Systems, devices and methods relating to drug dosage guidance - Patents.com

Abstract

Systems, devices and methods are provided for determining a patient or user's drug dosage. The dosage determination can take into account the patient's or user's recent and / or past analyte levels. The dosage determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity and / or behavior. Many different dosage determination embodiments are shown for a wide range of different aspects of the systems or environments in which the embodiments may be implemented. Systems, devices and methods are provided for displaying information related to glucose values, including graphs of analytes including time in target range and identification of pattern types for segments of the day.

Description

Related Applications

[0001] This application claims priority to U.S. Patent Application No. 63 / 237,769, filed August 27, 2021, U.S. Patent Application No. 63 / 225,140, ​​filed July 23, 2021, and U.S. Patent Application No. 63 / 145,131, filed February 3, 2021, the contents of which are expressly incorporated by reference herein in their entirety for all purposes. This application is also related to U.S. Patent Application No. 29 / 817,852, filed December 3, 2021, U.S. Patent Application No. 29 / 817,851, filed December 3, 2021, U.S. Patent Application No. 29 / 814,001, filed November 2, 2021, and U.S. Patent Application No. 29 / 813,998, filed November 2, 2021, the contents of which are expressly incorporated by reference herein in their entirety for all purposes. This application is also related to U.S. Patent Application No. 16 / 944,736, filed July 31, 2020, which claims priority to and benefit of U.S. Provisional Patent Application No. 62 / 882,249, filed August 2, 2019, U.S. Provisional Patent Application No. 62 / 979,578, filed February 21, 2020, U.S. Provisional Patent Application No. 62 / 979,594, filed February 21, 2020, U.S. Provisional Patent Application No. 62 / 979,618, filed February 21, 2020, and U.S. Provisional Patent Application No. 63 / 058,799, filed July 30, 2020, the contents of which are expressly incorporated herein by reference in their entireties for all purposes. [Technical field]

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

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

[0004] A growing body of clinical data indicates a strong correlation between frequency of glucose monitoring and glycemic control. However, despite this correlation, many individuals diagnosed with diabetes do not monitor their glucose levels as frequently as they should due to a combination of factors including the inconvenience, discretion of testing, pain associated with glucose testing, and cost.

[0005] For patients who rely on the administration of medications (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 medication dosage guidance in an easily accessible manner as needed. It would further be desirable for such a system, device, or method to take into account the physiology, diet, activity, and / or behavior of the user or patient receiving treatment in providing such medication dosage guidance, which may itself improve accuracy and reliability. Furthermore, in some circumstances it would also be desirable for such a system, device, or method to be able to automatically deliver a selected medication dosage. Summary of the Invention [Problem to be solved by the invention]

[0006] For these and other reasons, a need exists for improved systems, methods and devices relating to drug dosage guidance. [Means for solving the problem]

[0007] Provided herein are exemplary embodiments of systems, devices, and methods for providing drug dose guidance, and in some embodiments, drug delivery. According to one aspect, many of the embodiments described herein comprise a dose guidance system (DGS) including a display device, a sensor control device, and a drug delivery device. The dose guidance system can include a dose guidance application (e.g., software) that can determine and output dose guidance (e.g., recommendations regarding dosage, corrections, and titration) to the patient. Additionally, according to some embodiments, the dose guidance system can learn the patient's dosing strategy during a learning period during which the dose guidance system can estimate key dosing parameters. According to some embodiments, the dose guidance system can also provide guidance for titration and corrections once the system is configured with the patient's current dosing strategy. The dose guidance system can also provide guidance for different meal intake scenarios. For example, in some embodiments, the dose guidance system can provide dose guidance at or before the start of a meal or after the start of a meal. The dose guidance system can also provide dose guidance for added meals (e.g., desserts) or for "touch-up" dosing to address high post-meal glucose levels. Exemplary system and safety features of the dose guidance system are also described.

[0008] Many of the embodiments provided herein include improved software functionality or graphical user interfaces for use with analyte monitoring systems that are highly intuitive and user-friendly, providing rapid access to the user's physiological information. More specifically, these embodiments allow the user (or HCP) to quickly determine the appropriate drug therapy based on information related to the user's physiological condition, past dosing patterns and other factors, without having to go through the tedious task of reviewing large amounts of analyte data. Additionally, some of the GUIs and GUI features allow the user (and their caregivers) to better understand and improve the user's dosing patterns and subsequent hypoglycemic and hyperglycemic episodes. Similarly, many other embodiments provided herein include improved software functionality for dose guidance systems, improving the dose guidance provided to the user by enabling safe titration strategies that minimize hypoglycemic episodes, ways to modify dose guidance depending on the timing of dose 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 dosing strategies, post-meal alarms based on predicted probability of occurrence rather than thresholds, and the like, to name just a few examples.

[0009] Additionally, many of the embodiments described herein are improvements over conventional bolus calculators that require the HCP to configure numerous settings, a very time-consuming process. Alternatively, the system may require the patient to input numerous settings, which is often confusing to the patient. The embodiments described herein do not require or minimize input from the patient (e.g., typical meal doses), greatly reducing the manual input required by both the patient and the HCP, and the system can learn other necessary parameters related to the patient's insulin dosing habits. Additionally, many patients have poor adherence to the proposed dosing regimen. During the learning period, the system can collect data on both glucose levels and insulin administration to assess the patient's level of adherence. In this way, the system's functionality can solve problems related to the HCP's time investment and the patient's level of progress. Another advantage of the system is that while conventional bolus calculators use fixed parameters that need to be set, the system can automatically titrate (i.e., optimize) dosing regimen parameters over time, i.e., optimize the dosing regimen to reduce low or high glucose patterns. This relieves HCPs from the time burden of having to perform the task of regularly checking patients' glucose data and updating dosing parameters to address glycemic issues. Another benefit is that, with many patients frequently missing insulin doses, the system can detect and alert the patient when they have missed a dose and provide a means for the patient to safely administer it at a delay, which helps lower the patient's overall glucose level. The current standard practice is for patients to wait more than two hours after starting a meal to take a correction dose or wait until the next meal dose to correct hyperglycemia. It can be safely administered within two hours of starting a meal, lowering overall glucose levels while minimizing the additional risk of hypoglycemia. Other improvements and benefits are also provided.Various configurations of these devices are described in detail in accordance with exemplary embodiments only.

[0010] Other systems, devices, methods, features, and advantages of the subject matter described herein will become apparent to one of ordinary skill in the art upon examination of the following figures and detailed description. All such additional systems, devices, methods, features, and advantages are intended to be included herein, be within the scope of the subject matter described herein, and be protected by the accompanying claims. Features of the exemplary embodiments should not be construed as limiting the scope of the appended claims unless those features are expressly recited in the appended claims. [Brief description of the drawings]

[0011] Details of the subject matter described herein, both as to its structure and operation, may become apparent from a consideration of the accompanying drawings, in which like reference numerals refer to like parts. The components in the drawings are not necessarily to scale, with emphasis instead being placed upon illustrating the principles of the subject matter. Moreover, all figures are intended to convey concepts, and relative sizes, shapes, and other detailed attributes may be shown diagrammatically, rather than literally or precisely. [Figure 1A] FIG. 1 is a block diagram of an exemplary embodiment of a dose guidance system. [Figure 1B] FIG. 1 is a block diagram of an exemplary embodiment of a dose guidance system. [Figure 2A] FIG. 1 is a schematic diagram illustrating an exemplary embodiment of a sensor control device. [Figure 2B] FIG. 2 is a block diagram illustrating an exemplary embodiment of a sensor control device. [Figure 3A] 1 is a schematic diagram illustrating an exemplary embodiment of a drug delivery device. [Figure 3B] FIG. 1 is a block diagram illustrating an exemplary embodiment of a drug delivery device. [Figure 4A] FIG. 1 is a schematic diagram illustrating an exemplary embodiment of a display device. [Figure 4B] FIG. 1 is a block diagram illustrating an exemplary embodiment of a display device. [Diagram 5] FIG. 1 is a block diagram illustrating an exemplary embodiment of a user interface device. [Figure 6A-1] FIG. 1 illustrates an exemplary layout of a glucose pattern report. [Figure 6A-2-1] FIG. 1 illustrates an exemplary layout of a glucose pattern report. [Figure 6A-2-2] This is a continuation of Figure 6A-2-1. [Figure 6A-3-1] FIG. 1 illustrates an exemplary layout of a glucose pattern report. [Figure 6A-3-2] This is a continuation of Figure 6A-3-1. [Figure 6A-4-1] FIG. 1 illustrates an exemplary layout of a glucose pattern report. [Figure 6A-4-2] This is a continuation of Figure 6A-4-1. [Figure 6B-1] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6B-2] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6C-1] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6C-2] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6D-1] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6D-2] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6E-1] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6E-2] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6F-1] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6F-2] FIG. 2 shows an exemplary glucose concentration profile. [Figure 6G-1]FIG. 2 shows an exemplary glucose concentration profile. [Figure 6G-2] FIG. 2 shows an exemplary glucose concentration profile. [Figure 7A] FIG. 1 is a flow diagram illustrating an example embodiment of a process flow of a portion of a dose guidance application directed to a learning method for estimating a patient's insulin dosing habits. [Figure 7B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for parameterizing a patient's dosing habits to configure dose guidance settings. [Figure 7C] FIG. 7C is a flow diagram illustrating an additional optional element for the method shown in FIG. 7B. [Figure 7D] FIG. 7C is a flow diagram illustrating an additional optional element for the method shown in FIG. 7B. [Figure 7E] FIG. 7C is a flow diagram illustrating an additional optional element for the method shown in FIG. 7B. [Figure 8A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation by a dose guidance application to evaluate meal bolus titration for multiple injection (MDI) dosing therapy. [Figure 8B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a process flow for operation with a dose guidance application for glucose pattern analysis (GPA). [Figure 8C] FIG. 1 illustrates an exemplary embodiment of a graph showing information for determining hypoglycemia risk for GPA and other indicators. [Figure 8D] FIG. 1 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for assessing meal bolus titration for MDI insulin dosing therapy. [Figure 8E] FIG. 1 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for assessing meal bolus titration for MDI insulin dosing therapy. [Figure 8F]FIG. 1 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for assessing meal bolus titration for MDI insulin dosing therapy. [Figure 8G] FIG. 1 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for assessing meal bolus titration for MDI insulin dosing therapy. [Figure 8H] FIG. 1 is a flow diagram illustrating aspects of various exemplary embodiments of an algorithm for assessing meal bolus titration for MDI insulin dosing therapy. [Figure 9A-1] FIG. 1 illustrates an exemplary report for review by an HCP. [Figure 9A-2] FIG. 1 illustrates an exemplary report for review by an HCP. [Figure 9B] FIG. 1 illustrates an exemplary adherence report. [Figure 9C] FIG. 13 illustrates exemplary plots associated with a clustering analysis of meal periods. [Figure 9D] FIG. 1 shows an exemplary plot of meal dose clustering and dosage. [Figure 9E] FIG. 13 illustrates exemplary plots related to determining a pre-meal correction factor. [Figure 9F] FIG. 1 is a flow diagram illustrating an example embodiment of a process for facilitating HCP access to electronic medical records. [Figure 9G] FIG. 1 illustrates an exemplary summary report for an HCP. [Figure 9H] FIG. 1 illustrates an exemplary plot showing patient adherence to a recommended medication regimen. [Figure 9I] FIG. 1 illustrates an exemplary summary report of a patient's treatment. [Figure 10] FIG. 1 is a state transition diagram that defines when the insulin dosing algorithm may be invoked during a guidance period. [Figure 11] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for displaying dose guidance related to meal doses and correction doses. [Figure 12] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for displaying a dose guidance screen. [Figure 13] FIG. 1 is a flow diagram illustrating an example embodiment of a method for displaying multiple meal icons. [Figure 14] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for displaying dose calculations. [Figure 15] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for issuing a missed meal dose alert. [Figure 16A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for disabling a missed meal dose alert. [Figure 16B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for disabling a missed meal dose alert. [Figure 16C] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for disabling a missed meal dose alert. [Figure 16D] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for disabling a missed meal dose alert. [Figure 17] FIG. 13 is a flow diagram illustrating an exemplary embodiment of a method for issuing a correction dose alert. [Figure 18A] FIG. 13 is a flow diagram illustrating an exemplary embodiment of a method for disabling a correction dose alert. [Figure 18B] FIG. 13 is a flow diagram illustrating an exemplary embodiment of a method for disabling a correction dose alert. [Figure 18C] FIG. 13 is a flow diagram illustrating an exemplary embodiment of a method for disabling a correction dose alert. [Figure 19] FIG. 1 shows IOB remaining from previous injection as a function of DIA of fast-acting insulin. [Figure 20A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for classifying doses. [Figure 20B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for classifying doses. [Figure 20C]FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for classifying doses. [Figure 21A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for providing dose guidance in response to analyte data. [Figure 21B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for determining a glucose pattern index. [Figure 21C] FIG. 21C is a flow diagram illustrating certain additional operations that may be performed in combination with one or more of the methods illustrated by FIGS. 21A and 21B. [Figure 21D] FIG. 21C is a flow diagram illustrating certain additional operations that may be performed in combination with one or more of the methods illustrated by FIGS. 21A and 21B. [Figure 21E] FIG. 11 is a flow diagram illustrating supplemental or alternative operations for displaying glucose patterns. [Figure 22A] FIG. 1 is an exemplary data flow diagram. [Figure 22B] FIG. 1 is an exemplary data flow diagram. [Figure 22C] FIG. 13 is a flow diagram illustrating an exemplary embodiment of a delivery device method for determining whether stored dosage data is complete. [Figure 22D] FIG. 13 is a flow diagram illustrating an example embodiment of a method of an application for determining whether received dosage data is complete. [Figure 22E] FIG. 13 is a flow diagram illustrating an example embodiment of a method of an application for determining whether received dosage data is complete. [Figure 23A] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for tagging meals with recommended dosages. [Figure 23B] FIG. 1 is a flow diagram illustrating an exemplary embodiment of a method for tagging meals with recommended dosages. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Before describing the subject matter of the present invention in detail, it is to be understood that this disclosure is not limited to particular embodiments described herein, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, since the scope of the disclosure will be limited only by the appended claims.

[0013] In general, embodiments of the present disclosure include systems, devices, and methods related to drug dose guidance. Dose 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 medication history, and other physiological information about the user. According to one aspect of the embodiments, dose 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 impact of such information categories on the user's future analyte levels.

[0014] The dose guidance function may be implemented as a dose guidance application (DGA) that includes software and / or firmware instructions stored in a memory of a computing device and executed by at least one processor or its processing circuitry. The computing device may be owned by a user or a healthcare professional (HCP), and the user or HCP may interface to the computing device via a user interface. According to some embodiments, the computing device may be a server or a trusted computer system accessible over a network, and the dose guidance software may be presented to the user in the form of an interactive web page via a browser running on a local display device (having a user interface) that is in communication with the server or trusted computer system over the network. In this and other embodiments, the dose guidance software may run across multiple devices, or may run partly on the processing circuitry of the local display device and partly on the processing circuitry of the server or trusted computer system. It will be understood by those skilled in the art that where the DGA is described as performing an operation, such operation is performed according to instructions stored in a computer memory (including instructions hard-coded in a read-only memory) that, when executed by at least one processor of at least one computing device, causes the DGA to perform the described operation. In all cases, the operations may alternatively be performed by hardware (eg, dedicated circuitry) physically configured to perform the operations, as opposed to being executed by instructions stored in a memory.

[0015] Additionally, as used herein, a system in which DGA is implemented may be referred to as a dose guidance system. A dose guidance system may be configured solely for the purpose of providing dose guidance or may be a multi-function system in which dose guidance is only one aspect. For example, in some embodiments, the dose guidance system may also monitor the user's analyte levels. In some embodiments, the dose guidance system may also deliver medication to the user, such as with an injection or infusion device. In some embodiments, the dose guidance system is capable of both analyte monitoring and medication delivery.

[0016] These and other embodiments described herein represent improvements in the field of computer-based dose determination, analyte monitoring, and drug delivery systems. Particular features and potential advantages of the disclosed embodiments are further described below.

[0017] Before describing embodiments of dose guidance in detail, it is desirable to first describe an example of a dose guidance system in which the dose guidance application can be implemented.

[0018] Exemplary embodiments of the dose guidance system 1A is a block diagram illustrating an exemplary embodiment of a dose guidance system 100. In this embodiment, dose guidance system 100 is capable of providing dose guidance, monitoring one or more analytes, and delivering one or more medications. This multi-functional example is used to illustrate the high degree of interconnectivity and performance provided by system 100. However, in the embodiments described herein, the analyte monitoring component, the medication delivery component, or both, may be omitted as desired.

[0019] Here, system 100 includes a sensor control device (SCD) 102 configured to collect analyte level information from a user, a drug delivery device (MDD) 152 configured to deliver a drug to the user, and a display device 120 configured to present information to and receive input or information from the user, the structure and functionality of each device being described in detail herein.

[0020] The system 100 is configured for highly interconnected and highly flexible communication between the devices. Each of the three devices 102, 120, and 152 can communicate with each other directly (without going through an intermediate electronic device) or indirectly with each other (such as through the cloud network 190 or through another device and then through the network 190). The bidirectional communication capabilities between the devices and between the devices and the network 190 are shown in FIG. 1A with double-headed arrows. However, those skilled in the art will appreciate that any one of the devices (e.g., SCDs) is capable of 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 of each path can be the same or different, and can be proprietary or standardized. For example, the wireless communication between the devices 102, 120, and 152 can be performed according to the Bluetooth (including Bluetooth Low Energy) standard, the NFC (near field communication) standard, the Wi-Fi (802.11x) standard, the mobile telephony standard, and the like. All communications over the various paths may be encrypted, and each device in Figure 1A may be configured to encrypt and decrypt those communications that are sent and received. In any case, the communication paths in Figure 1A may be direct (e.g., Bluetooth or NFC) or indirect (e.g., Wi-Fi, mobile telephony, or other Internet protocols). An embodiment of system 100 need not have the capability to communicate across all of the paths shown in Figure 1A.

[0021] In addition, while FIG. 1A depicts a single display device 120, a single SCD 102, and a single MDD 152, one skilled in the art will appreciate that the system 100 may comprise a plurality of any of the aforementioned devices. By way of example only, the system 100 may comprise a single SCD 102 in communication with a plurality (e.g., two, three, four, etc.) of display devices 120 and / or a plurality of MDDs 152. Alternatively, the system 100 may comprise a plurality of SCDs 102 in communication with a single display device 120 and / or a single MDD 152. Furthermore, each of the plurality of devices may be the same or different device types. For example, the system 100 may comprise a plurality of display devices 120, including smartphones, handheld receivers, and / or smartwatches, each of which may communicate with the SCDs 102 and / or MDDs 152 as well as with each other.

[0022] Analyte data can be transferred between devices in system 100 autonomously (e.g., automatically according to a schedule) 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, which then sends the analyte data back to the first device). Other technologies for data communication, such as a cloud network 190, can also be employed to accommodate more complex systems.

[0023] 1B is a block diagram illustrating another exemplary embodiment of a dose 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 trusted computer system 180 accessible by a cloud network 190. The SCD 102 and the MDD 152 can communicate with each other and with the display device 120-1, which can act as a communication hub to aggregate information from the SCD 102 and the MDD 152, process and display the information at a desired location, and forward 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 may include or present software for data management and analysis and communication with components in the system 100. The computer system 170 may be used by a user or medical professional to display and / or analyze analyte data measured by the SCD 102. Additionally, although FIG. 1B illustrates a single SCD 102, a single MDD 152, and two display devices 120-1 and 120-2, one skilled in the art will appreciate that the system 100 may include a plurality of any of the aforementioned devices, and each of the multiple devices may include the same or different types of devices.

