Systems, devices, and methods relating to medication dose guidance

Abstract

To provide improved systems, devices, and methods that take into account the physiology, dietary habits, and the like of a user, improve accuracy and reliability, determine a medication dose, and automatically deliver the medication dose.SOLUTION: A method includes, by a processor, by executing a pattern analysis algorithm that receives time-correlated analyte data of a user, which has been acquired during an analysis period, as input, the steps of: determining an analyte pattern type during at least one time-of-day (TOD) period; determining a before-meal correction factor on the basis of the analyte pattern type and a defined dosing strategy of the user for the analysis period by the processor executing the algorithm; and storing an indicator of before-meal correction in a computer memory and outputting to at least one of the user or a medication dosing device. The pattern analysis algorithm may be a glucose pattern analysis (GPA) algorithm.SELECTED DRAWING: Figure 1A

Description

Related Applications

[0001] This application claims the priority and benefit of U.S. Provisional Patent Application No. 62 / 882,249, filed on August 2, 2019; U.S. Provisional Patent Application No. 62 / 979,578, filed on February 21, 2020; U.S. Provisional Patent Application No. 62 / 979,594, filed on February 21, 2020; U.S. Provisional Patent Application No. 62 / 979,618, filed on February 21, 2020; and U.S. Provisional Patent Application No. 63 / 058,799, filed on July 30, 2020, the entire contents of which are hereby incorporated by reference herein for all purposes. It should be noted that this application is a divisional application of Japanese Patent Application No. 2022-506627, filed on July 31, 2020, as the parent application.

Technical Field

[0002] The subject matter described herein generally relates to systems, devices, and methods related to drug dosage guidance, such as determining insulin dosages for treating elevated glucose levels, for example, due to diabetes.

Background Art

[0003] The detection and / or monitoring of analyte levels such as glucose, ketones, lactate, oxygen, hemoglobin A1C, etc. can be extremely important for the health of individuals with diabetes. Patients suffering from diabetes may be subject to complications including loss of consciousness, cardiovascular disease, retinopathy, neuropathy, and nephropathy. Diabetic patients generally need to monitor their glucose levels to confirm that their glucose levels are maintained within a clinically safe range, and can also use this information to determine whether insulin is needed and / or when it is needed to lower the glucose levels in the body, or whether additional glucose is needed to raise the glucose levels in the body.

[0004] Clinical data is increasingly showing a strong correlation between the frequency of glucose monitoring and blood glucose control. However, despite such a correlation, many individuals diagnosed with diabetes do not monitor their glucose levels as frequently as needed due to a combination of factors including inconvenience, test discretion, pain associated with glucose testing, and cost.

[0005] For patients who rely on the administration of drugs (e.g., insulin) to treat or manage diabetes, it would be desirable to have a system, device, or method that can automatically utilize glucose information collected by an analyte monitoring system to provide drug dosage guidance in an easily accessible manner as needed. It is further desirable that such a system, device, or method takes into account the physiology, diet, activity, and / or behavior of the user or patient being treated when providing such drug dosage guidance, which can itself improve accuracy and reliability. Additionally, in some situations, it would be desirable for such a system, device, or method to be able to automatically deliver the selected drug dosage. SUMMARY OF THE INVENTION PROBLEMS TO BE SOLVED BY THE INVENTION

[0006] For these and other reasons, there is a need for improved systems, methods, and devices related to drug dosage guidance. MEANS FOR SOLVING THE PROBLEMS

[0007] This specification provides exemplary embodiments of dosage guidance and, in some embodiments, systems, devices, and methods related to drug delivery. According to one aspect, many of the embodiments described herein include a dosage guidance system that includes a display device, a sensor control device, and a drug delivery device. The dosage guidance system can include a dosage guidance application (e.g., software) that can determine dosage guidance (e.g., recommendations regarding dosage amount, correction, and titration) and output it to a patient. Further, according to some embodiments, the dosage guidance system can learn a patient's dosing strategy during a learning period in which the dosage guidance system can estimate the patient's primary dosing parameters. According to some embodiments, the dosage guidance system can also provide guidance for titration and correction when the system is configured with the patient's current dosing strategy. The dosage guidance system can also provide guidance for different meal administration scenarios. For example, in some embodiments, the dosage guidance system can provide dosage guidance at the start or before or after the start of a meal. The dosage guidance system can also provide dosage guidance for an added meal (e.g., dessert) or for a "touch-up (additional)" administration to address a high glucose level after a meal. Exemplary systems and safety features of the dosage guidance system are also described.

[0008] Many of the embodiments provided herein include improved software functionality or graphical user interfaces for use in an analyte monitoring system, are highly intuitive and user friendly, and provide rapid access to a user's physiological information. More specifically, these embodiments enable a user (or HCP) to quickly determine appropriate pharmacotherapy based on information related to the user's physiological condition, past dosing patterns, and other factors without the need to perform the cumbersome task of examining large amounts of analyte data. Further, some of the GUI and GUI functionality enables a user (and their caregiver) to better understand and improve the user's dosing patterns as well as subsequent hypoglycemic and hyperglycemic episodes. Similarly, many of the other embodiments provided herein include improved software functionality of a dosage guidance system and provide dosage guidance to a user by enabling a safe titration strategy that minimizes hypoglycemic episodes, a method of changing dosage guidance in response to the timing of dosage administration relative to the start time of a meal (e.g., before, at, or after the start of a meal), consideration of real-world events that affect the dosing strategy, post-meal alarms based on predicted probability of occurrence rather than thresholds, among others, which are merely examples. Other improvements and advantages are also provided. The various configurations of these devices are described in more detail by way of non-limiting embodiments.

[0009] Other systems, devices, methods, features, and advantages of the subject matter described herein will be apparent to those skilled in the art upon consideration of the following drawings and detailed description. All such additional systems, devices, methods, features, and advantages are included within this specification, are within the scope of the subject matter described herein, and are intended to be protected by the accompanying claims. The features of the illustrative embodiments should not be construed as limiting the claims to the extent that such features are not expressly recited in the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The details of the subject matter described in this specification can become apparent with reference to the accompanying drawings, both as to its structure and operation (like reference numerals in the drawings refer to like parts). The components in the drawings are not necessarily to scale, and instead emphasis has been placed upon illustrating the principles of the subject matter. Further, all the figures are intended to convey concepts, and relative sizes, shapes, and other detailed attributes may be shown schematically, rather than literally or precisely.

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DETAILED DESCRIPTION OF THE INVENTION

[0011] Before describing the subject matter of the present invention in detail, it is to be understood that the present disclosure is not limited to the specific embodiments described herein and can, of course, vary itself. Also, the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of the present disclosure is defined only by the appended claims.

[0012] Generally, embodiments of the present disclosure include systems, devices, and methods related to drug dosage guidance. The dosage guidance can be based on a wide range of user-specific information and information categories, such as the user's current and past analyte levels, the user's current and past diet, the user's current and past physical activity, the user's current and past medical history, and other physiological information about the user. According to one aspect of the embodiments, the dosage guidance provided by the systems, devices, and methods of the present disclosure can be based not only on individual information categories but also on the predicted effects such information categories have on the user's future analyte levels.

[0013] The dosage guidance function can be implemented as a dosage guidance application (DGA) that includes software and / or firmware instructions stored in the memory of a computing device and is executed by at least one processor or its processing circuitry. The computing device can be owned by a user or a healthcare provider (HCP), and the user or HCP can form an interface to the computing device via a user interface. According to some embodiments, the computing device can be a server or a trusted computer system accessible via a network, and the dosage guidance software can be presented to the user in the form of an interactive web page executed via a browser on a local display device (having a user interface) that communicates with the server or the trusted computer system via the network. In this embodiment and other embodiments, the dosage guidance software can be executed across multiple devices, or part of it can be executed by the processing circuitry of the local display device and part of it can be executed by the processing circuitry of the server or the trusted computer system. When the DGA is described as performing an operation, it will be understood by those skilled in the art that such an operation is executed in accordance with instructions stored in a computer memory (including instructions hard-coded in read-only memory) that cause the DGA to perform the operation when executed by at least one processor of at least one computing device. In all cases, the operation can alternatively be executed by hardware (e.g., a dedicated circuit) that is hardware-implemented to perform the operation, as opposed to execution by instructions stored in memory.

[0014] Furthermore, as used herein, a system in which DGA is implemented can be referred to as a dosage guidance system. The dosage guidance system can be configured for the sole purpose of providing dosage guidance, or can be a multi-functional system where dosage guidance is only one aspect. For example, in some embodiments, the dosage guidance system can also monitor the analyte level of a user. In some embodiments, the dosage guidance system can also deliver a drug to the user using, for example, an injection or infusion device. In some embodiments, the dosage guidance system can perform both analyte monitoring and drug delivery.

[0015] These and other embodiments described herein represent improvements in the field of computer-based dosage determination, analyte monitoring, and drug delivery systems. Specific features and potential advantages of the disclosed embodiments are further described below.

[0016] Before describing embodiments of dosage guidance in detail, it is desirable to first describe an example of a dosage guidance system in which a dosage guidance application can be implemented.

[0017] Exemplary embodiments of a dosage guidance system FIG. 1A is a block diagram showing an exemplary embodiment of a dosage guidance system 100. In this embodiment, the dosage guidance system 100 can provide dosage guidance, monitor one or more analytes, and deliver one or more drugs. This multi-functional example is used to illustrate the high degree of interconnectivity and performance obtained by the system 100. However, in the embodiments described herein, the analyte monitoring component, the drug delivery component, or both can be omitted as desired.

[0018] Here, system 100 includes a sensor control device (SCD) 102 configured to collect analyte level information from a user, a medication delivery device (MDD) 152 configured to deliver medication to the user, and a display device 120 configured to present information to the user and receive input or information from the user. The structure and function of each device are described in detail herein.

[0019] System 100 is configured for highly interconnected and highly flexible communication between devices. Each of the three devices 102, 120, and 152 can communicate directly with each other (without passing through an intermediate electronic device) or indirectly with each other (e.g., via cloud network 190 or via another device and then via network 190). The bidirectional communication capabilities between the devices and the bidirectional communication capabilities between the devices and network 190 are shown by double-headed arrows in FIG. 1A. However, those skilled in the art will understand that any one or more of the devices (e.g., SCD) can support unidirectional communication such as broadcast, multicast, or advertising communication. In any case, whether bidirectional or unidirectional, the communication can be wired or wireless. The protocols controlling the communication on each path can be the same or different, and can be proprietary or standardized. For example, the wireless communication between devices 102, 120, and 152 can be carried out according to Bluetooth (including Bluetooth Low Energy) standards, NFC (Near Field Communication) standards, Wi-Fi (802.11x) standards, mobile telephony standards, etc. All communication on various paths can be encrypted, and each device in FIG. 1A can be configured to encrypt and decrypt those communications being transmitted and received. In any case, the communication paths in FIG. 1A can be direct (e.g., Bluetooth or NFC) or indirect (e.g., Wi-Fi, mobile telephony, or other Internet protocols). Embodiments of system 100 do not need to have the ability to communicate across all of the paths shown in FIG. 1A.

[0020] In addition, although FIG. 1A shows a single display device 120, a single SCD 102, and a single MDD 152, one of ordinary skill in the art will understand that system 100 may include multiple of any of the aforementioned devices. By way of example only, system 100 may include a single SCD 102 that communicates with multiple (e.g., two, three, four, etc.) display devices 120 and / or multiple MDDs 152. Alternatively, system 100 may include multiple SCDs 102 that communicate with a single display device 120 and / or a single MDD 152. Further, each of the multiple devices may be of the same or different device types. For example, system 100 may include multiple display devices 120 including smartphones, handheld receivers, and / or smartwatches, each of which can communicate with an SCD 102 and / or an MDD 152 and can also communicate with each other.

[0021] Analyte data can be transferred autonomously (e.g., automatically transmitted according to a schedule) between each device within system 100 or in response to a request for analyte data (e.g., a request for analyte data is sent from a first device to a second device, and then analyte data is sent from the second device to the first device). To accommodate more complex systems, other techniques for data communication, such as cloud network 190, can also be employed.

[0022] Figure 1B is a block diagram showing another exemplary embodiment of the dosage guidance system 100. Here, the system 100 includes an SCD 102, an MDD 152, a first display device 120-1, a second display device 120-2, a local computer system 170, and a reliable computer system 180 accessible via a cloud network 190. The SCD 102 and the MDD 152 are communicable with each other and with the display device 120-1. The display device 120-1 can aggregate information from the SCD 102 and the MDD 152, process and display that information at a desired location, and function as a communication hub for transferring some or all of the information to the cloud network 190 and / or the computer system 170. Conversely, the display device 120-1 can receive information from the cloud network 190 and / or the computer system 170 and communicate some or all of the received information to the SCD 102, the MDD 152, or both. The computer system 170 may be a personal computer, a server terminal, a laptop computer, a tablet, or other suitable data processing device. The computer system 170 can include or present software for data management and analysis and for communication with components within the system 100. The computer system 170 can be used by a user or a medical professional to display and / or analyze analyte data measured by the SCD 102. Further, Figure 1B shows a single SCD 102, a single MDD 152, and two display devices 120-1 and 120-2, but those skilled in the art will understand that the system 100 can include multiple of any of the aforementioned devices, and each multiple of devices can include the same or different types of devices.

[0023] Referring further to FIG. 1B, according to some embodiments, a trustworthy computer system 180 can be owned by a manufacturer or vendor of components of system 100, either physically or virtually via a secure connection, to authenticate devices of system 100 (e.g., devices 102, 120-n, 152), to securely store user data, and / or to provide a data analysis program (e.g., accessible via a web browser) for performing analysis of the user's measured analyte data and medical history. The trustworthy computer system 180 can also function as a data hub for routing and exchanging data among all devices communicating with the system 180 via the cloud network 190. In other words, all devices of system 100 that are communicable with the cloud network 190 (e.g., directly using an Internet connection or indirectly via other devices) can communicate directly or indirectly with all other devices of system 100 that are communicable with the cloud network 190.

[0024] A state in which the display device 120-2 is communicating with the cloud network 190 is shown. In this example, the device 120-2 can be owned by another user who has been permitted access to the analyte and drug data of the person wearing the SCD102. For example, the person owning the display device 120-2 can be, as an example, the parent of a child wearing the SCD102, or, as another example, a caregiver of an elderly patient wearing the SCD102. The system 100 can be configured to communicate analyte and drug data regarding the wearer to another user who has been permitted access to the data via the cloud network 190 (e.g., via the trustworthy computer system 180).

[0025] Exemplary embodiments of an analyte monitoring device The analyte monitoring function of the dosage guidance system 100 can be implemented by including one or more devices capable of collecting, processing, and displaying the user's analyte data. Exemplary embodiments of such devices and methods of using them are described in International Application No. 2018 / 152241 and U.S. Patent Application No. 2011 / 0213225, the entire contents of which are incorporated herein by reference for all purposes.

[0026] Analyte monitoring can be performed in a number of different ways. A "continuous analyte monitoring" device (e.g., a "continuous glucose monitoring" device) can continuously or repeatedly transmit data from a sensor control device to a display device, for example, automatically according to a schedule, regardless of whether a prompt is displayed. As another example, a "flash analyte monitoring" device (e.g., a "flash glucose monitoring" device or simply a "flash" device) can transfer data from a sensor control device in response to a user-initiated data request (e.g., a scan) by a display device, using, for example, an NFC (Near Field Communication) or RFID (Radio Frequency Identification) protocol.

[0027] An analyte monitoring device that utilizes a sensor configured to be partially or fully disposed within the user's body can be referred to as an in vivo analyte monitoring device. For example, an in vivo sensor can be placed within the user's body such that at least a portion of the sensor contacts a body fluid (e.g., an interstitial (ISF) fluid such as dermal fluid in the dermal layer or subcutaneous fluid under the dermal layer, blood, etc.), and the concentration of the analyte in that body fluid can be measured. In vivo sensors can use various types of sensing technologies (e.g., chemical, electrochemical, or optical). Some systems that utilize in vivo analyte sensors can also operate without the need for calibration by fingerstick.

[0028] An 「in vitro」 device is a device that contacts a sensor with a biological sample outside the body (or 「ex vivo」). These devices typically include a port for receiving an analyte test strip that conveys the user's body fluid, which can be analyzed to determine the user's blood glucose level. Other ex vivo devices attempt to non-invasively measure the user's in vivo analyte levels, for example, it has been proposed to use optical techniques that can measure in vivo analyte levels without mechanically penetrating the user's body or skin. In vivo and ex vivo devices often include in vitro functionality (e.g., an in vivo display device that also includes a test strip port).

[0029] This subject matter will be described with respect to sensors capable of measuring glucose concentration, but detection and measurement of the concentration of other analytes are also within the scope of the present disclosure. These other analytes include, for example, ketones, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, human chorionic gonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA, fructosamine, glutamine, growth hormone, hormones, peroxide, prostate specific antigen, prothrombin, RNA, thyroid stimulating hormone, troponin, and the like. Also, for example, the concentration of drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, ditoxin, abused drugs, theophylline, and warfarin can be monitored. The sensor can be configured to measure two or more different analytes, at the same or different times. In some embodiments, a sensor control device can be coupled to two or more sensors, one sensor being configured to measure a first analyte (e.g., glucose), and the other one or more sensors being configured to measure one or more different analytes (e.g., any of those described herein). In other embodiments, the user can wear two or more sensor control devices, each of which can measure a different analyte.

[0030] The embodiments described herein can be used in any type of in vivo, in vitro, and ex vivo devices capable of monitoring the aforementioned analytes and the like.

[0031] In many embodiments, the operation of the sensor can be controlled by an SCD102. The sensor can be mechanically and communicatively coupled to the SCD102 or communicatively coupled to the SCD102 simply using wireless communication technology. The SCD102 can include an electronic device and a power source that enable and control the sensing of analytes performed by the sensor. In some embodiments, the sensor or the SCD102 can be self-powered so as not to require a battery. The SCD102 can also include a communication circuit for communicating with another device (e.g., a display device), which may or may not be local to the user's body. The SCD102 can be present on the user's body (e.g., attached to the user's skin, otherwise disposed, or carried in the user's clothing). The SCD102 can also be embedded in the user's body together with the sensor. The functions of the SCD102 can be split between a first component embedded in the body (e.g., a component that controls the sensor) and a second component present on the body or the like (e.g., a relay component that communicates with the first component and further communicates with an external device such as a computer or a smartphone). In other embodiments, the SCD102 can be external to the body and configured to non-invasively measure the user's analyte level. The sensor control device can also be referred to as, among others, depending on the actual implementation or embodiment, a "sensor control unit", an "on-body electronic device" device or unit, an "on-body" device or unit, an "in-body electronic device" device or unit, an "in-body" device or unit, or a "sensor data communication" device or unit.

[0032] In some embodiments, the SCD102 can include a user interface (e.g., a touch screen), process analyte data, and be capable of displaying the resulting calculated analyte levels to the user. In this case, the embodiments of the dosage guidance described herein can be implemented, in whole or in part, directly by the SCD102. In many embodiments, the physical form factor of the SCD102 is minimized (e.g., to minimize its appearance on the user's body), the sensor control device may not be accessible to the user (e.g., if it is fully embedded), or for other reasons, it may be desirable for the user to have a display device available to read the analyte levels and interface with the sensor control device.

[0033] FIG. 2A is a side view of an exemplary embodiment of the SCD102. The SCD102 can include a case or mount 103 (FIG. 2B) for the sensor electronics, which can be electrically coupled to an analyte sensor 101 configured herein as an electrochemical sensor. According to some embodiments, the sensor 101 can be configured to be partially present within the user's body (e.g., through the outermost surface of the skin), where it makes fluid contact with the user's body fluid and can be used to measure the user's analyte-related data together with the sensor electronics. An attachment structure 105, such as an adhesive patch, can be used to secure the case 103 to the user's skin. The sensor 101 can extend through the attachment structure 105 and project away from the case 103. Those skilled in the art will understand that other forms of attachment to the body and / or case 103 may be used in addition to or in place of the adhesive and are fully within the scope of the present disclosure.

[0034] The SCD102 can be applied to the body in any desired manner. For example, an insertion device (not shown), sometimes referred to as an applicator, can be used to position all or a portion of the analyte sensor 101 through the outer surface of the user's skin to contact the user's body fluid. In so doing, the insertion device can also position the SCD102 on the skin. In other embodiments, the insertion device can first position the sensor 101 and then subsequently couple (e.g., insert into a mount) an associated electronic device (such as a wireless transmission circuit and / or a data processing circuit, etc.) to the sensor 101 manually or with the aid of a mechanical device. Examples of insertion devices are described in U.S. Patent Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, 2015 / 0173661, and 2018 / 0235520, the entire contents of which are hereby incorporated by reference for all purposes.

[0035] Figure 2B is a block diagram showing an exemplary embodiment of an SCD102 having an analyte sensor 101 and sensor electronics 104. The sensor electronics 104 can be implemented on one or more semiconductor chips (e.g., an application specific integrated circuit (ASIC), a processor or controller, memory, a programmable gate array, etc.). In the embodiment of FIG. 1B, the sensor electronics 104 forms an interface with the sensor 101 in an analog manner and is configured to convert an analog signal to and / or from a digital format (e.g., using an A / D converter), an analog front end (AFE) 110, a power supply 111 configured to supply power to the components of the SCD102, a processing circuit 112, a memory 114, a timing circuit 115 (e.g., an oscillator and phase locked loop for providing a clock or other timing to the components of the SCD102), and a communication circuit 116 configured to communicate, wired and / or wirelessly, with one or more devices external to the SCD102, such as a display device 120 and / or an MDD 152.

[0036] The SCD102 can be implemented in a highly interconnected manner, the power supply 111 is coupled to each component shown in FIG. 2B, and these components (e.g., the AFE 110, the processing circuit 112, the memory 114, the timing circuit 115, and the communication circuit 116) that communicate or receive data, information or commands can be communicatively coupled to all other such components via, for example, one or more communication connections or buses 118.

[0037] The processing circuit 112 can include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which can be discrete chips or distributed (and in part) among a number of different chips. The processing circuit 112 can include on-board memory. The processing circuit 112 forms an interface with the communication circuit 116 and can perform other functions that facilitate analog-to-digital conversion, encoding and decoding, digital signal processing, and conversion of data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuit 112 also forms an interface with the communication circuit 116 and can perform the reverse function necessary to receive a wireless transmission and convert it into digital data or information.

[0038] Processing circuit 112 can execute the instructions stored in memory 114. These instructions can cause processing circuit 112 to process raw analyte data (or preprocessed analyte data) and reach the final calculated analyte level. In some embodiments, the instructions stored in memory 114, when executed, cause processing circuit 112 to process the raw analyte data to determine one or more of the calculated analyte level, the analyte level averaged within a predetermined time window, the rate of change calculated for the analyte level within a predetermined time window, and / or whether the calculated analyte indicator exceeds a predetermined threshold condition. These instructions can also cause processing circuit 112 to read and operate on received transmissions, adjust the timing of timing circuit 115, process data or information received from other devices (e.g., calibration information received from display device 120, encryption or authentication information, etc.), execute tasks to establish and maintain communication with display device 120, interpret voice commands from the user, transmit to communication circuit 116, etc. In embodiments where SCD102 includes a user interface, the instructions can cause processing circuit 112 to control the user interface, read user input from the user interface, display information on the user interface, format data for display, etc. The functions described herein encoded as instructions can instead be implemented by SCD102 using a hardware or firmware design that does not rely on the execution of software instructions stored to achieve the functions.

[0039] Memory 114 can be shared by one or more of the various functional units present within SCD102 or can be distributed among two or more functional units (e.g., as separate memories present in different chips). Memory 114 can also be a separate chip by itself. Memory 114 is non-transitory and can be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0040] The communication circuit 116 can be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communication circuits) that perform functions for communication over respective communication paths or links. The communication circuit 116 can include or be coupled to one or more antennas for wireless communication.

[0041] The power source 111 can include one or more batteries, which can be rechargeable or single-use disposable batteries. The power management circuit can also include, for example, to regulate battery charging, monitor the usage of the power source 111, boost the power, and perform DC conversion.

[0042] Furthermore, an optional temperature sensor (not shown) can collect readings or measurements of the temperature on the skin or of the sensor. These readings or measurements can be communicated from the SCD102 to another device (e.g., the display device 120) (either individually or as aggregated measurements over time). However, the temperature readings or measurements can be used in combination with software routines executed by the SCD102 or the display device 120 to correct or compensate the analyte measurements output to the user, instead of or in addition to actually outputting the temperature measurements to the user.

[0043] Exemplary embodiments of a drug delivery device The drug delivery function of the dosage guidance system 100 can be realized by including one or more medical device dispensers (MDDs) 152. The MDD 152 can be any device configured to deliver a specific dosage of a drug. The MDD 152 can also include a device that transmits dosage-related data to the DGA even if the device itself does not deliver the drug, for example, a paint cap. The MDD 152 can be configured as a portable injection device (PID) that can deliver one dosage per single injection such as a bolus. The PID is basically a manually operated syringe, and the drug is pre-loaded in the syringe or must be drawn into the syringe from a container before injection. However, in most embodiments, the PID includes electronics for forming an interface to the user and performing drug delivery. The PID does not necessarily have the appearance of a pen, but is often referred to as a dosing pen. A PID with user interface electronics is often referred to as a smart pen. The PID may be discarded after being used to deliver one dosage, or may be durable and reused repeatedly for performing multiple dosings over a period of one day, one week, or one month. The PID is often used by users who perform multiple daily injections (MDI) therapy.

[0044] The MDD can also include a pump and an infusion set. The infusion set includes a tube cannula that is at least partially present within the recipient's body. This tube cannula is in fluid communication with the pump, and the pump can repeatedly deliver the drug little by little into the recipient's body through the cannula. The infusion set can be applied to the recipient's body using an infusion set applicator, and the infusion set often remains implanted for a period of two to three days or longer. The pump device includes electronics for forming an interface to the user and controlling the slow injection of the drug. Both the PID and the pump can store the drug in a drug reservoir.

[0045] The MDD152 can function as part of a closed-loop system (e.g., an artificial pancreas system that does not require user intervention for operation), a semi-closed-loop system (e.g., an insulin loop system that requires little user intervention for operation, such as to confirm a dosage change), or an open-loop system. For example, the analyte levels of a diabetic patient can be repeatedly and automatically monitored by the SCD102, and the information can be used by the dosage guidance embodiments described herein to automatically calculate or otherwise determine an appropriate drug dosage for controlling the analyte levels of the diabetic patient, and then deliver that dosage to the body of the diabetic patient. This calculation can be performed within the MDD152 or any other device of the system 100, and the resulting determined dosage can then be communicated to the MCD152.

[0046] In many embodiments, the dosage guidance provided by the embodiments described herein is for types of insulin (e.g., rapid-acting (RA), short-acting insulin, intermediate-acting insulin (e.g., NPH insulin), long-acting (LA), ultra-long-acting insulin, and mixed insulin), and is the same agent to be delivered by the MDD152. Types of insulin include human insulin and synthetic insulin analogs. Insulin can also include premixed formulations. However, the dosage guidance embodiments described herein and the drug delivery capabilities of the MDD152 can also be applied to other non-insulin drugs. Such drugs can include, but are not limited to, exenatide, exenatide extended-release formulations, liraglutide, lixisenatide, semaglutide, pramlintide, metformin, SLGT1-i inhibitors, SLGT2-i inhibitors, and DPP4 inhibitors. The dosage guidance embodiments can also include combination therapies. Combination therapies can include, but are not limited to, insulin and glucagon-like peptide-1 receptor agonists (GLP-1RAs), and insulin and pramlintide.

[0047] To facilitate the description of the dosage guidance embodiments of this specification, MDD152 is often described in the form of a PID, specifically a smart pen. However, those skilled in the art will readily understand that MDD152 can alternatively be configured as a pen cap, a pump, or any other type of drug delivery device.

