use of an artificial pancreas among adolescents for a missed snack bolus and an underestimated meal bolus. Pediatr Diabetes 2016; 17(1): 28-35. 14. Paldus B, Morrison D, Zaharieva DP, et al. A Randomized Crossover Trial Comparing Glucose Control During Moderate-Intensity, High-Intensity, and Resistance Exercise With Hybrid Closed-Loop Insulin Delivery While Profiling Potential Additional Signals in Adults With Type 1 Diabetes. Diabetes Care 2022; 45(1): 194-203. 15. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc 1982; 14(5): 377-81. 16. Garcia-Tirado J, Lv D, Corbett JP, Colmegna P, Breton MD. Advanced hybrid artificial pancreas system improves on unannounced meal response - In silico comparison to currently available system. Comput Methods Programs Biomed 2021; 211: 106401. 17. Corbett JP, Garcia-Tirado J, Colmegna P, Diaz Castaneda JL, Breton MD. Using an Online Disturbance Rejection and Anticipation System to Reduce Hyperglycemia in a Fully Closed-Loop Artificial Pancreas System. J Diabetes Sci Technol 2022; 16(1): 52-60. 18. Battelino T, Danne T, Bergenstal RM, et al. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care 2019; 42(8): 1593-603. 19. Garcia-Tirado J, Colmegna P, Villard O, et al. Assessment of Meal Anticipation for Improving Fully Automated Insulin Delivery in Adults With Type 1 Diabetes. Diabetes Care 2023; 46(9): 1652-8. 20. Jaser SS, Datye KA. Frequency of Missed Insulin Boluses in Type 1 Diabetes and Its Impact on Diabetes Control. Diabetes Technol Ther 2016; 18(6): 341-2.

[0178] It will be understood that modifications to the embodiments disclosed herein may be made to satisfy a particular set of design criteria. For example, any of the components, features, or steps of a system, apparatus, or method may be of any appropriate number or type to achieve a particular purpose. Thus, although certain exemplary embodiments of the systems and methods disclosed herein have been discussed and described, it is clear that the present invention is not limited thereto and can be embodied and practiced in various other ways within the scope of the following claims.

[0179] While some components, features, and / or configurations may be described in relation to a single specific embodiment, these components, features, and / or configurations may be applied to or used in many other embodiments and should be considered applicable to other embodiments unless otherwise stated, or unless it is technically impossible for such components, features, and / or configurations to be used in other embodiments. Accordingly, components, features, and / or configurations of various embodiments may be combined in any way, and such combinations are expressly considered and disclosed in this description.

[0180] Those skilled in the art will recognize that the present invention can be embodied in other specific forms without departing from its spirit or essential features. The embodiments disclosed herein are therefore considered illustrative and not limiting in all aspects. The scope of the present invention is indicated by the appended claims rather than by the foregoing description, and all modifications leading to its meaning, scope, and equivalents are intended to be encompassed within the claims. Furthermore, disclosure of a range of values ​​is a disclosure of any numerical value within that range, including its endpoints.

Claims

1. An insulin delivery controller that implements a processor configuration for efficiently achieving insulin delivery targets, The system comprises a processor and a memory associated with the processor, wherein the memory includes instructions stored therein, and when an instruction is executed by the processor, Processing glucose data received from the memory, including a data representation of glucose disturbances (d(t)), Determining the rate of change of glucose (G'(t)), To generate a command signal for dynamically reshaping blood glucose disturbances within the predicted range of the insulin delivery controller according to G'(t), To generate an insulin command signal to an insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate. An insulin delivery controller that causes the aforementioned processor to perform this operation.

2. Combined with an insulin delivery unit, The insulin delivery controller according to claim 1, wherein the insulin delivery unit includes a processor and a memory having instructions stored therein, the instructions, when executed by the insulin delivery processor, cause the insulin delivery processor to generate an insulin delivery dose based on the insulin delivery dose and / or the insulin delivery rate.

3. The command is threshold ( [Math 1] The insulin delivery controller according to claim 1, wherein the processor is instructed to keep d(t) constant or decrease d(t) based on ).