[0024] With further reference to FIG. 1B, according to some embodiments, the trusted computer system 180 can be owned by a manufacturer or distributor of the components of the system 100, either physically or virtually through a secure connection, and can be used as a server to perform authentication of the devices of the system 100 (e.g., devices 102, 120-n, 152), to securely store the user's data, and / or to provide data analysis programs (e.g., accessible via a web browser) to perform analysis of the user's measured analyte data and medical history. The trusted computer system 180 can also function as a data hub for routing and exchanging data between all devices in communication with the system 180 via the cloud network 190. In other words, all devices of the system 100 that can communicate with the cloud network 190 (e.g., directly using an Internet connection or indirectly via other devices) can also communicate directly or indirectly with all other devices of the system 100 that can communicate with the cloud network 190.

[0025] Display device 120-2 is shown in communication with cloud network 190. In this example, device 120-2 may be owned by another user authorized to access the analyte and medication data of the person wearing SCD 102. For example, the person owning display device 120-2 may be, as one example, a parent of a child wearing SCD 102, or, as another example, a caregiver of an elderly patient wearing SCD 102. System 100 may be configured to communicate analyte and medication data about the wearer via cloud network 190 (e.g., via trusted computer system 180) to another user authorized to access the data.

[0026] Exemplary embodiments of an analyte monitoring device The analyte monitoring functionality of dose guidance system 100 can be achieved through the inclusion of one or more devices capable of collecting, processing and displaying a user's analyte data. Exemplary embodiments of such devices and methods of their use are described in International Publication No. WO 2018 / 152241 and U.S. Patent Application Publication No. 2011 / 0213225, the contents of which are incorporated herein by reference in their entireties for all purposes.

[0027] Analyte monitoring can occur 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 the sensor control device to the display device, for example, automatically according to a schedule, whether prompted or not. A "flash analyte monitoring" device (e.g., a "flash glucose monitoring" device or simply "flash" device) can transfer data from the sensor control device in response to a user-initiated request for data (e.g., a scan) by the display device, such as using NFC (near field communication) or RFID (radio frequency identification) protocols, as another example.

[0028] Analyte monitoring devices that utilize sensors configured to be partially or entirely placed within a user's body may be referred to as in vivo analyte monitoring devices. For example, an in vivo sensor may be placed within a user's body such that at least a portion of the sensor is in contact with bodily fluid (e.g., interstitial (ISF) fluid, such as dermal fluid at the dermis layer or subcutaneous fluid below the dermis layer, blood, etc.) and may measure an analyte concentration in the bodily fluid. In vivo sensors may use various types of sensing technologies (e.g., chemical, electrochemical, or optical). Some systems utilizing in vivo analyte sensors may also operate without the need for fingerstick calibration.

[0029] An "in vitro" device is one 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 carries a user's bodily fluid, which can be analyzed to determine the user's blood glucose level. Other ex vivo devices attempt to measure a user's internal analyte levels non-invasively, for example, proposing to use optical techniques that can measure internal analyte levels without mechanically intruding into the user's body or skin. In vivo and ex vivo devices often include in vitro capabilities (e.g., an in vivo display device that also includes a test strip port).

[0030] Although the subject matter is described with respect to a sensor capable of measuring glucose concentration, detection and measurement of concentrations of other analytes are within the scope of the present disclosure. These other analytes may include, for example, ketones, lactate, oxygen, hemoglobin A1C, acetylcholine, amylase, bilirubin, cholesterol, 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. Concentrations of drugs may also be monitored, for example, antibiotics (e.g., gentamicin, vancomycin, and the like), digitoxin, gitoxin, drugs of abuse, theophylline, and warfarin. The sensor may be configured to measure two or more different analytes at the same or different times. In some embodiments, the sensor control device can be coupled to two or more sensors, where one sensor is configured to measure a first analyte (e.g., glucose) and one or more other sensors are configured to measure one or more different analytes (e.g., any of those described herein). In other embodiments, a user can wear two or more sensor control devices, each capable of measuring a different analyte.

[0031] The embodiments described herein may be used in any type of in vivo, in vitro, and ex vivo device capable of monitoring the aforementioned analytes and the like.

[0032] In many embodiments, the operation of the sensor can be controlled by the SCD 102. The sensor can be mechanically and communicatively coupled to the SCD 102, or can simply be communicatively coupled to the SCD 102 using wireless communication techniques. The SCD 102 can include electronics and a power source that enable and control the sensing of analytes performed by the sensor. In some embodiments, the sensor or the SCD 102 can be self-powered so as not to require a battery. The SCD 102 can also include communication circuitry for communicating with another device (e.g., a display device) that may or may not be local to the user's body. The SCD 102 can be present on the user's body (e.g., attached to the user's skin, otherwise positioned, or carried in the user's clothing). The SCD 102 can also be implanted in the user's body along with the sensor. The functionality of the SCD 102 can be split between a first component implanted in the body (e.g., a component that controls the sensor) and a second component present on the body or the like (e.g., an intermediary component that communicates with the first component and also with an external device such as a computer or smartphone). In other embodiments, the SCD 102 may be configured to be external to the body and to non-invasively measure the user's analyte levels. The sensor control device may also be referred to as a "sensor control unit," an "on-body electronics" device or unit, an "on-body" device or unit, an "in-body electronics" device or unit, an "in-body" device or unit, or a "sensor data communication" device or unit, to name a few, depending on the actual implementation or embodiment.

[0033] In some embodiments, the SCD 102 may include a user interface (e.g., a touch screen) and may be capable of processing the analyte data and displaying the resulting calculated analyte levels to the user. In this case, the dose guidance embodiments described herein may be implemented, in whole or in part, directly by the SCD 102. In many embodiments, where the physical form factor of the SCD 102 is minimized (e.g., to minimize appearance on the user's body), the sensor control device may not be accessible to the user (e.g., if entirely implanted), or other factors, it may be desirable to have a display device available to the user to read the analyte levels and interface with the sensor control device.

[0034] FIG. 2A is a side view of an exemplary embodiment of an SCD 102. The SCD 102 can include a case or mount 103 (FIG. 2B) for sensor electronics, which can be electrically coupled to an analyte sensor 101, here configured as an electrochemical sensor. According to some embodiments, the sensor 101 can be configured to reside partially within the user's body (e.g., through the outermost surface of the skin), where it is in fluid contact with the user's bodily fluids and can be used to measure analyte-related data of the user along 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 protrude away from the case 103. Those skilled in the art will appreciate that other forms of attachment to the body and / or case 103 can be used in addition to or instead of adhesives and are fully within the scope of the present disclosure.

[0035] The SCD 102 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 an external surface of the user's skin and in contact with the user's bodily fluids. In doing so, the insertion device can also position the SCD 102 on the skin. In other embodiments, the insertion device can first position the sensor 101 and then later couple (e.g., insert into a mount) the associated electronics (e.g., wireless transmission circuitry and / or data processing circuitry, etc.) to the sensor 101, either manually or with the aid of a mechanical device. Examples of insertion devices are described in U.S. Patent Application Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661, 2018 / 0235520, the entire contents of which are incorporated by reference herein for all purposes.

[0036] 2B is a block diagram illustrating an exemplary embodiment of an SCD 102 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 interfaces in an analog manner to the sensor 101 and includes an analog front end (AFE) 110 configured to convert analog signals to and / or from a digital format (e.g., with an A / D converter), a power supply 111 configured to provide power to the components of the SCD 102, processing circuitry 112, memory 114, timing circuitry 115 (e.g., oscillators and phase-locked loops for providing clocks or other timing to the components of the SCD 102, etc.), and communication circuitry 116 configured to communicate in a wired and / or wireless manner with one or more devices external to the SCD 102, such as the display device 120 and / or the MDD 152.

[0037] The SCD 102 may be implemented in a highly interconnected manner, with the power source 111 coupled to each of the components shown in FIG. 2B, and those components (e.g., the AFE 110, the processing circuitry 112, the memory 114, the timing circuitry 115, and the communication circuitry 116) that communicate or receive data, information, or commands may be communicatively coupled to all other such components, for example, via one or more communication connections or buses 118.

[0038] The processing circuitry 112 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed among (and portions of) many different chips. The processing circuitry 112 may include on-board memory. The processing circuitry 112 may interface to the communications circuitry 116 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuitry 112 may also interface to the communications circuitry 116 and perform the inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

[0039] The processing circuit 112 may execute instructions stored in the memory 114. These instructions may cause the processing circuit 112 to process the raw analyte data (or pre-processed analyte data) to arrive at a final calculated analyte level. In some embodiments, the instructions stored in the memory 114, when executed, may cause the processing circuit 112 to process the raw analyte data to determine one or more of a calculated analyte level, an average calculated analyte level within a predetermined time window, a calculated rate of change of the analyte level within a predetermined time window, and / or whether the calculated analyte indicator exceeds a predetermined threshold condition. The instructions may also cause the processing circuit 112 to read and act on received transmissions, adjust the timing of the timing circuit 115, process data or information received from other devices (e.g., calibration information, encryption or authentication information received from the display device 120, etc.), perform tasks to establish and maintain communication with the display device 120, interpret voice commands from a user, transmit to the communication circuit 116, etc. In embodiments in which SCD 102 includes a user interface, the instructions may 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 functionality described herein that is encoded as instructions may instead be implemented by SCD 102 using hardware or firmware designs that do not rely on the execution of stored software instructions to achieve the functionality.

[0040] Memory 114 may be shared by one or more of the various functional units present in SCD 102, or may be distributed among two or more functional units (e.g., as separate memories present in different chips). Memory 114 may also be a separate chip itself. Memory 114 is non-transitory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, Flash memory, F-RAM, etc.).

[0041] The communications circuitry 116 may be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communications circuitry) that perform functions for communication over respective communications paths or links. The communications circuitry 116 may include or be coupled to one or more antennas for wireless communication.

[0042] The power source 111 can include one or more batteries, which can be rechargeable or single-use disposable batteries. Power management circuitry can also be included to regulate battery charging, monitor usage of the power source 111, boost power, perform DC conversion, etc.

[0043] Additionally, 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 (individually or as aggregate measurements over time) from the SCD 102 to another device (e.g., the display device 120). However, the temperature readings or measurements can be used in conjunction with software routines executed by the SCD 102 or the display device 120 to correct or compensate the analyte measurements output to the user instead of, or in addition to, actually outputting a temperature measurement to the user.

[0044] Exemplary embodiments of a drug delivery device The drug delivery functionality of the dose guidance system 100 can be achieved by including one or more drug delivery devices (MDDs) 152. The MDDs 152 can be any device configured to deliver a particular dose of drug. The MDDs 152 can also include devices, such as pen caps, that transmit data regarding the dose to the DGA, even if the device itself does not deliver the drug. The MDDs 152 can be configured as portable injection devices (PIDs) that can deliver a single dose per injection, such as a bolus. The PID is essentially a manually operated syringe, where the drug is either preloaded into the syringe or must be drawn into the syringe from a reservoir prior to injection. In most embodiments, however, the PID includes electronics to interface with the user and effect the delivery of the drug. The PID is often referred to as a dosing pen, although it does not necessarily have a pen-like appearance. A PID with user interface electronics is often referred to as a smart pen. PIDs may be used to deliver a single dose and then discarded, or they may be durable and used repeatedly to deliver many doses of medication over a period of a day, week, or month. PIDs are commonly used by users of multiple injection (MDI) therapy, who inject multiple times per day.

[0045] The MDD may also include a pump and an infusion set. The infusion set includes a tubing cannula that resides at least partially within the recipient's body. The tubing cannula is in fluid communication with a pump that can repeatedly deliver small amounts of medication through the cannula into the recipient's body over time. The infusion set can be applied to the recipient's body with an infusion set applicator, and the infusion set often remains implanted for 2-3 days or longer. The pump device includes electronics to interface with the user to control the slow infusion of medication. Both the PID and the pump can store medication in a medication reservoir.

[0046] The MDD 152 can function as part of a closed loop system (e.g., an artificial pancreas system that does not require user intervention to operate), a semi-closed loop system (e.g., an insulin loop system that requires little user intervention to operate, such as to confirm a dose change), or an open loop system. For example, a diabetic patient's analyte levels can be repeatedly and automatically monitored by the SCD 102, and that information can be used by the dose guidance embodiments described herein to automatically calculate or otherwise determine an appropriate drug intake to control the diabetic patient's analyte levels, and then deliver that dose to the diabetic patient's body. This calculation can be performed within the MDD 152 or any other device of the system 100, and the resulting determined intake can then be communicated to the MCD 152.

[0047] In many embodiments, the dose guidance provided by the embodiments described herein is for the type of insulin (e.g., rapid-acting (RA), short-acting insulin, intermediate-acting insulin (e.g., NPH insulin), long-acting (LA), very long-acting insulin, and mixed insulin) and is the same drug delivered by MDD152. The types of insulin include human insulin and synthetic insulin analogs. Insulin can also include premixed formulations. However, the dose guidance embodiments described herein and the drug delivery capabilities of 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 dose guidance embodiments can also include combination therapies. Combination therapies can include, but are not limited to, insulin and a glucagon-like peptide-1 receptor agonist (GLP-1RA), insulin and pramlintide.

[0048] To facilitate the description of the dose guidance embodiments herein, the MDD 152 is often described in the form of a PID, specifically a smart pen, however, one of ordinary skill in the art will readily appreciate that the MDD 152 may alternatively be configured as a pen cap, a pump, or any other type of drug delivery device.

[0049] FIG. 3A is a schematic diagram illustrating an exemplary embodiment of a PID, specifically an MDD 152 configured as a smart pen. The MDD 152 can include a case 154 for electronics, an injection motor, and a reservoir for medication (see FIG. 3B) from which medication can be delivered via a needle 156. The case 154 can include a removable or detachable cap or cover 157 that can be attached to cover the needle 156 when not in use and then removed for injection. The MDD 152 can also include a user interface (UI) 158 that can be implemented as a single component (e.g., a touch screen for outputting information to a user and receiving input from a user) or as multiple components (e.g., a touch screen or display in combination with one or more buttons, switches, etc.). The MDD 152 can also include an actuator 159 that can be moved, pressed, contacted, or otherwise actuated to initiate delivery of medication from an internal reservoir through the needle 156 and into the recipient's body. According to some embodiments, cap 157 and actuator 159 may also include one or more safety mechanisms to prevent separation and / or actuation to reduce the risk of injecting harmful drugs. Details of these safety mechanisms and more are described in U.S. Patent Application Publication No. 2019 / 0343385 (the '385 publication), the entire contents of which are incorporated herein by reference for all purposes.

[0050] FIG. 3B is a block diagram illustrating an exemplary embodiment of an MDD 152 having electronics 160 coupled to a power source 161, and a motorized injection motor 162 also coupled to the power source 161 and to a reservoir 163 for a drug. A needle 156 is shown in fluid communication with the reservoir 163, and a valve (not shown) may be present between the reservoir 163 and the needle 156. The reservoir 163 may be permanent or may be removable and replaced with another reservoir containing the same or a different drug. The electronics 160 may 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. 3B, the electronics 160 may include higher level functional units including processing circuitry 164, memory 165, communication circuitry 166 configured to communicate wired and / or wirelessly with one or more devices external to the MDD 152 (e.g., the display device 120), and user interface electronics 168.

[0051] MDD 152 may be implemented in a highly interconnected manner, with power source 161 coupled to each of the components shown in FIG. 3B, and those components (e.g., processing circuitry 164, memory 165, and communication circuitry 166) that communicate or receive data, information, or commands may be communicatively coupled to all other such components, for example, via one or more communication connections or buses 169.

[0052] The processing circuitry 164 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed among (and portions of) many different chips. The processing circuitry 164 may include on-board memory. The processing circuitry 164 may interface to the communications circuitry 166 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuitry 164 may also interface to the communications circuitry 166 and perform the inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

[0053] The processing circuitry 164 may execute software instructions stored in memory 165. These instructions may cause the processing circuitry 164 to receive a selection or provision of a designated dose from a user (e.g., entered via user interface 158 or received from another device), process a command to deliver the designated dose (e.g., a signal from actuator 159), and control motor 162 to deliver the designated dose. These instructions may also cause the processing circuitry 164 to read and act on received transmissions, process data or information received from other devices (e.g., calibration information, encryption or authentication information received from display device 120, etc.), perform tasks to establish and maintain communication with display device 120, interpret voice commands from the user and transmit them to communication circuitry 166, etc. In embodiments in which MDD 152 includes user interface 158, the instructions may cause the processing circuitry 164 to control the user interface, read user input from the user interface (e.g., entering a drug dose for administration or entering a confirmation of a recommended drug dose), display information on the user interface, format data for display, etc. The functions described herein that are coded as instructions may instead be implemented by MDD 152 using hardware or firmware designs that do not rely on the execution of stored software instructions to achieve the functionality.

[0054] Memory 165 may be shared by one or more of the various functional units present in MDD 152, or may be distributed among two or more functional units (e.g., as separate memories present in different chips). Memory 165 may also be a separate chip itself. Memory 165 is non-transitory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0055] The communications circuitry 166 may be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communications circuitry) that perform functions for communication over respective communications paths or links. The communications circuitry 166 may include or be coupled to one or more antennas for wireless communication. Details of exemplary antennas may be found in the '385 publication, the contents of which are incorporated herein in their entirety for all purposes.

[0056] The power source 161 can include one or more batteries, which can be rechargeable or single-use disposable batteries. Power management circuitry can also be included to regulate battery charging, monitor power source 161 usage, boost power, perform DC conversion, etc.

[0057] The MDD 152 may also include an integrated or attachable in vitro glucose meter that includes an in vitro test strip port (not shown) for receiving in vitro glucose test strips for performing in vitro blood glucose measurements.

[0058] Communication features A connected insulin pen and pen cap device is a type of MDD 152 that measures the amount of insulin injected by the patient and then transmits that data to a display device 120, such as a smartphone. In a connected pen, the electronics and mechanics required to transmit the data are built into the insulin pen. In a connected pen cap, the electronics and mechanics are built into a "cap" that attaches to the insulin pen.

[0059] The connected MDD 152 is an important part of the DGS 100. Previously, bolus calculator applications required the patient to manually enter dosing information, limiting the usability of the application. Having the connected MDD 152 automatically transmit insulin delivery data to the DGA substantially improves the usability of the DGS 100.

[0060] Features relating to how and what information is communicated between the DGA and the connected MDD 152 can have a significant impact on how easy the DGS 100 is to use.

[0061] Current connected MDDs 152 may include circuitry that can broadcast a record of an insulin dose once a dose is administered. Additionally, many designs can rebroadcast the record until the receiving application acknowledges receipt of the dose record. As seen in FIG. 22A, in a dataflow design 2300, the MDD 152 can broadcast dose information 2302 to an application, which can send an acknowledgement of receipt to the MDD 152. This dataflow design may work well for applications that use the dose information for a specific function, such as a dose logging function.

[0062] However, this data flow design may not adequately address the needs of a software application intended to provide insulin dosing guidance. In particular, dose calculation typically requires knowledge of previous insulin doses within the time frame of the insulin's action time (e.g., typically about 4.5 hours for most fast-acting insulins). Dose calculators can track a metric commonly called IOB (insulin on board). The IOB is typically subtracted from the calculated dose before being displayed to the user. When a user requests dose guidance, the application needs to calculate the insulin dose and then calculate the patient's IOB. The problem with data flow design 2300 is that if MDD 152 does not function or the communication path is interrupted, the application may not receive information about the recent dose, which may then miscalculate the user's IOB and result in the user overdosing.

[0063] In one embodiment, this risk can be mitigated by having the DGA prompt the user to confirm that no doses other than those received by the DGA have occurred. If the DGA does not have a recent dose record to proceed with the dose guidance calculation, the DGA can provide the patient with a means to manually enter the dose and time. In another embodiment, the DGA can provide the user with instructions to correct the communication interruption and a means to retry the dose guidance calculation. However, this method can be cumbersome and can add user steps to the process of requesting dose guidance, seriously reducing the usability of the DGA.