[0048] Figure 3A is a schematic diagram showing an exemplary embodiment of MDD152 configured as a PID, specifically a smart pen. MDD152 can include a case 154 for an electronic device, an injection motor, and a reservoir for the drug (see Figure 3B), from which the drug can be delivered via a needle 156. The case 154 can include a removable or detachable cap or cover 157, which when worn can cover the needle 156 when not in use and can then be removed for injection. MDD152 can include a user interface 158 that can be implemented as a single component (e.g., a touch screen for outputting information to the user and receiving input from the user) or as multiple components (e.g., a touch screen or display combined with one or more buttons, switches, etc.). MDD152 can also include an actuator 159 that can be actuated by movement, pressing, contact, or other means to begin delivering the drug from the internal reservoir through the needle 156 into the recipient's body. According to some embodiments, the cap 157 and the actuator 159 can also include one or more safety mechanisms to prevent separation and / or actuation to reduce the risk of injection of harmful drugs. Details of such safety mechanisms and the like are described in U.S. Patent Publication No. 2019 / 0343385 (the '385 publication), the entire contents of which are hereby incorporated by reference for all purposes.

[0049] Figure 3B is a block diagram showing an exemplary embodiment of an MDD 152 having an electronic device 160 coupled to a power supply 161, and a motor 162 for an electric injection coupled to the power supply 161 and a reservoir for a drug as well. The needle 156 is shown in fluid communication with a reservoir 163, and a valve (not shown) may be present between the reservoir 163 and the needle 156. The reservoir 163 can be permanent or removable and can be exchanged with another reservoir containing the same or different drugs. The electronic device 160 can be implemented on one or more semiconductor chips (e.g., an application specific integrated circuit (ASIC), a processor or a controller, a memory, a programmable gate array, etc.). In the embodiment of Figure 3B, the electronic device 160 includes a high-level functional unit including a processing circuit 164, a memory 165, a communication circuit 166 configured to communicate with one or more devices external to the MDD 152 (such as a display device 120) in a wired and / or wireless manner, and a user interface electronic device 168.

[0050] The MDD 152 can be implemented in a highly interconnected manner, the power supply 161 is coupled to each component shown in Figure 3B, and these components (e.g., the processing circuit 164, the memory 165, and the communication circuit 166) that communicate or receive data, information, or commands can be communicatively coupled to all other such components via, for example, one or more communication connections or buses 169.

[0051] The processing circuit 164 can include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which can be discrete chips or distributed (and in part) among a number of different chips. The processing circuit 164 can include on-board memory. The processing circuit 164 forms an interface to the communication circuit 166 and can perform other functions that facilitate analog-to-digital conversion, encoding and decoding, digital signal processing, and conversion of data signals into formats (e.g., in-phase and quadrature) suitable for wireless or wired transmission. The processing circuit 164 also forms an interface to the communication circuit 166 and can perform the reverse functions necessary to receive a wireless transmission and convert this into digital data or information.

[0052] The processing circuit 164 can execute software instructions stored in the memory 165. These instructions can cause the processing circuit 164 to receive a selection or provision of a specified dosage from the user (e.g., input via the user interface 158 or received from another device), process a command to deliver the specified dosage (e.g., a signal from the actuator 159), and control the motor 162 to effect the delivery of the specified dosage. These instructions can also cause the processing circuit 164 to read and operate on received transmissions, process data or information received from other devices (e.g., calibration information received from the display device 120, encryption or authentication information, etc.), perform tasks to establish and maintain communication with the display device 120, interpret voice commands from the user, transmit to the communication circuit 166, etc. In embodiments where the MDD 152 includes the user interface 158, the instructions can cause the processing circuit 164 to control the user interface, read user input from the user interface (e.g., input of a drug dosage for administration or input of confirmation of a recommended drug dosage), display information on the user interface, format data for display, etc. The functions described herein that are encoded as instructions can instead be implemented by the MDD 152 using a hardware or firmware design that does not rely on the execution of software instructions stored to achieve the functions.

[0053] The memory 165 can be shared by one or more of the various functional units present within the MDD 152 or can be distributed among two or more functional units (e.g., as separate memories present in different chips). The memory 165 can also be a separate chip by itself. The memory 165 can be non - transient and can be a volatile memory (e.g., RAM, etc.) and / or a non - volatile memory (e.g., ROM, flash memory, F - RAM, etc.).

[0054] The communication circuit 166 can be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communication circuits) that perform functions for communication via respective communication paths or links. The communication circuit 166 can include or be coupled to one or more antennas for wireless communication. Details of exemplary antennas can be found in the ’385 publication, the entire contents of which are hereby incorporated by reference for all purposes.

[0055] The power supply 161 can include one or more batteries, which can be rechargeable or single-use disposable batteries. A power management circuit can also be included for regulating the charging of the batteries, monitoring the usage of the power supply 161, boosting the power, performing DC conversion, and the like.

[0056] The MDD 152 can also include an integrated or attachable in vitro glucose meter that includes an in vitro test strip port (not shown) for receiving an in vitro glucose test strip for performing in vitro blood glucose measurements.

[0057] Exemplary embodiments of a display device The display device 120 can be configured to display information related to the system 100 to the user and to receive or accept input from the user related to the system 100 as well. The display device 120 can display the most recently measured analyte level to the user in any number of forms. The display device can display not only the user's past analyte levels, but also other metrics that describe the user's analyte information (e.g., glucose control metrics (time in range), ambulatory glucose profile (AGP), hypoglycemia risk level, etc.). The display device 120 can display drug delivery information such as past dosage information and the time and date of administration. The display device 120 can display alarms, alerts, or other notifications related to analyte levels and / or drug delivery.

[0058] The display device 120 is not only a dedicated device for use with the system 100 (e.g., an electronic device designed and manufactured primarily for forming an interface with an analyte sensor and / or drug delivery device), but can also be used with multifunctional and general-purpose computing devices such as handheld or portable mobile communication devices (e.g., smartphones or tablets), or laptops, personal computers, or other computing devices. The display device 120 can be configured as a smart glass, or a mobile smart wearable electronic device assembly such as a smartwatch or a wristband. The display device and its variations can be referred to, among other things, as a "reader device", "reader", "handheld electronic device" (or handheld), "portable data processing" device or unit, "information receiver", "receiver" device or unit (or simply receiver), "relay" device or unit, or "remote" device or unit.

[0059] FIG. 4A is a schematic diagram showing an exemplary embodiment of the display device 120. Here, the display device 120 includes a user interface 121 and a case 124 in which the display device electronic device 130 (FIG. 4B) is held. The user interface 121 can be implemented as a single component (e.g., a touch screen capable of input and output) or a plurality of components (e.g., one or more devices configured to receive a display and user input). In this embodiment, the user interface 121 includes a touch screen display 122 (configured to display information and graphics and receive user input by touch) and an input button 123, both of which are coupled to the case 124.

[0060] The display device 120 can have stored software (e.g., software downloaded by the manufacturer or by the user in the form of one or more "apps" or other software packages) that forms an interface to the SCD102, MDD152, and / or the user. Additionally, or alternatively, the user interface can be controlled by a web page displayed in a browser or other Internet interface software executable on the display device 120.

[0061] Figure 4B is a block diagram of an exemplary embodiment of a display device 120 with a display device electronic device 130. Here, the display device 120 includes a user interface 121 including a display 122 and an input component 123 (such as buttons, actuators, touch sensor switches, capacitive switches, pressure sensitive switches, jog dials, microphones, speakers, etc.), a processing circuit 131, a memory 125, a communication circuit 126 configured to communicate with and / or from one or more other devices external to the display device 120, a power supply 127, and a timing circuit 128 (such as an oscillator and a phase locked loop for providing clock or other timing to components of the SCD102). Each of the foregoing components can be implemented as one or more different devices or integrated into a multifunctional device (for example, integration of the processing circuit 131, the memory 125, and the communication circuit 126 on a single semiconductor chip). The display device 120 can be implemented in a highly interconnected manner, the power supply 127 is coupled to each component shown in Figure 4B, and those components that communicate or receive data, information, or commands (such as the user interface 121, the processing circuit 131, the memory 125, the communication circuit 126, and the timing circuit 128) can be communicatively coupled to all other such components via, for example, one or more communication connections or buses 129. Figure 4B is a simplified representation of typical hardware and functions present within a display device, and those skilled in the art will readily recognize that other hardware and functionality (such as codecs, drivers, glue logic) may also be included.

[0062] The processing circuit 131 can include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which can be discrete chips or distributed among and within a number of different chips. The processing circuit 131 can include on-board memory. The processing circuit 131 forms an interface with the communication circuit 126 and can perform other functions that facilitate analog-to-digital conversion, encoding and decoding, digital signal processing, and conversion of data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuit 131 forms an interface with the communication circuit 126 and can also perform the reverse functions necessary to receive a wireless transmission and convert this into digital data or information.

[0063] The processing circuit 131 can execute software instructions stored in the memory 125. These instructions can cause the processing circuit 131 to process raw analyte data (or preprocessed analyte data) and reach the corresponding analyte levels suitable for display to the user. These instructions can cause the processing circuit 131 to read, process, and / or store administration instructions from the user and, consequently, transmit the administration instructions to the MDD 152. These instructions can cause the processing circuit 131 to configure system parameters (such as alarm thresholds, notification settings, display preferences, etc.), present current and past analyte level information to the user, present current and past drug delivery information to the user, collect other non-analyte information from the user (such as information regarding consumed meals, performed activities, administered drugs, etc.), and execute user interface software adapted to present to the user an interactive group of graphical user interface screens for the purpose of presenting notifications and alarms to the user. These instructions can also cause the processing circuit 131 to perform transmissions to the communication circuit 126, cause the processing circuit 131 to read and operate on received transmissions, read inputs from the user interface 121 (such as input of a drug dosage to be administered or confirmation of a recommended drug dosage), display data or information to the user interface 121, adjust the timing of the timing circuit 128, process data or information received from other devices (such as analyte data received from the SCD 102, calibration information, encryption or authentication information, etc.), perform tasks for establishing and maintaining communication with the SCD 102, interpret voice commands from the user, etc. The functions described herein, which are coded as instructions, can instead be implemented by the SCD 102 using a hardware or firmware design that does not rely on the execution of software instructions stored to achieve the functions.

[0064] Memory 125 can be shared by one or more of the various functional units present within display device 120, or can be distributed among two or more functional units (e.g., as separate memories present in different chips). Memory 125 can also be a separate chip by itself. Memory 125 is non-transitory and can be a volatile memory (e.g., RAM, etc.) and / or a non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0065] Communication circuit 126 can be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communication circuits) that perform functions for communication via respective communication paths or links. Communication circuit 126 can include or be coupled to one or more antennas for wireless communication.

[0066] Power supply 127 can include one or more batteries, and these batteries can be rechargeable or single-use disposable batteries. A power management circuit can also be included for adjusting the charging of the batteries, monitoring the usage of power supply 127, boosting the power, performing DC conversion, etc.

[0067] Display device 120 can also include one or more data communication ports (not shown) for wired data communication with external devices such as computer system 170, SCD 102, or MDD 152. Display device 120 can also include an integrated or attachable in vitro glucose meter that includes an in vitro test strip port (not shown) for receiving an in vitro glucose test strip for performing in vitro blood glucose measurement.

[0068] The display device 120 can display the measurement analyte data received from the SCD102, and can also be configured to output alarms, alert notifications, glucose values, etc., which may be visual, auditory, tactile, or any combination thereof. In some embodiments, the SCD102 and / or MDD152 can also be configured to output alarms or alert notifications in visual, auditory, tactile forms, or combinations thereof. Further details and other display embodiments can be found, for example, in U.S. Patent Publication No. 2011 / 0193704, the entire content of which is incorporated herein by reference for all purposes.

[0069] Exemplary embodiments related to dosage guidance The following exemplary embodiments relate to the dosing guidance function provided by the dosing guidance system 100. The dosing guidance function is implemented, in many embodiments, as a set of software instructions stored and / or executed on one or more electronic devices. This dosing guidance function will be referred to herein as the dosing guidance application (DGA). In some embodiments, the DGA is stored, executed, and presented to the user on the same single electronic device. In other embodiments, the DGA can be stored and executed on one device and presented to the user on a different electronic device. For example, the DGA can be stored and executed on a reliable computer system 180 and presented to the user via a web page displayed via an Internet browser executed on the display device 120.

[0070] Accordingly, there are many different embodiments related to the number and type of electronic devices used to store, execute, and present the DGA to the user. For presenting to the user, a device configured to implement this capability will be referred to herein as a user interface device (UID) 200. FIG. 5 is a block diagram showing an example of one embodiment of the UID 200. In this embodiment, the UID 200 includes a case 201 coupled to a user interface 202. The user interface 202 can output information to the user and receive input or information from the user. In some embodiments, the user interface 202 is a touch screen. As shown here, the user interface 202 includes a display 204, which may be a touch screen, and an input component 206 (e.g., buttons, actuators, touch sensor switches, capacitive switches, pressure sensitive switches, jog dials, microphones, touch pads, soft keys, keyboards, etc.).

[0071] Many of the devices described herein can be implemented as the UID 200. For example, the display device 120 is used as the UID 200 in many embodiments. In some embodiments, the MDD 152 can be implemented as the UID 200. In embodiments where the SCD 102 includes a user interface, the SCD 102 can be implemented as the UID 200. The computer system 170 can also be implemented as the UID 200.

[0072] Detection of MDI dosing strategies Turning to the aspect of the DGA, more specifically, the DGA can use knowledge of the patient's dosing strategy and analyte levels to provide accurate dosing guidance. Described herein are exemplary embodiments related to the automatic detection of a patient's dosing strategy that can facilitate and expedite the setup of the DGA. The detection of the dosing strategy can be based on a number of characteristics of the monitored drug (e.g., insulin) dose. For example, in this embodiment, based on the MDD152 used to administer the dose, the dose can be identified as basal or bolus. Some patients can have multiple MDD152s. For example, a patient can have one MDD for administering long-acting insulin (e.g., basal dose) and another MDD for administering rapid-acting insulin (e.g., meal dose). Also, the count (e.g., number of administrations) and timing of basal administrations per dose can be used to classify the basal strategy as a "single" or "split" basal dosing strategy. For example, in a "split" basal dosing strategy, a 20U basal dose per day can be split into two 10U doses, one dose can be administered before bedtime, and the other dose can be administered upon waking up.

[0073] When successive bolus doses are administered in sequence, the system can attempt to distinguish between the original meal dose, an increment to the original meal dose, or a correction dose for hyperglycemia between meals. If the DGA detects a small dose followed by a large dose (both occurring near the start of a meal), the DGA can group those doses as a single meal dose, even if the first dose was a priming dose that was not injected into the patient. If a dose occurs long after a dose (group) that has been tagged as a known meal and / or meal dose, the DGA can tag the later dose as a correction dose for post-meal hyperglycemia or as a dose that increases the previous meal dose in consideration of the extra food consumed. When a meal event is recognized based on a meal detector algorithm or a meal event input by the user, the DGA uses the amount of the previous dosing event and the timing relative to the currently detected meal to help clarify whether the previous dosing was the first of multiple meal dosings or a correction for hyperglycemia between meals. It is assumed that correction doses are smaller in size than doses at mealtime. Further, if the elapsed time between the previous dosing and the current meal event is long enough, it is reasonable to consider that these two events are not associated with the treatment of the same glucose excursion event, ruling out the possibility that the previous dosing was the first of multiple dosings related to a given meal. Thus, if a previous dose is small enough compared to the meal doses recorded within this window over the past few days and is far enough away from the current meal, the previous dose can be classified as a correction dose event.

[0074] The DGA can be configured to use a real-time meal detection algorithm and dosing times to identify doses added to the basal as bolus doses for breakfast, lunch, and / or dinner, and / or correction doses. The DGA can also be configured to use the number of daily bolus doses to identify dosing strategies as basal only, basal + 1, basal + 2, etc.

[0075] These different scenarios and aspects of DGA will be described in more detail elsewhere in this specification.

[0076] Onboarding To enhance the safety profile of the DGA, the HCP can approve the learned insulin dosing parameters and subsequent titrations calculated by the DGA. Embodiments of the DGA include numerous ways of interacting between the HCP and the DGA, such that relevant evidence for the HCP to approve the proposed dose learning and titration is provided in a concise and useful manner that improves the workflow.

[0077] For diabetic patients already on an insulin dosing regimen, the HCP can utilize existing reports that provide insights into the patient's glucose patterns to identify users who may benefit from dose guidance. Embodiments of the DGA provide a learning period (e.g., while using DGS100) that can classify the patient's dosing strategy and trends. If the combined insulin and glucose data further confirm that the user is a suitable candidate for the DGA, e.g., a candidate for which the DGA can learn a specific dosing strategy, the insulin dosing parameters learned during the learning period can be used as initial conditions for dose guidance for which titration can be performed by the DGA as needed. Also, an HCP notification method for the initialization of the DGA dosing parameters and titration can be presented. This process helps both the HCP and the user by streamlining DGA onboarding and titration, and also serves to ensure that the DGA is used only by those it is directed at. If the DGA is unable to learn the patient's dosing parameters, the DGA can indicate a discrepancy in the patient's dosing, which the HCP can use to address the dosing discrepancy.

[0078] The first step in identifying potential users of DGA can include an initial analysis of a patient's blood glucose control by glucose concentration profile. To enable as many users as possible to access DGA, this process can be made independent of the method of glucose monitoring that the user is currently performing.

[0079] In the case of diabetic patients currently using the SCD102, it is possible to utilize a glucose pattern report that includes key metrics, glucose concentration profiles (e.g., ambulatory glucose profiles (AGP)), patterns identified at different time intervals, and titrations and lifestyle suggestions for improving cases where glucose is consistently outside the target range. This pattern can be identified using the GPA algorithm, as described in detail elsewhere. As seen in FIG. 6, there is an exemplary glucose pattern report 250. One skilled in the art will understand that the glucose pattern report 250 can be a graphical user interface output on the display of a computing device. The glucose pattern report 250 can include a time in range (TIR) display 252 that shows the percentage of time the patient's glucose level was below the target range (e.g., below 70 mg / dL), within the target range (e.g., 70 - 180 mg / dL), and above the target range (e.g., above 180 mg / dL). Additionally, the TIR display 252 can report the amount of time the patient's glucose level was at a low threshold (e.g., below 54 mg / dL) below the lower limit of the target range or at a high threshold (e.g., above 250 mg / dL) above the upper limit of the target range. The TIR display 252 can include a histogram where different ranges are displayed in different colors. For example, the time below the target range can be displayed in red, the time within the target range can be displayed in green, and the time above the target range can be displayed in yellow or orange. The glucose pattern report 250 can also display the average glucose level 254 over the time period 264 of the report, e.g., for about 14 days. The glucose pattern report 250 can also display a glucose concentration profile 256 such as an ambulatory glucose profile (AGP). The glucose concentration profile 256 is a graph of the glucose data over the time period of the report, and the various data points on the graph can be color-coded according to whether their glucose analyte level is below the target range, within the target range, or above the target range. This color-coding can correspond to the color-coding of the TIR display 252.Boxes 258 surrounding different portions of the glucose concentration profile 256 highlight patterns (e.g., hyperglycemia, hypoglycemia, moderate or appropriate hyperglycemia, moderate or appropriate hypoglycemia, and combinations thereof) detected according to the GPA algorithm described elsewhere in this specification.

[0080] Dosage guidance 260 can also be provided in the glucose pattern report 250 when the patient's current therapy (e.g., basal + RA insulin, basal only, basal + SU, etc.) is known. The dosage guidance can be provided in the form of text recommendations. General advice regarding insulin dose titration can be provided based on the identified high and low glucose patterns highlighted in box 258 in the glucose concentration profile 256. Note that this general advice could be determined without accessing data regarding the actually administered insulin dose. The recommendations can generally follow the rule of reducing any low patterns first before reducing any high patterns. If the glucose pattern report includes a proposal regarding insulin dose titration, the glucose pattern report 250 can also include a proposal that the patient is a good candidate for DGS100, facilitating the conversation between the HCP and the patient before transitioning to the learning period.

[0081] When the GPA algorithm identifies a highly variable pattern, self-care guidance 262 can be displayed in the glucose pattern report 250. Alternatively, there may be cases where the glucose concentration profile 256 has a high variability value such that the logic underlying the report cannot make a specific proposal, in which case, instead of the aforementioned case, it is the default for the user to consult the HCP regarding lifestyle or therapy changes.

[0082] For those who are not currently using a device or system (e.g., SCD102) associated with an application capable of creating a glucose pattern report 250 as described above, the HCP can propose that the patient be monitored by another device or system so that a report 250 or similar can be created. For example, the patient may wear an SCD102 configured in a masking mode or blind mode where the user cannot access the measured glucose levels and thus cannot modify their behavior during a period of several days or weeks while glucose data is being collected. From these data, a glucose pattern report can be created. If the proposed insulin titration is included in the glucose pattern report, the glucose pattern report 250 can also include a proposal that the patient is a good candidate for the DGS100 and can propose a learning period for the drug dosing strategy.

[0083] During the learning period, MDD152 can be incorporated into the glucose sensing system used for the initial screening and can provide a more complete portrait of insulin intensive diabetes management. The learning period can utilize an algorithm such as those described elsewhere in this specification to detect the user's insulin dosing strategy. During the learning period, the DGA can be configured to determine how the user determines the meal-time dose. For example, the DGA can determine whether the user is determining the meal-time dose based on carbohydrate counting, whether the user is determining the meal-time dose based on an empirical method such as learning appropriate dosages based on past meals or similar meal experiences, whether the user is administering a fixed amount of insulin at meal-time, whether the user is changing the insulin meal dose (determined from fixed dosing, carbohydrate counting, or empirical dosing) based on the pre-meal glucose value, whether the user considers residual insulin (IOB) from previous injections or other techniques when determining the dose (determined from fixed dosing, carbohydrate counting, or empirical dosing). The DGA can also determine whether the user's meal-time dose is constant depending on the type of meal (e.g., breakfast, lunch, and dinner), or whether the meal dose changes. The determination that the meal-time dose is changing may indicate that the user is basing the meal-time dose on carbohydrate counting techniques. The DGA can also determine whether the user is adjusting the meal-time dose considering pre-meal hyperglycemia. In some embodiments, the DGA can also determine a target glucose level, and the user can adjust or correct the meal-time dose if their level is above or predicted to be above the target glucose level. The DGA can also determine which meals are associated with insulin dosing. The DGA can also determine patterns where meal-taking has been forgotten. For example, the DGA can detect whether the user has failed to administer the insulin dose associated with a meal or time period at least twice and instead at least three times during a certain period (e.g., one week or two weeks).

[0084] The learning period can continue for any period of time sufficient to obtain the necessary information. In many embodiments, this period is at least two days, more preferably one week or longer (e.g., 14 days), and can vary depending on how well the DGA can learn trends. The results can be summarized in a summary report for both the user and the physician.

[0085] Learning method Manual setting of the DGS100 requires time for the HCP, but there may be cases where sufficient time cannot be taken. In addition, even if time for the HCP can be secured, the setting is complex and errors may easily occur. To mitigate these problems, a patient parameter initialization (PI) module that requires no setting or only minimal setting can be included in the DGA. The PI module parameterizes the patient's dosing habit, for example, to learn the patient's dosing strategy including only basal, basal + 1, basal + 2, etc., and to configure the dosing guidance setting by the DGA.

[0086] According to one aspect of the embodiment, the learning process of the PI module can include the step of automatically configuring the patient's dosing guidance setting from the observed data. When the setting is learned normally, the DGS100 can enter the guidance mode, and the patient can request dosing guidance and receive notifications regarding administration. During the learning process preceding the guidance mode, the DGA can process the glucose and insulin data collected by the patient's SCD102, UID202, and / or other devices, and determine the administration information based on the processed data.

[0087] Administration information can include, for example, a dosing regimen, meal - dosing type, dosage parameters, and dosing range. The dosing regimen can include, for example, basal amount+BF, basal amount+LU, basal amount+DI, basal amount+BF / LU, basal amount+BF / DI, basal amount+LU / DI, and basal amount+3, where BF indicates "breakfast", LU indicates "lunch", and DI indicates "dinner". Additional regimens, such as an afternoon snack dosage, can also be included. The type of meal dosage can include, for example, a fixed meal dosage or a variable meal dosage. Dosage parameters can include, for example, the nominal fixed dosage or carbohydrate ratio for each meal, the pre - meal correction factor (CF), and the post - meal CF. The dosing range can include an estimate of the minimum meal dosage.

[0088] For each of the above - described administration information types, the DGA can determine whether the cumulative data is sufficient or insufficient to determine the administration information. In some embodiments, the patient's SCD102 can be configured to operate for a predetermined time period, for example, 14 days. In these embodiments, the DGA can determine, after the predetermined time period (or earlier if the sensor stops operating before the end of the period), whether the available analyte and administration data is sufficient to determine each of the above - described administration information. If sufficient, the DGA can execute the parameterization method 300 and initiate the dosage guidance mode. In alternative embodiments, during the learning period, periodically (e.g., once a day), the DGA can determine whether the data is sufficient to determine each of the above - described administration information. In any case, if the collected data is sufficient, the DGA can end the learning period, execute the parameterization, and start the guidance period. Otherwise, the DGA can continue the learning process.

[0089] Referring to FIG. 7, the DGA can be configured to execute method 300, alone or in any combination, on a suitable computing device such as UID200, SCD102, MDD152. The program instructions for executing method 300 can be grouped in a PI module or any other suitable code configuration. Generally speaking, method 300 can include a step in which, at step 302, the DGA classifies each of the doses of the drug received by the patient during the analysis period based on data characterizing the analyte of the patient and the doses of the drug received by the patient during the analysis period. Method 300 can further include a step of grouping each of the doses into one of a set of meal time groups at step 304. The method can further include a step of creating at least in part the patient's dose parameters by applying the data of each meal time group to a model at step 306. The method can include a step of storing the dose parameters in a computer memory and configuring dose guidance settings at step 308. In the embodiments described herein, the analyte can be glucose or can include an indicator of the patient's glucose level, and the drug can be insulin or can include insulin. The dose guidance settings can be used by the DGA to formulate dose guidance or provided to an interface device, such as UID200 or a healthcare provider's terminal, for output. A more detailed aspect of each operation in method 300 will be described below. As used herein, "PI module" refers to a part or portion of the DGA that executes the operations of method 300 and any auxiliary operations. The PI module is not limited to a particular configuration and can encompass various arrangements of computer code.

[0090] In one aspect, the classification operation 302 can include classifying each dose of a drug (e.g., insulin) into one of a meal dose, a correction dose, and / or an ambiguous dose. If the DGA cannot classify a drug dose as a meal dose or a correction dose with a defined confidence level, the DGA can classify the drug dose as ambiguous and omit the drug dose from use in creating dose parameters for dose guidance.

[0091] The DGA can classify drug doses by a sequence of two operations referred to herein as feature extraction and classification. In connection with FIG. 7, the classification operation 302 can include creating a feature matrix that correlates a set of classification features to each of the doses. In some embodiments, the DGA can compose, as inputs to a function that outputs a feature matrix for insulin dose classification, a vector of insulin injection timestamps, a data file containing analyte measurements from the patient's SCD102, and results from a meal detection algorithm module discussed elsewhere herein. The number of rows of the feature matrix can indicate the amount of injections, or equivalent drug administration events, during the associated analysis period. Each row of the feature matrix can be, or can include, a feature vector for a single dosing event. In embodiments for classifying insulin injections, each vector can include elements, described below, referred to herein as classification features. The DGA can determine each of the elements of the feature vector based on a corresponding segment of glucose monitoring data for a time range relative to the insulin injection time, e.g., a -2.5 hour to 1.5 hour range.

[0092] In embodiments, the classification features can include the dosing time of each administration, e.g., the time period when the insulin injection was recorded by the MDD152, or the time period recorded by the patient using the UID200.

[0093] The classification features can further include time-filtered analyte values, such as glucose values filtered using a Savitsky-Golay filter, a low-pass filter, a band-pass filter, a non-parametric smoothing filter such as locally estimated scatterplot smoothing, or other filters. In one aspect, the Savitsky-Golay filter can have a degree of 2 and a frame length of 7 with a sampling interval of 15 minutes.