4. The instruction is threshold [Math 2] The insulin delivery controller according to claim 3, wherein the processor is instructed to adjust the decrease of d(t) based on the above.

5. The insulin delivery controller according to claim 1, wherein the instruction causes the processor to estimate d(t).

6. The insulin delivery controller according to claim 5, wherein the instruction causes the processor to periodically estimate d(t).

7. The insulin delivery controller according to claim 5, wherein the instruction causes the processor to estimate d(t) every five minutes.

8. The insulin delivery controller according to claim 5, wherein the instruction causes the processor to estimate d(t) using Kalman filtering technique.

9. The insulin delivery controller according to claim 1, wherein the instruction causes the processor to process the glucose data, determine the (G'(t)), and generate the command signal for dynamically reshaping the glucose disturbance, via a closed-loop control (CLC) process.

10. The order is, Model Predictive Control (MPC) algorithms, The proportional-integral-derivative (PID) algorithm, or Fuzzy logic (FL) algorithm The insulin delivery controller according to claim 9, wherein the processor is instructed to perform the CLC process as part of the CLC process.

11. The instruction causes the processor to generate the insulin command signal to the insulin delivery unit, which adjusts the insulin delivery dose and / or the insulin delivery rate in order to coordinate insulin delivery, To minimize the risk of hypoglycemia, or to prevent hypoglycemia, and / or It mitigates glucose disturbances that tend towards hyperglycemia. The insulin delivery controller according to claim 10, which will be adjusted in such a way.

12. The instruction causes the processor to generate the insulin command signal to the insulin delivery unit to adjust the insulin delivery dose and / or the insulin delivery rate in order to coordinate insulin delivery, Approaching the target glucose level The insulin delivery controller according to claim 10, which will be adjusted in such a way.

13. A computer-readable medium containing instructions stored therein, wherein the instructions, when executed by a processor, Processing glucose data including data representation of glucose disturbances (d(t)), Determining the rate of change of glucose (G'(t)), To generate a command signal for dynamically reshaping glucose disturbances within the predictive range of the insulin delivery controller according to G'(t), To generate an insulin command signal to an insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate. A computer-readable medium that causes the processor to efficiently achieve the insulin delivery target.

14. The computer-readable medium according to claim 13, wherein processing the glucose data, determining the (G'(t)), and generating the command signals for dynamically reshaping the glucose disturbance are performed via a closed-loop control (CLC) process.

15. The CLC process described above is Model Predictive Control (MPC) algorithms, The proportional-integral-derivative (PID) algorithm, or Fuzzy logic (FL) algorithm A computer-readable medium according to claim 14, including the following:

16. Generating the insulin command signal to the insulin delivery unit includes generating the insulin command signal such that the insulin delivery dose and / or the insulin delivery rate are adjusted to coordinate insulin delivery, To minimize the risk of hypoglycemia, or to prevent hypoglycemia, and / or It mitigates glucose disturbances that tend towards hyperglycemia. A computer-readable medium according to claim 15, which is adjusted to such an extent.

17. Generating the insulin command signal to the insulin delivery unit includes generating the insulin command signal such that the insulin delivery dose and / or the insulin delivery rate are adjusted to coordinate insulin delivery, Approaching the target glucose level An insulin delivery controller according to claim 15, which is adjusted to such an extent.

18. A method for managing processor configuration to efficiently achieve insulin delivery targets, Processing glucose data including data representation of glucose disturbances (d(t)), Determining the rate of change of glucose (G'(t)), To generate a command signal for dynamically reshaping glucose disturbances within the predictive range of the insulin delivery controller according to G'(t), To generate an insulin command signal to an insulin delivery unit to adjust the insulin delivery dose and / or insulin delivery rate. Methods that include...

19. The method according to claim 18, wherein processing the glucose data, determining the (G'(t)), and generating the command signal for dynamically reshaping the glucose disturbance are performed via a closed-loop control (CLC) process.

20. The CLC process described above is Model Predictive Control (MPC) algorithms, The proportional-integral-derivative (PID) algorithm, or Fuzzy logic (FL) algorithm The method according to claim 19, including the method described in claim 19.

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