[0064] In another embodiment, as seen in FIG. 22B, the MDD 152 may be designed to provide a way for the DGA to query the pen for the most recent dose records. In addition to or instead of the data flow 2300 in which the DGA monitors the MDD 152 for alert conditions, in data flow 2320, after the user initiates (e.g., requests) dose guidance, the DGA may send a query to the MDD 152 to request dose information 2322. The query may be for recent dose information, or for dose information from a specific period, e.g., a period since the last time insulin dose data was received, as specified in the communication protocol. In response to the query, the MDD 152 may send the requested dose information back to the DGA 2324, which may send a receipt confirmation 2326 back to the MDD 152. Once the DGA has received all recent dose records, the DGA may calculate and display the IOB and dose guidance amounts to the user, as described elsewhere in this application.

[0065] The data flow design 2320 can handle situations where the communication channel is interrupted. However, to ensure that the IOB is accurate, the DGA can request confirmation from the MDD 152 that it has received all recent dose information, including confirmation that no other doses have been delivered by the MDD 152 recently or during a particular time period. Some scenarios in which this may occur include: (a) the MDD 152 battery may have died or other temporary failure may have prevented the MDD 152 from properly recording the delivered dose, or (b) in the case of a pen cap delivery device, the pen cap may not have been placed on the insulin pen.

[0066] In an exemplary embodiment, in a method 2340 as seen in FIG. 22C, in a first step 2342, the MDD 152 may store data of doses administered during a period of time. The data may include the administered dose and time. The data may also include the remaining amount of medication, e.g., insulin, remaining in the medication delivery device. In step 2344, the MDD 152 may determine whether the stored data includes all doses delivered during a period of time. To make this determination, the MDD 152 may include a self-test circuit that may periodically ensure proper functionality and battery power. This self-test circuit may maintain a counter that increments every self-test cycle at a fixed period of time, e.g., every minute. When the MDD 152 is queried, the MDD 152 may check the self-test counter to ensure that the counter value is equal to an estimated counter value based on the current elapsed time, which may be provided by a separate circuit in the MDD 152 electronics. In another embodiment, the MDD 152 can send the counter value as part of a query to the DGA, and the DGA can perform a counter value check that compares the MDD 152 counter value to an estimated counter value based on the elapsed time of a clock within the DGA. The DGS 100 may include some timing tolerance between the DGA clock and the MDD clock.

[0067] If, in step 2346, it is determined that the stored data includes all doses delivered during a period of time, the MDD may send the stored data to the application that sent the query.

[0068] If, in step 2348, it is determined that the stored data does not include all doses delivered during a period of time, the MDD 152 may create an indication of incomplete dose data. In step 2350, the MDD 152 may send an indication of the incomplete dose data to the application that sent the query.

[0069] In an alternative embodiment, the DGA may include circuitry to determine whether data transmitted from the MDD 152 includes all doses administered during a period of time. In an exemplary method 2360, as shown in FIG. 22D, in step 2362, the DGA may query the MDD 152, e.g., an insulin pen, and receive a first self-test counter value. This first counter value may be a current counter value at the time of the query. At a later time, in step 2364, the DGA may send an additional query to the MDD 152 and receive a second self-test counter value. The additional query may be in response to a request for dose guidance from the user. The second self-test counter value may be a current counter value at the time of the additional query. In step 2366, the DGA may calculate an estimate of the second self-test counter value. The estimate may be calculated based on the first counter value + (time elapsed between the query and the additional query / duration of the self-test). The self-test period may be a period during which the self-test circuitry of the MDD 152 is configured to increment a counter value by "1."

[0070] In step 2368, the DGA may compare the second counter value with the estimated counter value to determine whether the value is within an acceptable range. If the comparison (e.g., difference) of the values ​​is within an acceptable range, in step 2370, the DGA may calculate insulin dose guidance that may be displayed to the user. If the comparison is not within an acceptable range, in step 2372, the DGA may request the user to confirm that no other doses were delivered apart from the dose recorded by the DGA (e.g., received in a data transfer). If the user confirms that no other doses were delivered, in step 2370, the DGA may calculate insulin dose guidance that may be displayed to the user. If the user does not confirm that no additional doses were delivered, the DGA will not calculate and display dose guidance and the system may recheck the counter value.

[0071] In another alternative embodiment, in method 2380 as seen in FIG. 22E, the DGA can query the MDD 152 for dose data for a period of time in step 2382. In step 2384, the DGA can receive data from the MDD 152. The received data may include dose data or may include an indication of incomplete dose data. In step 2386, the DGA can determine whether an indication of an incomplete dose has been received from the MDD 152. If an indication of an incomplete dose has not been received, in step 2388, the DGA can calculate dose guidance based on the data sent from the MDD 152. If the DGA receives an indication of an incomplete dose, in step 2390, the DGA can output a prompt requesting the user to confirm that the received dose data includes all doses administered over the period of time. If in step 2392, the DGA determines that the DGA has received confirmation from the user that no other doses were delivered during the period of time, the DGA can calculate dose guidance in step 2388. If the DGA determines that it has not received confirmation from the user that no other doses have been delivered during a period of time, the DGA may again query the MDD 152 for dose data, similar to step 2382.

[0072] For DGS100 including a pen cap system as MDD152, when the cap is removed from the insulin pen for a period of time and then reinstalled, the system may be able to detect that a dose has been delivered and may also be able to detect the cumulative dose (if more than one dose delivery has occurred). However, the actual timing of these one or more doses may be unknown. In one embodiment, the pen cap may include a mechanism to detect when it is attached to or removed from the insulin pen, in addition to a mechanism to detect the current insulin remaining in the pen. The pen cap controller system may store the date and time of the last instance when the pen cap was reinstalled and the insulin level was different from when the pen cap was last removed. The date and time of this error indication may be sent to the DGA in response to a query. The pen cap controller system may exclude the storage of this error indicator in the special case where the pen cap is removed when the pen is empty (or nearly empty) and then reinstalled when the pen is filled. The DGA may process this indicator in a similar manner as described for the self-test indicator.

[0073] Exemplary embodiments of a display device The display device 120 can be configured to display information related to the system 100 to a user and to accept or receive input from the user also related to the system 100. The display device 120 can display recently measured analyte levels to the user in any number of forms. The display device can display the user's past analyte levels as well as other indicia describing the user's analyte information (e.g., time in range, ambulatory glucose profile (AGP), hypoglycemic risk level, etc.). The display device 120 can display past dosage information and drug delivery information such as time and date of administration. The display device 120 can display alarms, alerts or other notifications related to analyte levels and / or drug delivery.

[0074] Display device 120 may be dedicated for use with system 100 (e.g., an electronic device designed and manufactured primarily for interfacing with an analyte sensor and / or drug delivery device) as well as a handheld or portable mobile communication device (e.g., a smartphone or tablet), or a multifunction, general-purpose computing device such as a laptop, personal computer, or other computing device. Display device 120 may be configured as a mobile smart wearable electronics assembly such as smart glasses, or a smart watch or wristband. Display devices and variations thereof may be referred to as "reading devices," "readers," "handheld electronics" (or handhelds), "portable data processing" devices or units, "information receivers," "receiver" devices or units (or simply receivers), "relay" devices or units, or "remote" devices or units, to name a few.

[0075] 4A is a schematic diagram illustrating an exemplary embodiment of a display device 120, where the display device 120 includes a user interface 121 and a case 124 in which the display device electronics 130 (FIG. 4B) are held. The user interface 121 can be implemented as a single component (e.g., a touch screen capable of input and output) or multiple components (e.g., a display and one or more devices configured to receive user input). In this embodiment, the user interface 121 includes a touch screen display 122 (configured to display information and graphics and accept user input by touch) and input buttons 123, both of which are coupled to the case 124.

[0076] Display device 120 may 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 SCD 102, MDD 152, and / or a user. Additionally or alternatively, the user interface may be controlled by web pages displayed in a browser or other internet interface software executable on display device 120.

[0077] 4B is a block diagram of an exemplary embodiment of a display device 120 with display device electronics 130. Here, display device 120 includes a user interface 121 including a display 122 and input components 123 (e.g., buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, jog dials, microphones, speakers, etc.), processing circuitry 131, memory 125, communication circuitry 126 configured to communicate with and / or from one or more other devices external to display device 120, power supply 127, and timing circuitry 128 (e.g., oscillators and phase-locked loops for providing clocks or other timing to components of SCD 102, etc.). Each of the aforementioned components may be implemented as one or more distinct devices or may be integrated into a multi-function device (e.g., integration of processing circuitry 131, memory 125, and communication circuitry 126 on a single semiconductor chip). Display device 120 may be implemented in a highly interconnected manner, with a power source 127 coupled to each of the components shown in Figure 4B, and those components that communicate or receive data, information or commands (e.g., user interface 121, processing circuitry 131, memory 125, communication circuitry 126 and timing circuitry 128) may be communicatively coupled to all other such components, for example, via one or more communication connections or buses 129. Figure 4B is a simplified representation of typical hardware and functionality present in a display device, and one of ordinary skill in the art will readily recognize that other hardware and functionality (e.g., codecs, drivers, glue logic) may also be included.

[0078] The processing circuitry 131 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed among (and portions of) many different chips. The processing circuitry 131 may include on-board memory. The processing circuitry 131 may interface to the communications circuitry 126 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that facilitate converting data signals into a format suitable for wireless or wired transmission (e.g., in-phase and quadrature). The processing circuitry 131 may also interface to the communications circuitry 126 and perform the inverse functions necessary to receive wireless transmissions and convert them into digital data or information.

[0079] 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 pre-processed analyte data) and arrive at corresponding analyte levels suitable for display to the user. These instructions can cause the processing circuit 131 to read, process, and / or store dosing instructions from the user, which in turn can cause the processing circuit 131 to communicate dosing instructions to the MDD 152. These instructions can cause the processing circuit 131 to execute user interface software adapted to present an interactive group of graphical user interface screens to the user for the purposes of configuring system parameters (e.g., alarm thresholds, notification settings, display preferences, etc.), presenting current and past analyte level information to the user, presenting current and past drug delivery information to the user, collecting other non-analyte information from the user (e.g., information regarding meals consumed, activities performed, drugs administered, etc.), and presenting notifications and alarms to the user. The instructions may also cause the processing circuit 131 to transmit to the communications circuit 126, read and act on received transmissions, read input from the user interface 121 (e.g., input of a drug dose to be administered or input of a confirmation of a recommended drug dose), display data or information on the user interface 121, adjust the timing of the timing circuit 128, process data or information received from other devices (e.g., analyte data, calibration information, encryption or authentication information received from the SCD 102, etc.), perform tasks to establish and maintain communications with the SCD 102, interpret voice commands from a user, etc. The functionality described herein that is encoded as instructions may instead be implemented by the display device 120 using hardware or firmware designs that do not rely on the execution of stored software instructions to achieve the functionality.

[0080] Memory 125 may be shared by one or more of the various functional units present in display device 120, or may be distributed among two or more functional units (e.g., as separate memories present in different chips). Memory 125 may also be a separate chip itself. Memory 125 is non-transitory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0081] The communications circuitry 126 may be implemented as one or more components (e.g., transmitters, receivers, transceivers, passive circuits, encoders, decoders, and / or other communications circuitry) that perform functions for communication over respective communications paths or links. The communications circuitry 126 may include or be coupled to one or more antennas for wireless communication.

[0082] The power source 127 can include one or more batteries, which can be rechargeable or single-use disposable batteries. Power management circuitry can also be included to regulate battery charging, monitor power source 127 usage, boost power, perform DC conversion, etc.

[0083] The display device 120 may also include one or more data communication ports (not shown) for wired data communication with external devices, such as the computer system 170, the SCD 102, or the MDD 152. The display device 120 may also include an integrated or attachable in vitro glucose meter that includes an in vitro test strip port (not shown) for receiving in vitro glucose test strips for performing in vitro blood glucose measurements.

[0084] The display device 120 can display the measured analyte data received from the SCD 102 and can also be configured to output alarms, alert notifications, glucose values, and the like, which may be visual, audible, tactile, or any combination thereof. In some embodiments, the SCD 102 and / or MDD 152 can also be configured to output alarm or alert notifications in visual, audible, tactile form, or any combination thereof. Further details and other display embodiments can be found, for example, in U.S. Patent Application Publication No. 2011 / 0193704, the entire contents of which are incorporated herein by reference for all purposes.

[0085] Exemplary embodiments relating to dose guidance The following exemplary embodiments relate to dose guidance functionality provided by the dose guidance system 100. The dose guidance functionality is implemented in many embodiments as a set of software instructions stored and / or executed on one or more electronic devices. This dose guidance functionality will be referred to herein as a dose guidance application (DGA). In some embodiments, the DGA is stored, executed, and presented to a user on the same single electronic device. In other embodiments, the DGA can be stored and executed on one device and presented to a user on a different electronic device. For example, the DGA can be stored and executed on the trusted computer system 180 and presented to a user via a web page displayed via an internet browser executing on the display device 120. The DGA can be a standalone application or can be incorporated in whole or in part into other software applications.

[0086] Thus, there are many different embodiments related to the number and type of electronic devices used to store, execute, and present the DGA to a user. With respect to presentation to a 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 illustrating an example of one embodiment of a 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 a user and receive input or information from a 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-sensitive switches, capacitive switches, pressure-sensitive switches, jog dials, microphones, touch pads, soft keys, keyboards, etc.).

[0087] Many of the devices described herein may be implemented as a UID 200. For example, the display device 120 is used as a UID 200 in many embodiments. In some embodiments, the MDD 152 may be implemented as a UID 200. In embodiments in which the SCD 102 includes a user interface, the SCD 102 may be implemented as a UID 200. The computer system 170 may also be implemented as a UID 200.

[0088] the purpose The Dose Guidance System (DGS100) leverages glucose and insulin data to learn, deliver, and titrate insulin doses. The DGS includes applications, such as smartphone-based mobile applications, integrated with connected insulin pens and continuous glucose sensors to improve treatment management for patients with insulin-intensive diabetes (PWD) on multiple injections (MDI).

[0089] The DGS 100 can perform three primary tasks. First, the DGS 100 can learn the patient's insulin dosing regimen (i.e., how often and at what dosage insulin doses are administered) during the DGS's "learning period." Second, the DGS 100 can provide the patient with dose recommendations for mealtime dosing and post-prandial corrections. Third, the DGS 100 can titrate the current dose settings to maximize glycemic control. These second and third tasks may occur in parallel during the DGS's "guidance period."

[0090] As with insulin data, various forms of continuous glucose data (scanned, historical and streaming) may serve as inputs to perform all three functions above. The DGS 100 may receive glucose data by various means and in various forms, including scanned, historical and streaming. Scan data, including the most recent glucose and trend values, may be obtained on demand by a user. Glucose history data may be generated by a component of the DGS, which may generate and record glucose and trend values ​​at regular intervals, e.g., every 15 minutes. Past history data may be obtained by scanning by a user. Streaming data may include glucose and trend values ​​that are generated and recorded at regular intervals, e.g., every minute, and automatically transmitted to the DGA. Similarly, insulin data may be obtained from multiple sources. Insulin data may be manually logged or transferred from the MDD 152, e.g., an insulin pen. Glucose and insulin data may be transferred by any known means, e.g., through wireless communication technologies such as Bluetooth or NFC.

[0091] During the "learning period," the DGA can determine the user's insulin dosing regimen through clustering of insulin data by time of day and subsequent curve fitting when combined with glucose data. This learning portion of the DGA may require glucose and insulin data from the user for a period of time, e.g., about 14 days.

[0092] A guidance period, including providing dose recommendations and titration of dose settings, may begin after the learning period is completed and the initial insulin dosing regimen is determined. During the guidance period, the DGS may provide mealtime dose recommendations upon request of the user. The user may request dose recommendations for meals for which the DGS has determined the user is currently treated with insulin. The dose calculation may utilize a form of bolus calculator to provide dose recommendations that modify the learned fixed dose by accounting for pre-meal glucose highs and residual insulin remaining in the bloodstream from previous injections.

[0093] The DGS can also notify the user in case of missed mealtime dosing and recommend a revised dose. Fast-acting insulin analogs for prandial dosing are recommended to be taken either at or immediately before a meal. Missed prandial insulin dosing is common and therapy concordance is known to be a factor affecting glycemic control. To address these behavioral trends, the DGS can leverage streaming data, e.g., data transmitted at regular intervals such as every minute, to detect substantial periods of glycemic excursions without an associated insulin dose. Such periods may indicate instances where the user ate a meal but did not take the prescribed insulin dose. In this case, a prompt is displayed to notify the user. If a missed meal event is confirmed by the user, a revised prandial insulin dosing can be recommended by accounting for the timing discrepancy between meal start and insulin injection.

[0094] The DGS can also notify the user when it is appropriate to take a correction dose and recommend a dose to correct high glucose between meals. Every minute, the DGS can process the streaming data to identify periods when the user is experiencing high glucose between meal doses. If this is the case, the DGS can display a prompt to notify the user of this occurrence. The user can then request a dose recommendation from the DGS to correct the high glucose. Alternatively, the DGS can provide the dose recommendation within the notification, eliminating the need for the user to request it.

[0095] The DGS can also titrate the user's insulin dosage regimen to lower the user's glucose levels while avoiding excessive time below a low threshold, e.g., 70 mg / dL, thus maximizing blood glucose levels in a target range, e.g., 70-180 mg / dL. Once the user transitions into a "guidance period," the DGS can periodically analyze insulin and glucose data to titrate previously learned or titrated insulin dosage regimen parameters.

[0096] Detection of MDI medication strategies Turning now to the DGA aspect, in more detail, DGA can use knowledge of a patient's dosing strategy and analyte levels to provide accurate dose guidance. Described herein is an exemplary embodiment for automatic detection of a patient's dosing strategy, which can make DGA setup easy and fast. 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, a dose can be identified as basal or bolus based on the MDD 152 used to administer the dose. Some patients can have multiple MDDs 152. For example, a patient can have one MDD for administering long-acting insulin (e.g., basal doses) and another MDD for administering fast-acting insulin (e.g., prandial doses). Also, the count (e.g., number of doses) and timing of basal doses 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 daily basal dose of 20 U can be split into two 10 U doses, one dose can be administered before bedtime and one dose can be administered upon awakening.

[0097] When consecutive bolus doses are administered in succession, the system can attempt to distinguish between an original meal dose, an add-on to an original meal dose, or a correction dose for high glucose between meals. When the DGA detects a small dose followed by a large dose, both of which occur near the start of a meal, the DGA can group the 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 then occurs long after a known meal and / or group of doses tagged as meal doses, the DGA can tag the later dose as a correction dose for high glucose after a meal, or as a dose that adds to the previous meal dose to account for the extra food consumed. Once a meal event is recognized based on the meal detector algorithm or a user-entered meal event, the DGA can use the amount of the previous dosing event and its timing relative to the currently detected meal to help disambiguate whether the previous dose is the first of multiple meal doses or a correction for high glucose between meals. It is assumed that correction doses are small in size compared to mealtime doses. Furthermore, if the time elapsed between the previous dose and the current meal event is long enough, it is reasonable to assume that these two events are not associated with the treatment of the same glucose excursion event, ruling out the possibility that the previous dose is the first of multiple doses for a given meal. Thus, if the previous dose is sufficiently smaller than the meal doses recorded within this window in the past few days and sufficiently distant from the current meal, the previous dose can be classified as a correction dose event.

[0098] The DGA can be configured to use real-time meal detection algorithms and dosing times to identify doses added to basal as breakfast, lunch, and / or dinner bolus doses, 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.

[0099] These different scenarios and aspects of DGA are described in more detail elsewhere in this specification.

[0100] 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. DGA embodiments include multiple interaction methods between the HCP and the DGA, providing the HCP with relevant evidence for approving the proposed dose learning and titrations in a concise and informative manner that improves workflow.