[0094] The classification features can further include the rate of change of the analyte value closest to the dosing time, for example, the rate of change of the analyte (e.g., glucose) value calculated by linear regression of five analyte data points (e.g., using a sampling interval of 15 minutes) centered on the data point closest to the dosing (e.g., injection) time.

[0095] The classification features can further include a left-sided area under the curve (AUC) indicator showing the difference between the analyte value and the analyte value closest to the dosing time integrated over an interval before the dosing time. For example, to obtain the left-sided AUC indicator, the DGA can collect all data points from the filtered analyte data within a time window (e.g., 2.5 hours), count back from the injection time, then calculate the difference between the average analyte value of the collected data points and the data point closest to the injection time (i.e., the reference data point), and multiply this difference by the duration of the time window to calculate the increment of the left-sided AUC, thereby calculating the left-sided AUC indicator.

[0096] The classification feature can further include a right AUC indicator that represents the value obtained by integrating, within an interval after the dosing time, the difference between the analyte value and the analyte value closest to the dosing time. For example, the DGA can collect all data points from the filtered analyte data within a time window (e.g., 1.5 hours), count back from the injection time, then calculate the difference between the average analyte value of the collected data points and the data point closest to the injection time (reference data point), and multiply this difference by the duration of the time window to calculate the increment of the right AUC, thereby calculating the right AUC indicator.

[0097] The classification feature can further include the elapsed time between dosing times. For example, for each injection time, the DGA can calculate the elapsed time between the previous injection time and the current injection time by subtracting the previous injection time from the current injection time. For the first injection time in the insulin log, since there is no available previous injection time, the DGA can calculate the elapsed time from the first SCG time data point to the current injection time. Additionally, as a further example, the DGA can calculate the elapsed time between the current injection time and the next injection time by subtracting the current injection time from the next injection time. For the last injection time in the insulin log, since there is no available next injection time, the DGA can calculate the elapsed time from the current injection time to the last SCG time data point. In both backward and forward calculations, if the elapsed time is greater than a predetermined maximum value (e.g., 12 hours), the DGA can set the value of the elapsed time equal to the maximum time.

[0098] The classification feature can further include the probability that a meal is started within a defined interval before the dosing time, for example, the maximum value of the meal start probability within a time window before injection (e.g., 1.5 hours). This probability can be calculated by a meal detection module described elsewhere in this specification.

[0099] The classification features can further include the most accurate interval of the elapsed time from the most recent meal, e.g., the elapsed time from the maximum point of the meal start probability with respect to the injection time (e.g., determined by the meal detection module).

[0100] The classification features can further include the probability that a meal is started within a defined interval after the dosing time, e.g., the maximum point of the meal start probability within 2 hours after injection (determined by the meal detection module).

[0101] The classification features can further include the most accurate interval until the next meal, e.g., the predicted elapsed time from the injection time to the maximum point of the meal start probability after the meal injection (e.g., determined by the meal detection module).

[0102] As described above, the step of calculating a part of the classification features includes the step of estimating the time of each meal taken by the patient during the analysis period, and the method of estimating the meal time will be described in more detail below. Briefly, the step of estimating the time of each meal can further include the step of creating a feature matrix based on the time-correlated analyte data by the DGA, and the feature matrix correlates a set of analyte (e.g., glucose) data features to each of the distinct regions classified as rising, before falling, and falling. The set of analyte data features can be or can include the maximum analyte change rate, the maximum analyte acceleration, the analyte value at the maximum analyte acceleration point, the duration of the region, the height of the region, the maximum deceleration, the average change rate within the region, and the time of the maximum analyte acceleration. The estimating step can further include the step of creating an estimated meal time based on the feature matrix using the algorithm described below.

[0103] A more detailed aspect of the retrospective meal time detection algorithm for use in or for other uses with method 300 will be described in the following paragraphs. Thereafter, the description of other aspects of method 300 will continue. The DGA can perform retrospective meal time detection based on time-correlated analyte data by executing one or more code modules, such as a feature extraction module and a meal detection module. When executed by the DGA, the feature extraction module can cause the DGA to receive a glucose time series as an input and output a feature matrix that is passed to a retrospective meal detection module to detect glucose excursions in response to meal events.

[0104] The DGA can perform feature extraction using the following operations described below, which can be divided into a sequence of three sub-operations: smoothing, segmentation, and extraction.

[0105] In the smoothing sub-operation, the DGA can smooth the analyte (e.g., glucose) time series using a Savitzky-Golay filter (degree 2) and calculate the rate of change and acceleration rate at each analyte data point. The filter frame length parameter can be the number of data points collected in a first time interval (e.g., 60 minutes), and thus the sampling is interval-dependent. The DGA can calculate the rate of change by taking the average of the differences before and after the smoothed analyte values between a point of interest and points that are in a second interval (e.g., 15 minutes) before and after that point of interest, where the second interval is smaller than the first interval and, for example, equal to one-fourth of the first interval. Similarly, the DGA can calculate the acceleration rate by taking the average of the differences before and after the rate of change of the analyte between a point of interest and points that are in a second interval (e.g., 15 minutes) before and after that point of interest.

[0106] In the segmentation sub-operation, the DGA can segment the smoothed analyte trace into regions that monotonically increase (i.e., rising) and regions that decrease (i.e., falling). Each rising region is considered a candidate for a glucose excursion in response to a meal event.

[0107] In the extraction sub-operation, the DGA can extract features from the data. For example, it can extract 16 features that may or may not include or consist of features from each rising region (e.g., 8 features), the preceding falling region (e.g., 4 features), and the subsequent falling region (e.g., 4 features). Features that the DGA can extract from the rising features can include, for example, the following: 1) the maximum analyte change rate, 2) the maximum analyte acceleration, 3) the analyte value at the maximum analyte acceleration point (reference point), 4) the duration of the rising region (the elapsed time from the reference point to the last point of the region), 5) the height of the region (the difference in the smoothed analyte values between the last point and the reference point), 6) the analyte value at the maximum analyte acceleration point (reference point), 7) the average change rate within the region (height / duration), and 8) the time of the data at the reference point. Further examples are that the 4 features extracted from the preceding falling region and the subsequent falling region can include the following: 1) the height of the falling region, 2) the duration of the falling region, 3) the average change rate of the region (height / duration), and 4) the maximum absolute value of the glucose change rate. The number of rows of the feature matrix output by the feature extraction module can be the same as the number of rising regions in the smoothed glucose time series.

[0108] According to another aspect of the embodiment, the retrospective meal detection module can receive a feature matrix as input and output a binary detection result for each rising region. Such output can include a binary classification result and a probability value that each rising region is an analyte (e.g., glucose) excursion in response to a meal event. The DGA can assign the probability value of each rising region to its reference point. In some embodiments, for example, a pre-trained machine learning model for meal detection can be implemented using a random forest classifier by scikit learn (https: / / scikitlearn.org / stable / modules / generated / sklearn.ensemble.RandomForestClassifier.html). The meal detection module can detect meal-induced postprandial glucose excursions based on a large number of decision trees constructed and optimized during the training process. In alternative embodiments, the DGA can construct a pre-trained model based on alternative classification algorithms including, for example, gradient boosting, ADA boost, artificial neural networks, linear discriminant analysis, and extra trees.

[0109] Referring back to method 300 of FIG. 7, classification operation 302 can receive the patient's feature matrix as input and output a binary classification result for each relevant dosing event (e.g., for each insulin injection). For example, the DGA can output binary data "1" meaning a meal dose and "0" meaning a non-meal dose. According to some embodiments, classification operation 302 can use the meal detection result, in which case meal detection can be performed prior to insulin dose classification. As described for retroactive meal time detection, classification operation 302 can include a pre-trained machine learning model, e.g., a model implemented using a random forest classifier from scikit learn (referenced above). The machine learning model implemented by the DGA can perform classification based on the tree construction rules and thresholds for various features in each decision tree that were optimized during the training process. Alternatively, this model can also be trained by other machine learning algorithms including gradient boosting, ADA boosting, artificial neural networks, linear discriminant analysis, extra trees. When the DGA successfully classifies each dose, it can proceed to determine the dosing regimen and administration parameters.

[0110] In step 304, method 300 can include the step of the DGA grouping each of the doses into one of a set of meal time groups or clusters. For example, the DGA can determine the dosing strategy by clustering the analysis of the dosing (e.g., injection) times of the meal doses. The DGA can execute a clustering module implemented using the K-means algorithm together with the elbow method, which receives the injection times as input and outputs the optimal number of clusters K (up to 3) and the cluster index for each injection time. The optimal number of clusters K can be the number of meal doses the patient takes in a day. Using the cluster index for each injection, the DGA can classify the meal doses into K groups according to the cluster index.

[0111] The DGA can identify these groups as breakfast, lunch, or dinner (B, L, D) as follows. For each group, the DGA can determine the typical time of day (TOD) by calculating the median TOD of the group. Alternatively, the DGA can use other centroid metrics. For K = 3, the DGA can associate breakfast with the group after the period between the typical TODs of the groups is the longest. Then, the next group is lunch, and the last group is dinner. For K = 2, the DGA can estimate which group is associated with breakfast, lunch, or dinner using a hypothesized rule regarding the time between each meal. For example, if the two groups are more than 6 hours apart from each other, the DGA can identify those groups as breakfast and dinner. Additionally, if the first group occurs before 10 am, the DGA can identify the group as breakfast and lunch; otherwise, as lunch and dinner. In an alternative embodiment, after the DGA has identified the typical times of the meal events, it can display a prompt to the user to identify the meal associated with each typical time. As a further example, in an alternative embodiment, the DGA can combine the two methods described herein by displaying a confirmation prompt to the user after estimating the meal relevance. As a further alternative method, mention can be made of the analysis of glucose data for identifying meals and the clustering of meal times for detecting typical meal times. This can be useful for differentiating meals when K = 2, i.e., for identifying meals for which no dose has been taken.

[0112] When the dosages are grouped into meal time clusters, at step 306, the DGA can perform the step of creating at least partially the patient's dosage parameters by applying the data of each meal time group to the model. For example, for each meal group (B, L, D), the DGA can pair each set of corresponding pre-meal glucose levels with the corresponding meal dosage. The DGA can fit each group to a suitable model, such as a linear function with zero slope, a linear function with non-zero slope, a piecewise linear function joined at one point, or a non-linear function that approximates a joined piecewise model but has a smooth curvature around the joining points. Other models are also suitable.

[0113] The DGA can perform model fitting and parameter estimation by minimizing the sum of squared residuals (SSR) of the model parameters. Then, the DGA can use an optimization algorithm to find the optimal parameters such that the value of SSR is minimized. In the case of a linear model, the DGA can perform fitting using the Nelder-Mead simplex method. In the case of a non-linear model, the DGA can use the Levenberg-Marquardt algorithm. That is, the DGA can use the Nelder-Mead simplex numerical optimization method for linear models and the Levenberg-Marquardt optimization method for non-linear models. Other ways of fitting data to these models are also possible.

[0114] If the number of iterations during optimization exceeds the convergence criterion, the model will not fit, and the DGA can exclude the non-fitting model as a candidate model. Further, the DGA can apply certain rules to minimize the uncertainty of parameter estimation, for example, by verifying the estimation correction factor by requiring at least three pre-meal glucose data points greater than the estimated threshold glucose, or by verifying the estimated fixed volume by requiring at least three pre-meal glucose data points less than the estimated threshold glucose, or by requiring that the 95% confidence interval of the parameter intercept excludes zero, or by requiring that the 95% confidence interval of the slope of the model excludes zero.

[0115] If the data is insufficient, the model fitting fails, and as a result, a particular model may be excluded as a candidate model. The DGA can evaluate each model with the Akaike Information Criterion (AIC) and select the model with the minimum AIC value as the desired model for each meal group.

[0116] Once the DGA selects a model for each meal time cluster, it can then determine dosage parameters, including, for example, fixed-dose insulin amount, target glucose level, and correction factor, based on the selected models for each meal time cluster. The DGA can determine the target glucose level and correction factor as single values for all groups, respectively, as described in more detail in the following paragraphs. In an alternative embodiment, the DGA can determine the target glucose level and correction factor separately for each group and use the separately determined parameters for downstream dosage guidance operations.

[0117] According to another aspect of the embodiment, the DGA can form combined data groups in order to obtain more accurate correction factors for the patient. For example, after fitting the dosage data to various models of each meal group to select the optimal model and estimating the fixed insulin dosage, the DGA can subtract the fixed-dose insulin amount from the related meal dosages of each meal group. The remaining non-zero values correspond to dosages with correction dosages. These non-zero values can then be combined from all three meal groups (B, L, D) to form a combined group. If the amount of insulin at a fixed dose cannot be determined for a certain group, the DGA can exclude the data of that group from the combined group. The system can then repeat the operation to find the optimal model for the combined group or use the same model identified when analyzing the groups individually. By using this approach of the combined group, it is assumed that the patient has the same (or constant) correction factor and target glucose for all meals, and a more accurate fitting can be provided with a larger sample size in the combined group. After determining the target glucose level and correction factor based on the optimal fitting model, the DGA completed the estimation of the dosage parameters. Then, in step 308, the DGA can store the dosage parameters in the computer memory and configure the dosage guidance settings.

[0118] In an additional aspect, the DGA can determine whether the patient is potentially performing carbohydrate counting (e.g., whether changing the meal dosage considering carbohydrate consumption) by comparing the AIC value of the preferred model with a threshold value such as 50, or 75, or 100. If the AIC value is greater than the threshold value, the DGA can determine that the patient is performing carbohydrate counting and request confirmation from the patient via the UID200.

[0119] In alternative embodiments, one or more of the above-described operations may be omitted and replaced by requiring the patient or HCP to manually provide information or extracting information from another source such as an EMR or other software program. Nevertheless, method 300 should be useful for various applications that do not have more information than SCD and MDD can provide.

[0120] User feedback during the learning period Next, exemplary embodiments of a method for obtaining user feedback during or after the learning period of the DGA will be described. During an initial learning phase by the DGA, a feedback prompt can be displayed to the user. User feedback can indicate to the user that the system is making progress. The DGA can prompt the user for feedback (e.g., input or confirmation) regarding any aspect of dose guidance, such as the administered dose, analyte history, patient behavior or activity, dosing strategy in general, type of specific dose, type or strategy of dose (e.g., learned by the system) determined by the DGA.

[0121] During the learning period (or afterwards), the DGA can output a prompt or other display requesting user feedback to the UID200. This feedback can relate to dosing strategies, such as strategies regarding insulin action types (e.g., long-acting and / or short-acting or rapid-acting). If the feedback (or other decision) indicates the use of a long-acting strategy, the DGA can monitor the patient's basal dosing pattern during a first time period, e.g., the first three days, to classify each dose or dosing pattern as a single-dose or split-dose type and / or to characterize the doses by time period (e.g., a single morning dose, a single evening dose, or a split dose (e.g., both morning and evening)). The DGA can also determine trends (e.g., median, mean) regarding the dosage amount and associated dosage variability values. From this information, the DGA can formulate an expected basal dose. After the first time period, if the actually administered dose (e.g., as automatically registered by the MDD152 or entered by the user) is different from the prediction, a feedback prompt can be displayed to the user.

[0122] According to one aspect of the embodiment, prompts can be displayed to the user in many different situations. For example, the DGA can be configured to detect missed doses, such as when the user fails to administer a basal dose or a bolus dose during the time period when the previous basal dose or bolus dose was administered. If a missed dose is detected, the DGA can be configured to request an input from the user regarding whether a basal dose was administered during that time period. According to some embodiments, the DGA can be configured to detect differences in administration timing. For example, the DGA can be configured to detect this when the user administers a basal dose at a different time than the time when the previous basal dose was administered (for example, when a basal dose that is normally administered in the morning is administered in the evening). If such a difference in administration timing is detected, the DGA can be configured to request an input from the user regarding whether the basal dose was administered during a different time period. In another aspect of this embodiment, the DGA can also be configured to detect that an extra dose has been administered. For example, the DGA can be configured to detect a change in the number of basal doses administered per day. In yet another aspect of the embodiment, the DGA can be configured to detect whether the dosing strategy on the first day (for example, one basal dose was administered) is different from the dosing strategy on the second day (for example, two basal doses were administered). If different dosing strategies are detected, the DGA can be configured to request an input from the user regarding whether the user adopted the dosing strategy used on the second day as a new dosing strategy. In yet another aspect of the embodiment, the DGA can also be configured to detect whether different doses have been administered. For example, the DGA can be configured to detect whether the first amount of the dose administered during a certain time period is different (smaller or larger) from the previous dose administered during the same time period on the previous day. If different doses are detected, the DGA can be configured to request an input from the user regarding whether the user has changed the dose.

[0123] Based on the user's response to these prompts, the DGA can confirm that it has identified the correct pattern (for example, the user forgot to take the morning basal dose but normally confirms taking it), or provide the user with an opportunity to modify the pattern (for example, the user notifies the DGA to adjust the basal dose based on glucose before taking the dose).

[0124] In rapid - acting insulin dosing strategies, in addition to the above - mentioned prompts, the DGA can include prompts regarding dose classification. Dose classifications can include, but are not limited to, bolus, correction, split dose, bolus + correction, and bolus + carbohydrate count + correction classifications.

[0125] Regarding the administration of rapid-acting insulin, prompts can be displayed to the user in various situations. The DGA can be configured to detect whether a dose that is not related to a meal has been administered. For example, the DGA can be configured to determine whether a dose has been taken during a time period when a meal is not identified or detected. If the DGA detects the taking of a dose and no meal is detected within the time period of administration (e.g., within about 1 hour from administration), the DGA can request an input from the user regarding the reason for the dose administration (e.g., because a meal was had, to lower glucose, or to finish administration of the previous meal). Also, the DGA can also be configured to detect that a meal dose does not match a previous meal dose related to the same meal type. For example, the DGA can be configured to determine whether a bolus dose related to a first meal type and administered at a certain time of day is not the same as the previous bolus dose related to the first meal type and administered at that same time of day the previous day. If such a difference in the bolus dose is detected, the DGA can be configured to determine the reason for the different dose. For example, the DGA can determine the difference in pre-meal glucose values related to the bolus dose and the previous bolus dose, and be configured to determine whether the detected difference is a correction. The DGA can also request an input from the user regarding the reason for the difference in the bolus dose (e.g., the amount of meal eaten was less / more, and / or correcting for hyperglycemia, and / or correcting for other factors).

[0126] In addition to enabling the DGA to determine what types of rapid-acting doses are being taken throughout the day, the timing of dose prediction can be facilitated. After a learning period where no prompts are provided, the DGA can provide these prompts to the user when the dose taken is different from the dose predicted in order to improve the DGA model of the user's dosing strategy.

[0127] In both the long-acting and rapid-acting cases, the DGA can aim to minimize the number of prompts as time passes and the user responds. At the initial stage, emphasis can be placed on giving prompts frequently and gradually reducing them when repeated patterns are observed.

[0128] Glucose pattern analysis and meal bolus titration for MDI insulin dosing therapy Next, an exemplary embodiment of a method for determining meal bolus titration will be described. When the system learns (or configures according to) the patient's current dosing strategy, it can provide titration guidance for frequent injection (MDI) dosing therapy. For patients using fixed meal dosing, the fixed dose (e.g., breakfast, lunch, dinner, snacks, etc.) can be titrated. For patients performing carbohydrate counting, the carbohydrate ratio for the same meal or different time periods can be adjusted. For patients using empirical dosing, the dose can be titrated for each meal. In the titration guidance by the DGA, recommended values for changing the dose or carbohydrate ratio in a specific direction can be provided. The amount of change can be changed at an appropriate ratio, such as 5%, 10%, 15%, etc. The dosing guidance can also include the start of meal dosing. For example, if a patient is using basal + 1 (e.g., lunch dosing regimen) and the breakfast shows a high pattern, the DGA can provide a recommended value for administering RA insulin for breakfast.

[0129] The DGA can require defining administration categories such as time-of-day (TOD) periods, type of meal (e.g., breakfast), and composition of the meal (e.g., cereal with milk). For example, the administration category can be a time-of-day period defined by the period associated with the meal insulin dosage for the time-of-day period. In a further example, the post-breakfast time period can be defined to start when a meal insulin dose is taken during a defined time-of-day, e.g., between 5:00 a.m. and 10:00 a.m., and end either after a defined post-meal period (e.g., 6 hours later) or earlier, whichever is earlier, when the next meal insulin dose is taken. One or more measurement criteria may be required to define whether the post-meal blood glucose response is nominal or requires modification, or to rank the post-meal blood glucose pattern more favorably or unfavorably than another. The low glucose likelihood (LLG) index and glucose median can be used to quantify the degree of hypoglycemia risk and hyperglycemia risk, respectively.

[0130] U.S. Patent Publication No. 2018 / 0188400 ('400 publication), the entire contents of which are incorporated herein by reference for all purposes, describes embodiments for deriving and determining risk metrics available in glucose pattern analysis (GPA) of embodiments of the DGA. This embodiment, among other things, utilizes central tendency (e.g., mean, median, etc.) and variability value data from a multi-day period to determine a risk metric corresponding to the degree of hypoglycemia risk ("hypo risk"). This embodiment is summarized herein, and a more comprehensive description of the embodiment and its variations can be obtained by reference to the '400 publication.

[0131] An alternative to the embodiment described in the publication of the '400 Gazette is described in U.S. Patent Publication No. 2014 / 0350369, which is also hereby incorporated by reference in its entirety for all purposes. For example, instead of using median and variability values, this method can employ any two statistical measures that define the distribution of the data. As described in the '369 Gazette, the statistical measures can be based on a glucose target range (e.g., G LOW = 70 mg / dL and G HIGH = 140 mg / dL). Common measurements related to the target range are time in range (TIR), time above target (t AT ), and time below target (t BT ). When glucose data is modeled as a distribution (e.g., gamma distribution), for predefined thresholds G LOW and G HIGH , t AT and t BT can be calculated. For the thresholds, the algorithm can also define t BT_HYPO , and when it exceeds t BT , it can be determined that the patient has a high risk of hypoglycemia. For example, a high risk of hypoglycemia can be defined as when t LOW exceeds 5% when G BT = 70 mg / dL. Similarly, an indicator of t AT_HYPER can be defined, and when it exceeds t AT , it can be determined that the patient has a high risk of hyperglycemia. The degree of the risk of hypoglycemia and hyperglycemia can be adjusted by adjusting either G LOW or t BT_HYPO , G HIGH or t AT_HYPER . A control grid can be defined using any two of the three measures of TIR, t BT and t AT . Using these options (and other options), the risk indicators of the embodiments of the DGA described herein can be determined.

[0132] The embodiments of the DGA described herein can operate based on a quantitative evaluation of the user's analyte data during the TOD period. This quantitative evaluation can be performed in various ways. For example, the embodiments described herein can evaluate analyte data over multiple days to determine one or more metrics that describe the associated risk indicated by that analyte data for the corresponding TOD. Using these metrics, the analyte data during the TOD period can be classified into one of a plurality of patterns. For example, these patterns can indicate the glucose behavior or trend that is common to or generalized for that TOD. Embodiments of the DGA can utilize any number of two or more patterns. For ease of reference herein, these patterns are referred to as glucose pattern types, and embodiments described herein refer to embodiments that utilize three glucose pattern types (e.g., a low pattern, a high / low pattern, and a high pattern), although in other embodiments only two types, or three or more types, can be utilized, and these types may differ from those described herein.

[0133] Taking a fixed meal dose as an example, once the DGA has learned the dosing strategy and the amount of dose or carbohydrate ratio, it can initiate a titration evaluation, which can be classified into four titration categories: "night", "after breakfast", "after lunch", and "after dinner". For each of these categories, the DGA can map the above two metrics (LLG and glucose median) to four logical "pattern" variables by the GPA method described below. FIG. 8A shows the operation of an exemplary method 400 by the DGA for evaluating meal bolus titration for multiple daily injection (MDI) therapy. Method 400 can include, at 402, determining at least one TOD analyte pattern type by the DGA executing a glucose pattern analysis (GPA) algorithm that receives as input time-correlated analyte data transmitted from a sensor control device worn by the patient during an analysis period. Method 400 can further include, at 404, selecting an MDI dosing recommendation value based on the analyte pattern type and the defined dosing strategy of the patient during the analysis period by the DGA executing a recommendation algorithm. Method 400 can further include, at 406, storing in computer memory by the DGA a pointer to a recommended action for output to at least one of the UID200 or MDD152 that administers a drug to the patient. The UID200 can control the user interface by using the pointer to a recommended action to, for example, display a human-readable representation of the pointer on a display or create an audio output that expresses the pointer in a human language. The MDD152 can use the pointer to adjust or maintain the next relevant dose administration. Further details of method 400 are described below.

[0134] FIG. 8B is a flowchart showing an exemplary embodiment of a GPA method 410 that can be implemented as the GPA algorithm referred to at 402. The method 410 can be executed for a particular TOD period that can be the entire day (e.g., a 24-hour period), or a time block (e.g., three 8-hour periods) or a portion of the day defined by a user's activities (e.g., meals, exercise, sleep, etc.). In many embodiments, the plurality of TOD periods can correspond to meals (e.g., after breakfast, after lunch, after dinner) and sleep (e.g., at night). These TOD periods can correspond to fixed times of the day when the activity would normally occur (e.g., from 5:00 am to 10:00 am after breakfast), and such time blocks can be set by the user or determined conditional on the meal or activity actually occurring, such as by automatic detection of the meal or activity, or a user indication (e.g., using UID200) indicating these things.

[0135] The DGA can execute method 410 independently for each TOD period and obtain an individual pattern evaluation for that period. At 412, the DGA can determine a central tendency value and a variability value from the user's analyte data for a particular TOD period. The user's analyte data can be obtained from the user's own records or the records of the user's healthcare professional, or the user's analyte data can be, for example, that collected by the DGS100. The analyte data preferably spans a period of multiple days (e.g., 2 days, 2 weeks, 1 month, etc.) so that there is sufficient data within the TOD period to make a reliable determination. In other embodiments, this method can be executed in real time for limited data. The DGA can use any type of central tendency indicator that correlates to the central tendency of the data, including but not limited to the median or mean value. Also, any variability indicator can be used, including but not limited to, the variability range across the entire data set (e.g., from the minimum value to the maximum value), the variability range that spans most of the data but not the entire data set to reduce the significance of outliers (e.g., from the 90th percentile to the 10th percentile, from the 75th percentile to the 25th percentile), or a variability range targeted at a particular asymmetric range (e.g., a low range variability that can span, for example, from the central tendency value to a low value of the data, such as the 25th percentile, 10th percentile, or minimum value). The selection of the indicators representing central tendency and variability can be varied depending on the embodiment.

[0136] At 414, the DGA can evaluate a hypoglycemia risk (“hypo risk”) indicator based on central tendency values and variability values. One such methodology for determining hypoglycemia risk is described with respect to FIG. 8C, which illustrates an exemplary embodiment of a framework for determining hypoglycemia risk and other indicators. FIG. 8C is intended to convey the framework to the reader; however, this framework can be implemented electronically in a number of different ways, such as a software algorithm (e.g., a set of mathematical equations, if-else statements, etc.), a look-up table, firmware, combinations thereof, and the like.

[0137] FIG. 8C is a graph of central tendency versus variability (e.g., low-range variability) that can be used to evaluate or identify a region or zone that holds or corresponds to a data pair of central tendency and variability determined for a particular TOD. Any number of two or more zones can be used. In this embodiment, the data pair can correspond to one of a target zone 425 or three hypoglycemia risk (hypo risk) zones: a low zone 426, a moderate zone 428, or a high zone 430. A first hypoglycemia risk function, referred to as the moderate risk function 422 (e.g., a curved or linear boundary), distinguishes the low zone 426 from the moderate zone 428. A second hypoglycemia risk function, referred to as the high risk function 424, distinguishes the moderate zone 428 from the high zone 430. The data pair of central tendency and variability can be evaluated against or compared to the zones to determine a hypoglycemia risk indicator for the corresponding TOD period.

[0138] The hypoglycemia risk functions 422 and 424 can be explicitly implemented in the DGA as a mathematical function (e.g., polynomial), or can be implicitly implemented by defining each zone by a pair of included, using a look-up table, a set of if-else statements, threshold comparison, etc. The hypoglycemia risk functions 422 and 424 can be pre-loaded into the DGA, downloaded from a reliable computer system 480, or set by another party such as an HCP. Once implemented in the DGA, the hypoglycemia risk functions 422 and 424 can be treated as fixed or adjusted by the user or HCP. An exemplary methodology for determining the hypoglycemia risk function is described in the '400 publication.

[0139] At 416, the DGA can evaluate a hyperglycemic risk metric (``hyper risk'') based on the central tendency value. In this embodiment, the hyperglycemic risk can be evaluated by comparing the central tendency value for a particular TOD period to a central tendency target or threshold 432. The magnitude and / or sign of the difference between the central tendency value and the target 432 can identify the amount of hyperglycemic risk. For example, if the central tendency value is less than the target 432 (e.g., a negative value), a low hyperglycemic risk may exist. A moderate hyperglycemic risk can exist if the central tendency value is less than a threshold (e.g., 5%, 10%, etc.) and exceeds the target 432 (e.g., a positive value). A high hyperglycemic risk can exist if the central tendency value exceeds the target 432 by a value greater than the threshold. The use of three separate groupings (e.g., low, medium, high) for hyperglycemic risk is one example, and any number of two or more groupings can be used.

[0140] In other embodiments, the DGA can evaluate the hyperglycemic risk metric at 416 prior to evaluating the hypoglycemia risk at 414. Alternatively, in another embodiment, the evaluation of the hypoglycemia risk at 414 and the evaluation of the hyperglycemic risk at 416 can be performed simultaneously in parallel.

[0141] Other metrics such as volatility risk can also be evaluated. For example, a volatility value smaller than the first volatility threshold 434 can indicate a low volatility risk, a volatility value greater than the first volatility threshold 434 and smaller than the second volatility threshold 436 can indicate a medium volatility risk, and a volatility value greater than the second volatility threshold 436 can indicate a high volatility risk. Here too, the use of three separate groupings for volatility risk is an example. The DGA can use any number of two or more groupings.

[0142] In step 418, the DGA can determine the pattern type of the TOD period based on one or more of the evaluated risk metrics. In an exemplary embodiment, pattern determination can be evaluated using a hypoglycemia risk metric and a hyperglycemia risk metric. If the hypoglycemia risk metric is high, the pattern can be set as a low pattern. Additionally, if the hypoglycemia risk is medium and the hyperglycemia risk is high or medium, the pattern can be set as a high / low (or medium) pattern. Additionally, if the hyperglycemia risk is high or medium and the hypoglycemia risk is low, the pattern can be set as a high pattern. If both the hyperglycemia risk and the hypoglycemia risk are low, the identified pattern can be No Problem (no issue) (e.g., an "OK" message is displayed and output).

[0143] Thus, method 410 is an example of a method by which the DGA outputs one of a plurality of pattern types for each TOD period. The number of pattern types themselves may be different from those described in this embodiment (e.g., low, high / low, high). Once the pattern type of the TOD period is determined, the DGA can store the index of the pattern type in a memory location for use in determining the titration recommendation value. Referring back to FIG. 8A, the DGA can proceed in 404 to determine the titration recommendation value once the GPA for each relevant TOD period is completed.

[0144] The recommended method can branch according to other factors such as pattern type (e.g., low, high / low, high), TOD period, dosing strategy, compliance with the strategy (e.g., whether the dose is insufficient), and whether sufficient data is available for evaluation. The DGA does not make titration recommendations until sufficient data becomes available for the corresponding TOD period. For example, if the amount of available data is below a threshold, e.g., if the number of other days with more than a minimum portion (e.g., 90%) of available data is less than a threshold (e.g., 5), the DGA can omit the evaluation and generate an error message.

[0145] Figures 8D - 8H show examples of branches of a recommended algorithm or method for determining dosing titration recommendations based on the input information described above. Other branches may also be useful. Figure 8D shows a recommended method branch 440 for a TOD with a low pattern type that has sufficient data and a possible cause including one or more of a basal dose, meal dose, pre - meal correction dose, or post - meal dose higher than the optimal amount. At 442, the DGA evaluates whether the pattern type during the nighttime TOD period is low. If the pattern is low, at 444, the DGA generates recommendations to reduce all relevant doses, including at least the basal dose and optionally one or more of the meal dose, pre - meal correction dose, or post - meal dose, by the same amount, e.g., 10%. The titration recommendation rule for a low pattern can include, for the nighttime TOD period, at 444, the step of generating recommendations to reduce the dose or basal rate of long - acting insulin. If other TOD periods are of low pattern at 446, the DGA can, at 448, generate recommendations to reduce only the fixed meal dose for the relevant TOD period.

[0146] In this embodiment, if there is at least one low pattern, no titration guidance is provided for the TOD period of the high pattern. The idea here is to prioritize the prevention of hypoglycemia and increase the dose only when the risk of hypoglycemia is low during all TOD periods. Also, in some cases, when the TOD period has a high pattern, this may be due to the fact that the pattern of the previous TOD period was low and the patient overate to compensate for it. Therefore, dealing with the low pattern itself can be helpful in dealing with the subsequent high pattern. In 449, if the pattern is not high, the process 440 either waits or ends without generating recommendations, or passes the high pattern evaluation 450.

[0147] Therefore, in the case of high / low patterns, the DGA does not generate titration guidance. If there is no TOD period for which titration guidance is given and the data for all time periods are sufficient, the DGA can provide the patient with a message indicating that it is necessary to address glucose variability before further titration guidance can be given. Also, the DGA can provide a report to the patient's HCP recommending consideration of alternative drug therapies or treatments that can address glucose variability.

[0148] FIG. 8E shows the operation by the DGA to generate high pattern titration recommendations when there is no low pattern TOD period. At 452, if the nighttime period has a high pattern and there are no other periods with a moderate risk of hypoglycemia, the DGA can, at 454, increase the long-acting insulin dose or basal rate recommendation. At 456, if the nighttime period has a high pattern and there is at least one other non-dinner period with a moderate risk of hypoglycemia, at 458, the DGA can reduce the meal insulin dose associated with any period with a moderate risk of hypoglycemia. At 460, if the nighttime TOD period has no moderate risk of hypoglycemia and no high pattern, at 462, the DGA can generate a recommendation to increase the meal insulin amount associated with the first TOD period having a high pattern. At 464, if the nighttime period has a moderate risk of hypoglycemia and the only post-meal period with a high pattern is dinner, at 466, the DGA can generate a recommendation to increase the long-acting insulin dose or basal rate. If the nighttime period has a moderate risk of hypoglycemia and there is no post-dinner period, at 462, the DGA can generate a recommendation to increase the meal insulin dose associated with the first TOD period having a high pattern.

[0149] In an alternative embodiment, the pre-meal glucose may be higher or lower than the target glucose (e.g., 120 mg / dL). The glucose data for each meal contributing to the calculation of the hypoglycemia and hyperglycemia risk metrics can be modified to compensate for the effects of previous meals or conditions that affect glucose not resulting from the current meal. The DGA can modify these data by subtracting an offset so that the resulting starting glucose is at the target level. Alternatively, the DGA can modify these data by a "triangle" function in which, for the start time of the meal, the difference between the meal start glucose and the target glucose is subtracted, but this modification is linearly reduced over a defined period (e.g., 3 hours) or by another decay function.

[0150] Alternatively, this function can itself be a function of the glucose level or glucose trend at the start of a meal, and / or of when the previous meal dose was taken.

[0151] According to another aspect of an embodiment, an algorithm for generating recommendations for meal bolus titration can become more complex when considering additional aspects such as, for example, missed meal administrations, missed basal administrations, post - meal corrections, and pre - meal corrections. An algorithm for providing appropriate recommended values in the presence of these factors may need to exclude some data while still meeting a data sufficiency threshold after excluding data for guidance.

[0152] For example, referring to FIG. 8F, if a high pattern is detected at 461 and there are several days where the meal dose has been missed, the days on which the meal dose has been missed are excluded and the GPA analysis 410 is repeated at 463. Then, if a high pattern is detected at 465, based on the patterns identified at other TODs, the dose can be increased at 467 or further input or a return can be awaited at 469. Alternatively, the system can evaluate only the high patterns using data excluding the days on which the meal dose has been missed. An algorithm 470 having this branching pattern is shown in FIG. 8F. If the system detects a low pattern at 473, it can execute a low - pattern algorithm 472 described in the next paragraph. If the system does not detect a high or low pattern, it may return to block 469 for further input or a return.

[0153] At 472, if a meal administration has been missed, the DGA can detect a low pattern during the TOD period and, if the meal administration has been missed on some days during this TOD, the DGA can generate recommendations for reducing the dose. The recommendations can include, for example, reducing a fixed dose or a correction dose portion.

[0154] Regarding the omission of basal administration, if the DGA detects a low pattern 473 at night TOD, the omission of basal administration should not affect the dose titration logic. Similarly, if a low pattern is detected at a TOD other than at night, the omission of basal administration should not affect the dose titration logic.

[0155] If the DGA detects a high pattern 461 at a TOD using data including at least one day (or TOD) on which basal administration has been omitted, it can exclude 463 the data for any day (or TOD) on which basal administration has been omitted and repeat the pattern analysis 410. Subsequent operations can depend on the specific TOD at which the high pattern was detected. For example, if the DGA detects a high pattern at the night TOD of data including at least one day on which basal administration has been omitted, it can exclude the data for any day on which basal administration has been omitted and repeat the pattern analysis. If a high pattern is detected at the night TOD when the days on which basal administration has been omitted are excluded, the result of the night TOD can be used as a guideline for adjusting the basal administration, and the basal dose can be increased. If a high pattern is detected at a TOD other than at night, the days on which basal administration has been omitted can be excluded and the pattern analysis can be repeated. If a high pattern is detected when the days on which basal administration has been omitted are excluded, as described herein, the meal dose related to the TOD having the high pattern can be analyzed and titrated. In any case, the logic flow 470 is as illustrated in FIG. 8F.

[0156] FIG. 8G shows an example of a logic flow 474 for formulating recommendations with post-meal correction. After the GPA 410, if the DGA detects a low pattern 479 of TOD over several days including post-meal correction, the following analysis can be used to perform correction or titration of the meal dose. At 475, if the DGA first detects the low pattern 479, it is first tested that sufficient data is available at 487 to exclude data for days without post-meal correction. If sufficient data is not available, the DGA may execute an error recovery routine 489, such as displaying an error message. If sufficient data is available, the DGA may repeat the pattern analysis 410. Subsequently, if the DGA detects a low pattern, the post-meal correction dose can be decreased (i.e., the correction factor can be increased) at 476, conditional on the pattern analysis results at other TODs. Thereafter, if the DGA does not detect a low pattern, the meal dose can be reduced at 477.

[0157] In all embodiments described herein, modification of the correction dose in one direction (e.g., titration) can be achieved by modification of the correction factor in the opposite direction. These two parameters have an inverse relationship such that an increase in the correction dose can be achieved by a decrease in the correction factor and a decrease in the correction dose can be achieved by an increase in the correction factor. Thus, in all embodiments described herein, the DGA can recommend or perform correction by either modification of the correction factor or modification of the correction dose. Thus, within the scope described herein, embodiments can be configured to achieve the same effect by inverse modification of the correction dose, and conversely, within the scope described herein for modification or titration of the correction dose, embodiments can be configured to achieve the same effect by inverse modification of the correction factor. Considering this interchangeability, both options are available in all embodiments described herein, although not all embodiments are described for the sole purpose of facilitating explanation.

[0158] Additionally, or alternatively, starting from the original dataset 491, at 478, the DGA can exclude the days when the dietary administration was forgotten. After finding sufficient data at 487, if the pattern analysis 410 of these data excluding the days when post-meal correction was performed at 490 does not show a low pattern, the DGA can recommend reducing the post-meal correction dose at 476. Additionally, the DGA can implement the logic 510 of FIG. 10B and as a result, recommend reducing either the meal-time insulin or the pre-meal correction part of the dose guidance. If the DGA does not detect a low value at 479 and does not detect a high glucose pattern at 492, it can wait for further input or return at 469. If the DGA detects a high pattern at 492, the process 480 can be implemented at block 471 (FIG. 8H).

[0159] Referring to FIG. 8H, if the DGA detects a high pattern 493 for a TOD with several days including post-meal correction, the following procedure 480 can be implemented to formulate recommendations for titrating the correction and meal doses. At 481, the DGA can repeat the pattern analysis 410 including data for fasting days and days when post-meal correction was performed. Then, if the DGA detects a high pattern at 494, at 482, the post-meal correction dose can be increased (i.e., the correction factor can be decreased) conditional on the pattern analysis results at other TODs. If no high pattern is detected at 494, at 495, a low pattern is checked, and if a low pattern is detected, return to 474 of FIG. 8G, or otherwise wait for further input at 469 or return. Although not shown in FIG. 8H, after excluding any data of the GPA410 and before running the GPA, the DGA can test the data sufficiency and execute an error recovery routine if the available data is insufficient.

[0160] Alternatively, or in addition, if a pattern analysis of the data that excludes the days on which meal administration was missed at 483, starting from the original dataset at 493, shows a high pattern along either one of branches 2.1 or 2.2, the DGA can continue with procedure 480 as follows. In the case of branch 2.1, if the pattern analysis 410 of the data for the days excluding post-meal corrections at 484 (i.e., data with only bolus administration) does not show a high pattern at 497, the DGA can generate a recommendation to increase the post-meal correction dose at 482, conditional on the pattern analysis of other TODs. Otherwise, the DGA can generate a recommendation to increase either the meal-time insulin or the pre-meal correction portion according to procedure 550 in FIG. 10C.

[0161] In the case of branch 2.2, if the pattern analysis of the data for only the days including post-meal corrections at 485 does not show a high pattern at 496, the DGA can increase either the meal-time insulin or the pre-meal correction portion according to procedure 550 in FIG. 10C. If no high pattern is detected at 496, the DGA may return to block 484.

[0162] If the recommendations for correction factor titration from different TODs are contradictory and the patient is currently using the same correction factor for all TODs, the DGA can increase the correction factor. Procedure 480 can first increase the meal dose if all three components of the meal dose, pre-meal correction, and post-meal correction are not optimal. The pre-meal correction can be titrated and increased after the titration of the meal dose has been performed. The post-meal correction can be titrated and increased after the meal dose and pre-meal correction have been completed.

[0163] During subsequent analysis, TODs for which the DGA generates a recommendation to "increase the correction factor" by the method described above will now result in a recommendation to "leave the correction factor unchanged". Conversely, if a TOD produced a recommendation to "decrease the correction factor" by the method described above, it is likely that different TODs were optimized using different correction factors when a recommendation to "decrease the correction factor" was produced this time.

[0164] Figures 8D - 8H illustrate aspects of various recommendation algorithms 404 for use in method 400, but it should be understood that these are exemplary. Various other algorithms may also be suitable.

[0165] Meal bolus titration hysteresis Next, an exemplary embodiment of a method for reducing oscillations near the optimal meal bolus dose will be described. When the DGA reaches or approaches the optimal titration, a situation may occur where titration is still being requested but is actually not necessary. That is, the metrics used to determine whether titration is needed may have a level of error or variability, and this variability may cause the titration algorithm to oscillate near the optimal dose. To reduce this oscillation problem, a PPC (Patient Parameter Convergence Tracking) module can be incorporated into the DGA.

[0166] In one embodiment, the PPC module can collect past information from the user, and the past information includes sufficient data to determine the impact of various events on the patient's glucose level at different times. The PPC module can be configured to calculate and track how multiple outcome metrics change over time. The outcome metrics can include, but are not limited to, hypoglycemia risk, hyperglycemia risk, and glucose control metrics. The PPC module can also be configured to calculate and track changes in dosage guidance parameter estimates and / or dosage guidance proposals. Examples of dosage guidance parameter estimates include, but are not limited to, estimated insulin sensitivity coefficient / insulin correction factor, estimated insulin-to-carbohydrate ratio, and estimated average carbohydrate at each meal time. Examples of dosage guidance proposals include, but are not limited to, recommended basal insulin dosage, recommended meal dosage, recommended touch-up dosage, and recommended correction dosage.

[0167] In one embodiment, the PPC module of the DGA can be configured to create a correlation for each outcome metric. The correlation can be a multi-dimensional correlation. The PPC module can be configured to map the change over time of each dosage guidance parameter and / or the change over time of the guidance proposal to the change over time of the outcome metric. The PPC module can also be configured to track the gradient of a specific metric. For example, if changes in multiple dosage guidance parameters and / or changes in the guidance proposal cause a predicted small change in the outcome metric, the PPC module can be configured to determine to delay the adjustment proposal for a certain time period to prevent unnecessary adjustment requests to the user.

[0168] In another embodiment, the PPC module can be configured to use multiple gradients and compare each gradient to a set of predetermined gradient thresholds. The PPC module can be configured to determine to delay the adjustment recommendation if the number of gradients exceeding the threshold is not greater than a predetermined value. The predetermined value can be a universal value or a user-specific value.

[0169] Physiological dosage guidance algorithm Next, an exemplary embodiment of a method for determining dosage guidance based on physiologically relevant processes will be described. Many model-based control systems, specifically those utilizing model predictive control (MPC) algorithms, are based on black box diagrams and do not have a rigorous physiological basis. As a result, these models may not be able to account for the specific pharmacokinetic and pharmacodynamic differences of different insulin analogs used in intensive MDI therapy. Further, in MDI therapy, it is necessary to manage the user's blood glucose levels over several hours with a given insulin dose, but many of the currently utilized dosage guidance algorithms are designed for use in conjunction with insulin pumps that continuously supply the user with only one type of insulin analog (usually rapid-acting). The MPC algorithm can communicate with the CGM to frequently (e.g., every 5 minutes) receive glucose feedback regarding the current insulin delivery rate and then can change the pump flow rate in real-time to maintain normoglycemia. As a result, current MPC algorithms that can frequently vary the pump-driven insulin delivery amount based on glucose feedback do not function in MDI therapy where the insulin delivery amount is not as large and thus longer time horizons must be predicted. The longer these horizons (e.g., in hours), the more the pharmacokinetics and pharmacodynamics of the drug can play a significant role in making accurate predictions. This requirement calls for the design of methods for insulin dosage guidance and glucose control based on more physiological insights.

[0170] This specification describes an insulin dosing guidance algorithm that takes into account physiologically relevant processes such as insulin diffusion, subcutaneous pharmacokinetics (PK), and glucose-insulin kinetics. The parameters can be determined by numerically solving a modified minimal model of insulin absorption and glucose control (see Bergman, et al. 1978 and Dalla Man, et al., 2007, which are hereby expressly incorporated by reference in their entirety) during a "learning phase" to establish user-specific model parameters. Once these parameters are determined, the user-specific minimal model can be solved for both meal-time and correction doses to determine the optimal insulin dose at that time.

[0171] As similarly described in other embodiments, the DGA can receive or otherwise access glucose values and trends from a glucose monitoring system. The DGA can be configured to query the user as to whether a dose should be administered soon, and can also be configured to query the user as to which meals require dosing guidance. The DGA can be configured to output dose recommendations based on an algorithm, such as a physiological dosing guidance algorithm or another algorithm described herein. The DGA can then be configured to observe whether the user is following the dosing guidance and can also track the resulting glucose trace for future consideration of dosing guidance.

[0172] The DGA can be configured to include a physiological dosing guidance algorithm that determines the optimal dose output in the dosing guidance. In one embodiment, dosing guidance can be provided to users having a multiple daily injection (MDI) regimen with correction of once-daily long-acting analogs and three-times-daily rapid-acting meal analogs. This methodology can also be applied to other MDI strategies, such as basal-only or basal with a single rapid-acting injection.

[0173] In an exemplary embodiment, as described in the flowchart of FIG. 9A, in an exemplary method 900 starting from step 904, the DGA can automatically generate possible bolus insulin doses (u1, u2,..., u n ). In step 906, the physiological dosing guidance algorithm can process the possible insulin doses and generate and output a glucose time course (g 1, g2,..., g n )(e.g., a data array) for each bolus insulin dose input. Then, in step 908, the DGA can calculate cost function values (C1, C2,..., C n ) for each glucose time course. The cost function can be defined to minimize times outside the range of the time course of each glucose time course (e.g., outside the range of about 70 mg / dL to about 180 mg / dL). In step 910, the DGA can determine the optimal insulin bolus dose that minimizes the cost function value.

[0174] The optimal insulin bolus amount (u) delivered at time t can be determined from the estimated glucose trace after simulating insulin dosing and carbohydrate meal input (if required). This dose is given by the (next) simple bolus formula, namely

[0175]

Equation

[0176] It can be decomposed into component parts such as a glucose correction part, a meal compensation part, and an insulin on board part using []. However, the physiological dosage guidance algorithm does not necessarily have to use Equation (1) to determine the optimal dosage. Instead, the physiological dosage guidance algorithm can use the individual components of the equation to enable the user and the HCP to understand to what extent a dosage is intended for the current glucose level in relation to meal compensation. In the equation, u is the optimal insulin bolus dosage. The first term in the equation within the parentheses TIFF2025106388000003.tif13114

[0177] is the glucose correction part and is intended to correct any offset between the user's current glucose (BG(t)) at time t and the target value (BG target ). The correction factor (CF) represents the user's specific sensitivity to insulin, i.e., how effective one insulin unit is in lowering the user's blood glucose level. The second term (CHO * IC) represents the portion of the insulin dosage required to cover the meal when a meal is taken. The CHO value indicates the amount of carbohydrates contained in the upcoming meal, and the IC value indicates the subject-specific insulin:carbohydrate ratio, i.e., how many grams of carbohydrates can be covered by a given insulin dosage. Finally, the IOB value is intended to account for the active "insulin on board" from which the therapeutic effect can still be drawn to avoid insulin stacking. This value can be based on a pharmacokinetic model of insulin and endogenous insulin production. BG target , IC, and the total daily insulin dose (TDD) values can be defined by the user's HCP. In one embodiment, the initial condition of CF can be defined according to the "1800 rule" defined as 1800 / TDD. Further, in one embodiment, during the learning phase, CF can be improved based on data of the correction dosage.

[0178] The physiological dosage guidance algorithm can use different methods to determine the dosage guidance for correction dosage, dietary dosage, and basal dosage. However, for all three types of dosage guidance, since rapid-acting insulin analogs (e.g., insulin lispro, aspart, or glulisine) are used and the PK profiles are similar subcutaneously for all three types, it can be assumed that the respective action times can be considered equivalent.

[0179] Correction dose When dosage guidance is required by the DGA for the correction dosage, since the correction dosage is not related to the diet, there is no newly considered carbohydrate, so CHO = 0. Therefore, the second term in Equation (1) is equal to 0. Therefore, the component parts of the correction dosage are the glucose correction part and the IOB.

[0180]

Number

[0181] In an exemplary embodiment, as described in the flowchart of FIG. 9B, in an exemplary method 911, when the correction dosage is prompted, starting from step 912, the DGA can generate a plurality of insulin dosage candidates. In one embodiment, the plurality of insulin dosage candidates can be generated based on the past history of the subject's insulin dosage.

[0182] In step 914, the DGA can determine a plurality of glucose time courses corresponding to a plurality of insulin dose candidates. In one embodiment, the DGA can calculate a subject-specific glucose time course using a physiological dosing guidance algorithm and a modification of a minimal model for a range of possible dosages. Each dosage candidate can be considered as the sum of the injected insulin dose and the amount of long-acting insulin currently on board. The amount of IOB can be calculated from the pharmacokinetics specific to the subject's long-acting analog and can be made the same across all candidate input dose candidates. The numerical difference between all candidate insulin doses can be due to the difference in the bolus component of the overall dose.

[0183] In step 916, the DGA can calculate a plurality of cost function values corresponding to the plurality of glucose time courses. In one embodiment, for each glucose time course, different glucose control metrics can be calculated and used to determine a cost function value that describes the risk of time outside a target range (e.g., from about 70 mg / dL to about 180 mg / dL).

[0184] An exemplary cost function is shown in Equation 5 below, although many other forms can be used. The area under the curve (AUC) is used in these calculations to incorporate both the magnitude and duration of hyperglycemic / hypoglycemic events.

[0185]

Number

[0186]

Number

[0187]

Number

[0188] AUC HYPO and AUC HYPER The threshold values can be defined as the minimum time and period spent in any regime. Each term of C can have an associated weighting factor w, and the whole set {w1, w2,..., w n} must sum to 1. This weighting enables prioritizing protection from hypoglycemic events. The insulin dose associated with the minimum cost function value can be the proposed dose of the dose guidance output by the DGA.

[0189] In step 918, the DGA can determine an optimal insulin dose, where the optimal insulin dose has the lowest cost function value among a plurality of cost function values. In one embodiment, the DGA can be configured to determine, as the optimal insulin dose, a dose candidate that minimizes the out-of-range time-related cost function C.

[0190] In step 920, the DGA can output dose guidance including the determined optimal insulin dose.

[0191] In another embodiment, instead of simulating glucose traces resulting from a speculative set of possible insulin doses and selecting the best option, in an alternative embodiment, the DGA can provide an initial dose estimate from which the glucose profile and cost function values can be tracked. Then, a constrained minimization of the cost function can be performed using either a gradient-based or non-gradient-based (e.g., genetic algorithm) approach. Thus, the estimate of each insulin dose can be selected following the first dose to minimize the cost function.

[0192] Once the optimal insulin dose is determined, the optimal insulin dose can be further segmented for the user to understand in what scenarios the correction is covered and how much insulin is currently on board.

[0193] Meal dose When dosage guidance is requested from the DGA for meal dosage, similar to the calculation of the corrected dosage described above, the DGA can calculate the user-specific glucose time course within the possible dosage range using a modification of the minimal model. Since the dosage guidance is related to the meal, unlike the process of determining the correction dosage, the DGA can also consider additional meal carbohydrate information when determining the dosage guidance. Since the dosage guidance is related to the meal, the CHO is greater than zero, and the components of the meal dosage can include additional meal carbohydrate information (CHO * IC). Thus, the components of the meal dosage include, according to formula (1), the glucose correction portion, the meal compensation portion, and the IOB.

[0194] In an exemplary embodiment, as described in the flowchart of FIG. 9C, in an exemplary method 921 starting from step 922, in response to a user inquiry regarding meal dosage guidance, the DGA can determine the distribution of carbohydrate values for the meal. The distribution of carbohydrate values can include a central tendency carbohydrate value, a low carbohydrate value smaller than the central tendency carbohydrate value, and a high carbohydrate value larger than the central tendency carbohydrate value. In one embodiment, the meal can be represented as a meal-specific distribution of carbohydrate values having known descriptive statistics that describe the central tendency (e.g., mean or median) and variability (e.g., standard deviation, coefficient of variation, 25 / 75% values). These data can be transformed to provide these summary statistics. As a result, each meal can have its own carbohydrate distribution.

[0195] In one embodiment, the central tendency carbohydrate value can be an average value or a median value. In one embodiment, the low carbohydrate value can be, for example, the 25th percentile value of glucose data, alternatively the 30th percentile value, alternatively the 35th percentile value, alternatively a value between the 20th percentile value and the 40th percentile value, or alternatively μ - σ if the data is normally distributed. In one embodiment, the high carbohydrate value can be, for example, the 75th percentile value, alternatively the 80th percentile value, alternatively the 65th percentile value, alternatively the 60th percentile value, alternatively a value between the 60th percentile value and the 80th percentile value of glucose data, or alternatively μ + σ if the data is normally distributed.

[0196] In step 924, the DGA can determine a plurality of insulin dose candidates for each of the central tendency carbohydrate value, the low carbohydrate value, and the high carbohydrate value. In one embodiment, the plurality of insulin dose candidates can be generated based on the past history of the subject's insulin doses.