[0101] For diabetic patients already on an insulin dosing regimen, HCPs can leverage existing reports that provide insight into the patient's glucose patterns to identify users who may benefit from dose guidance. DGA embodiments provide a learning period (e.g., while using the DGS100) during which the patient's dosing strategy and trends can be classified. If the combined insulin and glucose data further confirms that the user is a suitable candidate for the DGA, such as a candidate that the DGA can learn a particular dosing strategy, the insulin dosing parameters learned during the learning period can serve as initial conditions for dose guidance that can be titrated by the DGA as needed. Also, HCP notification methods for initialization and titration of DGA dose parameters can be presented. This process can help both HCPs and users by streamlining DGA onboarding and titration, while also helping ensure that the DGA is only used by those indicated. If the DGA is unable to learn the patient's dosing parameters, the DGA can indicate a patient dosing discrepancy that the HCP can utilize to address the patient dosing discrepancy.

[0102] The first step in identifying potential users of DGA can include an initial analysis of the patient's glycemic control with a glucose concentration profile. To make DGA accessible to as many users as possible, this process can be independent of the user's current method of glucose monitoring.

[0103] For diabetic patients currently using SCD102, a glucose pattern report is available that includes key indicators, a glucose concentration profile (e.g., an ambulatory glucose profile (AGP)), patterns identified over different time periods, and titration and lifestyle suggestions to improve when glucose is consistently outside of the target range, as described in more detail below with respect to Figures 6A-1-6G-2. The patterns can be identified using the GPA algorithm, as described in more detail elsewhere.

[0104] As discussed above, for those not currently using a device or system (e.g., SCD 102) associated with an application capable of generating glucose pattern report 250, the HCP may suggest that the patient be monitored by another device or system so that report 250 or similar may be generated. For example, a patient may wear an SCD 102 configured in a masking or blind mode in which the user does not have access to the measured glucose levels and therefore cannot modify their behavior during this time to collect glucose data over a period of days or weeks. From these data, a glucose pattern report may be generated. If a suggested insulin titration is included in the glucose pattern report, the glucose pattern report 250 may also include a suggestion that the patient is an appropriate candidate for DGS 100 and may suggest a learning period of drug dosing strategies.

[0105] During the learning period, the MDD 152 can be integrated into the glucose sensing system used for the initial screening to provide a more complete portrait of insulin-intensive diabetes management. The learning period can utilize algorithms such as those described elsewhere herein to detect the user's insulin dosing strategy. During the learning period, the DGA can be configured to determine how the user determines prandial doses. For example, the DGA can determine whether the user determines prandial doses based on carbohydrate counting, whether the user determines prandial doses based on an empirical approach such as learning appropriate doses based on experience with past meals or similar meals, whether the user administers a fixed amount of insulin at mealtimes, whether the user varies prandial insulin doses (determined from fixed dosing, carbohydrate counting, or empirical dosing) based on pre-meal glucose values, whether the user considers residual insulin from previous injections (IOB) or other techniques when determining dosages (determined from fixed dosing, carbohydrate counting, or empirical dosing). The DGA can also determine whether the user's prandial doses are constant based on meal type (e.g., breakfast, lunch, and dinner) or whether the prandial doses vary. A determination that the mealtime dose has changed may indicate that the user is basing the mealtime dose on carbohydrate counting. The DGA can also determine whether the user is adjusting the mealtime dose to account for pre-meal high glucose. In some embodiments, the DGA can also determine a target glucose level, and the user adjusts or corrects the mealtime 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 of missed meal doses. For example, the DGA can detect whether the user has missed at least two insulin doses associated with a meal or time period in a period (e.g., one week or two weeks), instead at least three times.

[0106] In some embodiments, the DGS can also prompt the user to input a typical meal dose and a typical time period during which the meal dose is usually administered. In some embodiments, the DGS can prompt the user to input the amount of fast acting insulin that is usually taken at each meal when the glucose level is at a particular level. For example, the DGS can prompt the user to input the amount of fast acting insulin that is usually taken at each meal when the glucose level is about 120 mg / dL. For fast acting insulin, the DGA can prompt the user for the start and end times of each of breakfast, lunch, and dinner. The fast acting doses at the specified time periods for each of the various meals can then be logged as the doses for that meal. In some embodiments, the fast acting doses may be logged as the doses for that meal without additional input from the user, e.g., the user may not be prompted after the dose is administered to confirm that the dose is for a particular meal. For example, the bolus time period for breakfast can be from about 2:00 a.m. to about 11:00 a.m. The lunch bolus time period can be from about 11:30 AM to about 2 PM. The dinner bolus time period can be from about 5 PM to about 8 PM. In this exemplary embodiment, a fast acting insulin dose administered at 12:30 PM can be automatically logged as a lunch dose without additional confirmation from the user.

[0107] In some embodiments, the DGS can also prompt the user for the amount of a typical basal dose to be administered, and the time or time period that the typical basal dose will be administered. The DGS can also prompt the user to confirm and verify all of the dosage amounts and administration times and time periods before finalizing.

[0108] The learning period can last 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 is able to learn trends. Results can be compiled into a summary report for both the user and the physician.

[0109] Glucose Pattern Report As seen in FIGS. 6A-1-6G-2, the glucose pattern report 250 can include various elements that can be arranged in different layouts. One skilled in the art will appreciate that the glucose pattern report 250 can be a graphical user interface output to a display of a computing device. The elements can include an identification of a most significant glucose pattern 278, a dosing guidance 260, a variability statement 286, lifestyle guidance 284, and an excursion statement 288. The glucose pattern report 250 can also include an identification of a time period 264 that the report covers, an identification of an amount of time 266 that the CGM sensor is active, an average number of scans or views per day 268, a glucose metrics or statistics section 270, a time in target range (TIR) ​​section 272, a guidance for clinicians 276 section, and a glucose pattern 282 section. The guidance for clinicians 276 section can include dosing guidance 260, a variability statement 286, lifestyle guidance 284, and an excursion statement 288.

[0110] A time period 264 that the report covers may be included in the glucose pattern report 250. The time period 264 may be about 7 days, about 14 days, about 1 month, about 2 months, or about 3 months. The time period 264 may be reported as a start and end date, a total number of days, and / or both a start and end date and a total number of days (e.g., "May 31, 2018 - June 13, 2018 (14 days)"). The time period 264 may be listed at the top of the report, e.g., under the report name, or may be listed at the bottom of the report, in the header or footer, or elsewhere in the layout of the report.

[0111] The amount of time 266 that the CGM sensor is active may also be reported, for example, as a percentage, in the glucose pattern report 250. The amount of time 266 that the CGM sensor is active may be listed at the top of the report, for example, near the time period 264. Alternatively, the amount of time 266 that the CGM sensor is active may be listed at the top of the report, for example, under the report name, or at the bottom of the report, in the header or footer, or elsewhere in the report's layout.

[0112] The average number of scans or views per day 268 during the time period 264 may also be included in the glucose pattern report 250. The average number of scans or views per day 268 may be listed at the top of the report, e.g., near the amount of time the CGM sensor is active 266. Alternatively, the average number of scans or views per day 268 may be listed at the top of the report, e.g., under the report name, or at the bottom of the report, in the header or footer, or elsewhere in the layout of the report.

[0113] A glucose indicator section 270 may also be included in the glucose pattern report 250. The glucose indicator section 270 may include an average glucose over the time period 264. The glucose indicator section 270 may also include a glucose management indicator (GMI) for the time period 264. Goals for each of the average glucose and GMI may optionally be listed next to the average glucose and GMI values, allowing the user to quickly discern how close or far away they were from achieving their goals for the time period 264. The goals may be displayed in a different color (e.g., gray text versus black for the actual calculated average glucose and GMI values) and may also be displayed in a smaller font size.

[0114] The time in target range (TIR) ​​section 272 can include the TIR graphical display 252 and a text component 274 describing the amount of time in each different range. The TIR graphical display 252 can be a bar graph, a pie chart, a histogram graph, or any other graphical display showing the relative amount of time in a number of different concentration ranges. The TIR graphical display 252 can include at least three, or at least four, or at least five, or at least six different concentration ranges. The graphical display of each concentration range can reflect the time in target range for that concentration range. For example, the relative area or relative height of the graphical display of each concentration range can be proportional to or related to the determined percentage of time for that concentration range during the time period 264.

[0115] Ranges can include below a very low threshold 290 (e.g., below about 54 mg / dL), between a very low threshold 290 and a low threshold 291 (e.g., between about 54 mg / dL and about 69 mg / dL), between a low threshold 291 and a high threshold 292 (e.g., between about 70 mg / dL and about 180 mg / dL), between a high threshold 292 and a very high threshold 293 (e.g., between about 181 mg / dL and about 250 mg / dL), and above a very high threshold 293 (e.g., above about 250 mg / dL).

[0116] The very low threshold 290 can be about 50 mg / dL to about 65 mg / dL, alternatively about 50 mg / dL to about 60 mg / dL, alternatively about 53 mg / dL, alternatively about 54 mg / dL, alternatively about 55 mg / dL, alternatively about 56 mg / dL, alternatively about 57 mg / dL, alternatively about 58 mg / dL, alternatively about 59 mg / dL, alternatively about 60 mg / dL, alternatively about 61 mg / dL, alternatively about 62 mg / dL, alternatively about 63 mg / dL, alternatively about 64 mg / dL, alternatively about 65 mg / dL. The low threshold can be about 60 mg / dL to about 75 mg / dL, alternatively about 65 mg / dL to about 75 mg / dL, alternatively about 67 mg / dL to about 72 mg / dL, alternatively about 67 mg / dL, alternatively about 68 mg / dL, alternatively about 69 mg / dL, alternatively about 70 mg / dL, alternatively about 71 mg / dL, alternatively about 72 mg / dL, alternatively about 73 mg / dL, alternatively about 74 mg / dL, or alternatively about 75 mg / dL. The high threshold can be about 170 mg / dL to about 190 mg / dL, alternatively about 175 mg / dL to about 185 mg / dL, alternatively about 175 mg / dL, alternatively about 176 mg / dL, alternatively about 177 mg / dL, alternatively about 178 mg / dL, alternatively about 179 mg / dL, alternatively about 180 mg / dL, alternatively about 181 mg / dL, alternatively about 182 mg / dL, alternatively about 183 mg / dL, alternatively about 184 mg / dL, or alternatively about 185 mg / dL. The very high threshold can be about 230 mg / dL to about 270 mg / dL, alternatively about 240 mg / dL to about 260 mg / dL, alternatively about 245 mg / dL, alternatively about 246 mg / dL, alternatively about 247 mg / dL, alternatively about 248 mg / dL, alternatively about 249 mg / dL, alternatively about 250 mg / dL, alternatively about 251 mg / dL, alternatively about 252 mg / dL, alternatively about 253 mg / dL, alternatively about 254 mg / dL, alternatively about 255 mg / dL. The various ranges of thresholds and limits can be user customizable. Alternatively, the various ranges of thresholds and limits can be non-user customizable.

[0117] Different concentration ranges of the TIR graphical representation can be colored differently, for example, the graphical representation below a very low threshold can be colored dark red or maroon, the graphical representation of the range between a very low threshold and a low threshold can be colored bright red, the graphical representation of the range between a low threshold and a high threshold can be colored green, the graphical representation of the range between a high threshold and a very high threshold can be colored yellow, and the graphical representation of the range above a very high threshold can be colored orange.

[0118] The text component 274 of the TIR display 272 may include a label for each range. Ranges below a very low threshold may be labeled "Very Low", ranges between a very low threshold and a low threshold may be labeled "Low", ranges between a low threshold and a high threshold may be labeled "Target", ranges between a high threshold and a very high threshold may be labeled "High", and ranges above a very high threshold may be labeled "Very High". The text component 274 of the TIR display 272 may include the numerical limits of the concentration ranges of the multiple ranges or alternatively include a description thereof. The different concentration ranges may be listed next to or in close proximity to the corresponding graphical element and / or the label for that range. For example, for the graphical element of time below the very low threshold 290, the text may read "Very Low < 54" or "Below 54 mg / dL", "Low 54-69" or "54-69 mg / dL", "Target 70-180" or "Target 70-180 mg / dL", "High 181-250" or "181-250 mg / dL", and / or "Very High > 250" or "Above 250 mg / dL". The text component 274 may also include a numerical value, e.g., a percentage, of the time spent in each range of the concentration ranges. Instead of or in addition to individual numerical values ​​for each concentration range, the text component 274 may include a combined numerical value, e.g., a combined percentage, of two or more ranges. For example, the numerical value of time below the very low threshold 290 and the numerical values ​​of time between the low threshold 291 and the very low threshold 290 may be reported as a single combined numerical value. Additionally, the time values ​​above the very high threshold 293 and the time values ​​between the high threshold 292 and the very high threshold 293 may be reported as a single combined number. The numbers or combined number may be located adjacent or in close proximity to the graphical elements and / or legend for each concentration range. If both individual and combined numbers are reported, the combined number may be visually distinct from the individual numbers.For example, the combined numerical value may be displayed in bold, italics, or a different color. The reported numerical value or the sum of the combined numerical values ​​may equal 100 or may equal a numerical value other than 100. The text component 274 may also include a target 275 for each concentration range (see, for example, FIG. 6A-2 and FIG. 6A-3), such as a percentage. Alternatively, the text component 274 may include a combined target for two or more concentration ranges. For example, a target for time below a very low threshold and a target for time between a low threshold and a very low threshold may be reported as a single target. Additionally, a target for time above a very high threshold and a target for time between a high threshold and a very high threshold may be reported as a single target. The target for each concentration range and / or the combined target may be listed next to or in close proximity to the determined numerical value of the time period 264 for each concentration range.

[0119] Sections detailing guidance for the clinician, HCP, or patient 276 may also be included in the glucose pattern report 250. The guidance for clinicians 276 section may include a top patterns section 278, a medication guidance section 260, and a lifestyle guidance section 284.

[0120] The most significant patterns section 278 may identify the most significant patterns for the time period 264 as determined by an algorithm, including but not limited to the GPA algorithm described elsewhere in this application. The most significant patterns section 278 may identify patterns including but not limited to "Lows", "Highs with Some lows", and "Highs". The most significant patterns section 278 may also identify the time period of the day during which the most significant patterns occurred, e.g., overnight, morning, afternoon, and / or evening. Each pattern and the time period of the day during which the identified patterns occurred may be identified with text, e.g., a sentence or phrase, and each may be identified with a tag 280. If multiple patterns are detected, including a "low" pattern, the glucose pattern report 250 may identify the "low" pattern in the most significant patterns section 278, allowing the clinician to address these "low" patterns first before addressing the "highs with some lows" or "highs". If multiple patterns are identified, the patterns identified in the most important pattern section 278 may be prioritized to first identify "low" patterns, then "high with some low" patterns, and then "high" patterns. However, additional patterns may be identified in the outline or box of the glucose pattern profile 256 even if they are not identified in the most important pattern statement 278. The pattern tags 280 in the most important pattern section 278 may be color coded to match the color of the outline or box or partial box or bracket highlighting those sections of the glucose concentration profile 256 in the glucose pattern section 282. For example, the tag 280 identifying the evening "low" may be colored red (e.g., white text on a red background), the box or partial box surrounding the evening period of the glucose concentration profile 256 may have a red line color, and the color coded red tag identifying the low may be placed at the top of the box.The time periods in which the most significant patterns occur may be listed next to the tags 280. The time periods may be displayed as text according to the order of the glucose concentration profile 256, e.g., "night", "morning", "afternoon" and "evening" from left to right. Alternatively, the time periods may be identified with tags (not shown), but in a different color than the pattern tags. For example, the time period tags may be gray. If the pattern occurs in all four time periods of the day, the most significant patterns section 278 may include two tags, "all day" and "night". If the pattern occurs in all time periods of the day except "night", the most significant patterns section 278 may include a single tag that reads "all day" or may include three tags, "morning", "afternoon" and "evening". If the pattern occurs in multiple time periods, a single box or partial boxes may outline adjacent time periods with a single label (see, e.g., FIGS. 6C-6E).

[0121] Dosing guidance 260 may also be provided in the glucose pattern report 250 if the patient's current therapy (e.g., basal+RA insulin, basal only, basal+SU, etc.) is known. Dosing guidance may be provided in the form of text recommendations. General advice regarding insulin dose titration may be provided based on identified patterns of high and low glucose, highlighted in box 258 in the glucose concentration profile 256. Note that this general advice may have been determined without access to data regarding the actual insulin doses administered. The recommendations may generally follow the rule of first mitigating any low patterns before mitigating any high patterns. If the glucose pattern report includes suggestions regarding insulin dose titration, the glucose pattern report 250 may also include a suggestion that the patient is a suitable candidate for DGS 100, facilitating a conversation between the HCP and the patient prior to transitioning into the learning period.

[0122] The medication guidance section 260 can include different statements and / or findings regarding medications administered during the time period 264. The medication guidance section 260 can include a question asking if medications contributed to low values. Alternatively, the medication guidance section 260 can include a statement that medications added to address high values ​​may worsen low values. Alternatively, the medication guidance section 260 can include a statement that if the patient is starting or adjusting medication to address high values, consider how the medication may induce low values. The medication guidance section 260 can also include a statement advising the clinician and / or patient to consider a different therapy to address glucose fluctuations. The medication guidance section 260 can also indicate to consider insulin adjustments for T1 patients. For T2 patients, the medication guidance section can include a statement advising to consider medication adjustments for T2 patients currently taking insulin or sulfonylureas, or to consider medication adjustments or initiation of medications other than insulin or sulfonylureas for other T2 patients. The medication guidance section 260 may also indicate consideration for initiating insulin for other T2 patients.

[0123] The medication guidance section 260 may include one or more statements regarding, but not limited to, the following topics: - Could medications be contributing to low values? - Medications added to treat high levels may make low levels worse.

[0124] -When initiating or adjusting medications to address high levels, consider how the medication may induce low levels.

[0125] - Consider different therapies to address glucose fluctuations.

[0126] -For T1 patients, consider insulin adjustment.

[0127] -For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication.

[0128] -For other T2 patients, consider initiating insulin.

[0129] -For other T2 patients, consider adjusting medication or initiating a medication other than insulin or a sulfonylurea.

[0130] The lifestyle guidance section 284 may include a variability statement 286, an excursion statement 288, and self-care guidance 262. If variability is determined to be low for a period of time, no statement regarding variability may be included. In some embodiments, if no pattern is detected for any of the periods of time, the variability statement 286 may not be displayed. In some embodiments, the variability statement 286 may be displayed if variability is determined to be high and a pattern is determined to exist. If variability is determined to be high for a period of time, the glucose pattern report 250 may include a variability statement 286. The variability statement 286 may indicate that low values ​​are often associated with high glucose variability. The variability statement 286 may alternatively or additionally indicate that high values ​​are often associated with high glucose variability. The variability statement 286 may also indicate that certain behaviors may contribute to high glucose variability, in which case it may include a list of the specific behaviors. The variability statement 286 may also indicate that certain behaviors may contribute to glucose variability, in which case it may include a list of the specific behaviors that contribute to glucose variability.

[0131] The variation statements 286 may include, but are not limited to, one or more statements regarding the following topics: - Low values ​​are often associated with high glucose variability.

[0132] -The following actions may contribute to glucose fluctuations:

[0133] - The following actions may contribute to high glucose fluctuations:

[0134] - High levels are often associated with high glucose variability.

[0135] Determining variation is described elsewhere in this application. Alternative determinations for determining variation are described in WO 2014 / 145335 and WO 2014 / 106263, both of which are expressly incorporated herein by reference in their entirety for all purposes.