[0197] In step 926, the DGA can determine a plurality of glucose time courses corresponding to the plurality of insulin dose candidates for each of the central tendency carbohydrate value, the low carbohydrate value, and the high carbohydrate value. In one embodiment, the DGA can calculate a subject-specific glucose time course using a physiological dosing guidance algorithm, using a modification of a minimal model for a range of possible dosing amounts, as described above for correction doses. Each dosing candidate can be considered the sum of the injected insulin dose and the amount of long-acting insulin currently on board. The amount of IOB can be calculated from the pharmacokinetics specific to the subject's long-acting analog and can be made the same across all candidate input doses. The numerical difference between all candidate insulin doses can be due to the difference in the rapid-acting component of the overall dose.

[0198] In step 928, the DGA can calculate a plurality of cost function values corresponding to a plurality of glucose time courses for each of the central tendency carbohydrate value, the low carbohydrate value, and the high carbohydrate value. In one embodiment, for each glucose time course, different glucose control metrics can be calculated and used to determine a cost function value that describes the risk of time outside a target range (e.g., from about 70 mg / dL to about 180 mg / dL). Although many other forms can be used, in one embodiment, the DGA can be configured to calculate a plurality of cost function values using the exemplary AUC analysis described with respect to correction dose calculation. In other embodiments, for each glucose time course, different glucose values at a predetermined percentile of the glucose values within a predetermined (rolling) time window can be determined. For example, instead of the time percentage of hypoglycemia or instead of the AUC below a low glucose threshold, the 5th percentile value (or other percentile less than 30%) can be calculated within the time window and compared to the low glucose threshold. The time window can be a fixed function of the time of day, e.g., from 9:00 am to 11:00 am, related to the start and / or end of an event, e.g., about 30 minutes after a meal, instead up to about 300 minutes after a meal, or other definitions. Also, different low percentiles with corresponding low glucose thresholds can be used. Instead of the time percentage of hyperglycemia or instead of the AUC above a high glucose threshold, the 90th percentile value (or other percentile greater than 70%) can be calculated within the time window and compared to the high glucose threshold.

[0199] In step 930, the DGA can determine an optimal insulin dose for each of the central tendency carbohydrate value, the low carbohydrate value, and the high carbohydrate value. The optimal insulin dose can be the dose having the lowest cost function value among the plurality of cost function values for each of the central tendency carbohydrate value, the low carbohydrate value, and the high carbohydrate value.

[0200] In step 932, the DGA can output a plurality of dosage guidances including the optimal insulin dosages determined for each of the central tendency carbohydrate value, the low carbohydrate value, and the high carbohydrate value. The dosage guidance related to the central tendency carbohydrate value can correspond to the median meal dosage guidance. The dosage guidance related to the low carbohydrate value can correspond to a "smaller than normal" meal dosage guidance. The dosage guidance related to the high carbohydrate value can correspond to a "larger than normal" meal dosage guidance. In one embodiment, the DGA can output not only the range of meal dosage guidance considering the current glucose value but also the distribution of meal carbohydrate values. In extreme cases, it can be modified or distorted as necessary. For example, the optimal insulin dosage associated with a meal that is more than normal may need to be based on the 60th percentile value instead of the 75th percentile value to avoid post-meal hypoglycemia as a result of an excessive bolus dose. Using Equation (1), the optimal insulin dosage can be further divided to show the amount allocated to the meal or the amount allocated to the pre-meal glucose level to the user and the HCP. In one embodiment, each component of the dosage guidance (e.g., glucose correction portion, meal compensation portion, and IOB) can be output and displayed to the user and / or the HCP.

[0201] Basal dose The basal dosage of the long-acting insulin analog is given once to twice a day depending on the selected drug. For example, insulin glargine has a duration of action of 24 hours and is administered once a day, and insulin detemir has a duration of action of 12 hours and is administered twice a day. Since these time scales are much longer than the 4-5 hour duration of action associated with rapid-acting analogs, different subcutaneous insulin PK profiles can be included for long-acting analogs. The time course of action is the difference in the case of the basal dosage compared to the other two. Similar to the correction dosage, the basal administration is not related to meals but rather is present during all meals due to its extended pharmacokinetic profile.

[0202] In an exemplary embodiment, referring to the flowchart of FIG. 9B, starting from step 912, when the DGA is prompted with basal dosage guidance, the DGA can generate a plurality of basal insulin dosage candidates. In one embodiment, the plurality of basal insulin dosage candidates can be generated based on the past history of the subject's basal dosage.

[0203] In step 914, the DGA can determine a plurality of glucose time courses corresponding to the plurality of basal insulin dosage candidates. In one embodiment, the DGA can calculate a subject-specific glucose time course using a physiological dosage guidance algorithm and using a modification of the minimal model for a range of possible dosages. The amount of IOB can be calculated from the pharmacokinetics specific to the subject's insulin analog dosage and can be made the same across all candidate input dosages.

[0204] In one embodiment, the DGA can be configured to expand the time courses of multiple glucoses with respect to time to reflect a long action time (e.g., 12 hours or 24 hours) associated with long-acting analogs. Usually, since basal insulin is a once-daily drug, a long-acting basal guidance can be formulated to prompt the user at the same time every day. Similar to the determination of correction doses and meal doses, the DGA can be configured to generate a blood glucose profile for a basal dose candidate. Each of the multiple glucose time courses includes three meal events, and these meal events may each include an input of rapid-acting administration to mimic the variations within the 24-hour window of basal action. Each meal event may include not only an input of carbohydrates but also an input of rapid-acting insulin. In one embodiment, the value of the insulin input can be based on the subject-specific median rapid-acting analog requirement from MDD152 data. In one embodiment, the meal input can be represented by the amount of carbohydrates of the central tendency (e.g., the median or average amount of carbohydrates) as described in the determination of meal-time dose guidance. In one embodiment, the meal events can be the same for all basal simulation events within a single dose guidance. In one embodiment, the basal dose is the only input value that changes.

[0205] In step 916, the DGA can calculate a plurality of cost function values corresponding to a plurality of glucose time courses. In one embodiment, for each glucose time course, different blood glucose control metrics can be calculated and used to determine a cost function value that describes the risk of time outside the target range (e.g., from about 70 mg / dL to about 180 mg / dL). In one embodiment, the cost function analysis for the plurality of glucose time courses generated for a plurality of basal dose candidates can be the same as that described for the correction dose analysis. In another embodiment, the cost function analysis for the plurality of glucose time courses generated for a plurality of basal dose candidates can be different from the cost function analysis used for the correction dose analysis.

[0206] In step 918, the DGA can determine an optimal basal insulin dose having the lowest cost function value among a plurality of cost function values. In one embodiment, the DGA can be configured to determine a basal dose candidate that minimizes the out-of-range time-related cost function C as the optimal insulin dose.

[0207] In step 920, the DGA can output dose guidance including the determined optimal insulin dose.

[0208] Correction factor titration Next, an exemplary embodiment of a method for determining correction factor titration will be described. Although initiated by a physician, insulin dosing for diabetic patients has mainly been done at the patient's discretion to manage the disease over time. Many of these management methods are empirical, where the patient learns from trial and error to improve diabetes management. More quantitative determination methods when using MDI often rely on simple approaches such as insulin bolus calculators and depend on values that are difficult to determine and understand from the patient's perspective, such as the insulin:carbohydrate ratio or insulin correction factor. The algorithmic subroutine described here aims to titrate previously learned patient-specific dosing parameters to an optimal level during a passive observation period by leveraging dense glucose data from a CGM device and insulin dosing information from a Bluetooth-enabled insulin pen, thereby providing personalized dose guidance that evolves with the user with minimal input from the user.

[0209] As described elsewhere in this specification, to provide initial dosage guidance, the DGA can go through a "learning phase" and can confirm user-specific dosing parameters such as fixed meal-time dosages, target glucose, and insulin correction factors. The method for the learning phase is described in connection with FIG. 7 of this specification. These parameter values function as initial estimates and can be further titrated to more optimal patient-specific values depending on the use of the system. By such an approach of continuously titrating the parameters following such initial learning, it becomes possible to accommodate not only the progression of the disease but also external changes in both the lifestyle habits and drug therapies that the user may undergo. With the increasing popularity of the class of insulin-based diabetes therapies that can enhance both endogenous insulin production and insulin sensitivity, it is not possible to keep the user's correction factor as a static value throughout the life of the system. This specification describes a method for titrating the user's insulin correction factor (also referred to as insulin sensitivity).

[0210] As used in many embodiments of DGA, the user's correction factor need not be a single value and may instead be a set of distinct values specific to a given meal type (e.g., breakfast, lunch, dinner). The correction factor can have units of mg / dL glucose per insulin unit. A high correction factor indicates that the user has a high sensitivity to insulin because a small dose will cause a large decrease in glucose. Conversely, a low correction factor suggests that the user has a low sensitivity to insulin. An effective bolus insulin dose should return the post-meal blood glucose level to a safe range within its pharmacodynamic window. For example, assuming a dosing rule for a bolus calculator as described in relation to FIGS. 8A - 8H, the correct correction factor takes into account both the carbohydrates in the meal and the pre-meal glucose rise, and should contribute to an insulin dose that returns the glucose level to a safe and stable target value. Thus, if GPA plots all insulin doses for a particular meal as the ordinate against a particular metric that quantifies glucose readings for the time period associated with the meal (e.g., a set amount of time after dosing (e.g., 4 hours), a metric that quantifies glucose data or excursions associated with the meal, or a percentile of glucose values during the meal time period (e.g., 5th percentile value, 10th percentile value)), then theoretically, the best fit of these data is a straight line with a slope of zero and a y-intercept equal to the user's target glucose. In practice, all bolus insulin doses return the user's blood glucose level to the target value or range.

[0211] The relationship between insulin and post-meal glucose having a slope of zero represents an ideal scenario for dosing effectiveness. DGA can be assumed to have pre-learned the user's fixed dose, for example, through user input or transmission from MDD152. In the case of a dose greater than the fixed dose, the system can observe the relationship between the insulin dose from SCD102 or other sources and post-meal glucose. DGA can evaluate the effectiveness of the dose using one or more of several methods including, but not limited to, the following.

[0212] Step of determining the centroid (median / average value) of postprandial glucose values In this method, the difference between the centroid of the postprandial glucose values of the insulin dose without any correction and the centroid of the postprandial glucose values of the insulin dose with correction is used. The ideal scenario is that there is no significant difference or the significant difference between the two centroids is zero. A positive difference exceeding a predetermined threshold is considered to indicate a suboptimal correction factor. Similarly, a negative difference less than the predetermined threshold is considered to indicate a correction factor higher than optimal.

[0213] Step of applying a linear fit to the data If the degree of fitness is met, the DGA can perform a statistical test to determine whether there is a statistically significant difference between the slope of the best fit and the ideal case where the slope is zero. If the slope is less than zero, it means that the insulin dosage is too high, corresponding to the need to increase it because the correction factor is too low. If the slope is greater than zero, it indicates that the postprandial glucose is rising and the insulin dosage can be increased, corresponding to the need to reduce the correction factor because the correction factor is too high. The amount for titrating these values can vary depending on the slope of the fit. When the slope is steep, titration needs to be performed more aggressively than when it is close to zero. This titration method is independent of the fixed-dose titration and can be carried out in parallel with the fixed-dose titration.

[0214] Step of determining the area under the postprandial glucose / insulin curve In the ideal dosing scenario, it can have a certain area, indicating that it can return to a certain glucose level after all administrations. Therefore, the DGA can evaluate any change within the area as an indication of an error in the correction factor. Using the integration method, it can be determined whether the meal-time administration component corresponding to the fixed meal dose is effective or further titration is required. When the integration level increases compared to the ideal scenario, it indicates that the fixed dose can be increased to bring the postprandial value closer to the target level.

[0215] During a defined period, e.g., a learning phase of up to 14 days depending on the characteristics of the SCD or other factors, the DGA can learn the user's dosing strategy, or other user-specific parameters including but not limited to the dose, correction factor, and insulin action time (IAT) / insulin on board (IOB). It may be desirable to titrate the correction factor for any one or more of the following reasons: (1) when the correction factor is insufficient; (2) changes due to therapeutic intervention or environmental changes. In the case of bolus administration that includes only correction (without carbohydrate counting), the DGA can analyze the post-meal glucose value to gradually titrate the correction factor. For example, when the post-meal glucose peak is above 180 mg / dL, or the difference between the post-meal peak and the pre-meal value is higher than the threshold and there is no risk of post-meal hypoglycemia, the correction factor can be gradually titrated for each measurement cycle. For example, the correction factor can be titrated in preset values, e.g., 1 unit increments, or alternatively 2 unit increments. In the case of separate correction dosing (e.g., without a meal) administered to treat hyperglycemia when the glucose level is high, the difference between the value at the time of dosing and the glucose value at a predetermined time (e.g., 4 hours after dosing) can be used to titrate the correction factor.

[0216] Insulin dosing at mealtime consists of two different amounts: a) the portion intended to cover the meal consumed, and b) the portion intended to address the situation when the pre-meal glucose level exceeds the target range. The meal-related portion is usually either fixed (e.g., the portion taken for breakfast is always a specific amount, which can be titrated over time) or varies according to the amount of carbohydrates the patient is expected to consume. In the following example, a fixed meal dose is assumed. However, the following explanation can be applied to a variable portion where the actual portion is determined by a fixed ratio of carbohydrate to insulin, and this ratio itself can be titrated. As used herein, the portion for dealing with pre-meal glucose refers to the correction dose or correction portion determined by the pre-meal correction factor.

[0217] The pre-meal correction factor can be used to control MDD152. Reducing or down-titrating the correction factor is comparable to increasing the corrected dose, as described herein. For example, FIG. 10A shows a method 500 by a DGA that provides a pre-meal correction factor in response to analyte data for use in controlling MDD152. At 502, method 500 can include determining an analyte pattern type for at least one TOD period by executing a GPA algorithm that receives, as input, time-correlated analyte data transmitted from a sensor control device worn by a patient over an analysis period by at least one processor. At 504, method 500 can include determining a pre-meal correction factor by at least one processor executing the algorithm, based on the analyte pattern type and a defined dosing strategy for the patient over the analysis period. At 506, method 500 can include storing an indicator of the pre-meal correction factor in computer memory for output to at least one of a user or a dosing device by at least one processor. In contrast to the linear fit embodiments described above, the embodiments described here are a unified approach that can simultaneously perform titration of a fixed dose and a correction factor via one technique or software function (e.g., a single logic tree).

[0218] In related aspects, FIG. 8B and the related descriptions above disclose a method 410 of GPA for classifying a patient's glucose pattern as high, low, or high / low over various TOD periods. FIG. 8A and the related description disclose a method for a DGA to use the output from GPA410 to provide MDI dosage guidance recommendations. FIGS. 8D-8H and the related descriptions show an example of an algorithm for a DGA to provide specific MDI dosage guidance recommendations based on the glucose pattern type output by GPA410 and the defined dosing strategy of the patient during the analysis period. The recommendations may be provided for automatic or semi-automatic control of MDD152, or for operation of a user interface to guide manual dosing control.

[0219] FIG. 10B shows, for example, a method 510 for evaluating recommendations for titrating meal doses and pre-meal corrections to raise high analyte levels, with or without pre-meal corrections, when GPA indicates a low pattern. In related aspects, FIG. 8G shows an example of an algorithm 474 for formulating recommendations for post-meal corrections. When executed by the DGA, algorithm 474 solicits consideration of whether to perform a pre-meal correction when GPA indicates a low pattern, considering analyte data excluding data for TOD periods where bolus administration was missed. Other low glucose pattern conditions 512 during the TOD period may also be appropriate to trigger execution of method 510. If both the meal dose and the pre-meal correction are higher than optimal, method 510 can output a pre-meal correction factor that can only reduce the meal dose. In the presence of a low pattern, this may be due to a high fixed dose, a high pre-meal correction, or both. In one embodiment, the fixed dose and the pre-meal correction can be titrated sequentially, with the fixed dose being titrated first as it forms the basis of the dosing amount. If a low pattern is still observed after the fixed dose has been appropriately titrated, the correction dose can be titrated. In another embodiment, the fixed dose and the pre-meal correction dose can be titrated in parallel.

[0220] If a low pattern is detected during the TOD period, the DGA can determine, at 514, whether the dietary dose of the defined dosing strategy includes a pre-meal correction. If it does, at 534, the DGA can exclude days with missed boluses from the original data set or pre-meal correction by including only a portion of the analyte data, for example, for days when dietary dosing was performed and the bolus was not missed. At 536, the DGA can test whether sufficient data remains after excluding data for days when the bolus or pre-meal correction was missed to achieve a minimum confidence level. If sufficient data exists, at 410, the DGA can repeat GPA410 on the input analyte data set with data for days when the bolus or pre-meal correction was missed excluded.

[0221] At 538, the DGA can determine whether the subsequent glucose pattern is still low, and if so, at 540, reduce only the dietary portion of the insulin dose. If the pattern is not low, at 542, the DGA can reduce the pre-meal correction factor, which, if implemented by the user or MDD, will result in a reduction of the corresponding pre-meal correction dose.

[0222] In 536, if the DGA determines that the data for determining the pattern is insufficient, in 516, the DGA can include data for the day on which dietary administration and pre-meal correction were performed. Then, in 518, the DGA can re-test the sufficiency of the data set. If the data is sufficient, in 410, the DGA can repeat the GPA for the expanded data set. In 540, if the resulting glucose pattern is not low, the DGA can reduce the recommended value of the dietary portion of each dose for the relevant TOD period without reducing the pre-meal correction factor. If the DGA determines in 520 that the glucose pattern is low, in 526, the DGA can include data for only the dietary dose. Next, in 530, the DGA can re-test whether the data set is sufficient. If the data set is not sufficient, in 528, the DGA can reduce both the dietary dose and the correction dose. If the data set is sufficient, the DGA can execute the GPA at 410 and determine at 532 whether the glucose pattern is low. If the glucose pattern is low, at 540, the DGA can reduce the recommended value of the dietary portion of each dose for the relevant TOD period without reducing the pre-meal correction factor. If the glucose pattern is not low, in 542, the GPA can reduce the pre-meal correction factor, which, if implemented by the user or the MDD, can reduce the corresponding pre-meal correction dose.

[0223] FIG. 10C shows a method 550 for evaluating recommended values for titrating meal doses and pre-meal corrections to lower high analyte levels, with or without pre-meal correction, for example, when GPA410 shows a high pattern. In a related aspect, FIG. 8H shows an example of an algorithm 480 for formulating recommendations for post-meal correction. When executed by the DGA, the algorithm 480 asks whether to perform a pre-meal correction when the GPA shows a high pattern. Other high glucose pattern conditions 552 during the TOD period may also be suitable for triggering the execution of the method 550. If both the meal dose and the pre-meal correction are lower than the optimal values, the method 550 can first increase the meal dose. The high glucose pattern can be addressed sequentially so that the correction dose can be up-titrated after the meal dose has been titrated to avoid hypoglycemia from both increasing the fixed dose and decreasing the correction factor (similar to being able to increase the correction dose at a given blood glucose level). (Patterns that are not high with data without correction). The high pattern can be addressed first by increasing the fixed dose. If the high pattern remains and occurs only when a correction dose is included, the correction factor can be titrated.

[0224] If a high pattern is detected for a particular TOD, at 554, the DGA can determine whether the dietary dose for which pre - meal correction has been made is included in the analyte data. If no pre - meal correction is included, at 564, the DGA can exclude the data for the day on which pre - meal correction was made and the day on which the bolus was missed from the original data set and repeat the glucose pattern analysis at 410. At 566, the DGA can determine whether the subsequent glucose pattern is still high. If not, at 572, the DGA can increase the pre - meal correction factor, which will cause a decrease in the pre - meal correction dose by outputting to the UID or MDD. Otherwise, if the glucose pattern is high, at 562, the DGA can increase the recommended value of the dietary portion of each dose for the relevant TOD period. At 554, if it is determined that pre - meal correction is included, at 556, the DGA can exclude the data for the day on which meal administration and pre - meal correction were made and the data for the day on which the bolus was missed from the original data set and repeat the glucose pattern analysis at 410. At 558, the DGA can determine whether the subsequent glucose pattern is still high. If the pattern is not high, at 562, the DGA can increase the recommended value of the dietary portion of each dose for the relevant TOD period. If the pattern is high at 558, at 564, the DGA can exclude the data for the day on which pre - meal correction was made, exclude the data for the day on which the bolus was missed, and repeat the pattern analysis at 410 and the subsequent steps described above.

[0225] Figures 10B - 10C show aspects of various algorithms for determining the pre - meal correction factor, but it should be understood that these are exemplary. Various other algorithms may also be suitable.

[0226] Dosage guidance method for insulin dosage according to administration timing (at the start of a meal or before and after the start of a meal) Next, an exemplary embodiment of a method for determining dose guidance according to administration timing will be described. To maximize glycemic control, it is desirable for insulin dosing to be temporally synchronized with meals such that the peak of circulating insulin coincides with the postprandial glucose rise. However, patients may forget to take their pre-meal insulin dose before eating. As a result, a mismatch can occur between the glucose rise associated with the meal and insulin action, and the appropriate dose may become unknown. Taking the meal dose after the start of the meal rather than immediately before the start of the meal can cause hypoglycemia. This is because active insulin may still be circulating even after the postprandial glucose rise has subsided. Additionally, patients correcting glucose trends may tend to increase their dose because the glucose trend at the start of the meal is upward. Such behavior can also cause hypoglycemia. To minimize hypoglycemic episodes related to timing, the DGA can be configured to determine a delay in dosing time and take the delay in dosing time into account when providing dose guidance. A positive delay time indicates that the insulin dose is administered after the start of the meal. A zero delay time indicates that the insulin dose is administered approximately simultaneously with the start of the meal. A negative time delay indicates that the insulin dose is administered before the start of the meal.

[0227] In many embodiments, the DGA can be configured to include a method for detecting in real time a missed meal administration for the purpose of determining whether the meal dose guidance calculation should be an "on-time" calculation (i.e., zero or negative time delay) or a "late dose" calculation (i.e., positive time delay). In some embodiments, the DGA can also be configured to determine and output an estimated value of the start time of the meal that can be used in the delayed dosing calculation. In some embodiments, if the meal-time insulin dose is not detected +X minutes or -Y minutes after the estimated start of the meal, the DGA can be configured to notify the patient that there may have been a missed administration.

[0228] Algorithm for real-time detection of missed meal administrations In many embodiments, the DGA can be configured to detect missed meal administrations using a real-time meal detection algorithm. Systems and processes for detecting missed meal administrations in real-time and then alerting the patient are described herein. The process for detecting missed meal administrations can be run periodically (e.g., each time new glucose data becomes available in the system). Alternatively, the process can be run whenever it is appropriate to provide the patient with a "missed dose" alert or whenever the alert is enabled.

[0229] In an exemplary embodiment, real-time meal detection can be performed by a feature extraction module and a meal detection module. The feature extraction module can receive CGM data points one at a time as they become available. When the feature extraction module detects that the glucose value is increasing, the feature extraction module can extract a plurality of features and pass the plurality of features to the meal detection module for meal detection.

[0230] In one embodiment, each time the feature extraction module receives a new glucose data point, it is configured to perform data smoothing by using a quadratic function to fit the data within a time window and back-calculating from the current data point. The time window can be about 60 minutes. The feature extraction module can be configured to store the fitted value at the center of the time window as the current smoothed data. The feature extraction module can also be configured to store the coefficients of the linear term and the quadratic term of the fitted value at the center of the time window as the current glucose rate of change and acceleration value, respectively. In addition to being configured to store the fitted value at the center point, the feature extraction module can also be configured to store the fitted value at the current point for feature extraction. The feature extraction module can be configured to compare the current smoothed glucose value with the previous smoothed glucose value (e.g., the smoothed glucose value immediately preceding the current smoothed glucose value) to determine whether the smoothed glucose data is rising or falling. The feature extraction module can extract a plurality of features and then, after determining that the plurality of features are rising in the comparison of the current smoothed glucose value and the previous smoothed glucose value, pass the plurality of features to the meal detection module.

[0231] The feature extraction module can be configured to extract a plurality of features from two segments of the smoothed data. These two segments can be the current ascending segment and the previous descending segment. The plurality of features extracted from the current ascending segment include: 1) maximum acceleration, 2) the time of the maximum acceleration point, 3) the glucose value at the maximum acceleration point, 4) the height calculated from the difference in glucose values between the current time point (fitted value) and the maximum acceleration point (reference point), 5) the duration of the current segment calculated from the elapsed time from the reference point to the current point, 6) the average ascent rate of the current segment calculated by dividing the height by the duration, 7) the maximum increase in acceleration (the increase in acceleration at a given time point is obtained by subtracting the acceleration at the previous time point from the acceleration at that time point), and 8) the increment of the area under the curve (obtained by subtracting the glucose value at the reference point from the average glucose value and multiplying the difference by the duration of the segment), but are not limited thereto. The plurality of features extracted from the previous descending segment include: 1) duration, 2) height, 3) average descent rate (height / duration), 4) maximum descent rate (the maximum value of the absolute value of the rate of change), and 5) maximum deceleration (the maximum value of the absolute value of acceleration), but are not limited thereto. The feature extraction module can be configured to pass the extracted plurality of features to the meal dose module.

[0232] The meal detection module can be configured to receive a feature vector as input and output a binary detection result indicating whether the current rising segment is a meal response glucose excursion. The meal detection module can also be configured to output a probability value along with the binary detection result. In one embodiment, the pre-trained machine learning model in the meal detection module can be implemented using a random forest classifier by scikit learn (https: / / scikit-learn.org / stable / modules / generated / sklearn.ensemble.RandomForestClassifier.html). The meal detection module can be configured to detect the start of a meal based on tree construction rules and feature thresholds for each feature of each tree, which can be optimized during the training process. In one embodiment, the pre-trained model can also be constructed based on alternative classification algorithms including gradient boosting, ADA boost, artificial neural networks, linear discriminant analysis, and extra trees.

[0233] The meal detection model can also be configured to estimate the start time of a meal when the meal is detected. In one embodiment, the start time of the meal can be estimated as the point with the maximum increase in glucose value acceleration by looking back from the detection point within a time window size of about 1.25 hours. For example, if a meal omission is detected by the algorithm at 1:15 PM, the model can determine the start time of the meal by looking back to around 12 PM. The acceleration of the glucose value at each point can be calculated by fitting five data points centered on the data point of interest using a quadratic function, i.e., y = ax 2 + bx + c. The fitted parameter "a" is the acceleration at the point of interest. The increase in the acceleration of the glucose value at a certain point k can be defined as a(k + 1) - a(k).

[0234] The meal detection model can also be configured to output a notification regarding missed dosing to the UID200 if it is detected that meal-time insulin administration was not performed within a period before and after the estimated start of the meal. In one embodiment, if the estimated start of the meal is less than 2 hours away, the notification can indicate that the patient can still obtain dosing guidance for the meal and that the dosing is delayed.

[0235] Details of other types of meal detection methods and algorithms are described in U.S. Patent Publication No. 2017 / 0185748 and PCT Application No. PCT / US2020 / 12134, the entire contents of which are hereby incorporated by reference.

[0236] Dosage guidance administered at the start of a meal If the dosing delay is negative or zero (i.e., the dosing guidance is for administration at or before the start of the meal), the DGA can be configured to consider other factors that can modify the hypoglycemia and hyperglycemia risks to account for the span of time during which bolus insulin can take effect before the presence of the meal. For example, the DGA can be configured to include a risk factor related to the circadian rhythm.

[0237] In an exemplary embodiment, as described in the flowchart of FIG. 11, in an exemplary method 600, the DGA can provide dosing guidance for administration to the subject at the start of the meal, for example, if the time delay is negative or zero. Starting at step 602, the DGA can determine a first dosing guidance for the meal to be administered at the time of day of the start of the meal. The first dosing guidance can be a fixed meal dose (with or without correction) or can be based on the carbohydrate content of the meal.

[0238] In step 604, then, the DGA can determine whether there is a risk of hypoglycemia based at least on the first dosage guidance and the time period during which the dosage is to be administered. The DGA can be configured to determine the risk of hypoglycemia by referring to a risk map. In one embodiment, historical user data (e.g., glucose level and insulin dosing data), population data, or a combination of both historical user data and population data can be used to develop a risk map related to time periods, days of the week, and / or other available patterns. In one embodiment, the DGA can be configured to use the risk map to identify the edge of the distribution closest to the highest risk rather than to identify typical behavior.

[0239] In step 606, the DGA can output a second dosage guidance that is different from the first dosage guidance. The second dosage guidance can be output to the UID200 and can include a lower drug amount than the first dosage guidance. The second dosage guidance can also be associated with a lower risk of hypoglycemia than the first dosage guidance. For example, if the first time period has a higher risk of hypoglycemia in the near future than another (second) time period and the user requests dosage guidance during the first time period at or before the start of a planned meal, the DGA can be configured to output a dosage guidance that is lower than the nominal proposed meal dosage guidance (e.g., a fixed meal dosage (with or without correction) or a meal dosage based on carbohydrate content).

[0240] If the dosage is to be administered after the start of a meal (i.e., if the dosing time delay is positive), the DGA can be configured to determine the dosing time delay and output a dosage guidance that takes into account the dosing time delay and any associated risks.

[0241] In an exemplary embodiment, as described in the flowchart of FIG. 12A, in an exemplary method 607, starting at step 608, the DGA can determine a first dose guidance for a meal in response to an inquiry from a subject, and the first dose guidance is determined for administration to the subject at the start of the meal. In one embodiment, the DGA can be configured to assume that the initial optimal meal dosing is performed simultaneously with the start of the meal. Thus, in one embodiment, when the user opens the DGA and requests dose guidance, the algorithm can first calculate the optimal insulin dose if administered at the start of the meal. The optimal insulin dose for a meal can be determined in a number of ways, including, but not limited to, a fixed meal dose (with or without correction) or a meal dose based on carbohydrate content.

[0242] In step 610, the DGA can determine whether there is a time delay between the start of a meal and an inquiry from the subject (i.e., the start of dosage guidance). In one embodiment, the DGA can be configured to identify a dosing time delay by multiple methods. In one embodiment, the DGA can be configured to determine an estimated start of a meal. In one embodiment, the DGA can be configured to identify a dosing time delay using the above-described meal detection algorithm that can detect an estimated start of a meal, and the DGA can be configured to record the time between the start and the dosage guidance prompted to the user. In another embodiment, the DGA can be configured to identify a dosing time delay between the start of a meal and the start of bolus insulin dosing, which can be recorded by the user. In this embodiment, the DGA can be configured to prompt the user for an input regarding the dosing time delay. For example, the DGA can be configured to ask the user whether the required dosage guidance is for a meal or a corrective dose for correcting high glucose. If the required dosage guidance is for a meal, the DGA can be configured to ask whether the meal has already started and, if so, for how long. This input can then be used as the meal-to-dosing time delay for dosage guidance.

[0243] In step 612, in response to the determination of the time delay, the DGA can determine whether there is a risk of hypoglycemia based on at least the first dosage guidance and the time delay. In one embodiment, the DGA can determine whether the patient is in a high or low blood glucose risk zone or has a risk of hypoglycemia compared to the remaining user population based on the first dosage guidance and the time delay.

[0244] In one embodiment, the DGA can determine the risk of hypoglycemia by referring to a multi-dimensional surface map. In one embodiment, the map can be population-based. For example, the map can include observations across all DGA users until sufficient data is collected to personalize recommendations based on specific users who require dosage guidance. In one embodiment, the DGA can be configured to receive and store multiple types of data related to the meal-time dosage of a DGA user. The multiple types of data related to the meal-time dosage include, but are not limited to, dosing time delay, proposed or recommended dosage guidance, administered dosage, and glucose time series for a predetermined time period after dosage administration. The glucose time series can have a fixed time interval, a varying time interval, or a combination thereof between glucose value samples (e.g., from a CGM). Postprandial glucose data in the glucose time series can be used to calculate a hypoglycemia indicator specific to a meal / dosing episode. This hypoglycemia indicator can be, but is not limited to, the time below about 70 mg / dL, the time below about 54 mg / dL, or a calculated risk factor such as the low blood glucose index (LBG). The hypoglycemia indicator can also be normalized to the glucose concentration value at the start of the meal. The dosage guidance and the administered dosage can be treated as variables not only by the difference between the two but also individually. Subsequently, a multi-dimensional surface map can be derived in which the resulting hypoglycemia indicator is a function of various variables. Considering the effects of different meal sizes and circadian rhythms, the DGA can create multiple multi-dimensional surface maps, including those for breakfast, lunch, and dinner respectively. Once the maps are created, the DGA can also determine a cut-off value for an acceptable hypoglycemia risk. One of the important variables used in this system can be the meal-to-dose time delay.

[0245] The DGA can be configured to output dosage guidance to the UID200 based on whether the risk of hypoglycemia is determined. If the DGA determines that the first dosage guidance and time delay do not expose the subject to the risk of hypoglycemia, at step 614, the DGA can output the first dosage guidance. At step 616, the DGA can output a second dosage guidance in response to a determination that there is a risk of hypoglycemia, and the second dosage guidance is associated with a lower risk of hypoglycemia than the first dosage guidance. The second dosage guidance can be output to the UID200. In one embodiment, if the DGA determines that there is a risk of hypoglycemia in the first dosage guidance at the time delay, the DGA can search for a change in dosage along the Δ dosage / hypoglycemia risk isopotential of the multidimensional surface map that reduces the risk below a predetermined cutoff value. This dosage change can be applied to the first dosage guidance to provide updated dosage guidance for minimizing hypoglycemia. The system can continue to collect information to improve and / or update the multidimensional surface map as a function of multiple variables (e.g., dosing time delay, proposed or recommended dosage guidance, administered dosage, and glucose time series over a predetermined time period after dosage administration).

[0246] In another embodiment, the DGA can be configured to determine a late-onset dosage guidance that does not consider endogenous insulin production. The problem with using conventional meal dosage calculations when determining dosage guidance after a meal has started is that usually, even when the user has dosed insulin, the user's glucose increases. If the user administers a meal dosage after the start of a meal and determines the meal dosage using the current glucose value, the user may inject too much insulin and hypoglycemia may ultimately occur. The DGA can be configured to determine dosage guidance for administration after the start of a meal that mitigates the problem of the user's glucose increase by considering the user's glucose value at the estimated start time of the meal to determine a correction portion of the dosage guidance.

[0247] In an exemplary embodiment, as shown in the flowchart of FIG. 12B, in an exemplary method 617 starting from step 618, the DGA can receive a query for meal dosage guidance having a start time. At step 620, the DGA can determine whether a query for dosage guidance was received after the start of the meal. In one embodiment, in response to a query for dosage guidance from a user, the DGA can use the delay dosage detection algorithm described above to determine whether the dosing is delayed. In one embodiment, the delayed dosage detection algorithm can determine and output an estimated start time of the meal, which the DGA can compare with the time when the query from the user was received.

[0248] At step 622, the DGA can determine the user's glucose level associated with the start time of the meal. In one embodiment, the DGA can determine a glucose value associated with the start time of the meal (e.g., the glucose value closest in time to the start time of the meal).

[0249] At step 624, the DGA can output delayed dosage guidance, which includes meal dosage guidance and correction dosage guidance. In one embodiment, the correction dosage guidance can be based on the determined glucose level at the start time of the meal. The correction dosage guidance can be determined using a bolus calculator to correct high glucose and can include a glucose correction portion. As previously described with respect to Equation (1), the glucose correction portion of the correction dosage guidance can be determined based on the following equation, where (BG(t)) is the current glucose value and BG target is the target glucose.

[0250]

Number

[0251] In one embodiment, for determining the glucose correction portion for delayed dose guidance, the current glucose value is the glucose value associated with the estimated start time of the meal. In one embodiment, the correction factor may be the user's insulin sensitivity factor. In one embodiment, the delayed dose guidance is the meal dose guidance plus the correction dose guidance. The meal dose guidance can be determined in a number of ways. In one embodiment, the meal dose guidance can be determined based on the estimated carbohydrate content of the meal. In another embodiment, the meal dose guidance may be based on a "fixed" or "fixed + correction" dosing strategy, and the user takes a predetermined amount of insulin for the meal dose regardless of the carbohydrate content of the meal.

[0252] In another embodiment, the DGA can be configured to determine a delayed dose that takes into account the production of endogenous insulin. The problem with using conventional meal dose calculations when dosing after a meal has started is that type 2 diabetes patients often still produce their own (endogenous) insulin. Thus, if the user doses late after the start of the meal, the user may have additional "insulin" produced by the user's pancreas, and if this is not taken into account in the dose calculation, the user may ultimately become hypoglycemic. The DGA can be configured to determine dose guidance that takes into account the production of additional endogenous insulin.

[0253] In an exemplary embodiment, as described in the flowchart of FIG. 12C, in an exemplary method 630 starting from step 632, the DGA can determine a first dose guidance for a meal in response to an inquiry from a subject. In one embodiment, the first dose guidance is determined for administration to the subject at the start of the meal. In other embodiments, the first dose guidance can be a fixed meal dose (with or without correction) or a meal dose determined based at least on the carbohydrate content of the meal.

[0254] In step 634, the DGA can calculate the time delay between the start of a meal and the inquiry from the subject. In one embodiment, the time delay can be calculated using the delayed dose detection algorithm described above. In one embodiment, the delayed dose detection algorithm can estimate the start time of the detected meal and calculate the time delay by comparing the time when the dose guidance inquiry from the user is received by the DGA with the estimated start time. The estimated start time can be determined by the DGA as described for other embodiments.

[0255] In step 636, in response to a determination that the time delay is greater than zero (>0), the DGA can determine a factor corresponding to the estimated amount of endogenous insulin and the amount of the time delay. In one embodiment, the factor can be a fraction. In one embodiment, the factor can be determined from a universal look-up table in which the fractional value decreases as the amount of time between the inquiry and the estimated start time of the meal increases, for example.

[0256] In step 638, the DGA can output a first dose guidance and a second dose guidance associated with the factor. The second dose guidance can be output to the UID200. The second dose guidance can be calculated by multiplying the first dose guidance by the factor determined in step 636, which can be a fraction. According to some embodiments, for example, the value obtained by subtracting the fraction from 1 can take into account the endogenous insulin production until the time of injection. In one embodiment, this fraction can be based on simulating a type 2 metabolic reaction to the meal. Although it may not be known for each subject, by summing this value with the fraction of the delayed dosage, a dosage considering endogenous production can be supplied, and a corrected dosage can be supplied to keep the postprandial blood glucose level of the user within a safe range.

[0257] Dosage guidance for additional meals (e.g., desserts) In some situations, a patient may dose meal-time insulin in an amount that covers a planned meal, but later may take additional food in the form of a "refill" or dessert. A common strategy is to re-dose the new amount regardless of glucose levels and trends, and this is actually commonly done. However, there may be situations where this is not the correct course of action. For example, if the patient originally did not take enough to cover the first dose, the new dose may be too much and lead to a hypoglycemic episode. Conversely, if the patient takes a conservative approach and does not dose additional insulin to cover the extra food, a hyperglycemic episode may occur. In one embodiment, the DGA can provide dosage guidance for the added meal by (1) confirming that the user is continuing to extend the original meal with more food, (2) notifying the user of the risk of hypoglycemia before an extra dose is administered, and (3) monitoring such risk after the meal.

[0258] The DGA can be configured to output a first query to the user with UID200 to confirm that the meal is being extended with extra food. If the user wishes to receive dosage guidance, it is assumed that the dosage guidance app is opened first. In an exemplary embodiment, as described in the flowchart shown in FIG. 13, in an exemplary method 660 starting from step 662, the DGA can be configured to determine whether the user's query regarding dosage guidance was made within the period of a first episode, where the first episode includes a meal having a start time. The start of the period can be determined from the start time of the meal determined by the real-time meal detection algorithm, and the length of the period can also be determined by the real-time meal detection algorithm. The start time of the meal can be determined in several ways including, but not limited to, the last time the meal was detected (e.g., by the real-time meal detection algorithm), or the last time dosage guidance and / or insulin dosing was provided in combination with meal detection.

[0259] As seen in step 664, if the user inquires about dosage guidance from the DGA within the period, the DGA can request input from the user to confirm whether the meal was extended with additional food. The DGA can output a prompt or other display requesting the user's feedback to the UID200. In one embodiment, the DGA can also request input from the user to confirm whether the purpose of the dosage guidance was for high glucose correction. In another embodiment, the DGA can prompt the user for an explanation regarding the most recent dosage guidance request and optionally provide selectable options as answers. Examples of the options can include, for example, the option that the additional request was made in response to an extended meal and the option that the additional request was made to correct high glucose not related to additional food intake.

[0260] As seen in step 666, the DGA can determine the risk of hypoglycemia from the start time of a meal. In one embodiment, to avoid hypoglycemic episodes due to insulin dose stacking, the DGA can be configured to determine the risk of hypoglycemia by determining that the user is in a current blood glucose excursion before extending the meal with additional food. The DGA can further be configured to create a forward prediction value of the glucose level from the determined point in the current excursion to determine whether the user's glucose level is still rising or falling. As a safety measure to avoid insulin stacking, the DGA can be configured not to provide dose guidance until the glucose level reaches its post-meal maximum. For example, if dose guidance for the added meal is requested while the user's glucose is still rising, the DGA can output a notification that it cannot provide guidance at this time for safety reasons. In one embodiment, if the risk of hypoglycemia is determined, at step 668, the DGA can be configured to notify the user not to administer any dose or to exercise extreme caution when dosing. In another embodiment, if there is no risk of hypoglycemia, at step 670, the DGA can be configured to notify the user to dose according to the HCP-recommended value for the extra food. Further, in one embodiment, if the DGA detects that an extra dose has been delivered, the DGA can be configured to advise the user to check the glucose level at least about 2 hours later to confirm that there is no hypoglycemia.

[0261] Dosage guidance method for correction dose (taking of the dose) Under certain circumstances, external factors can affect the effectiveness of insulin dosing, and the dose may result in a greater glucose-lowering effect than expected. In these cases, the user may conservatively estimate the insulin dose to avoid hypoglycemia. Also, if the user does not know how much insulin is sufficient for a particular meal type or amount, dosing can be done conservatively individually. For example, a certain meal type may have more fat and / or protein compared to the meals the user normally consumes. To enable conservative dosing, the DGA can be configured to provide dose guidance to the user after a meal and, incidentally, perform an initial meal insulin dosing. In one embodiment, the DGA can be configured to perform at least four functions following the initial meal dose guidance and administration. The DGA can be configured to confirm that the user has administered an amount less than the dose indicated by the initial meal dose guidance. Next, the DGA can be configured to determine the risk of hypoglycemia before an additional dose is proposed and notify the user of that risk. The DGA can further be configured to provide additional dose guidance and monitor the subsequent risk of hypoglycemia during the period after the introduction of the additional dose guidance.

[0262] In an exemplary embodiment, at the start or near the start of a meal, the user queries the DGA for the recommended dose. As described in FIG. 14, in an exemplary method 700 starting from step 702, the DGA can output a first dose guidance in response to the first user query. The first dose guidance can be output to the UID200. In one embodiment, the first dose guidance can be calculated to be administered at the start of the meal. The first dose guidance can also be a fixed dose (with or without correction) meal guidance. The first dose guidance can also be determined based on the carbohydrate content of the meal.

[0263] However, the user can choose whether to follow the recommended dosage guidance when administering insulin. When the administered dosage is different from the recommended dosage, the system records the difference. As step 704, the DGA can determine whether the first administered dosage is different from the first dosage guidance. The first administered dosage can be lower or higher than the first dosage guidance. In one embodiment, the first administered dosage is lower than the first dosage guidance. The DGA can be configured to record when the administered dosage is lower than the recommended dosage guidance.

[0264] In step 708, the DGA can determine whether it has received a second user inquiry regarding the second dosage guidance during the period of administration of the first dosage. This period can be determined by a real-time meal detection algorithm from the administration of the first dosage or can be a predetermined time.

[0265] In step 710, the DGA can request an input from the user and determine whether the second user inquiry, if determined to have been received within the period, is to adjust the high glucose level after the meal. The DGA can output a prompt or other notification requesting the user's feedback to the UID200. For example, then, if there is an inquiry for a further dosage recommendation value to the DGA following a conservative dosage (e.g., an amount less than the recommended dosage) within a certain amount of time from the first meal dosing, the DGA can use both the inquiry time and the record of the dosing discrepancy to prompt the user about the reason for the second dosage guidance. In one embodiment, the DGA can be configured to request an input to determine whether the subsequently requested dosage is for covering additional meal-time food or for considering post-meal high glucose. If the user requests the second dosage guidance for covering additional meal-time food, the DGA can follow the flow described in the "Dosage Guidance for Added Meals" section elsewhere in this specification.

[0266] As seen in step 712, if the user requests a second dose guidance to correct post-meal hyperglycemia, the DGA can determine the risk of hypoglycemia by determining at least whether the user's blood glucose is rising. In one embodiment, to avoid hypoglycemic episodes due to insulin dose stacking, the DGA can be configured to determine the risk of hypoglycemia by determining the point at which the user is at the current blood glucose excursion. The DGA can further be configured to create a forward prediction value of the glucose level from the determined point of the current excursion to determine whether the user's glucose level is still rising or falling. In one embodiment, as a safety measure to avoid insulin stacking, the DGA can be configured not to provide dose guidance until the glucose level reaches its post-meal maximum. If a dose recommendation for correction is requested while the user's glucose is still rising, the DGA can be configured to provide a notice that it cannot provide guidance at this time for safety reasons. In one embodiment, the DGA can be configured to provide additional reasons for not providing dose guidance. For example, the DGA can be configured to provide the latest estimated value of the insulin on board, determined from a combination of population-based aggregated parameters and user-specific parameters, if available.

[0267] In one embodiment, when the DGA determines that the user's blood glucose is decreasing, the DGA can be configured to calculate the risk of future hypoglycemia in the absence of a correction "touch-up" dose. In one embodiment, the DGA can be configured to calculate the risk of hypoglycemia by calculating a forward prediction value of the current glucose level to examine the likelihood of a hypoglycemic episode. In another embodiment, the DGA can be configured to analyze past events to observe the frequency with which a given dose induces postprandial hypoglycemia for a given pre-meal glucose range. In one embodiment, if the DGA determines that the current hypoglycemia risk exceeds a predefined threshold at step 716, the DGA can be configured to output a recommendation to UID200 not to take any more insulin at the current time.

[0268] If the DGA determines that there is no current hypoglycemia risk for the user, at step 714, the DGA can be configured to calculate and output dose guidance as if the subsequent dose were a postprandial correction. Thus, in one embodiment, since the meal has already ended, the subsequent dose can be determined without considering the intake of new carbohydrates. As described with respect to Equation (2), the DGA can be configured to calculate the second dose (correction dose guidance) as a function of subtracting the residual insulin on board (IOB) of the initial meal-time dose from the glucose correction portion. That is,

[0269]

Number

[0270] wherein, TIFF2025106388000010.tif18114

[0271] is the glucose correction portion. In one embodiment, the DGA sets the glucose correction portion of the correction dose guidance to the difference between the current glucose (BG(t)) and the target glucose (BG target) can be configured to be calculated as the difference from [[ID=]] divided by a correction factor. The correction factor can be the user's insulin sensitivity factor (ISF), and the ISF is an index used as a measure of how much 1 unit of insulin lowers the fasting blood glucose level. The ISF can be personalized for each user during the algorithm learning period. In one embodiment, for initial estimation, population-based statistics regarding insulin pharmacokinetics (PK) can be used to estimate the IOB. Rapid-acting insulin analogs generally achieve peak plasma concentration in about 45 minutes and then have an exponentially decaying profile, but this time can be shorter if the user is taking an ultra-rapid-acting insulin analog. This time window may correspond to a dormant period when the DGA cannot provide guidance due to an increase in glucose level. The IOB can be estimated from this profile by directly measuring the exponential decay or by estimating the linear decay from the peak insulin concentration to the pre-meal value. The IOB value can explain the current decrease in glucose level from the initial insulin dosing and can minimize insulin stacking by subtracting it from the glucose correction dose. When an extra dose is delivered, the application's user interface advises the user to scan for glucose after 2 hours to confirm the absence of hypoglycemia.

[0272] Postprandial hypoglycemia and hyperglycemia alarm methods in postprandial insulin therapy The DGA can be configured to pre-notify the user of predicted or potentially future hypoglycemic and / or hyperglycemic episodes by generating and / or outputting an alarm or by other means. With these alarms, the user can take actions to maintain the blood glucose level, which is the main goal of diabetes management, within the normal blood glucose range.

[0273] In contrast to threshold-based alarm methods, DGA's predictive alarm method can be based on the predicted probability of occurrence, the predicted time, and the predicted severity of hypoglycemic / hyperglycemic episodes. There are several advantages associated with the predictive alarm approach. The predictive alarm approach overcomes the problem of how to set the optimal threshold, which is one of the most common problems with threshold-based alarms. Setting the threshold too low may result in many false alarms, while setting the threshold too high may result in the alarm not reaching the patient in time. Further, the predictive alarm approach can provide the patient with more specific information about future blood glucose episodes, including the probability of occurrence, the timing, and the severity. The additional specificity provided by the alarm enables the patient to take more appropriate action. Further, the predictive alarm approach can provide the patient with personalized options for appropriate action.

[0274] In an exemplary embodiment, as described in the flowchart of FIG. 15, in an exemplary method 720 starting at step 722, the DGA can be configured to receive a plurality of data including analyte (e.g., glucose) time series data and event data. The DGA can be configured to learn specific patterns from the past data of individual patients. The plurality of data received by the DGA can include glucose time series data and timestamps associated with other event markers. In one embodiment, the higher order differentials and integrals of the glucose time series data are also inputs related to the alarm system. Further, the plurality of data can also include, but is not limited to, patient location data, calendar day data, TOD data, and stress level data. The event data can include, but is not limited to, meal data, snack data, exercise data, and dosing data, along with the associated timestamps for each event. The dosing data includes the dose and amount of bolus insulin and / or the dose and amount of basal insulin.

[0275] In step 724, the DGA can be configured to process at least a portion of the plurality of data to determine the probability of occurrence, predicted time, and predicted severity of future hypoglycemic or hyperglycemic episodes. In one embodiment, past records of each event type can be profiled to generate a prediction of the most likely occurrence in the near future. The prediction of future blood glucose value episodes can be made with reference to TOD, time of week, or relevance to other events. For example, the DGA can be configured to predict the relevance to exercise events occurring before or after a particular meal on a particular day of the week.

[0276] The DGA can be configured to predict future hypoglycemic or hyperglycemic episodes using a glucose value prediction algorithm. In one embodiment, this algorithm can be implemented using a naive Bayes classifier. In another embodiment, this algorithm can use a long short-term memory (LSTM) architecture of a recurrent neural network (RNN), random forest, or a combination of various methods. In one embodiment, first, a machine learning model can be learned using glucose time series data collected in clinical studies and real-world databases to formulate a population-based model. In one embodiment, the population-based model can be an initial model for each patient at the starting point, and the model can be trained on subject-specific patterns by continuously training the algorithm using data from the patient. Therefore, the performance of the DGA can be improved for each subject as the subject uses the DGA.

[0277] In step 726, the DGA can be configured to alert the patient of a predicted future hypoglycemic or hyperglycemic episode. In one embodiment, the DGA outputs an alarm to the UID200 to notify the patient, and can be configured such that the patient can act based on the alarm, for example, by ingesting carbohydrates to treat a future hypoglycemic state or by ingesting insulin to treat a future hyperglycemic state. In another embodiment, the DGA can be configured to be coupled with a dosing algorithm for the alarm system to propose an appropriate amount of treatment. In one embodiment, if a future hyperglycemic episode is predicted, the DGA can be configured to output an alarm including recommended dose guidance to the UID200. In another embodiment, when determining whether post-meal insulin dosing is recommended, the DGA can be configured to provide a display accessible to the user to the UID200. For example, if it is calculated that there is a high likelihood of future hypoglycemia, the DGA can be configured to output dose guidance indicating that additional insulin is not recommended. The DGA can also be configured to output recommendations unrelated to dose guidance to the UID200. For example, the DGA can output a message recommending carbohydrate consumption, re-check the glucose level after a short period (e.g., 15 minutes), set a reminder to check glucose after a preset time or a time set by the user, and / or enable a hypoglycemia threshold alarm. The DGA can also be configured to include, in addition to the output of the alarm, a detailed display of the status of future hypoglycemic / hyperglycemic episodes.

[0278] In one embodiment, the prediction of a particular episode can be modified by the probability of occurrence. If the probability level is different, the output of the system can also be different. For example, the initiation of an alarm may require a higher probability than the display of post-meal dose guidance requested by the patient.

[0279] In another embodiment, the DGA can be configured such that the sensitivity or specificity of the prediction method is adjustable. In one embodiment, different levels of sensitivity and / or specificity enable the user or the DGA to select a level of sensitivity and / or specificity appropriate for the level of user involvement at that time. For example, when the user needs to focus on other aspects of life, by selecting a higher specificity, the user can be alerted only in urgent situations. In another embodiment, a higher sensitivity can be selected. For example, when the user decides to allocate more time to improve glucose management, a predictive system with higher sensitivity can be proactively selected to prevent emergency situations. In one embodiment, similar to the learning module itself, the sensitivity and specificity settings may initially be based on population data. As more user selections are recorded, pattern recognition attempts to associate day of the week (e.g., individual days over a seven-day period, or weekdays vs. weekends), time of day, and potentially the density of the user's calendar activity log, and can evaluate the most likely sensitivity and specificity setting preferences at any given time.

[0280] Features of the system The DGS100 can include considerations on the system for events that commonly occur during the management of diabetes with high insulin concentration. Current insulin bolus dosimeters do not consider these real-world events, and the user will modify the recommended dose according to their best judgment. It is desirable for diabetic patients to be able to modify the ideal dose recommendation according to real-world events, but such considerations can be a significant cognitive burden for diabetic patients. To reduce such a burden and create an even more user-friendly dosing system, the DGS100 utilizes insulin and glucose data to make appropriate dose adjustments according to real-world situations. Furthermore, the features of these systems are also enhanced by the physician's ability to modify dosing parameters.

[0281] Override of dosage guidance settings by HCP The DGA can provide dosage guidance based on individualized parameters such as fixed dosage amounts, target glucose, correction factors, and insulin action time. The user can display these various parameters in various ways. For example, the user can display the parameters on the UID200 via the settings tab within the DGA, or display them as a breakdown of the displayable dosage recommendations. The user can display these parameters which are part of the recommended dosage, but these values are strictly for providing information and may not be editable by the user.

[0282] In contrast, the DGA can be configured such that the user's HCP can display these values, for example, as part of a clinician-facing system web application, and can also be configured such that the HCP can edit one or more of these parameters. This web application can display the patient's performance metrics through a set of the patient's glucose values, such as not only the glucose concentration profile but also insulin dosing statistics. If the HCP wants to modify the dosage recommendations to accommodate changes in other treatment regimens, the clinician-facing web application can allow the HCP to edit user-specific dosing parameters including, but not limited to, those described above.

[0283] In an exemplary embodiment, as described in the flowchart of FIG. 16A, in an exemplary method 800, at step 801, the HCP can create a new insulin dosage guidance by adjusting at least one parameter used to provide the target's insulin dosage guidance in the dosage guidance application. For example, the HCP can adjust at least one of a fixed dosage, a target glucose level, a correction factor, and an insulin action time. Before adjusting at least one parameter to create a new insulin dosage guidance, the HCP can display a glucose concentration profile and insulin statistics to determine whether the target has experienced a low or high glucose pattern during any time period of the day and can notify if an adjustment to the insulin dosage guidance is necessary. A high or low glucose pattern can be determined by the GPA as described elsewhere in this specification.