[0136] As seen in FIG. 6A-3, an excursion statement 288 may be included in the glucose pattern report 250 when an excursion is detected. An excursion may be an instance where a glucose level below a very low threshold is detected. The very low threshold may be about 50 mg / dL to about 65 mg / dL, alternatively about 50 mg / dL to about 60 mg / dL, alternatively about 53 mg / dL, alternatively about 54 mg / dL, alternatively about 55 mg / dL, or as described elsewhere in this application. If an excursion is detected, the excursion statement 288 may suggest that the clinician discuss with the patient the occasional occurrence of hypoglycemia below the very low threshold and may refer to a Weekly Summary Report or Daily View Report that may list excursions below the very low threshold (e.g., below about 54 mg / dL). Alternatively, the excursion statement 288 may indicate occasional occurrence of hypoglycemia below a very low threshold 290 and may refer to a weekly summary report or a daily view report (e.g., "occasional occurrence of hypoglycemia below 54 mg / dL. See daily view report"). If one or more excursions below the very low threshold are detected, the excursion statement 288 may be included in the glucose pattern report 250. In some embodiments, even if one or more excursions below the very low threshold are detected, an excursion statement may not be included in the glucose pattern report 250 if a low pattern is detected. In some embodiments, the excursion statement 288 may be displayed for a "high" pattern, a "high with some lows" pattern, or if no pattern is detected.

[0137] Self-care guidance 262 may also be included in the glucose pattern report 250. If the GPA algorithm identifies a pattern of high variability, the self-care guidance 262 may be displayed in the glucose pattern report 250. Alternatively, the glucose concentration profile 256 may have such high variability that the logic behind the report cannot make any specific recommendations, instead defaulting to the user consulting an HCP about lifestyle or treatment changes.

[0138] The self-care guidance 262 displayed in the glucose pattern report 250 may depend on the type of pattern detected, the amount of variation, the median glucose value, whether there is a risk of hypoglycemia, and whether there is an excursion. The self-care guidance 262 may include one or more statements on the following topics: - Do you miss meals sometimes or have inconsistent carbohydrate intake? -Does your activity level vary from day to day? - Does your alcohol intake vary from day to day? -Do you sometimes forget to take your medication? - Are your meals or snacks frequently high in carbohydrates? - Are your meals or snacks sometimes high in carbohydrates? The glucose pattern section 282 may include a glucose concentration profile 256 that displays glucose data over a 24-hour period. The glucose concentration profile 256 may be an ambulatory glucose profile (AGP). Alternatively, the glucose pattern profile may display various data points as points or dots on a graph. These points or dots may or may not be connected by lines showing the analyte curves for each day. An exemplary glucose concentration profile 256 is shown in Figures 6B-1-6G-2. The glucose concentration profile 256 may be a graph of the glucose data for the time period 264 of the report 250, and the various data points on the graph may be color coded (not shown) to correspond to which concentration range the glucose analyte levels fall within. This color coding may correspond to the color coding of the TIR display 252. Boxes or partial boxes 295 surrounding different portions of the glucose concentration profile 256 highlight detected patterns (e.g., "highs," "lows," and "highs with some lows"). Various thresholds and boundaries for different concentration ranges may be highlighted in the glucose concentration profile 256, including labeling or other highlighting (e.g., text labels and / or horizontal lines) of the very low threshold 290, the low threshold 291, the high threshold 292, and the very high threshold 293. The target or goal range between the low threshold 291 and the high threshold 292 may also be labeled in the glucose concentration profile 256, for example, shaded or highlighted in a different color (e.g., green). The median glucose level at each time may be highlighted with a solid line 294 and labeled as the median or 50%. The median glucose line 294 may change color depending on the median value of that portion of the line.For example, the glucose median line 294 may be colored dark maroon or dark red for medians below a very low threshold, red for medians that fall between a very low and low threshold, green for medians that fall between a low and high threshold, yellow for medians that fall between a high and very high threshold, and orange for medians that exceed a very high threshold. The four time periods may be night 296 (e.g., from about 12:00 AM to about 8:00 AM), morning 297 (e.g., from about 8:00 AM to about 12:00 PM), afternoon 298 (e.g., from about 12:00 PM to about 6:00 PM), and evening 299 (e.g., from about 6:00 PM to about 12:00 AM).

[0139] For each time slot, the algorithm may determine that one of three possible patterns is present or that no pattern is present for that time slot. Thus, each of the four time slots may be determined to be assigned the following patterns: (1) low values ​​281, (2) high values ​​with some low values ​​283, (3) high values ​​285, or (4) no adverse pattern detected. Each identified pattern is labeled with a colored tag over the appropriate section of the graph, and the time slot is outlined with a box or partial box, which may also be color coded. The "low" 281 pattern may be displayed in red (see, e.g., Figures 6B-1-6B-2). The "high values ​​with some low values" 283 may be displayed as amber or maroon (see, e.g., Figures 6E-1-6E-2). The "high values" 285 may be displayed in orange (see, e.g., Figures 6C-1-6C-2 and 6D-1-6D-2). As seen in Figures 6B-1-6B-2, if multiple patterns are detected, multiple boxes or multiple partial boxes 295 may display all of the patterns in the relevant section on the glucose concentration profile 256. In some embodiments, the "low" pattern 281 may be prioritized and highlighted in a different color, e.g., red, compared to the boxes and labels of the other patterns. As seen in Figures 6C-1-6C-2, if the same pattern occurs in two or more adjacent time periods, a single box or partial box 295 may outline all of the relevant time periods with a single label on the box. As seen in Figures 6D-1-6D-2 and 6E-1-6E-2, if the same pattern occurs in all time periods of the day (night, morning, afternoon and evening), a single box or partial box 295 may outline all of the time periods with a single label on the box 295.

[0140] In some embodiments, the pattern may be determined according to a GPA algorithm discussed elsewhere herein. Multiple variables may be used to determine the output of the GPA algorithm and the content of the glucose pattern report 250. The variables include, but are not limited to, a preferred pattern, additional patterns in combination with the preferred pattern, variability (high or low), median glucose (e.g., above or below 180 mg / dL), moderate hypoglycemia risk (yes or no), and excursion below 54 mg / dL (yes or no). The layout and text associated with each element may vary depending on the output of the GPA algorithm.

[0141] The specific guidance text displayed in any given report may be based on a combination of the three patterns described herein, a "hidden" pattern defined as a "moderate hypoglycemia risk" pattern determined according to the GPA algorithm described elsewhere herein, glucose variability, and hyperglycemia defined by an overall glucose median compared to a 180 mg / dL threshold. In some embodiments, the patterns and indicators described herein may be replaced with similar patterns and indicators. In some embodiments, the low pattern may be replaced by a calculation that the number of low events (e.g., glucose less than 70 mg / dL) exceeds a threshold. For example, the threshold may require that the number of low events is at least four low events in a 14-day period. In some embodiments, the variability indicator may be replaced with any other common glucose variability indicator. In some embodiments, the overall hyperglycemia indicator may be defined as the mean glucose value instead of the median. In some embodiments, the "highs with some lows" pattern may be replaced with a calculation of about 2-3 low events and about 4 or more high events (where a high event may be defined as glucose >180 mg / dL) occurring in a 14-day time period. In this way, guidance may be driven by comparable patterns and indicators.

[0142] Table 1 below outlines an exemplary mapping of guidance or look-up tables that may be provided in a glucose pattern report. Inputs include: (1) whether there is a large variation; (2) a determination of low, high with some lows, high, or no pattern; (3) whether there is a risk of hypoglycemia; and (4) the median glucose (G med ) is greater than 180 mg / dL. Table 1 can define the output of guidance text based on the inputs. The outputs can include (1) medication guidance, (2) lifestyle statements and guidance, (3) low excursions, and (4) identification of top-priority patterns. In other embodiments, the lookup table may have fewer inputs. In some embodiments, the table may exclude, for example, overall hyperglycemia and corresponding guidance, or additional inputs. The various scenarios outlined in Table 1 are described in more detail below.

[0143] [Table 1-1]

[0144] [Table 1-2]

[0145] [Table 1-3]

[0146] "Any" = at least one of the four TOD periods "None" = 0 out of 4 TOD periods G med = Median glucose concentration Medication Guidance Statement A1: For T1 patients, consider adjusting insulin.

[0147] Statement A2: For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication.

[0148] Statement A3: For other T2 patients, adjust medication or consider initiating a medication other than insulin or a sulfonylurea.

[0149] Statement A4: For other T2 patients, consider initiating a new medication, such as insulin.

[0150] Statement A5: When initiating or adjusting medications to address high levels, consider how the medication may induce low levels.

[0151] Statement A6: Consider different therapies to address glucose variability.

[0152] Statement A7: Do medications contribute to low values? Statement A8: Medications added to address high levels may worsen low levels.

[0153] Lifestyle Statements and Guidance Statement B1: The following actions may contribute to high glucose excursions:

[0154] Statement B2: The following actions may contribute to glucose excursions:

[0155] Statement B3: Low values ​​are often associated with high glucose variability.

[0156] Statement B4: High values ​​are often associated with high glucose variability.

[0157] Statement B5: Are your meals or snacks frequently high in carbohydrates? Statement B6: Are your meals or snacks sometimes high in carbohydrates? Statement B7: Do you sometimes forget to take your medication? Statement B8: Do you sometimes miss meals or have carbohydrate variability? Statement B9: Does your activity level vary from day to day? Statement B10: Does your alcohol intake vary from day to day? As seen in FIGS. 6A-1-6A-4, the layout of the glucose pattern report 250 can have, for example, a time period 264 and a time the CGM is active 266, which may be displayed at the top of the report, for example, under the title of the report. In some embodiments, the average number of scans / views per day may also be displayed at the top of the report. The time period 264, the time the CGM is active 266, and the average number of scans / views per day 268 may be listed adjacent to each other, separated by a line, or outlined by a box. The glucose indicator section 270 and the TIR section 272 may be displayed side-by-side below the listing of the time period 264, the time the CGM is active 266, and the average number of scans / views per day 268. The glucose indicator section 270 may display an average glucose value 271a along with a target value 271b, and may display a GMI 273a along with a target value 273b. In some embodiments, the average glucose value with the target value may be displayed above the display of the GMI with the target value. The TIR display section 272 may be displayed on the left and the glucose indicator section 270 may be displayed on the right. Alternatively, the TIR display section 272 may be displayed on the right and the glucose indicator section 270 may be displayed on the left. The glucose pattern report 250 may include a guidance for clinicians section 276 below the glucose indicator section 270 and the TIR display section 272. The guidance for clinicians section 276 may include an identification of a top pattern 278 at the top of the section 276, below which the medication guidance 260 and the lifestyle guidance 284 may be listed side by side. Within the lifestyle guidance section 284, the variability statement 286 may be listed first, the self-care guidance 262 may be listed second, and the excursion statement 288 may be listed last, if all three statements are reported.Alternatively, each of the three statements 286, 262, and 288 may be listed in a different order, for example, the self-care guidance 262 may be listed first, middle, or last, the variation statement 286 may be listed first, middle, or last, and the excursion statement 288 may be listed first, middle, or last. The glucose pattern section 282, which may include the glucose concentration profile 256, may be displayed below the guidance for the clinician 276. Thus, in one embodiment, the glucose indicator section 270 and the TIR display section 272 may be displayed in the top third of the report 250, the guidance for the clinician 276 may be displayed in the middle third of the report 250, and the glucose pattern section 282 may be displayed in the bottom third of the report. Alternatively, in other embodiments, the glucose indicator section 270 and the TIR display section 272 may be displayed in either the top, middle, or bottom third of the report 250, the guidance for the clinician 276 may be displayed in the top, middle, or bottom third of the report 250, and the glucose pattern section 282 may be displayed in the top, middle, or bottom third of the report.

[0158] In some embodiments, if no patterns are found and no excursions are detected during the time period 264, the guidance for the clinician 276 may include a statement that no adverse glucose patterns were detected. Additionally, the glucose concentration profile may not include boxes highlighting any time periods during the day.

[0159] In some embodiments, if no pattern was found but at least one excursion was detected during the time period 264, the guidance for the clinician 276 may include an overriding pattern statement 278 stating that no adverse glucose patterns were detected. The guidance for the clinician 276 may also include an excursion statement 288 that may suggest that the clinician discuss with the patient the occasional occurrence of hypoglycemia below a very low threshold and may refer the clinician to additional reports, such as a weekly summary report. The glucose concentration profile may not include boxes highlighting any time periods during the day, but may include several data points highlighted in dark red or maroon that are below a very low threshold 290 corresponding to one or more excursions detected.

[0160] In some embodiments, if a "low" pattern 281 is detected during a time period and low variability is detected, the guidance 276 for the clinician may include a most important pattern statement 278 that may highlight the "low" pattern 281 with a red colored tag and may also include a tag for the time period of the day during which the "low" pattern occurred. If the "low" pattern 281 occurs during all times of the day, the most important pattern statement 278 may include two tags, "all day" and "overnight". If the "low" pattern 281 occurs during all times of the day, as seen in Figures 6F-1-6F-2, the glucose concentration profile may include a single box 295, e.g., a red box, that highlights the entire graph with a "low" heading at the top. Alternatively, if the "low" pattern 281 occurs during at least two non-adjacent times periods, the times periods may be highlighted by separate or partial boxes 295, as seen in Figures 6G-1-6G-2.

[0161] In some embodiments, if a "low" pattern 281 is detected for at least one time period and high fluctuations are detected, or if a "low" pattern 281 and other patterns are detected, the guidance for the clinician 276 may include a top priority pattern statement 278 that may highlight the "low" pattern 281 with a tag 280 colored red and may also include an identification of the times of day in which the "low" pattern occurred, which may be colored a different color, such as gray. The medication guidance 260 may include statements for the clinician to consider when determining treatment for the "low" pattern. The statements may include, but are not limited to, "Do medications contribute to lows?" and "Medications added to address highs may make lows worse." The guidance for the clinician 276 may also include a fluctuation statement 286 regarding the detected high fluctuations. The statements may include, but are not limited to, "Lows are often associated with high glucose fluctuations" and "The following behaviors may contribute to glucose fluctuations (which may be followed by a list of behaviors)." The guidance 276 for the clinician may also include self-care guidance 262. The statements 262 regarding self-care may include, but are not limited to, "Do you miss meals or have carbohydrate variability?", "Does your activity level vary from day to day?", and "Does your alcohol intake vary from day to day?". In the glucose concentration profile, time periods with a "low" pattern may be highlighted with a red box and labeled "low" at the top, as seen, for example, in FIGS. 6G-1-6G-2. In some embodiments where adjacent time periods have the same pattern, a single box may outline adjacent time periods with the same pattern (see, for example, FIGS. 6F-1-6F-2). When different types of patterns are detected in time periods, as seen in FIGS. 6G-1-6G-2, all patterns may be identified with a box that outlines the relevant time period of the day and an appropriate heading label that identifies the type of pattern.In some embodiments, if a "low value" pattern 281 is detected along with a "high value" 285 and / or a "high value with some low values" 283 pattern, the "low value" pattern 281 may be highlighted in a different color, e.g., red, compared to other patterns, e.g., gray (see, e.g., Figures 6B-1-6B-2 and 6G-1-6G-2).

[0162] In some embodiments, if a "highs with some lows" pattern 283 and a high excursion are detected, or if a "highs with some lows" pattern 283 and a "high" pattern 285 are detected, the guidance for the clinician 276 may include a most important pattern statement 278 that may highlight the "highs with some lows" pattern 283 with a red colored tag 280 and may also include an identification of the time of day during which the "highs with some lows" pattern 283 occurred. In some embodiments, the "highs with some lows" pattern may be prioritized over the "highs" pattern as it is more important for the clinician to address first. The dosing guidance 260 may include statements for the clinician to consider when determining treatment for the low pattern. The statements may include, but are not limited to, "When initiating or adjusting medication to address highs, consider how the medication may induce lows" and "Consider different therapies to address glucose excursions." The guidance for the clinician 276 may also include an excursion statement 286 regarding the detected high excursion. The statements may include, but are not limited to, "The following behaviors may contribute to high glucose fluctuations (which may be followed by a list of behaviors)." The guidance 276 for the clinician may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Do you sometimes miss your medication?", "Do you sometimes miss meals or have carbohydrate variability?", "Does your activity level vary from day to day?", and "Does your alcohol intake vary from day to day?". In the glucose concentration profile 256, time periods having a "high with some low" pattern 283 may be highlighted with a dark red box or partial box with a "high with some low" heading at the top (see, e.g., Figures 6E-1-6E-2), and time periods having a "high" pattern 285 are highlighted with a box 295, e.g., a gray box, with a "high" heading at the top of the box.

[0163] In some embodiments, if only a "high" pattern 285 is detected, with low variability, median glucose <180 mg / dL, no hypoglycemia risk, and no excursions detected, the guidance 276 for the clinician may include a prime pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. To distinguish the "high" pattern 285 from the "low" pattern 281 and the "high with some low" pattern 283, each of the "high" patterns 285 may be highlighted in orange, the "low" pattern 281 may be highlighted in red, and the "high with some low" pattern 283 may be highlighted in dark red / purple when that particular pattern is detected, reported, and identified as the prime pattern. The dosing guidance 260 may include a statement for the clinician to consider when determining treatment for the "high" pattern 285. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, adjust medication or consider initiating medication other than insulin or sulfonylureas." Because low variability was detected, the glucose pattern report 250 may not include a variability statement 286. The guidance 276 for the clinician may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Are your meals or snacks frequently high in carbohydrates?" In the glucose concentration profile, time periods having a "high" pattern 285 may be highlighted with a box, for example, an orange box. As seen in Figures 6D-1-6D-2, if adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods.

[0164] In some embodiments, if only a "high" pattern 285 is detected, with low variability, median glucose >180 mg / dL, no hypoglycemia risk, and no excursions detected, the guidance for the clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include statements for the clinician to consider when determining treatment for the "high" pattern. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, consider initiating insulin." The guidance for the clinician 276 may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Are your meals or snacks frequently high in carbohydrates?" In the glucose concentration profile, time periods having a "high" pattern 285 may be highlighted with a box, for example an orange box (see, e.g., FIGS. 6C-1-2 and 6D-1-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box or partial box 295 may outline the adjacent time periods (see, e.g., FIGS. 6C-1-2 and 6D-1-2).

[0165] In some embodiments, if only a "high" pattern is detected, with low variability, any median glucose, moderate hypoglycemic risk, and no excursions detected, the guidance for the clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include a statement for the clinician to consider when determining treatment for the "high" pattern 285. The statement may include, but is not limited to, "When initiating or adjusting medication to address highs, consider how the medication may induce lows." The guidance for the clinician 276 may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Are your meals or snacks frequently high in carbohydrates?" In the glucose concentration profile, time periods having a "high" pattern may be highlighted with an orange box (see, e.g., FIGS. 6C-1-6C-2 and 6D-1-6D-2). If adjacent time periods are all determined to have a "high" pattern, a single box may outline the adjacent time periods (see, e.g., FIGS. 6C-1-6C-2 and 6D-1-6D-2).

[0166] In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose <180 mg / dL, no hypoglycemia risk, and no excursions detected, the guidance for the clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include statements for the clinician to consider when determining treatment for the "high" pattern. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, adjust medication or consider starting a medication other than insulin or sulfonylureas." The guidance for the clinician 276 may also include a variability statement 286 regarding the detected high variability. The statements may include, but are not limited to, "High values ​​are often associated with high glucose fluctuations" and "The following behaviors may contribute to high glucose fluctuations (which may be followed by a list of behaviors)." The guidance 276 for the clinician may also include self-care guidance 262. Statements regarding self-care may include, "Do you sometimes miss your medication?" and "Do you sometimes miss meals or have carbohydrate variability?". In the glucose concentration profile, time periods having a "high" pattern 285 may be highlighted with a box 295, for example an orange box (see, e.g., Figures 6C-1-6C-2 and Figures 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1-6C-2 and Figures 6D-1-6D-2).

[0167] In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose >180 mg / dL, no hypoglycemia risk, and no excursions detected, the guidance for the clinician 276 may include an overriding pattern statement 278 identifying the "high" pattern and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The dosing guidance 260 may include statements for the clinician to consider when determining treatment for the "high" pattern 285. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, consider initiating insulin." The guidance for the clinician 276 may also include a variability statement 286 regarding the detected high variability. The statements may include, but are not limited to, "High values ​​are often associated with high glucose variability," and "The following behaviors may contribute to high glucose variability (which may be followed by a list of behaviors)." The guidance 276 for the clinician may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Do you sometimes miss taking your medication?" and "Do your meals or snacks sometimes contain a lot of carbohydrates?". In the glucose concentration profile 256, time periods having a "high" pattern may be highlighted with an orange box 295 (see, e.g., FIGS. 6C-1-6C-2 and 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., FIGS. 6C-1-6C-2 and 6D-1-6D-2).

[0168] In some embodiments, if only a "high" pattern 285 is detected, with high variability, any median glucose, no hypoglycemic risk, and no excursions detected, the guidance for the clinician 276 may include an overriding pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include a statement for the clinician to consider when determining treatment for the "high" pattern. This statement may include, but is not limited to, "When initiating or adjusting medication to address high values, consider the possibility that the medication may induce low values." The guidance for the clinician 276 may also include a variability statement 286 regarding the detected high variability. The statement may include, but is not limited to, "High values ​​are often associated with high glucose variability" and "The following behaviors may contribute to high glucose variability (which may be followed by a list of behaviors)." The guidance for the clinician 276 may also include self-care guidance 262. Statements regarding self-care may include, "Do you sometimes forget to take your medication?" and "Do your meals or snacks sometimes contain a lot of carbohydrates?" In the glucose concentration profile 256, time periods having a "high" pattern 285 may be highlighted with a box, for example an orange box (see, e.g., FIGS. 6C-1-2 and 6D-1-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., FIGS. 6C-1-2 and 6D-1-2).

[0169] In some embodiments, if only a "high" pattern 285 is detected, with low variability, median glucose <180, no hypoglycemic risk, and low excursions, the guidance for clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include statements for the clinician to consider when determining treatment for the "high" pattern 285. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, adjust medication or consider starting a medication other than insulin or sulfonylureas." Because low variability was detected, the glucose pattern report 250 may not include the variability statement 286. The guidance for clinician 276 may also include self-care guidance 262. The self-care statements may include, but are not limited to, "Are your meals or snacks occasionally high in carbohydrates?" The guidance for clinicians 276 may also include excursion statements 288. The statements may include, but are not limited to, "Occasional hypoglycemia below 54 mg / dL. See weekly summary report." In the glucose concentration profile 256, time periods having a "high" pattern may be highlighted with an orange box, for example an orange box (see, e.g., Figures 6C-1-6C-2 and 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1-6C-2 and 6D-1-6D-2).

[0170] In some embodiments, if only a "high" pattern 285 is detected, with low variability, median glucose >180, no hypoglycemic risk, and low excursions, the guidance for clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The dosing guidance 260 may include a statement for the clinician to consider when determining treatment for the "high" pattern 285. The statement may include, but is not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, consider initiating insulin." Because low variability was detected, the glucose pattern report 250 may not include the variability statement 286. The guidance for clinician 276 may also include self-care guidance 262. A statement regarding self-care may include, "Are your meals or snacks frequently high in carbohydrates?". The guidance for clinicians 276 may also include an excursion statement 288. This statement may include, but is not limited to, "Occasional hypoglycemia below 54 mg / dL. See weekly summary report." In the glucose concentration profile 256, time periods having a "high" pattern 285 may be highlighted with a box, e.g., an orange box (see, e.g., Figures 6C-1-6C-2 and 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1-6C-2 and 6D-1-6D-2).

[0171] In some embodiments, if only a "high" pattern 285 is detected, with low variability, any median glucose, moderate hypoglycemic risk, and low excursions are detected, the guidance for clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include a statement for the clinician to consider when determining treatment for the "high" pattern 285. The statement may include, but is not limited to, "When initiating or adjusting medication to address highs, consider how the medication may induce lows." Because low variability was detected, the glucose pattern report 250 may not include the variability statement 286. The guidance for clinician 276 may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Are your meals or snacks frequently high in carbohydrates?" The guidance for the clinician 276 may also include an excursion statement 288, which may include, but is not limited to, "Occasional hypoglycemia below 54 mg / dL. See weekly summary report." In the glucose concentration profile 256, time periods having a "high" pattern 285 may be highlighted with a box, for example an orange box (see, e.g., FIGS. 6C-1-6C-2 and 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., FIGS. 6C-1-6C-2 and 6D-1-6D-2).

[0172] In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose <180, no hypoglycemic risk, and low excursions, the guidance for the clinician 276 may include a top priority pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time period may be identified in text form. The medication guidance 260 may include statements for the clinician to consider when determining treatment for the "high" pattern 285. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, adjust medication or consider starting a medication other than insulin or sulfonylureas." The guidance for the clinician 276 may also include a variability statement 286 regarding the detected high variability. The statements may include, but are not limited to, "High values ​​are often associated with high glucose excursions" and "The following behaviors may contribute to high glucose excursions (which may be followed by a list of behaviors)." The guidance for clinicians 276 may also include self-care guidance 262. The self-care statements may include, but are not limited to, "Do you sometimes miss medications?" and "Do your meals or snacks sometimes contain high carbohydrates?" The guidance for clinicians 276 may also include excursion statements 288. The statements may include, but are not limited to, "Occasional hypoglycemia below 54 mg / dL. See weekly summary report." In the glucose concentration profile 256, time periods having a "high" pattern 285 may be highlighted with a box, for example an orange box (see, for example, Figures 6C-1-6C-2 and 6D-1-6D-2).If adjacent time periods are determined to all have a "high" pattern 285, then a single box 295 may outline the adjacent time periods (see, for example, Figures 6C-1-6C-2 and 6D-1-6D-2).

[0173] In some embodiments, if only a "high" pattern 285 is detected, with high variability, median glucose >180, no hypoglycemia risk, and low excursions, the guidance for the clinician 276 may include an overriding pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange and the time period may be identified in text form. The dosing guidance 260 may include statements for the clinician to consider when determining treatment for the "high" pattern 285. The statements may include, but are not limited to, "For T1 patients, consider adjusting insulin," "For T2 patients currently taking insulin or sulfonylureas, consider adjusting medication," and "For other T2 patients, consider initiating insulin." The guidance for the clinician 276 may also include a variability statement 286 regarding the detected high variability. The statements may include, but are not limited to, "High values ​​are often associated with high glucose variability," and "The following behaviors may contribute to high glucose variability (which may be followed by a list of behaviors)." The guidance for clinicians 276 may also include self-care guidance 262. Statements regarding self-care may include, but are not limited to, "Do you occasionally miss medication?" and "Do your meals or snacks sometimes contain a lot of carbohydrates?". The guidance for clinicians 276 may also include excursion statements 288. These statements may include, but are not limited to, "Occasional hypoglycemia below 54 mg / dL. See weekly summary report." In the glucose concentration profile 256, time periods having a "high" pattern 285 may be highlighted with an orange box (see, e.g., Figures 6C-1-6C-2 and Figures 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1-6C-2 and Figures 6D-1-6D-2).

[0174] In some embodiments, if only a "high" pattern 285 is detected, with high variability, any median glucose, moderate hypoglycemic risk, and low excursions detected, the guidance for the clinician 276 may include an overriding pattern statement 278 identifying the "high" pattern 285 and the time period in which the pattern was detected. The "high" pattern tag 280 may be highlighted in orange, and the time period may be identified in text form. The medication guidance 260 may include a statement for the clinician to consider when determining treatment for the "high" pattern 285. The statement may include, but is not limited to, "When initiating or adjusting medication to address high values, consider how the medication may induce low values." The guidance for the clinician 276 may also include a variability statement 286 regarding the detected high variability. The statement may include, but is not limited to, "High values ​​are often associated with high glucose variability" and "The following behaviors may contribute to high glucose variability (which may be followed by a list of behaviors)." The guidance for the clinician 276 may also include self-care guidance 262. Self-care statements may include, but are not limited to, "Do you occasionally miss taking your medication?" and "Do your meals or snacks occasionally contain high carbohydrates?". The guidance for clinicians 276 may also include excursion statements 288. These statements may include, but are not limited to, "Occasional hypoglycemia below 54 mg / dL. See weekly summary report." In the glucose concentration profile 256, time periods having a "high" pattern 285 may be highlighted with an orange box (see, e.g., Figures 6C-1-6C-2 and Figures 6D-1-6D-2). If adjacent time periods are determined to all have a "high" pattern 285, a single box 295 may outline the adjacent time periods (see, e.g., Figures 6C-1-6C-2 and Figures 6D-1-6D-2).

[0175] As seen in FIG. 6B-1-FIG. 6B-2, if multiple types of patterns are detected, all patterns may be identified in the glucose concentration profile 256. In some embodiments, the glucose concentration profile 256 may include up to three patterns per patient. The boxes or partial boxes or brackets 295 that outline the various patterns may be prioritized. The priority from first to last may be "low values", "high values ​​with some low values" and "high values". The various patterns may be color coded based on the priority. In some embodiments, the highest priority pattern among the detected patterns may be the only pattern highlighted in color. The remaining lower priority patterns may be colored a different color, for example, gray or black. For example, if at least one of each of the "low" pattern 281, the "high with some low" pattern 283, and the "high" pattern 285 are detected, the "low" pattern 281 may be displayed in red (in partial box 295 and label format), and the "high with some low" pattern 283 and the "high" pattern 285 may be displayed in gray on the glucose concentration profile 256. In other embodiments, if at least one of each of the "high with some low" 283 and "high" 285 patterns are detected, the "high with some low" 283 may be displayed in dark red or maroon (in partial box 295 and label format), and the "high" pattern 285 may be displayed in gray on the glucose concentration profile 256.

[0176] If a patient is found to have met all of the TIR targets for a period, but a "low" pattern is detected for at least one period, the report may still identify the "low" pattern in all of the appropriate sections, including a box or partial box or bracket 295 that generally indicates the "low" pattern on the glucose concentration profile 256, and an overriding pattern statement 278. In an alternative embodiment, if a patient is found to have met all of the TIR targets for a period, the report may not identify or highlight the pattern.

[0177] In embodiments where only a single pattern is detected, the single pattern may be displayed in color on the glucose concentration profile 256. For example, a "low" pattern 281 may be displayed in red (in partial box 295 and label format), a "high with some low" pattern 283 may be displayed in dark red or maroon (in partial box 295 and label format), and a "high" pattern 285 may be displayed in orange (in partial box 295 and label format).

[0178] How to learn Manual configuration of the DGS100 requires time from the HCP, which may not be available. In addition, even if the HCP has time, the configuration may be complex and error-prone. To mitigate these issues, the DGA can include a Patient Parameter Initialization (PI) module that requires no or minimal configuration. The PI module learns the patient's dosing strategy, which may include, for example, basal only, basal +1, basal +2, etc., and parameterizes the patient's dosing habits to configure the dose guidance settings by the DGA.

[0179] According to one aspect of an embodiment, the learning process of the PI module can include automatically configuring the patient's dose guidance settings from the observed data. Once the settings are successfully learned, the DGS 100 can enter a guidance mode in which the patient can request dose guidance and receive dosing notifications. During the learning process that precedes the guidance mode, the DGA can process glucose and insulin data collected by the patient's SCD 102, UID 202, and / or other devices and determine dosing information based on the processed data.

[0180] The dosing information can include, for example, dosing regimen, meal-dose type, dose parameters, and dose range. Dosing regimen can include, for example, basal dose+BF, basal dose+LU, basal dose+DI, basal dose+BF / LU, basal dose+BF / DI, basal dose+LU / DI, and basal dose+3, where BF indicates "breakfast", LU indicates "lunch", and DI indicates "dinner". Additional regimens can also be included, for example, afternoon snack doses. Meal-dose type can include, for example, fixed meal dose or variable meal dose. Dosage parameters can include, for example, nominal fixed dose or carbohydrate ratio for each meal, pre-meal correction factor (CF) and post-meal CF. Dose range can include an estimate of the lowest meal dose. CF is also known as insulin sensitivity factor. It is a ratio that reports how much 1 U of insulin lowers blood glucose in either fasting or pre-meal state. DGA can have two CF values ​​that take into account fasting and preprandial physiology, i.e., differences in insulin sensitivity between pre- and post-prandial. The units of CF are (mg / dL) / insulin unit.

[0181] For each of the above dosing information types, the DGA can determine whether the cumulative data is sufficient or insufficient to determine the dosing information. In some embodiments, the patient's SCD 102 can be configured to operate for a predetermined period of time, e.g., 14 days. In these embodiments, the DGA can determine after the predetermined period of time (or earlier if the sensor stops operating before the end of the period) whether the available analyte and dosing data is sufficient to determine each of the above dosing information. If so, the DGA can execute the parameterization method 300 and initiate the dose guidance mode. In alternative embodiments, periodically during the learning period (e.g., once a day), the DGA can determine whether the data is sufficient to determine each of the above dosing information. In either case, if the collected data is sufficient, the DGA can terminate the learning period, execute parameterization, and initiate the guidance period. If not, the DGA can continue the learning process.

[0182] With reference to FIG. 7A, the DGA can be configured to execute the method 300 on a suitable computing device, for example, the UID 200, the SCD 102, the MDD 152, alone or in any combination. Program instructions for executing the method 300 can be grouped in a PI module or any other suitable code configuration. In general, the method 300 can include classifying, by the DGA, at step 302, each of the doses of the drug received by the patient during the analysis period based on data characterizing the patient's analytes and the doses of the drug received by the patient during the analysis period. The method 300 can further include grouping, at step 304, each of the doses into one of a set of meal time groups. The method can further include generating, at least in part, a dose parameter for the patient by applying the data for each of the meal time groups to a model, at step 306. The method can include storing the dose parameter in a computer memory and configuring a dose guidance setting, 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 for output to an interface device, such as the UID 200 or a medical practitioner's terminal. More detailed aspects of each operation in method 300 are described below. As used herein, a "PI module" refers to a portion or part of the DGA that performs the operations of method 300 and any ancillary operations. The PI module is not limited to a particular configuration and can encompass various arrangements of computer code.

[0183] In one embodiment, the classification operation 302 can include classifying each dose of a medication (e.g., insulin) as either a meal dose, a correction dose, and / or an ambiguous dose. If the DGA cannot classify a medication dose as a meal dose or a correction dose with a defined confidence, the DGA can classify the dose as an ambiguous dose and omit the medication dose from use in generating dose parameters for dose guidance.

[0184] DGA can classify drug doses through a sequence of two operations, referred to herein as feature extraction and classification. With reference to FIG. 7A, 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 configure a vector of insulin injection timestamps, a data file containing analyte measurements from the patient's SCD 102, and results from a meal detection algorithm module, discussed elsewhere herein, as inputs to a function that outputs a feature matrix for insulin dose classification. The number of rows in the feature matrix can indicate the amount of injections, or equivalent drug administration events, during the associated analysis period. Each row in the feature matrix can be or can include a feature vector for a single medication event. In an embodiment 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 from a time range relative to the insulin injection time, e.g., from -2.5 hours to 1.5 hours.

[0185] In an embodiment, the classification features may include the time of administration of each administration, for example the time period during which the insulin injection was recorded by the MDD 152 or by the patient using the UID 200.

[0186] The classification features may further include time filtered analyte values, e.g., 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 embodiment, the Savitsky-Golay filter may be of order 2, frame length 7 with a sampling interval of 15 minutes.

[0187] Classification features can further include the rate of change of the analyte value closest to the time of dosing, e.g., the rate of change of the analyte (e.g., glucose) value calculated by linear regression of five analyte data points (e.g., using a 15 minute sampling interval) centered on the data point closest to the time of dosing (e.g., injection).

[0188] The classification features may further include a left area under the curve (AUC) index that indicates the difference between the analyte value and the analyte value closest to the time of dosing, integrated over the interval prior to the time of dosing. For example, to obtain the left AUC index, the DGA may collect all data points from the filtered analyte data within a time window (e.g., 2.5 hours), count back from the time of injection, and then calculate the left AUC index by calculating the difference between the average analyte value of the collected data points and the data point closest to the time of injection (i.e., the reference data point), and multiplying this difference by the duration of the time window to calculate the left AUC increment.

[0189] The classification feature may further include a right-side AUC index, which indicates the difference between the analyte value and the analyte value closest to the time of dosing, integrated over an interval after the time of dosing. For example, the DGA may calculate the right-side AUC index by collecting all data points from the filtered analyte data within a time window (e.g., 1.5 hours), counting back from the time of injection, and then calculating the difference between the average analyte value of the collected data points and the data point closest to the time of injection (the reference data point), and multiplying this difference by the duration of the time window to calculate the increment of the right-side AUC.

[0190] The classification features may further include the elapsed time between dosing times. For example, the DGA may calculate for each injection time the elapsed time between the last injection time and the current injection time by subtracting the last injection time from the current injection time. For the first injection time of the insulin log, there is no last injection time available, so the DGA may calculate the elapsed time from the first SCG time data point to the current injection time. In addition, as a further example, the DGA may 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 of the insulin log, there is no next injection time available, so the DGA may calculate the elapsed time from the current injection time to the last SCG time data point. In both the backward and forward calculations, if the elapsed time is greater than a predefined maximum value (e.g., 12 hours), the DGA may set the elapsed time value equal to the maximum time.

[0191] The classification features may further include the probability that a meal begins within a defined interval before the dosing time, e.g., the maximum probability of meal initiation within a time window (e.g., 1.5 hours) before the injection, which may be calculated by a meal detection module described elsewhere herein.

[0192] The classification features may further include the most probable interval of time since the most recent meal, for example, the time since the point of maximum probability of meal onset relative to the time of injection (eg, as determined by a meal detection module).

[0193] The classification features may further include the probability that a meal will be initiated within a defined interval after the time of administration, for example, the maximum probability of a meal initiation within 2 hours after injection (as determined by a meal detection module).

[0194] The classification features may further include the most probable interval until the next meal, for example, the predicted elapsed time from the injection time to the point of maximum probability of meal onset after meal injection (eg, as determined by a meal detection module).

[0195] As mentioned above, the step of calculating a portion of the classification features includes estimating the time of each meal taken by the patient during the analysis period, and the method of estimating the meal times is described in more detail below. Briefly, the step of estimating the time of each meal can further include creating a feature matrix based on the time-correlated analyte data by DGA, the feature matrix correlating a set of analyte (e.g., glucose) data features to each of the distinct regions classified as up, before, and down. The set of analyte data features can be or include maximum analyte rate of change, maximum analyte acceleration, analyte value at the point of maximum analyte acceleration, duration of the region, height of the region, maximum deceleration, average rate of change within the region, and time of maximum analyte acceleration. The estimating step can further include creating an estimated meal time based on the feature matrix using an algorithm described below.

[0196] More detailed aspects of retrospective meal time detection algorithms for use in method 300 or elsewhere are described in the following paragraphs, followed by a description of other aspects of method 300. The DGA can perform retrospective meal time detection based on time-correlated analyte data by executing one or more code modules, e.g., a feature extraction module and a meal detection module. The feature extraction module, when executed by the DGA, can cause the DGA to receive a glucose time series as input and output a feature matrix that is passed to the retrospective meal detection module to detect glucose excursions in response to meal events.

[0197] DGA can perform feature extraction using the following operation, which can be divided into a sequence of three sub-operations: smoothing, segmentation and extraction.

[0198] In the smoothing sub-operation, the DGA may smooth the analyte (e.g., glucose) time series using a Savitzky-Golay filter (order 2) and calculate the rate of change and acceleration rate at each analyte data point. The frame length parameter of the filter may 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 may calculate the rate of change by taking the average of the difference before and after the smoothed analyte value between the point of interest and a point that is a second interval (e.g., 15 minutes) before and after the point of interest, the second interval being smaller than the first interval, e.g., equal to one-quarter of the first interval. Similarly, the DGA may calculate the acceleration rate by taking the average of the difference before and after the difference of the rate of change of the analyte between the point of interest and a point that is a second interval (e.g., 15 minutes) before and after the point of interest.