[0284] After the HCP changes at least one parameter, at step 802, the target can be notified of the new insulin dosage guidance. Further, an explanation of the reason for the change in the dosage guidance can also be provided to the target. In one embodiment, one or both of the user and the HCP can approve any change in the dosage guidance before the change becomes effective. The parameters and dosage guidance can be fixed for a certain period (e.g., 14 days in accordance with the sensor's lifespan), but as part of the algorithm's continuous learning, any parameter that has not been changed can also be further varied.

[0285] In step 804, the DGA can determine whether the subject experiences a hypoglycemic episode during a time period after at least one parameter has been adjusted, for example, within 14 days. If any case of hypoglycemia is observed during this time period, the HCP can be immediately notified and it can be determined whether the parameter should be returned to its value before override or adjustment. At the end of this time period, the HCP can be required to examine in detail the subject's performance during the time period, for example, the subject's glucose concentration profile and insulin statistics, and confirm whether the adjustment of the dosing parameter should be maintained. If the HCP decides to maintain the adjustment of the dosing parameter, these values can be used as initial conditions for adjusting future doses. The history of all past data associated with these values will be negated or very lightly considered in future dose recommendations.

[0286] If the HCP only verbally notifies the user of a change in the dose without updating the DGA, for example, via a web application, there may be a difference between the output dose guidance and the administered dose. If such a difference is consistently observed over a long period (e.g., 3 days), the DGA can provide a notification to both the user and the HCP and inquire whether there has been any change in the dose. If both the user and the HCP confirm, the algorithm can adopt the strategy described above. Additionally, the system can prompt the user to enter the reason for such a persistent change.

[0287] In addition, the DGA can also be equipped with a "10%" reduction button. When the HCP selects in relation to a specific dosage recommendation value, the dosage recommendation value automatically decreases by 10% (for example, 1U). For example, the HCP may want to reduce the patient's insulin dosage as a result of a change in the dosage of a non-insulin drug. This change in dosage may be known to make the patient more sensitive to insulin. Therefore, the patient's current insulin therapy may have too strong an effect on lowering glucose and may cause hypoglycemia. As a preventive measure, the HCP can reduce the insulin dosage. In this example, the DGA can be titrated based on the adjusted value in the future.

[0288] Fault detection of an on-board glucose sensor from insulin and glucose data The glucose sensor 101 can incorporate fault detection to notify the user to remove and replace the current sensor, but the DGS100 can also be configured to provide fault detection through the total amount of insulin.

[0289] In an exemplary embodiment, as described in the flowchart of FIG. 16B, in an exemplary method 807 starting from step 808, insulin dosage data of the subject can be received from the MDD152.

[0290] In step 810, the DGA can determine whether a plurality of recommended insulin dosages differ as compared to a plurality of previous insulin dosages administered over a period of time. Each of the plurality of insulin dosage guidances and each of the plurality of previous insulin dosages administered are associated with a TOD period, and each of the plurality of insulin dosage guidances can be compared to one of the plurality of previous insulin dosages administered that is associated with the same TOD period to determine a difference. A difference is detected when the dosage guidance differs from the corresponding preceding dosage administered during the same time period. If sensor 102 is reading a higher glucose level compared to past sensors, the recommended dosage can be similarly increased for increased high glucose correction. Conversely, if sensor 102 has a much lower value compared to past sensors, the recommended dosage can be immediately lowered to prevent hypoglycemia.

[0291] In step 811, the DGA can determine whether a new sensor has been connected to SCD102 at a time close to the start of the period in which the difference was detected. If it is determined that a new sensor has recently been connected to the SCD, in step 812, the DGA can recommend replacing it with the new sensor. There are multiple reasons why the glucose readings of the sensor may be high or low, but if the onset of these abnormal glucose levels and dosage guidances coincides with the placement of the new sensor and persists while sensor 102 is being worn, the faulty sensor may be the root cause of such deviations. In this case, the DGA may notify the user to replace the glucose sensor 102.

[0292] Change of dosing strategy in response to combination therapy or new insulin type Intensive diabetes treatment for type 2 diabetes patients is often very complex. Diabetic patients often take various drugs that act complementarily or synergistically to improve glucose homeostasis while insulin sensitivity gradually decreases in addition to insulin. Changes in adjuvant therapies such as secretagogues or incretin-based therapies may affect both endogenous insulin production and tissue insulin sensitivity. As a result, changes in adjuvant therapies may affect the effectiveness of subsequent exogenous insulin and should be considered as part of insulin dosage guidance. A similar situation may also occur when the user switches to a different insulin (e.g., from rapid-acting to ultra-rapid-acting, or from once-daily basal to twice-daily basal).

[0293] Changes in dosing strategies can occur when the HCP changes the patient's adjuvant therapy or insulin type. In this case, as described above, the HCP can provide an adjustment of insulin dosing parameters to minimize hypoglycemic episodes as described above. However, in some cases, the DGA may not be informed of changes in adjuvant therapy or dosing recommendations. In this case, the system can monitor the trend of differences related to insulin dosage over a period of time.

[0294] In an exemplary embodiment, as described in the flowchart of FIG. 16C, in an exemplary method 813 starting from step 814, insulin dosage data for a subject can be received from the MDD 152.

[0295] In step 816, a DGA can detect a trend of differences associated with a plurality of insulin doses administered in a first time period over a certain time period. The trend of differences can include, but is not limited to, not only the difference between the proposed dose and the administered dose, but also the difference in the effectiveness of that dose compared to the previous administration at a given dose value (either in terms of the magnitude of the response or the duration of action). If a trend of differences over a period is observed, in step 818, the DGA can provide a notification regarding the trend of differences to the user, the HCP, or both. This period can be about two days, or about three days, or about four days. In one embodiment, the DGA can also output, on the UID200, a prompt asking the HCP and / or the user to confirm a change in the treatment method in order to explain the trend of differences.

[0296] As a trend of differences, if the administered dose is consistently different from the DGA-recommended dose, there is a possibility that the HCP has overridden the user's dosing parameters, as described in the section "Override of Dose Guidance Setting by HCP" above. As a trend of differences, if the recommended dose has a significant and consistent difference in postprandial glucose control compared to past administrations, there is a possibility that the insulin effectiveness has changed. This change in insulin effectiveness can be either in magnitude (indicating a change in adjunctive therapy) or duration (indicating a change in insulin analog).

[0297] If the adjunctive drug is changed, when the user and HCP confirm the change, the DGA can shift to a conservative mode and set the dosage recommendation to a fraction of the previous dosage guidance. The DGA can then titrate the dosing parameters and dosages to optimize these new conditions. If the insulin type is changed (e.g., from rapid-acting to ultra-rapid-acting), since the difference in the market between the two lies in the rapid onset / offset of the drug, the magnitude of the response should not change. Rather, the duration of the response may change. As a countermeasure, the insulin duration of action can be estimated using the population-based values of the new insulin type until the system can determine new individualized values regarding this.

[0298] When the user takes a dose different from the recommended dosage guidance The DGA can recommend a dosage to the user, but the DGS100 does not guarantee that the user will strictly follow the dosage guidance. The DGA can record the difference between the administered dosage and the recommended dosage and detect the trend of that difference. For the observed persistent trend, it is described in the sections of "Override of Dosage Guidance Setting by HCP" and "Change of Dosing Strategy in Response to Adjuvant Therapy or New Insulin Type" above.

[0299] In an exemplary embodiment, as described in the flowchart of FIG. 16D, in an exemplary method 820 starting from step 822, the DGA can detect the difference associated with the insulin dosage administered to the subject during a first time period compared to the dosage guidance provided during the first time period. The DGA can compare the dosage guidance with the insulin dosage data received from the MDD152 to determine whether a dosage other than the dosage indicated by the dosage guidance has been administered to the user. The administered dosage may differ in the amount of insulin ingested or the type of insulin ingested compared to the provided dosage guidance.

[0300] As a special example of taking a dose different from the dose guidance, there is a case where the user takes the wrong insulin in the correct amount (for example, injecting a long-acting type instead of a rapid-acting type or vice versa). The DGA can calculate the optimal dose for a given insulin analog type for any dosing scenario, that is, use a rapid-acting type for mealtime and correction doses and a long-acting type for basal administration. To avoid any errors, the DGA can output both the type and amount of insulin in the dose guidance of the UID200. The MDD152 can record and transmit both the amount and type of insulin administered. The DGA can collect information about which insulin type was used for the injection and detect any differences. When a difference is detected, a notification can be sent to the user on the UID200. This notification may be for the DGA to detect that a different insulin type was used and ask the user to confirm it.

[0301] If the user administers the wrong type of insulin, even before the hypoglycemia alarm is triggered, an insulin mismatch notification can be output to the UID200 to alert the user as soon as possible of the possibility of a severe hypoglycemic episode. For example, if a rapid-acting dose is taken instead of a long-acting dose, severe hypoglycemia may be imminent. There are three reasons for this: (1) As the name implies, rapid-acting insulin has a more pronounced effect immediately after administration than long-acting insulin; (2) The once-daily long-acting dosing can be a much larger amount than a rapid-acting dose at a single meal; and (3) Depending on the timing of the last meal dosing, a wrong rapid-acting bolus can cause insulin stacking. If a long-acting dose is taken instead of a rapid-acting meal-time dose, the results can be more unpredictable. Since long-acting insulin has a slower pharmacokinetic and pharmacodynamic profile compared to rapid-acting insulin, postprandial blood glucose levels after accidentally dosing with long-acting insulin can cause hyperglycemia in the patient immediately after injection. Depending on the timing of the previous long-acting dosing, there is a possibility of insulin stacking and subsequent hypoglycemia, especially since the current dose reaches its maximum plasma concentration at about 6 hours after injection. The user can be made aware of the possibility of severe hypoglycemia within the UID200 notification. In either situation, the DGA does not have to make any recommendations regarding insulin dosage until the on-board insulin value for injection approaches zero (e.g., about 5 - 6 hours for rapid-acting and 12 - 24 hours for long-acting). The glucose data during these periods can also be flagged by the system so that it is not used for further parameter improvement and dose titration.

[0302] In step 824, the DGA can detect a hypoglycemic episode associated with the insulin dose administered during the first time period. The DGA can determine a hypoglycemic episode, for example, when the glucose level drops below 70 mg / dL.

[0303] In step 826, the DGA can notify the subject and / or the HCP that any hypoglycemic episodes associated with the insulin dose administered during the first time period have been detected. This notification can be output to the UID200. If the DGA observes that the user has experienced hypoglycemia by continuously ingesting an amount higher than the recommended insulin dose, a predicted hypoglycemia alert can be output to the UID200 to notify the user during the period before the actual hypoglycemic event itself occurs and reduce the associated side effects on the spot. However, if hypoglycemia occurs due to continuous dosing beyond the recommended value (e.g., defined as three separate episodes, or four separate episodes), notifications can be sent to both the user via the system phone application and the HCP via the clinician-facing web application to alert both of this trend.

[0304] Differences that do not appear to be part of a broader trend are noted by the DGA and a given dose can be incorporated into the continuous learning system of individual dosing patterns. Similar to any administered dose, post-meal metrics can be noted. Whenever different doses are associated with post-meal hypoglycemia (e.g., less than 70 mg / dL) or other negative outcomes, that dose can be flagged so that both the user and the HCP can improve their dosing approaches respectively. This flag can be presented to the HCP as a note in the glucose pattern report and to the user as a note in the DGA insulin logbook.

[0305] Mechanism of a system for performing multiple dosages continuously To ensure accurate dose recording, the DGA must be able to correctly interpret situations where multiple insulin injections are performed continuously in a short period of time. Examples of situations where this can occur include priming before actual dosing (potentially multiple times) and multiple injections for a given dosing recommendation.

[0306] In an exemplary embodiment, as described in the flowchart of FIG. 16E, in an exemplary method 827 starting from step 828, target insulin dose data can be received from MDD152.

[0307] In step 830, the DGA can detect the administration of a plurality of insulin doses. The plurality of insulin doses includes at least an initial dose and a final dose, and the final dose is administered within the time period of the initial dose. If a plurality of doses are administered within a short time period, at least one of the administered doses can be a priming dose. A best practice in insulin delivery is to use a new needle for each injection and prime each new needle with insulin prior to dose administration. This is done by dispensing a small amount of insulin (typically 2U) into the air until insulin is visible at the tip of the needle, indicating that the needle is full with the injectate. To distinguish the priming amount, the DGA can assume that (1) the priming dose is generally much less than the actual dose, (2) the priming dose is the same amount each time, and (3) little time has elapsed between priming and dosing. Alternatively, if a plurality of doses are administered within a short time period, due to the large amount to be injected, it may actually be a split dose. For example, the user's MDD152 may have less insulin remaining than the required dose, requiring a cartridge replacement and subsequent extra injections.

[0308] In step 832, the DGA can record the last dose as the administered dose if the first criterion is met. The first criterion can be met if the last dose is administered within about 1 minute, or about 2 minutes, or about 3 minutes of the first dose. In such a situation, the DGA can consider that the first dose and any subsequent intermediate doses (excluding the last dose) are priming doses and thus not considered to be the actually administered dose. Therefore, the DGA can be configured to record only the last dose as the administered dose. Usually, since a priming dose contains a small amount of insulin, in another embodiment, the first criterion can be met if the first dose is substantially smaller than the last dose. For example, the first criterion can be met if the first dose is about one-tenth, or about one-fifth, or about one-fourth, or about one-third of the last dose.

[0309] Alternatively, in step 833, if the second criterion is met, the DGA can record the amount obtained by combining the first dose and the last dose as the amount of the administered dose. The second criterion can be met when a single dose is delivered via a plurality of smaller injections performed during a time period longer than the time period for priming (e.g., 1 to 3 minutes), such as about 4 to about 35 minutes, or about 5 to about 30 minutes. For example, the user's MDD152 may have less insulin remaining than the required dosage, so a cartridge replacement and subsequent extra injections may be required. In this case, instead of interpreting these injections as separate episodes, the DGA can interpret them as two operations within the same dosing episode. To that end, the DGA can adopt a waiting time for dosing records and can record the value of the insulin dose only starting about 30 minutes after the first dose administration. For example, if the DGA recommends a 10U dose at mealtime but only 4U remains in the insulin cartridge, the injection will be performed in two parts: 4U first and the remaining 6U. If a new needle is used for the second injection, an intermediate priming dose may also occur. When the first 4U is administered, the DGS100 goes to look for the remaining 6U. If the 6U dose arrives within 30 minutes of the first dose, the DGA can consider this as one 10U dose. If one or more prime doses are detected between the actual doses, the DGA can consider all doses between the first and the last dose as prime and thus not include them in the final dosage. Since the DGA is communicating with the MDD152, the DGA can be configured to import the remaining amount of the insulin cartridge along with the insulin dosage information. By knowing the amount of insulin remaining in the cartridge, the DGA can predict split dosages due to cartridge replacement and can also be configured to notify the user about the remaining insulin amount.

[0310] In another embodiment, the DGA can be configured to treat the priming dose and subsequent doses as split doses and add the two doses together to obtain a single meal-time value that includes the prime dose. If the prime dose is much less than the meal dose, the prime dose may have little impact on algorithmic dose guidance titration. This is similar to the split dose logic described below. Although not exact, the assumption that the priming amount is much less than the injection amount can be reasonable for type 2 diabetes patients suffering from increased insulin resistance.

[0311] Titration using glucose data at the time of missed dosing To provide optimal dosing advice for a progressive disease, the DGA can continuously improve the estimated values of the user's specific dosing parameters. As a result, it is necessary to identify an appropriate data stream that forms the basis of this algorithm learning. To avoid confusion of results, the DGA can be configured to learn based only on insulin and glucose data that follows the user's clinically recommended dosing strategy. Such strategies include, but are not limited to, basal-only, basal plus one meal-time rapid-acting insulin dosing, basal plus two meal-time rapid-acting insulin dosing, and complete frequent injection strategies of basal plus three meal-time rapid-acting insulin dosing.

[0312] In an exemplary embodiment, as described in the flowchart of FIG. 16F, in an exemplary method 833 starting from step 834, insulin dose data for a subject can be received from MDD152.

[0313] In step 836, the DGA can detect missed insulin doses, and the insulin doses have a time period related to the duration of action. For example, the DGA can first identify the user's dosing strategy during an initial learning period before dosing guidance can be provided. Also, using an automated meal detection method and data from the Bluetooth-connected MDD152, the system can also identify meal events and their accompanying doses. Therefore, the DGA can determine whether dosing has been missed for a given meal. In the absence of data reported from the user's long-acting MDD152, missed basal dosing can be detected.

[0314] In step 838, the DGA can ignore glucose analyte data associated with that time period when determining adjustments to insulin dosage guidance. A single missed dose at mealtime can cause an increase in blood glucose and insulin bolus compared to what has occurred in the past. Not only these changes in mealtime dosages, but also glucose levels after a meal has been missed can distort the current dosage titration that the DGA has determined for a given dosing strategy. Similarly, if basal dosing is missed, it can cause a continuous increase in glucose during the generally assumed insulin duration of action of one day. As a result, the DGA algorithm will only include glucose and insulin data obtained from meals with insulin dosages. In the case of rapid-acting insulin, the duration of action can be about 4 hours, or about 5 hours, or about 6 hours, or about 4 hours to about 6 hours. For example, if the breakfast dose is missed, the 4 hours of glucose data obtained after the start of breakfast may not be included in the dosage titration. In the case of long-acting insulin, the duration of action can be about 18 hours, or about 20 hours, or about 24 hours, or about 20 hours to about 24 hours. Since long-acting insulin serves both to maintain normal blood glucose during meals and to prevent diabetic ketoacidosis, the system algorithm may not include data within the duration of action in the event of a missed basal dose. For example, insulin glargine has been reported to have an insulin duration of action of 24 hours. If a user taking glargine misses a daily basal dose, none of the subsequent 24 hours of data will be used by the system for dosage titration.

[0315] Prompt the user to avoid missed administrations By adhering to an appropriately titrated insulin dosage regimen, diabetes management can be improved by reducing not only hyperglycemia associated with missed dosing but also hypoglycemia due to overcompensating corrective dosing. The DGA can be configured to provide the user with practical and easily interpretable data that emphasizes the positive impact of medication adherence. One such way is to provide regular updates that compare glucose control metrics or other relevant statistical values for time periods with missed dosing and time periods without missed dosing.

[0316] In an exemplary embodiment, as described in the flowchart of FIG. 16G, in an exemplary method 840 starting from step 841, insulin dosage data for a subject can be received from the MDD 152.

[0317] In step 842, the DGA can detect missed administration of the insulin dosage. In step 846, the DGA can determine the amount of time the subject's glucose level remained within the target range (TIR) during the first and second time periods. The target range can be set by the DGA, the user, or the HCP. The target range can be from about 70 mg / dL to about 180 mg / dL, or from about 70 mg / dL to about 190 mg / dL, or from about 70 mg / dL to about 200 mg / dL. The first time period and the second time period can be the same amount of time, the first time period does not include missed administration of the insulin dosage, and the second time period includes missed administration of the insulin dosage. In this way, a comparison of the TIR for the period with missed dosing and the period without missed dosing can be prepared.

[0318] In step 848, the DGA can notify the user of the determined TIRs for the first and second time periods. If the difference between the two determined TIRs is greater than a threshold value (i.e., subtracting the TIR of the second period from the TIR of the first period), a positive message can be displayed to the user on the UID200 to not only encourage good dosing behavior but also educate the user about the benefits of medication adherence. For example, the user can be notified that the glucose control index increases by 10% on days when all doses are taken compared to days when meal dosing is forgotten.

[0319] Safety function Exemplary embodiments of the safety features of the DGS100 described herein prioritize the safety of the user in the guidance and titration processes. Current insulin bolus dose calculators use static dosing parameters to calculate dose proposal values. When the user or HCP updates these values, it becomes a trial-and-error process, which may in some cases cause hypoglycemia. The DGA, as an automated process, can utilize the user's insulin and glucose data to titrate these parameters and provide dose guidance that can be adjusted according to the user. Safety measures can be incorporated to ensure that the recommended titration does not cause hypoglycemia after dosing.

[0320] Safe titration method The titration logic of the DGA can be set so as not to increase the dose titration until the time period of hypoglycemia is alleviated. When the HCP performs titration manually, the HCP may want to increase some doses and decrease other doses in order to perform a complete titration as soon as possible to save time. However, in an automated system, titration cannot be performed as aggressively and does not require the time of the HCP, so it takes time (becomes more conservative and safer). Therefore, hypoglycemic patients may experience high average glucose values at the beginning of starting titration, but when hypoglycemia is alleviated, the insulin dose may be safely increased to achieve the blood glucose target.

[0321] One problem when detecting a post-meal high glucose pattern is that the previous meal may have post-meal glucose, and as a result, the next meal may start with high glucose. If this occurs, because the starting glucose is high, there is a possibility that a high pattern may be wrongly indicated in the next meal. To address this problem, the titration strategy of the DGA can include the step of first titrating the nighttime dose before titrating the meal dose, if necessary. Further, the meal dose can be titrated in the order of first titrating the earliest meal having a high glucose pattern and then sequentially titrating the subsequent meals of that day having a high glucose pattern. For example, the recommendations for titrating the insulin dose related to the nighttime period may be done first. Next, for any high glucose pattern detected during any post-meal time period, the recommendations for titrating the insulin dose related to breakfast may be provided before providing the recommendations for the dose related to lunch, and the recommendations for the dose related to lunch may be provided before providing the recommendations for the dose related to dinner. By titrating the previous meal first, the pre-meal high glucose for the next meal is minimized, and the likelihood that the recommended titration will affect or interfere with the titration of the next meal is reduced.

[0322] For the embodiments described herein, the DGA can detect high / low patterns, as described for the GPA elsewhere in this specification.

[0323] For the embodiments described herein, the DGA can be configured to recommend a change (e.g., an increase or decrease) in insulin dosage. The amount of change recommended can be any desired amount of insulin, such as a fraction of a unit (0.1 unit or 0.5 unit), or one unit (1.0 unit), or two or more units of insulin (2.0 or more), or any combination thereof. For ease of explanation, the embodiments described herein refer to adjustments in one-unit intervals.

[0324] The DGA can perform the various steps described in the safe titration embodiments in a variety of different ways. For example, these steps can be performed before each meal, or at the beginning of the day, or at the end of the day, or daily, or every other day, or every third day, or when the user queries the DGA for dosage recommendations, or in combinations thereof.

[0325] In an exemplary embodiment, the DGA can access the measured glucose data (e.g., from SCD102). The DGA can determine whether there is a high glucose pattern during the night period. If a high pattern is detected during the night period, the DGA can change the dosage guidance. For example, the DGA can increase the amount of the drug for the dosage guidance of the basal dose if it is safe to do so, e.g., if it can be done without causing any low glucose patterns during any period of the day. Then, the DGA can determine whether there is a high glucose pattern during at least one post-meal period of the day. If a high pattern is detected, the DGA can increase the amount of the drug for the dosage guidance related to the earliest period among at least one post-meal period of that day. The DGA can then be configured to increase the amount of the drug for the dosage guidance related to the next earliest time period among at least one post-meal period of the day determined to have a high glucose pattern.

[0326] In another exemplary embodiment, as described in the flowchart of FIG. 17A, in step 852 of the exemplary method 850, in response to detecting a low glucose pattern in the analyte data of the subject in at least a first time zone, the DGA can output a first dosage guidance that is less than the previous dosage for at least the first time zone. The first dosage guidance can be output to the UID200. If a low glucose pattern is detected, the DGA can output the dosage guidance related to the period in which the low glucose pattern is detected. The DGA can be configured not to recommend any reduction in the insulin dosage for dealing with the high glucose pattern until the low glucose pattern ceases to be detected.

[0327] In step 856, in response to detecting a high glucose pattern during a nighttime period in the analyte data of the subject, the DGA can output a second dosage guidance that is less than the prior dosage for the nighttime period. For example, if it is safe for the DGA to recommend an increase in the basal dosage, for example, if such an increase does not cause a low glucose pattern during another time period of the day, it can be recommended.

[0328] In step 860, in response to detecting a high glucose pattern during at least one post - meal period in the analyte data of the subject, the DGA can output a third dosage guidance that is less than the prior dosage for the at least one post - meal period. In one embodiment, the DGA can be configured to detect all high glucose patterns found during any time period of the day. In another embodiment, the DGA can be configured to detect whether the period after breakfast has a high glucose pattern, then the DGA can be configured to detect whether the period after lunch has a high glucose pattern, and then the DGA can be configured to detect whether the period after dinner has a high glucose pattern.

[0329] If it is determined that there are high glucose patterns in multiple post - meal periods, the third dosage guidance output by the DGA can be associated with the post - meal period having the high glucose pattern that occurs earliest in the day. For example, if high glucose patterns are detected in both the periods after breakfast and lunch, the DGA can increase the recommended insulin dosage related to breakfast before recommending an increase in the recommended insulin dosage related to lunch. Further, the DGA can re - evaluate whether the high glucose pattern in the period after breakfast has been alleviated before recommending an increase in the recommended insulin dosage related to lunch.

[0330] In another exemplary embodiment, as described in the flowchart of FIG. 17B, in an exemplary method 862 starting from step 864, the DGA can detect a low glucose pattern during any period of a day. The low pattern can be detected based on the GPA as described elsewhere. If a low glucose pattern is detected, at step 866, the DGA can output a first dosage guidance for the period of the day when the low glucose pattern is detected. The first dosage guidance can include a lower amount of drug as compared to the previous dosage administered during the period of the day when the low glucose pattern is detected. The DGA can be configured not to recommend any increase in the insulin dosage for dealing with high glucose patterns until the low glucose pattern is no longer detected during any time period.

[0331] At step 868, the DGA can detect whether a high glucose pattern exists during the night period. If a high glucose pattern is detected, at step 870, the DGA can output a second dosage guidance for the basal dosage. For example, the second dosage guidance can include a higher amount of drug than the previous basal dosage if the DGA determines that such a modification is safe, e.g., if such an increase does not cause a low glucose pattern during another time period of the day. In one embodiment, the second dosage guidance can include a higher amount of drug than the basal dosage administered the previous day.

[0332] At step 872, the DGA can detect whether a high glucose pattern exists during the post - breakfast period. If a high glucose pattern is detected, at step 874, the DGA can output a third dosage guidance. The third dosage guidance can increase the recommended insulin dosage associated with breakfast, i.e., the third dosage guidance can include a higher amount of drug than the previous post - breakfast dosage. In one embodiment, the third dosage guidance can include a higher amount of drug than the post - breakfast dosage administered the previous day.

[0333] In step 876, the DGA can detect whether there is a high glucose pattern during the post-lunch period. If a high glucose pattern is detected, in step 878, the DGA can output a fourth dosage guidance. The fourth dosage guidance can increase the recommended insulin dosage related to lunch, that is, the fourth dosage guidance can include a higher drug amount than the previous post-lunch dosage. In one embodiment, the fourth dosage guidance can include a higher drug amount than the post-lunch dosage administered the previous day.

[0334] In step 880, the DGA can detect whether there is a high glucose pattern during the post-dinner period. If a high glucose pattern is detected, in step 882, the DGA can output a fifth dosage guidance, that is, the fifth dosage guidance can increase the recommended insulin dosage related to dinner, that is, the fifth dosage guidance can include a higher drug amount than the previous post-dinner dosage. In one embodiment, the fifth dosage guidance can include a higher drug amount than the post-dinner dosage administered the previous day. In one embodiment, the DGA can increase the recommended insulin amount related to dinner only if it is safe to do so, for example, if it can be increased without causing a low glucose pattern during the night period.

[0335] In another exemplary embodiment, as described in the flowchart of FIG. 17C, in step 885 of the exemplary method 883, the DGA can detect whether there is a high glucose pattern during the night period as described with reference to the GPA.