[0199] In the segmentation sub-operation, the DGA can segment the smoothed analyte trace into monotonically increasing (i.e., rise) and decreasing (i.e., fall) regions, each of which is considered a candidate glucose excursion in response to a meal event.

[0200] In the extraction sub-operation, the DGA can extract features from the data. For example, 16 features can be extracted that may be or include features from each up-regime (e.g., 8 features), the preceding down-regime (e.g., 4 features), and the following down-regime (e.g., 4 features). Features that the DGA can extract from the up-regime features can include, for example: 1) maximum analyte rate of change, 2) maximum analyte acceleration, 3) analyte value at the point of maximum analyte acceleration (reference point), 4) duration of the up-regime (time elapsed from reference point to the last point of the region), 5) height of the region (difference in smoothed analyte value between the last point and reference point), 6) maximum deceleration (negative acceleration at maximum absolute value), 7) average rate of change within the region (height / duration), and 8) time of data for the reference point. By way of further example, the four features extracted from the preceding and following down-regime regions can include: 1) Height of the declining region, 2) Duration of the declining region, 3) Average rate of change of the region (height / duration), 4) Maximum absolute value of the glucose rate of change. The number of rows in the feature matrix output by the feature extraction module may be equal to the number of rising regions in the smoothed glucose time series.

[0201] According to another aspect of the embodiment, the retrospective meal detection module can receive the feature matrix as input and output a binary detection result for each up-regulation region. Such output can include a binary classification result and a probability value that each up-regulation region is an analyte (e.g., glucose) excursion in response to a meal event. The DGA can assign the probability value of each up-regulation 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 post-prandial glucose excursions based on a number of decision trees that are constructed and optimized during the training process. In an alternative embodiment, the DGA can construct a pre-trained model based on alternative classification algorithms, for example, gradient boosting, Ada boost, artificial neural networks, linear discriminant analysis, and extra trees.

[0202] Referring again to the method 300 of FIG. 7A, the classification operation 302 can receive as input the patient's feature matrix and output a binary classification result for each relevant medication 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, the classification operation 302 can use meal detection results, in which case the meal detection can be performed before the insulin dose classification. As mentioned for retrospective meal time detection, the classification operation 302 can include a pre-trained machine learning model, for example a model implemented using a random forest classifier by scikit learn (see above). The machine learning model implemented by the DGA can perform the classification based on tree construction rules and thresholds for various features in each decision tree that are optimized during the training process. Alternatively, the model can be trained with other machine learning algorithms, including gradient boosting, Ada boost, artificial neural networks, linear discriminant analysis, and extra trees. Once the DGA has successfully classified each dose, it can proceed to determine the medication regimen and administration parameters.

[0203] In step 304, the method 300 can include the DGA grouping each of the doses into one of a set of mealtime groups or clusters. For example, the DGA can determine a dosing strategy by clustering an analysis of the dosing (e.g., injection) times of the meal doses. The DGA can execute a clustering module implemented using a K-means algorithm with the elbow method, which receives the injection times as input and outputs an optimal number of clusters K (up to 3) and a cluster index for each injection time. The optimal number of clusters K can be the number of meal doses taken by the patient in a day. Using the cluster index of each injection, the DGA can classify the meal doses into K groups according to the cluster index.

[0204] The DGA can identify these groups as breakfast, lunch or dinner (B,L,D) as follows: For each group, the DGA can determine a typical time of day (TOD) by calculating the median TOD of the group. Alternatively, the DGA can use other centroid measures. If K=3, the DGA can associate breakfast with the group after the longest period between the group's typical TODs. Then the next group is lunch and the last group is dinner. If K=2, the DGA can infer which group is associated with breakfast, lunch or dinner using assumption rules regarding the time between each meal. For example, if two groups are more than 6 hours apart from each other, the DGA can identify the groups as breakfast and dinner. Else, if the first group occurs before 10:00 a.m., the DGA can identify the groups as breakfast and lunch, otherwise as lunch and dinner. In an alternative embodiment, after the DGA identifies the typical times of meal events, the user can be prompted to identify the meals associated with each typical time. As a further example, in an alternative embodiment, the DGA can combine the two methods described herein by estimating meal associations and then prompting the user for confirmation. A further alternative method can include analysis of glucose data to identify meals and clustering meal times to detect typical meal times. This can be useful for distinguishing meals when K=2, i.e., identifying meals where no dose was taken.

[0205] Once the doses have been grouped into mealtime clusters, in step 306, the DGA can perform a step of generating dose parameters for the patient at least in part by applying the data for each of the mealtime groups to a model. For example, for each meal group (B,L,D), the DGA can pair each set of corresponding pre-prandial glucose levels with a corresponding meal dosage. The DGA can fit each group with an appropriate model, for example, a linear function with zero slope, a linear function with non-zero slope, piecewise linear functions connected at a point, or a nonlinear function that approximates the connected piecewise model but has smooth curvature around the connection points. Other models are also suitable.

[0206] DGA can perform model fitting and parameter estimation by minimizing the sum of squared residuals (SSR) of the model parameters. DGA can then use a search algorithm to find the optimal parameters such that the value of SSR is minimal. For linear models, DGA can perform fitting using the Nelder-Mead simplex method. For nonlinear models, DGA can use the Levenberg-Marquardt algorithm; that is, DGA can use the Nelder-Mead simplex numerical optimization method for linear models and the Levenberg-Marquardt optimization method for nonlinear models. Alternative methods of fitting data to these models are also possible.

[0207] If the number of iterations during optimization exceeds a convergence criterion, the model will not fit and the DGA can eliminate the non-fitting models as candidate models. Additionally, the DGA can apply certain rules to minimize the uncertainty in the parameter estimates, such as by requiring at least three pre-meal glucose data points greater than the estimated threshold glucose to validate the estimated correction factor, or by requiring at least three pre-meal glucose data points less than the estimated threshold glucose to validate the estimated fixed volume, or by requiring that the 95% confidence interval of the parameter intercept exclude zero, or by requiring that the 95% confidence interval of the model slope exclude zero.

[0208] Insufficient data may result in failure to fit the model, which may result in certain models being excluded as candidate models.DGA can evaluate each model with the Akaike Information Criterion (AIC) and select the model with the smallest AIC value as the desired model for each dietary group.

[0209] Once the DGA has selected a model for each mealtime cluster, it can then determine dose parameters, including, for example, fixed dose insulin amounts, target glucose levels, and correction factors, based on the selected model for each mealtime cluster. The DGA can determine the target glucose levels and correction factors as single values ​​for all groups, respectively, as described in more detail in the next paragraph. In an alternative embodiment, the DGA can determine the target glucose levels and correction factors for each group separately and use the separately determined parameters for downstream dose guidance operations.

[0210] According to another aspect of the embodiment, the DGA can form a combined data group to obtain a more accurate correction factor for the patient. For example, after fitting the dose data to various models for each meal group to select the best model and estimate the fixed insulin dose, the DGA can subtract the fixed dose insulin amount from the associated meal dose for each meal group. The remaining non-zero values ​​correspond to doses with correction doses. These non-zero values ​​can then be combined from all three meal groups (B, L, D) to form a combined group. If the fixed dose insulin amount cannot be determined for a group, the DGA can exclude that group's data from the combined group. The system can then repeat the operation to find the best model for the combined group, or use the same model identified when analyzing the groups individually. Using this combined group approach, it is assumed that the patient has the same (or constant) correction factor and target glucose for all meals, and the combined group can provide a more accurate fit with a larger sample size. After the DGA determines the target glucose level and correction factor based on the best fitting model, the dose parameter estimation is completed. The DGA can then store the dose parameters in computer memory and configure the dose guidance settings, step 308.

[0211] In additional embodiments, the DGA can determine whether the patient is potentially carb counting (e.g., varying meal dosage to account for carbohydrate consumption) by comparing the AIC value of the preferred model to a threshold such as 50, or 75, or 100. If the AIC value is greater than the threshold, the DGA can determine that the patient is carb counting and ask the patient for confirmation via UID 200.

[0212] In alternative embodiments, one or more of the above operations may be omitted and replaced by requiring the patient or HCP to manually provide the information, or by extracting the information from another source, such as an EMR or other software program. Nevertheless, method 300 should be useful for a variety of applications that do not have more information than the SCD and MDD can provide.

[0213] Alternative learning methods In an alternative embodiment, the DGS 100 (e.g., SCD 102, display device 120, or MDD 152) can be configured with an automatic or semi-automatic learning method that classifies and characterizes drug doses based on patient input and the patient's GPA analyzed over a learning period. The alternative learning embodiment encompasses the processing of regimen input from the user, recorded glucose measurements (glucose readings and associated date / timestamps) and insulin dosing information (dosages and associated date / timestamps). However, the inputs and algorithms may be optimized to reduce the burden on the patient and HCP. For example, in the embodiments described herein, HCP involvement may not be required to initiate dose guidance. This is advantageous because in many cases, HCPs do not have the time to set up previous systems for their patients. Additionally, this embodiment provides an additional safety mechanism compared to the common practice of having the patient or HCP directly set up the dose guidance system (or, more commonly, called a bolus calculator). Specifically, as connected insulin pens are becoming more common and have recently been used in studies, it is apparent that many MDI patients are not adhering to their medication regimen, such as missing meal doses, administering them late, or taking less insulin than required. The first two are likely due to inconvenience and forgetfulness, while the third may stem from fear of hypoglycemia combined with a lack of confidence in managing hypoglycemia.

[0214] The underlying problem with the common practice is that the dose guidance system may be set based on what the patient or HCP considers necessary based on their glycemic profile. However, that profile may be based on a previous regimen with poor adherence. For example, the HCP may recommend that the patient increase their fixed insulin dose at breakfast to 10-12 to alleviate post-breakfast glucose hypertension, but this hypertension was actually triggered by the patient forgetting 50% of their breakfast dose or underdosing by taking only 8 units in many cases. If the patient suddenly decides to increase their adherence, hypoglycemic problems may result. It is therefore important that the dose guidance setting process includes an analysis of past adherence before presenting the dose guidance to the patient.

[0215] As mentioned above, the patient or HCP may input all or only a portion of the key dose guidance parameters. As an example, it is assumed here that the patient inputs the following portions of the dose guidance parameters, although other examples are contemplated. Specifically, the system provides a means for inputting the following parameters prior to providing dose guidance: typical fixed doses for breakfast, lunch and dinner; and typical times of day during which breakfast (and / or each meal) occurs.

[0216] The above is an example of a compact set of parameters that should be easy enough for most patients to understand and enter correctly. Complex parameters such as correction factors that many patients cannot understand are avoided. Alternatively, the system may allow the patient to enter more parameters, optionally for patients with more sophisticated dosing regimens and / or who understand the details of these regimens well enough to enter the parameters correctly. In another embodiment, the patient can enter information about the patient's level of adherence, such as how often they miss doses during certain times of the day or how often they skip meals and therefore do not need to take insulin. Another embodiment is a system in which the patient is prompted to manually categorize the recorded doses. However, this type of system is likely to require a high degree of interaction with the patient due to the lack of readily available values, which may not be desirable for most patients.

[0217] During the learn mode to analyze the patient's adherence to the user-entered regimen, the system acquires glucose data and insulin dose information over a period of, for example, two weeks. If the system does not begin dose guidance after this period, the system can allow the patient to begin a subsequent observation period.

[0218] Once the learning period is complete, the system processes the entered regimen parameters, observed glucose and insulin dose information using an analytical algorithm executed by the processor. The algorithm may include classifying the doses as previously described. Alternatively, or in addition, the classification may be based on dosing information entered by the user and may take into account absolute and relative times of day between the measured analyte (e.g., glucose) data and the administration data provided by the user. The classification may alternatively, or in addition, be based on dynamic relationships between different data inputs, e.g., rate of change of glucose. The system may perform the classification using a classification model that may be developed and trained using common machine learning techniques applied to clinical and simulated data.

[0219] Once the system processor classifies the doses, it associates each dose with one of a number of meal events during the day. The clustering analysis described above is one way to perform the association. For example, the system can associate one of the clusters whose central measurement (e.g., median, mean) for that time period is closest in time to the value entered by the user for breakfast with breakfast, the next cluster in time with lunch, and the next cluster with dinner.

[0220] An alternative clustering method may include not only dose information but also glucose data in the cluster determination by an algorithm to identify clusters and which doses are associated with which clusters. Furthermore, the processor may further include data indicative of absolute and relative times of day between glucose data and dose data in its cluster analysis. For example, a 10-unit dose in the morning may be associated with the lunch cluster if the 10-unit dose is a typical dose for this cluster and not a dose for the breakfast cluster, and the dose was taken late enough in the morning. The association may also be based on dynamic relationships between different data inputs, such as the rate of change of glucose. The determination is made by a classification model, which may be developed and trained using common machine learning techniques applied to clinical and simulated data.

[0221] Once the clustering process is complete, the system estimates the regimen parameters as described above. The parameter estimation process may include using standard numerical analysis techniques to determine the degree of confidence of the parameter estimates. In the following description, for simplicity, the degree of confidence may be described as a confidence interval, although there are other common measures of confidence that can be used. These parameters are referred to herein as "learned" regimen parameters, which include the parameter values ​​and associated confidence intervals.

[0222] Once a regimen is learned, the system may perform the following checks between the entered and learned parameters: The system processor may check the confidence intervals (CIs) of the learned meal dose parameters and typical administration times by comparing them to a maximum threshold appropriate for each parameter. If the CI exceeds that threshold, the processor may flag the system configuration as suspect. For meal doses, suitable maximum CIs are ±30%, or 20%, or 50%, and for typical meal administration times, suitable maximum CIs are ±1.5 hours, 1 hour, 2 hours.

[0223] In another aspect, the system processor can compare the learned parameters to user-entered parameters, specifically, breakfast, lunch, and dinner dosages. If the entered and learned parameters differ by more than a confidence interval (or some multiple of the confidence interval), the processor can flag the system configuration as suspect.

[0224] The system processor can further compare the input typical breakfast administration time with the learned meal administration times of the temporally closest dose clusters, and if these parameters differ significantly, the processor can flag the confidence interval (or some multiple of the confidence interval) as suspect.

[0225] Furthermore, the system can additionally check whether the learned parameters are plausible. For example, the maximum gap between learned typical meal administration times should occur before the learned typical meal administration times to ensure that nighttime periods are properly taken into account. If the plausibility check fails, the system configuration can be flagged as suspect. Some parameters do not need to be estimated with a high degree of confidence to avoid the system configuration being flagged as suspect. For target glucose (GT), pre-prandial CF, and post-prandial CF, if the CI is within the maximum threshold appropriate for that parameter, the learned value is used for the dose guidance regimen, otherwise a conservative default value is used. Suitable maximum CI thresholds for GT and CF are ±2, 5, 10 mg / dL or ±5, 10, 20 mg / dL / unit, respectively. Suitable default values ​​for GT, pre-prandial CF, and post-prandial CF are 120, 125, 130 mg / dL, or 40, 50 mg / dL / unit, or 60, 70 mg / dL / unit.

[0226] Additionally, the system can evaluate the acquired data for patient compliance with the entered regimen. For example, the system's processor can estimate the frequency of missed meals (or meals with a small impact on postprandial glucose levels). This is useful for calculating subsequent indicators. This estimate can be calculated using a model developed using machine learning or other techniques from glucose and insulin dose data and meal records - the model can be trained using clinical, real-world or simulated data. In general, if during a time period when a meal is expected to be administered, no insulin dose is administered and the blood glucose pattern during this time period does not show a rise in glucose, this is indicative of a missed meal.

[0227] The system processor can estimate the frequency of missed meal doses for each of breakfast, lunch, and dinner. For example, the frequency can be calculated as (number of missed dose periods - number of periods tagged as missed meals) divided by (total number of periods - number of periods tagged as missed meals).

[0228] In another embodiment, the system can estimate for each meal (breakfast, lunch, and dinner) the difference between the median dose and the entered dose, i.e., how much the patient is under- or overdosed compared to the dose entered. Before starting the dose guidance, the system processor can output the dosing adherence findings on a display device or equivalent device. Similarly, the system can report to the patient the estimated impact on glycemic control and A1c due to lack of adherence, or conversely, show the patient the likelihood of improvement in glucose indicators such as average glucose or time in target range, or laboratory values ​​such as A1c, if the patient corrects the adherence issue. This information can be generated by the processor from a model developed by correlating each diabetes control measure with the adherence measures for a particular regimen. A simple model can find correlation parameters that relate adherence measures to glycemic measures based on real or simulated population data, and relate them to A1c measurements. More sophisticated models for relating specific patient characteristics, such as tracked regimens and glycemic profiles, to adherence measurements can also be developed and implemented by the system processor.

[0229] As a summary of the foregoing, and as an additional example, the DGS 100 or components thereof (e.g., one or more of the SCD 102, the display device 120, or the MDD 152) for parameterizing a patient's medication habits to configure dose guidance settings can include an input component configured to receive measured analyte data, meal data, and medication data, a display component configured to visually present the information, and one or more processors coupled to the input, the display, and the memory. The memory can hold instructions and time-correlated data characterizing the patient's analytes over an analysis period, which, when executed by the one or more processors, cause the device to perform the method 1000, as shown in FIG. 7B. In one embodiment, the medication can be or include insulin.

[0230] The method 1000 may include, at 1002, receiving, by one or more processors, patient dosage regimen information for an analysis period. The one or more processors may hold the patient dosage regimen information in a memory for processing. The method 1000 may further include, at 1004, evaluating a measure of consistency between the time correlation data and the patient dosage regimen information. In one aspect, the patient dosage regimen information may be or may include a typical fixed drug dose taken at mealtime and a typical time of day for eating breakfast. As a further example, the patient dosage regimen information may be or may include information defining a frequency of scheduled doses or patient compliance with meals. In some embodiments, the patient dosage regimen may include information regarding the type, amount and / or timing of dosing. In particular, information regarding the dosing schedule of one or more drugs.

[0231] The measure of concordance may include any numerical measure for comparing the concordance between data sets, such as the mean and standard deviation, or the interquartile range (IQR). The assessment of the measure of concordance may include comparison with a fixed or variable threshold. The measure of concordance may also be, for example, a measure of the variability between the time-correlated data and the predicted data calculated based on the patient dose regimen. The variable threshold may be determined using machine learning or other algorithms. More specific examples are provided herein above and below.

[0232] The method 1000 may further include, at 1006, determining dose guidance information based on the measure of concordance. Once determined, the processor may output the dose guidance information to a display or other output device for use by the patient or HCP, or may store in memory for later use. In one embodiment, the dose guidance information may be or may include dose guidance information as exemplified herein above. Dose guidance may also be information that alters the type, amount, or timing of administration of a particular medication.

[0233] Method 1000 may include additional operations 1100, 1200, and / or 1300 shown in Figures 7C-7E. The additional operations may be performed in any operable order by one or more processors of DGS 100, and the presence or absence of one or more operations shown in any figure does not necessarily imply the presence or absence of other corresponding operations shown in that figure. Instructions for performing method 1000 and / or any one or more of the additional operations 1100, 1200, and 1300 may be retained in memory for execution by one or more processors of the DGS.

[0234] 7C, operations 1100 of method 1000 may include outputting dose guidance information to a display or other output device at 1102. The method may further include receiving patient dose regimen information from an input component, for example via input from a touch screen of the display device, at 1104. Alternatively, or additionally, the method may include receiving patient dose regimen information by transmission from a remote data server at 1106.