[0336] If a high glucose pattern is detected during the night period, at step 886, the DGA can determine whether an increase in the basal dose causes a low glucose pattern during any time period of the day. If the increase in the basal dose can cause a low glucose pattern during any time period of the day, at step 887, the DGA can output a first dose guidance for the day period determined to have the low glucose pattern. The first dose guidance can reduce the recommended insulin dose associated with the period determined to have the low glucose pattern, that is, the first dose guidance can include a lower drug amount than the previous dose administered during the same period of the day. In one embodiment, the first dose guidance can include a lower drug amount than the dose administered the previous day during the same period of the day.

[0337] If it is determined that the increase in the basal dose does not cause a hypoglycemia pattern at any time period, at step 889, the DGA can output a second dose guidance. The second dose guidance can increase the recommended basal dose to address the high glucose pattern during the night period, that is, the second dose guidance can include a higher drug amount than the previous basal dose. In one embodiment, the second dose guidance can include a higher drug amount than the basal dose administered the previous day. The DGA can then be configured to perform the next titration iteration at step 888 after the DGA outputs the first dose guidance that reduces the recommended insulin amount for the day period associated with the time period determined to have the low glucose pattern at step 887, and then determine at step 885 whether there is a high glucose pattern during the night period.

[0338] In another exemplary embodiment, as described in the flowchart of FIG. 17D, in an exemplary method 890 starting from step 891, the DGA can detect whether a high glucose pattern exists during the post-dinner period. If a high glucose pattern is detected during the post-dinner period, at step 892, the DGA can determine whether it can safely increase the insulin dose associated with dinner. For example, if the insulin dose associated with dinner may cause a low glucose pattern during the night period, the dinner dose cannot be safely increased.

[0339] If the insulin dose associated with dinner can be safely increased, at step 893, the DGA can output a first dose guidance. The first dose guidance can increase the recommended insulin dose associated with dinner to address the high glucose pattern during the post-dinner period, that is, the first dose guidance can include a higher drug amount than the previous post-dinner dose. In one embodiment, the first dose guidance can include a higher drug amount than the post-meal dose administered the previous day.

[0340] If the dinner dose cannot be safely increased, at step 894a, the DGA can output a second dose guidance. The second dose guidance can decrease or reduce the recommended basal insulin dose, that is, the second dose guidance can include a lower drug amount than the previous basal dose. In one embodiment, the second dose guidance can include a lower drug amount than the basal dose administered the previous day. The DGA can also be configured such that after the DGA reduces the recommended basal dose at step 894a, then performs the next titration iteration at step 894b, and then can determine whether a high glucose pattern exists during the post-meal period at step 891.

[0341] Problems related to the connectivity of MDD For proper dosage guidance, the DGA needs to have access to the latest glucose analyte data and insulin dosage data. If there are gaps in either data, inaccurate recommendations may be made, leading to serious hypoglycemic episodes.

[0342] In an exemplary embodiment, as seen in FIG. 17E, in an exemplary method 895 starting from step 896, the DGA can receive or otherwise access the subject's insulin data (e.g., from MDD152). For example, the DGA can check the latest insulin delivery information by requesting delivery information from various sources including, but not limited to, MDD152, MDD-related applications, or an interface (e.g., MDD application web server) storing the latest insulin delivery information, or by checking the memory of various applications for the latest insulin delivery information.

[0343] In step 897, the DGA can determine whether it has received data related to the last dose administered to the subject. This step can be particularly applicable to embodiments where the device or software responsible for recording dose administration is different from the DGA or the device executing the DGA. This step may not be applicable in embodiments where the DGA is automatically provided with dose administration data (e.g., the DGA is being executed by MDD152).

[0344] The DGA can determine whether it has the latest available data based on various factors. In one embodiment, it can be determined based on the temporal gap in the received insulin dose data. For example, if the user is performing a complete frequent injection therapy (basal + 3 boluses at mealtimes), the DGA can communicate with the MDD152 or its related application at least approximately every 6 hours. If communication has not occurred at that time, the DGA can be configured to determine that it is necessary to connect the MDD152 to the DGA before providing dose guidance. In one embodiment, the DGA can assume that it has not received data related to the last administered dose if the gap difference in time since the last dose administration is longer than the time between meals that is expected. For example, the expected mealtime duration can be approximately 5 hours, or approximately 6 hours, or approximately 6.5 hours, or approximately 7 hours, or approximately 7.5 hours, or approximately 8 hours. In another embodiment, the DGA can detect whether Bluetooth communication is enabled between the user's display device 120, such as a smartphone, and the MDD152. For example, Bluetooth communication may not be enabled on either the device 120, the MDD152, or both. In another embodiment, the DGA can detect whether it is necessary to replace the power source related to the dosing device.

[0345] If it is determined that data related to the last dose administered to the subject has not been received, at step 898, the DGA can notify the user that it cannot provide dose guidance. In one embodiment, the UID200 can display a message to the user indicating that the DGA cannot output dose guidance until the DGA receives the latest insulin delivery information. In one embodiment, the UID200 can display a message to the user indicating that the DGA cannot provide dose guidance until the DGA receives the latest insulin delivery information. In another embodiment, the DGA can also generate a prompt to notify the user to turn on the Bluetooth of the display device 120, the MDD152, or both, and can be displayed on the UID200. In another embodiment, the DGA can also indicate that the battery of the MDD152 needs to be replaced. Further, the DGA can determine the remaining battery life of the MDD152, and if the battery life falls below a certain threshold, for example, if the battery life is less than 10%, a warning that can be displayed on the UID200 can be output to the patient. In another embodiment, the DGA can also output a notification to be displayed on the UID200 to inform the user of the last recorded insulin dose and timestamp, and can also warn the user that the dose guidance is not based on any dose that may follow that last dose and timestamp.

[0346] If it is determined that data related to the last dose administered to the subject has been received, at step 899, the DGA can output dose guidance to the UID200 based on the received glucose analyte data and insulin dose data.

[0347] Recommendations for Additional Tests in the Case of Abnormal Insulin Delivery The DGA can be configured to perform statistics corresponding to various measurements of the administered insulin and the measured glucose levels. Using the correlation relationships between the various measurements of insulin and glucose, anomalies of the DGA can be identified, which may include, but are not limited to, incorrect recording of insulin dosage, glucose readings that may be biased low or high, and decreases or increases in insulin resistance.

[0348] The insulin metric used can be a rolling insulin metric. In one embodiment, the rolling insulin metric can be the total dosage of insulin within a time period. The time period can be about 24 hours, about 48 hours, or about 72 hours. Further, the total dosage of insulin can be the total dosage of long-acting and rapid-acting insulin within the time period. In another embodiment, the rolling insulin metric can be the insulin on board at the elapsed time from the start of a meal. Such a metric can have different pre-determined insulin-related parameters, such as DIA or insulin duration of action, for different meal-time insulin types.

[0349] The glucose metric used can be a rolling glucose metric. In one embodiment, the rolling glucose metric can be the rolling average glucose, the rolling median glucose, or the rolling mode glucose. In another embodiment, the rolling glucose metric can be the meal-start normalized glucose AUC or the change in meal glucose (meal delta).

[0350] In another exemplary embodiment, as depicted in the flowchart of FIG. 17F, at step 851 of the exemplary method 849, the DGA can determine a first rolling insulin metric related to a first time from insulin dosage data. At step 853, the DGA can determine a first rolling glucose metric related to the first time from glucose dosage data. The first rolling insulin metric and the first rolling glucose metric can be associated together to form a first complementary pair.

[0351] Many different complementary pairs of rolling insulin metrics and rolling glucose metrics can be formed. For example, in one embodiment, the rolling insulin metric of the total insulin dosage within a rolling time period can be paired with one of the rolling average glucose, rolling median glucose, or rolling mode glucose of the same or a similar time window. In another embodiment, complementary pairs such as the glucose mode over the rolling past 48 hours and the total insulin dosage delivered over the rolling past 48 hours can also follow the same general procedure outlined above. In another embodiment, the IOB at a specific elapsed time from the start of a meal can be paired with either the meal-start normalized glucose AUC or the meal delta (e.g., the change in glucose due to the meal). Examples of the time horizons for pairing IOB and AUC include, but are not limited to, about 60 minutes, or about 120 minutes, or about 150 minutes, or about 300 minutes post-meal.

[0352] In step 855, the DGA can determine which of the first, second, and third zones contains the first complementary pair with reference to a data space that includes the first, second, and third zones. The data space can be defined by a plurality of complementary pairs, and each complementary pair includes a rolling insulin index and a rolling glucose index associated with the same time. For the plurality of complementary pairs, the paired values can be collected at regular time intervals, such as every about 2 hours, or every about 6 hours, or every about 12 hours, or every about 24 hours, or other intervals that balance appropriate data density and minimum data storage requirements. The amount of complementary pairs stored can be held in a FIFO (First-in-First-Out) buffer implemented in software or hardware in some embodiments. Among the complementary pairs in the FIFO buffer, a correlation can be taken between the complementary pairs in the same way as fitting a curve of a scatter plot created with pairs of insulin and glucose indices collected over time. This correlation can be a curve of a predetermined structure (e.g., a cubic polynomial) that potentially has one or more parameters determined based on the paired data in the FIFO buffer and one or more parameters predetermined from pre-population data. This curve can represent the nominally expected relationship between corresponding observed values. In addition to the nominally expected relationship, two safety boundary curves with a predetermined structure can be constructed above and below the curve of the nominally expected relationship. Also, some of the parameters can be determined based on the paired data in the FIFO buffer, while other parameters can be predetermined from pre-population data. To improve numerical stability, the parameter fitness of the nominal curve, upper curve, and lower curve for different time horizons can be associated by a priori rules. To be able to cover this insulin-glucose balance check, other instances can interpolate from existing time horizons. The curve of the nominally expected relationship and the two safety boundary curves can form three zones of the data space. The first zone is defined as the zone between the upper and lower safety boundary curves and includes the curve of the nominally expected relationship.The second zone is defined as the zone above the upper safety boundary curve, and the third zone is defined as the zone below the lower safety boundary curve.

[0353] In step 857, in response to a determination that a complementary pair including the first rolling insulin index and the first rolling glucose index is included in the second zone or the third zone, the DGA can output a notification regarding checking at least one of SCD102 or MDD152.

[0354] FIG. 17G is a diagram showing an example of tracking a complementary pair in which the rolling insulin index is the estimated IOB 90 minutes after each meal and the rolling glucose index is the meal-start normalized glucose AUC 90 minutes after each meal. The complementary pair 867 being analyzed (e.g., the latest complementary pair indicated by the solid circle in FIG. 17G) is excluded from the fit to the curve of the nominally expected relationship 861 and represents an event of interest, such as the state of the user's glucose-insulin balance 90 minutes after a meal. The latest complementary pair 867 is analyzed to determine which zone it corresponds to. By examining its position relative to the two safety boundaries 863, 865, it can be determined which zone this complementary pair 867 is included in. For example, the solid circle 867 shown in FIG. 17G appears "below" the lower safety boundary of the third zone 873. For this occurrence, there are several possibilities: (1) the possibility that the recorded amount of insulin is erroneously high compared to the amount of insulin actually delivered, (2) the possibility that the glucose readings are biased low, or (3) the possibility that other confounding factors have occurred, such as a decrease in insulin resistance as a result of exercise or a large difference in the composition of the meal consumed. Depending on the actual combination of events, if the latest pair is well below the lower safety boundary, there may be a risk of a false notification of impending hypoglycemia or a risk that post-meal hyperglycemia is not detected.

[0355] In contrast, if the position of the noted complementary pair is above the upper safety margin of the second zone 871, one or more opposing scenarios may occur, namely: (1) the amount of insulin recorded may be erroneously low compared to the actual amount of insulin delivered, including omission of a meal dose; (2) the glucose reading may be skewed high; or (3) other confounding factors may be occurring, such as an increase in insulin resistance as a result of illness or a significantly different composition of the meal that may have been consumed. Depending on the combination of actual events, if the latest pair significantly exceeds the upper safety margin, there may be a risk of false notification of impending hyperglycemia or a risk that postprandial hyperglycemia is not detected.

[0356] If the complementary pair is mapped such that it exceeds the upper safety margin in the second zone or falls below the lower safety margin in the third zone, a prior determination was made using sufficient research data to conclude that an integrity check of the DGA100 may be required. Thus, the DGA can notify the user to perform self-monitoring of blood glucose (SMBG). When blood glucose measurement (BGM) readings can be linked to the DGS100 or otherwise input into the DGA, this can be used to determine whether the sensor 102 needs to be replaced using one or more threshold comparisons not covered in this discussion. Otherwise, the DGA can notify the user to check the MDD152 or the cap of the smart insulin pen for possible causes of error.

[0357] Incorporate Trends into Bolus Calculation Conventional bolus calculators determined the dose based on the difference between the user's current glucose and the target glucose. In these conventional bolus calculators, the current glucose value was considered an individual snapshot in time and the trend of the glucose value at that point was not taken into account. However, during periods of high glucose rate of change, the current glucose value can rapidly increase or decrease from one point in time to the next, so the recommended dose can vary significantly.

[0358] In December 2018, the Endocrine Society published consensus guidelines for using glucose trend arrows in diabetes management (the entire content of which is incorporated herein by reference; see Y. C. Kudva, et al., “Approach to Using Trend Arrows in the Freestyle Libre Flash Glucose Monitoring Systems in Adults,” Journal of the Endocrine Society, vol. 2, pp. 1320-1337, 2018). Glucose trend arrows can be classified into five categories based on the rate of change of glucose, namely: (1) rapidly rising (rising at a rate of more than 2 mg / dL / min), (2) rising (1-2 mg / dL / min), (3) changing slowly (no change greater than 1 mg / dL / min), (4) falling (decreasing at a rate of 1-2 mg / dL / min), and (5) rapidly falling (decreasing at a rate of more than 2 mg / dL / min).

[0359] To attempt to explain such rapid changes, for each category of trend arrow, heuristics can be developed that incorporate the rate of change of glucose in addition to the current glucose value to calculate a recommended dose. Depending on the reported rate of change, the recommended dose utilizing an additional rate-of-change term can be increased or decreased to account for the dynamic glucose response. It is envisioned that developing these heuristics will minimize the incidence of hypoglycemia after dosing, thereby improving glucose control metrics.

[0360] Rotation of Insulin Sites The method described herein promotes appropriate site rotation for insulin injection by automatically detecting the injection sites of insulin pen needles and infusion sets.

[0361] The most common method of insulin delivery is administration into the subcutaneous tissue by individual injections or continuous infusion. To properly perform both insulin injection and placement of the infusion set, it is necessary to "rotate" the injection sites, i.e., by circulating various locations on the body, local skin reactions caused by repeatedly and continuously piercing the needle can be avoided. Two common consequences associated with insufficient rotation of injection sites are the formation of scar tissue and lipohypertrophy. Scar tissue is formed as a result of continued trauma or inappropriate healing of the initial wound, and is characterized by fibrous tissue and avascular tissue rich in collagen. Lipohypertrophy is clinically defined as the local accumulation of fat deposits at the insulin injection site. Scar tissue is a problem that is widespread throughout injection therapy, while lipohypertrophy is a symptom that is seen almost exclusively in relation to subcutaneous administration of insulin. Both appear as hard nodules under the skin and are associated with local areas that are mainly avascular, and have an adverse effect on the uptake and action of insulin throughout the body. Therefore, these effects on insulin absorption are often considered together. These effects include a decrease in insulin absorption (as high as 40%), an increase in the total daily dose (TDD) of insulin, and a decrease in blood glucose control. The morbidity is also high, and it is estimated that approximately 50% of people receiving insulin therapy are affected.

[0362] Currently, there is no technical solution to the problem of poor site rotation. Site rotation depends on education of patients by physicians and diabetes educators and subsequent patient adherence.

[0363] As described herein, the DGS100 can include a site rotation application configured to detect common injection sites and provide guidance to the user to rotate to a new injection site when the same location is repeatedly detected, which can promote better injection habits, higher insulin efficacy, and a greater time in range by presenting site rotation information to the user as part of a dosing guidance regimen.

[0364] In one embodiment, as seen in FIGS. 18A and 18B, in an exemplary method 275 starting from step 276, the site rotation application can be configured to determine a first distance 270 between an electronic device such as a display device 120 (e.g., a smartphone) and the SCD102 and a second distance 272 between the electronic device and the MDD152. In one embodiment, the site rotation application can employ Bluetooth communication (BLE) between three separate devices, namely (1) a display device 120 such as a smartphone, (2) the SCD102, and (3) the MDD152 such as a connected pen needle. As seen in FIG. 18A, the display device 120 can be configured to function as a central connection point for communication. When dosing guidance is requested from the DGA, the display device 120 can determine the first distance 270 between the display device 120 and the SCD102 and the second distance 272 between the display device 120 and the MDD152. In one embodiment, the first distance 270 can be determined by a first signal strength between the display device 120 and the SCD102, and the second distance 272 can be determined by a second signal strength between the display device 120 and the MDD152.

[0365] In step 278, the site rotation application can be configured to calculate a third distance 274 between SCD102 and MDD152, where SCD102 is at a fixed location of the subject. Since the display device 120 is not fixed at one location, the first and second signals can be triangulated to determine the third distance 274 between SCD102 and MDD152, which is at a location fixed to the subject's body. The calculated third distance 274 between MDD152 and SCD102 can then be recorded within the site rotation application of the display device 120.

[0366] In step 282, the site rotation application can be configured to output a message recommending that the MDD be moved to a new injection site of the subject in response to a determination that the calculated third distance is substantially similar to a pre-calculated third distance. In one embodiment, if the calculated third distance 274 between SCD102 and MDD152 is continuously repeated beyond a predetermined threshold, the site rotation application can be configured to supply a guidance message suggesting that the user inject insulin at the location of the new site. The threshold can be once, or twice, or three times. In one embodiment, the site rotation application can further be configured to supply the user with a list of acceptable location sites to present ideas for new injection sites. These alternative areas can include the arm, thigh, abdomen, and buttocks.

[0367] Additional Exemplary Embodiments Additional exemplary embodiments related to drug delivery are also described herein. These embodiments are described in the context of an insulin pen, but are not limited to such. In many embodiments, the MDD152 (e.g., an insulin pen) can communicate directly with the SCD102 and thus may not require an additional reader device. In some embodiments, the reader device can also communicate with a sensor control device that is communicating with the MDD152. In these embodiments, the reader device can also communicate with the MDD152. In other embodiments, the reader device can communicate with the MDD152, and the MDD152 itself is communicating with an SCD102 that is not communicating with the reader device. In other embodiments, the SCD102 is communicating with the reader device, and the reader device itself is communicating with the MDD152, but the MDD152 is not communicating with the SCD102. With these various communication schemes, any type of information (e.g., analyte measurements, alarms, user information or settings, dosage guidance, etc.) can be communicated from one device in the system to another device via an intermediate device. The communication can be performed via Bluetooth or Bluetooth Low Energy, or another wireless protocol (e.g., NFC, RFID, Wi-Fi, etc.). The communication can be continuous (e.g., active Bluetooth pairing) or intermittent (e.g., NFC proximity scan).

[0368] In some embodiments, the SCD102 or display device 120, such as a reader device (RD), can check whether the MDD152 is maintaining a wireless connection with the SCD102 or reader device for safety purposes of notifying the user when the MDD152 is potentially out of an area in close proximity. If the SCD102 or RD detects a loss of connection, an alarm is generated at the SCD102 (if configured to generate an audible or visual alarm) or the RD (or the SCD102 can notify the RD to generate an alarm). Similar alarms can be generated when a priming problem or failure, or a depletion of the drug supply (e.g., low cartridge) is detected. If the SCD102, RD, or MDD152 detects a high analyte state (e.g., hyperglycemia), the SCD102 or RD (via communication with the MDD152), or the MDD152 itself, can determine whether a dose was recently administered and refrain from creating an alarm (at the SCD102, RD, or MDD152).

[0369] Other notifications or alarms that can be generated for the user can be related to the remaining amount of the pen, which may include estimating that there is not enough drug remaining to administer within a certain time period (e.g., one day) and / or reminding the user. When it is determined that the next scheduled dose is more than the remaining amount of the drug, a message can be output recommending the use of a new pen or the loading of a new cartridge. MDD152 can be configured to monitor the amount of the drug and generate an alarm or notification if the expected amount does not match the actual (sensed) amount. MDD152 can also be configured to monitor the duration for which the delivery button is pressed to ensure complete delivery and notify the user if incomplete delivery is suspected. If MDD152 or DGS100 detects that the time the button is pressed is too short, MDD152 or DGS100 can assume that the user has administered a smaller amount than recommended. DGA or MDD152 can output an inquiry to the user to verify whether the administered dose is an incomplete dose or an intended low dose. If MDD152 or DGS100 detects that the time the delivery button is pressed is too long (e.g., longer than the time required to administer the recommended dose or the dosage shown in the dosage guidance), MDD152 or another system device (e.g., DGA installed in the display device 120) can notify or alert the user that a dose that is too large may have been administered. In one embodiment, MDD152 can be configured to output an audible notification (e.g., a beep sound), a tactile notification (e.g., vibration or click sound), and / or a visual notification (e.g., LED, light) when the completion of the dose is reached so that the user can stop pressing the actuator or button.After a dose is administered, SCD102 or RD can be configured to send a blackout time to MDD152, such that if MDD152 is disconnected or otherwise unable to communicate with SCD102 or RD120, which is configured to provide dose guidance, MDD152 knows that further dose administration should be prevented or limited, at least during the blackout period. The proposed feed data can be sent to the integrated pen based on the latest best estimate.

[0370] If the connection between MDD152 and SCD102 and / or RD120 is lost and then re-established, the user can be queried to enter the amount of any dose administered while the connection was down. In embodiments where MDD152 monitors the time period during which an injection or delivery button is pressed, it can include an accelerometer or other push-sensing mechanism. Based on the time of depression measured by the push-sensing mechanism (or the pattern and / or orientation of the pen movement when the button is depressed), MDD152 can determine whether the button was pressed for priming or dosing purposes. MDD152 can be configured to distinguish different types of presses. For example, MDD152 can assume that a short set of presses is for priming purposes and is thus not counted as a dose administration.

[0371] In one embodiment, DGS100 (e.g., SCD102, display device 120, or MDD152) can detect the occurrence of food consumption and then be configured to prompt the user to administer if the user has not already done so. Since analyte data is collected during or immediately after a meal and before dose administration, DGS100 can update the dose guidance.

[0372] MDD152 can also include a temperature sensor, and DGS100 can adjust insulin dosage guidanc...

Claims

**Claim 1** A method for providing a pre-meal correction factor in response to analyte data, the method comprising: determining, by at least one processor, an analyte pattern type for at least one time-of-day (TOD) period by executing a pattern analysis algorithm that receives, as input, time-correlated analyte data of a patient obtained during an analysis period; determining, by the at least one processor executing the algorithm, a pre-meal correction factor based on the analyte pattern type and a defined dosing strategy of the patient during the analysis period; storing, by the at least one processor, an indicator of pre-meal correction in a computer memory and outputting it to at least one of a user or a dosing device and including the method. **Claim 2** The method according to claim 1, wherein the pattern analysis algorithm is a glucose pattern analysis (GPA) algorithm. **Claim 3** The GPA algorithm outputs a display of a low glucose pattern, and the method further comprises determining, by the at least one processor, whether the meal dose of the defined dosing strategy includes pre-meal correction. The method according to claim 2. **Claim 4** The method according to claim 3, further comprising including, by the at least one processor, a portion of the analyte data only for days on which meal administration has occurred and bolus administration or pre-meal correction has not been forgotten. **Claim 5** The method according to claim 4, further comprising testing, by the at least one processor, whether sufficient data remains to achieve a minimum confidence level. **Claim 6** The method according to claim 5, further comprising, by the at least one processor, if sufficient data remains, using a portion of the analyte data to execute the GPA algorithm only for days on which meal administration has occurred and bolus administration or pre-meal correction has not been forgotten. **Claim 7** The method according to claim 6, further comprising determining, by the at least one processor, whether the glucose pattern from the GPA algorithm is low, and if so, reducing the recommended value of the meal portion of each dose for the associated TOD period. **Claim 8** The method according to claim 7, further comprising, by the at least one processor, reducing a pre-meal correction factor when the glucose pattern is not low.

9. The method according to claim 5, further comprising, by the at least one processor, including data for the day on which pre-meal correction was performed when insufficient data remains.

10. The method according to claim 9, further comprising, by the at least one processor, testing whether sufficient data exists to achieve a minimum confidence level, including data for the day on which pre-meal correction was performed.

11. The method according to claim 10, further comprising, by the at least one processor, executing a second GPA algorithm using analyte data including data for the day on which one or more pre-meal corrections were performed as input.

12. The method according to claim 11, further comprising, by the at least one processor, determining whether the glucose pattern from the second GPA algorithm is not low, and if so, reducing the recommended value of the meal portion of each dose for the relevant TOD period.

13. The method according to claim 12, further comprising, by the at least one processor, executing a third GPA algorithm using analyte data including only data for meal doses as input when the glucose pattern from the second GPA algorithm is low.

14. The method according to claim 13, further comprising, by the at least one processor, determining whether the glucose pattern from the third GPA algorithm is not low, and if so, reducing the recommended value of pre-meal correction.

15. The method according to claim 14, further comprising, by the at least one processor, reducing the recommended value of the meal portion of each dose for the relevant TOD period when the glucose pattern from the third GPA algorithm is low.

16. The method for the GPA algorithm to output a display of a high glucose pattern, wherein the at least one processor determines whether the analyte data includes a meal dose with pre-meal correction, and if not, excludes from the original data set the data of the day when the bolus was forgotten and the data of the day when pre-meal correction was performed, and further includes the step of executing the GPA algorithm based thereon, the method according to claim 2.

17. The method according to claim 16, further including the step of, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if not, increasing the pre-meal correction coefficient.

18. The method according to claim 16, further including the step of, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if so, increasing the recommended value of the meal portion of each dose in the relevant TOD period.

19. The method for the GPA algorithm to output a display of a high glucose pattern, wherein the at least one processor determines whether the analyte data includes a meal dose with pre-meal correction, and if so, excludes from the original data set the data of the day when the bolus was forgotten, including the meal dose and the data of the day when pre-meal correction was performed, and further includes the step of executing the GPA algorithm based thereon, the method according to claim 2.

20. The method according to claim 19, further including the step of, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if so, excluding from the original data set the data of the day when pre-meal correction was performed and the data of the day when bolus administration was forgotten, and further includes the step of executing the GPA algorithm based thereon.

21. The method according to claim 20, further including the step of, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if not, increasing the pre-meal correction coefficient.

22. The method according to claim 20, further comprising, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if so, increasing the recommended value of the dietary portion of each dose for the relevant TOD period.

23. A method in which the GPA algorithm outputs a display of a high glucose pattern, further comprising, by the at least one processor, determining whether the analyte data includes a meal dose for which pre-meal correction has been performed, and if not, excluding from the original data set the data for the day on which pre-meal correction has been performed and the day on which the bolus has been forgotten, and based on this, executing the GPA algorithm.

24. The method according to claim 23, further comprising, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if so, increasing the recommended value of the dietary portion of each dose for the relevant TOD period.

25. The method according to claim 23, further comprising, by the at least one processor, determining whether the glucose pattern determined by the GPA algorithm is high, and if not, increasing the pre-meal correction factor.

26. The method according to claim 1, wherein the analyte data includes an indicator of glucose level and the agent of the defined dosing strategy includes insulin.

27. The method according to claim 1, wherein the pattern analysis algorithm is a TIR algorithm.

28. The step of executing the TIR algorithm is a step of determining, by the at least one processor, a time (t ＢＴ ) below the target of at least one TOD period and a time (t ＡＴ ) above the target, the method according to claim 27.

29. The step of executing the TIR algorithm is performed by the at least one processor based on at least a time (t ＢＴ ) below the target and a time (t ＡＴ ) above the target, and further includes the step of evaluating at least one of a hypoglycemia risk index or a hyperglycemia risk. The method according to claim 28.

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