[0235] The method 1000 may further include an operation 1200 for evaluating the measure of concordance, as shown in FIG. 7D. The method 1000 may include, at 1202, classifying each dose of the patient dosage regimen into a medication class based on the time correlation data. The method may include, at 1204, grouping each dose into a set of meal time groups, e.g., one of breakfast, lunch, and dinner, based on time of day and / or other factors. Alternatively, the method may include grouping each dose by time of day, e.g., hourly analysis. The method may include, at 1206, generating, at least in part, dose parameters for the patient by applying data for each group of meal times to a model. In some embodiments, the model may be based on historical data from a user or from a population survey. The method may further include, at 1208, storing the dose parameters for configuring dose guidance settings.

[0236] The method 1000 may include further operations 1300, as shown in FIG. 7E. The method 1000 may include, at 1302, accumulating time correlation data characterizing the patient's analytes over a period of time, e.g., 10 days, 14 days, or 21 days, prior to evaluating the measure of concordance. The method may include, at 1304, determining dose guidance information, at least in part, by reducing a dosing recommendation based on detecting an analyte excursion in the time correlation data that exceeds a lower threshold. The recommendation should correlate with a meal dose associated with the excursion.

[0237] The method may include, at 1306, determining patient adherence to the patient dosing regimen information based on the time correlation data. The method may include, at 1308, determining whether to output dose guidance parameters based on the measure of concordance. For example, if the measure of concordance indicates sufficient patient adherence, dose guidance information is provided as determined by the DGS. If adherence is marginally consistent, the DGS provides dose guidance but provides a warning regarding adherence. If adherence is inconsistent, the DGS provides information to the patient or HCP on what to correct before providing guidance.

[0238] In one aspect, at 1310, the method may include outputting dose guidance parameters including predefined dose suggestions if the measure of concordance indicates an unreliable system configuration, e.g., unreliable data. The predefined dose suggestions may be retrieved from memory as a fallback if dose guidance is unavailable by the method in use.

[0239] Involvement of HCPs during the learning period If the system configuration is tagged as suspicious, dose guidance may not start automatically. The DGS100 can offer the patient two options: (a) address the deficiencies noted in the learning / adherence report and repeat the learning period, or (b) review the learning period results / output at the next HCP visit. For the second option, the HCP can configure the system to start dose guidance and / or advise the patient on how to address the deficiencies noted in the report and repeat the learning period.

[0240] Many HCPs do not have the time to obtain a web portal account and configure the dose guidance system for their patients. Therefore, the DGS100 can provide a very efficient means to facilitate assisting HCPs with this configuration. Once the learning period is completed and the system configuration is tagged as questionable, the DGA can display control features that allow the patient to initiate the following process that facilitates HCP configuration of the system:

[0241] The next time the patient has contact with the HCP (e.g., in person or via telehealth), the patient can press a button or select an option to initiate this process. The DGA can then display a URL and / or code for the HCP to enter into their web browser. The code can be randomly generated by the DGA and associated with that patient. When the HCP enters the URL into their internet browser, the DGA can open a web page that provides UI controls for entering these codes. The code can be, for example, four alphanumeric characters, or any other acceptable format. The DGA can treat this code as only valid for a limited time, for example, 5 or 15 minutes. Once the HCP enters the code, the DGA checks if the code is valid, and if so, provides the HCP with access to the patient's dose guidance learning and setup information. In an alternative embodiment, the HCP can also be asked to enter a medical license, and the system can only give the HCP access if the entered license matches the medical license format requirements. The HCP's browser can save the medical license number for subsequent interactions with this web page.

[0242] If the HCP has access, the DGA may display a GUI report 1330, an example of which is shown in Figures 9A-1 and 9A-2, that provides information about what the patient entered into the DGS 100 1332, and the DGS 100 observed and learned values ​​1334 determined during the learning period. The parameters listed in the report 1330 may include dosage 1342, administration time 1341 (times when various doses are typically taken or will be taken), administration time range 1344 (start and end administration time for each meal dose), and correction parameters 1345. The dosage 1342 may include the basal dose and each meal (breakfast, lunch, and dinner) dose. The correction parameters 1345 may include target glucose (mg / dL), pre-meal correction factor (mg / dL / U), and post-meal correction factor (mg / dL / U). The patient input data 1332 may include dosages 1342, administration times 1341 (e.g., the times when the basal dose and each meal (breakfast, lunch, and dinner) dose are typically taken), and administration time ranges 1344 (e.g., start and end times defining the time ranges when the meal doses are typically taken for each meal). The observed or learned values ​​1334 may include dosages 1342 for the basal dose and each meal dose, administration times 1341 for the basal dose and each meal dose, administration time ranges 1344 (observed start and end times for each meal administration time range), and correction parameters 1345 (target glucose, pre-prandial correction factor, and post-prandial correction factor). The report 1330 may also provide information and / or warnings about adherence issues, for example, in observation notes 1336 for any or all of the listed parameters. The report may also provide conservative values ​​1340 for the correction parameters 1345 or other parameters listed in the report 1330. For example, if adherence observations indicate underdosing, a warning message may be provided to the HCP indicating that it may be safe to set the initial dosing regimen to a lower learning dose (e.g., a conservative value) 1340 .The report 1330 may also provide a means for the HCP to manually enter dosing regimen parameters or to copy values ​​from other appropriate parts of the report, for example by entering values ​​in fields under dose guidance initial therapy 1338. The report may include a means for the HCP to approve this initial regimen. If the HCP selects the approval UI function 1337, the system may download this initial dosing regimen to the patient's device and begin dose guidance. The HCP may also reject a suggested treatment in the report 1330 by selecting a reject treatment option 1339. If dosing regimen parameters are not fully entered or are out of range, an error message may be provided to the HCP. In another alternative embodiment, the control function of the DGA may initiate the same process (i.e., a user interface that allows for the entry of dose guidance parameters) on the patient's mobile phone, except for the display of a URL and code for the HCP to enter. In yet another embodiment, instead of (or in addition to) displaying a URL and code, the system can provide a UI means for entering an email address; the patient can enter, for example, the email address of an HCP, and the system can then send an email to this address with an embedded hyperlink that, when selected by the user receiving the email, can open a report web page on the user's web browser containing the patient's data and information.

[0243] In some embodiments, the DGA can provide a status summary of patients under the care of the HCP or treatment facility. The DGA can display a GUI report 1450, an example of which is shown in FIG. 9G, that provides information about all patients under the care of the HCP or treatment facility along with a summary of various statistics related to the patient's diabetes management. The report 1450 can include records for multiple subjects, including columns or fields for subject identifier 1452, study type 1454, subject status 1456, approval request pending 1458, time in target range (TIR) ​​1460, time below low threshold 1462 (e.g., 70 mg / dL (TB70)), time above high threshold 1464 (e.g., 180 mg / dL (TA180)), basal dose % taken 1466, average number of bolus doses taken per day 1468, glucose capture (%) 1470, and subject days in study 1472. Any of the fields may include filter options, allowing the HCP to reorder the records as needed. The report 1450 may display at least one, or at least two, or at least three, or at least four, or at least five, or at least six of the following: time in target range, time below a low threshold, time above a high threshold, percentage of basal dose taken, and average bolus dose taken per day.

[0244] The report 1450 may display a column that includes a subject identifier 1452, such as a subject number or a subject name.

[0245] The report 1450 may display a field including the type of study 1454 in which the subject is enrolled. The study may be an exploratory study or other type of study.

[0246] The report 1450 may display a column containing a subject's status 1456. The status may be either "Pre-Observation", "Observation 1", "Observation 2", "Regimen Approved", "Regimen Updated" or "End of Study". If the status is "Regimen Approved" or "Regimen Updated", the date 1474 when the regimen was approved or updated may be listed below the status. In some embodiments, the date 1474 may be grayed out or in a lighter font than the status.

[0247] The report 1450 may display a column 1458 regarding the status of the approval requests. The approval requests column 1458 may include an icon 1476 (e.g., an orange triangle with an "!" in the center) indicating that approval by the HCP is required if there are outstanding requests awaiting review and approval by the HCP. The report may also include a statement 1478 indicating the number of subjects with dosing regimens that require approval by the HCP.

[0248] The report 1450 may display a column reporting the time that the subject's glucose levels have been within target range ("Time in Target" or "TIR") 1460, as described elsewhere in this application. The TIR may be calculated for the number of days the subject has participated in the study and / or the number of days the subject has been on their current dosing regimen.

[0249] The report 1450 can display a column reporting the time that the subject's glucose level was below a low threshold, for example, 70 mg / dL ("Time Below 70 mg / dL" or "TB70") 1462. The time below the low threshold can be calculated for the number of days the subject was in the study and / or the number of days the subject was on their current dosing regimen. In some embodiments, the low threshold can be about 60 mg / dL, alternatively about 65 mg / dL, alternatively about 70 mg / dL, alternatively about 75 mg / dL, alternatively about 80 mg / dL, alternatively about 85 mg / dL, alternatively about 90 mg / dL.

[0250] The report 1450 can display a column reporting the time that the subject's glucose level is above a high threshold, for example 180 mg / dL ("Time Above 180 mg / dL" or "TA180") 1464. The time above the high threshold can be calculated for the number of days the subject has participated in the study and / or the number of days the subject has been on their current dosing regimen. In some embodiments, the high threshold can be about 170 mg / dL, alternatively about 175 mg / dL, alternatively about 180 mg / dL, alternatively about 185 mg / dL, alternatively about 190 mg / dL, alternatively about 195 mg / dL, alternatively about 200 mg / dL.

[0251] The report 1450 can display a column reporting the basal dose taken 1466, for example as a percentage. The % basal dose taken 1466 can be calculated for the number of days the subject was in the study and / or the number of days the subject was on their current dosing regimen.

[0252] The report 1450 can display a column reporting the average number of bolus doses taken per day 1468. The average number of bolus doses taken per day 1468 can be calculated for the number of days the subject was in the study and / or the number of days the subject was on their current dosing regimen.

[0253] The report 1450 can display a column that reports the amount of glucose capture 1470, for example as a percentage. The glucose capture percentage 1470 can be calculated as a percentage of the glucose readings received by the system from the sensor during a set period of time, for example, the number of days the subject was in the study and / or the number of days the subject was on a current dosing regimen (e.g., prior to titration).

[0254] The report 1450 may display a column reporting the number of days 1472 the subject was enrolled in the study.

[0255] In some embodiments, the DGA can provide a report summarizing the approved therapies along with a statistical analysis of glucose levels and doses that differ from the approved therapies. The DGA can display a GUI report 1486, an example of which is shown in FIG. 9I, that provides information about the approved therapies 1488 for a particular patient 1496 along with an AGP plot 1490, a graph of missed doses 1492, and a graph of user overrides 1494. The clinician 1498 can switch back to the complete list of their patients to view a GUI report 1486 for another patient.

[0256] The approved therapies 1488 can be presented in a table that includes columns for the date the therapy was approved, the type of dosing strategy (e.g., initial, titration, manual override), various insulin doses, the length of the treatment period (e.g., in days), the average number of boluses per day, the number of low glucose events, TIR (%), time below low threshold (e.g., TB70 (%)), time above high threshold (e.g., TA180) (%), and glucose capture (%). The dosing strategy types can include, but are not limited to, initial, titration, and manual override. The various insulin dose types for which amounts are reported include, but are not limited to, basal dose; fixed dose for breakfast, lunch, and dinner; and dose with correction factor for breakfast, lunch, and dinner. The percentages in the table can be calculated for the indicated time period of data, e.g., the last two weeks of data, the period the subject was enrolled in the study, or the period the subject was on a particular dosing strategy.

[0257] The AGP graph 1490 shows an ambulatory glucose profile (AGP) graph, displaying the 5th, 25th, 50th (median), 75th, and 95th hourly percentiles of glucose readings presented over a 24-hour "typical" day based on all days in the selected time frame. Alternatively, the AGP can display other hourly percentiles of glucose readings, such as the 10th, 25th, 50th (median), 75th, and 90th percentiles, presented over a 24-hour "typical" day based on all days in the selected time frame. The AGP graph can also include two horizontal lines indicating the boundaries of the target range. For example, the first line can correspond to the lower limit of the target range (e.g., 70 mg / dL) and the second line can correspond to the upper limit of the target range (e.g., 180 mg / dL). The first and second lines can also be color coded. Data points below the lower limit can be colored a different color, e.g., red, than other data points to highlight these data points. In this way, the AGP graph simply illustrates the amount of time spent within the target range (or the amount of readings that fall within the target range) and the amount of time spent outside the target range. Other exemplary AGP graphs can be found, for example, in US Patent Application Publication No. 2018 / 0235524, US Patent Application Publication No. 2014 / 0188400, US Patent Application Publication No. 2014 / 0350369, US Patent Application Publication No. 2018 / 0226150, all of which are incorporated herein by reference in their entirety for all purposes.

[0258] The missed dose graph 1492 graphs the percentage of missed doses out of the total number of doses received or administered over a period of time for each of basal, breakfast, lunch, and dinner doses. This graph can easily indicate to the HCP if a subject has missed a large number of doses of a particular type and can recommend corrective action. The percentages on the graph can be calculated for a designated time period of data, for example, the last two weeks of data during which the subject was enrolled in the study or during which the subject was on a particular dosing strategy. The graph may be configurable to allow the HCP to select the desired time window from a drop-down menu. The missed dose graph 1492 may be a bar graph with one axis of dose type (basal, breakfast, lunch, and dinner) and the other axis of percent missed out of total doses.

[0259] The user override graph 1494 graphs the percentage of doses the subject did not take the recommended dose for each of basal, breakfast, lunch, and dinner doses out of the total number of doses received or administered by the subject. This graph can easily indicate to the HCP if the subject is ignoring the recommended dose for a particular type of administration or meal and recommend corrective action. The percentages on the graph can be calculated for a designated time period of data, for example, the last two weeks of data during which the subject was enrolled in the study or during which the subject was on a particular dosing strategy. The graph may be configurable to allow the HCP to select the desired time window from a drop-down menu. The user override graph 1494 may be a bar graph with one axis of dose type (basal, breakfast, lunch, and dinner) and the other axis of user override as a percentage of total dose.

[0260] In some embodiments, the DGA can provide a graph showing the correlation between the recommended dose and the dose actually taken by the subject. The DGA can display a Recommended Dose vs. Taken Dose GUI 1480, an example of which is presented in FIG. 9H, which graphs the dose taken at various times during the day vs. the dose taken against the recommended dose. As seen in the GUI 1480, the X-axis 1481 can be time in minutes, hours, days, or weeks. In some embodiments, the graph can be 12:00 AM to 12:00 PM. The graph may display data accumulated over a period of days, weeks, or months, and may graph the doses on the same graph. For example, the graph can include all doses administered during a period of time when the subject is in a particular study period or following a particular dosing regimen.

[0261] The y-axis 1482 can be the difference between the taken dose and the recommended dose. If the taken dose is the same as the recommended dose, the y-coordinate of the dose is zero and the x-coordinate is the time the dose was administered or taken by the user. If the taken dose is X1 units more than the recommended dose, the y-coordinate of the dose is X1. (For example, if the taken dose is 2 units more than the recommended dose, the y-coordinate of the dose is 2). If the taken dose is X2 units less than the recommended dose, the y-coordinate of the dose is -X2. (For example, if the taken dose is 1 unit less than the recommended dose, the y-coordinate of the dose is -1). For example, if a fixed breakfast dose is taken at 7:50 AM, if the taken dose is the same as the recommended dose, the corresponding point 1483 on the graph is (7:50 AM, 0). If the basal dose taken at 9pm was 2 units more than the recommended basal dose, then the corresponding point 1484 on the graph would be (9pm, 2).

[0262] Doses that may be presented on the graph include, but are not limited to, a basal dose; a fixed dose for breakfast, lunch and dinner; and a dose with correction factors for breakfast, lunch and dinner.

[0263] In some embodiments, a report 1350 showing details of regimen adherence may also be displayed, as seen in FIG. 9B. The report 1350 may be accessed by the patient, HCP, or other interested parties. The adherence report 1350 may cover a period of time (e.g., one week, or two weeks, or a study period) and may include a table 1352 listing different analyses for each of the different types of doses 1351. The different types of doses 1351 may include basal doses, breakfast doses, lunch doses, dinner doses, and correction (e.g., post-prandial correction) doses. The table 1352 may list dose counts 1354 of taken doses, missed doses 1356, guidance requested 1358, delta mean 1360 of taken doses and guidance doses, and delta IQR 1362 of taken doses and guidance doses for each of basal, breakfast, lunch, dinner, and / or post-prandial correction for the different types of doses.

[0264] Definitions and / or descriptions 1364 for each of these categories may be listed under table 1352. Dose count 1354 may be a simple count of the dose type in the relevant time period (e.g., last week). Missed doses 1356 may be reported as a percentage and for basal doses in the last week, may be calculated by (7-basal count dose) / 7. Missed doses 1356 may be calculated as (missed dose detections with no associated dose) / (all missed dose detections). Missed post-meal correction doses 1356 may be calculated as (post-meal correction alerts with no associated dose) / (all post-meal correction alerts). Requested guidance 1358 may be reported in units of insulin and may be calculated as (dose associated with guidance indication) / (total dose). Delta average (intake guidance) 1360 may be reported in units of insulin and may be calculated by calculating the average difference for doses associated with guidance. Delta IQR (Intake Guidance) 1362 can be calculated by calculating the IQR for the dose associated with the guidance. Delta IQR has a standard meaning to those skilled in the art and refers to the difference between the 3rd quartile and the 1st quartile.

[0265] Additional metrics 1366, and statistics related to these metrics, may also be listed in the report 1350, including late dose frequency, post-meal frequency, automatic vs. manual dose classification, non-meal and non-post-meal correction frequency, snack dose frequency, percent dose taken per guidance (which may be doses that are temporally linked to the guidance rather than just aggregated), and percent dose taken per alert.

[0266] FIG. 9C shows an exemplary plot 1370 related to a clustering analysis to determine meal periods, which can be optionally displayed to the HCP. A graph 1372 plots the cumulative counts of all doses in the learning period versus the time periods in which administration occurred (e.g., ascending time periods). User-entered meal period range values ​​1374, if available, may be shown in the plot 1370, for example near the x-axis, along with user-entered meal period midpoints 1376. The portions of the curve that increase sharply indicate each meal period cluster. In alternative embodiments, the graph may be plotted as a histogram or pie chart of counts. Learned typical meal periods 1378 may be indicated by vertical lines in the plot 1370. The line segments 1378 have a length equal to the amount of insulin injected. If there are multiple injections within 15 minutes, the length will be equal to the sum of all injected amounts. This is necessary because a patient may inject multiple times for a meal, for example if the insulin pen the patient is using has less than a full amount needed to cover the required dose and the patient refills the pen (or gets a new full pen) and completes the required dose, some patients require insulin doses that exceed the injection capacity of the pen and therefore require multiple injections, and some patients find injecting large amounts of insulin in a single injection painful and therefore administer in multiple injections. The learned meal time range 1380 may be indicated by a horizontal line located at the intersection of the typical time and dose lines.

[0267] FIG. 9D shows an exemplary plot 1390 associated with meal dose clustering and dosage. The graph 1392 plots each dose of all doses in the study period against the TOD on which the dose was administered. Each dose is indicated by a dot 1394 to indicate different doses in the same time period. The thickness of the bar associated with the time period can be changed corresponding to the number of doses to indicate when identical doses occurred in the same time period. As seen in the inset window, the dots 1394 and the thickness of the bars allow the viewer to distinguish between various doses taken at the same time of day. Doses taken within a short time, for example about 15 minutes, on the same day may be added together (e.g., 7.5+1.5=9 units). Additional doses may be indicated by a line 1396 of a different color or type. Other means may be employed to illustrate this.

[0268] A user interface control can be provided to switch between displaying results for all data and displaying only results for doses associated with pre-prandial glucose below a certain threshold, for example 150 mg / dL, or the user interface control can be provided to allow the user to set the threshold.

[0269] FIG. 9E shows an example plot 1400 associated with pre-meal correction factor determination. Graph 1402 plots dots 1404 of each insulin dose for all initial meal doses during the learning period against the pre-meal glucose associated with each dose for a particular meal. Curves of each model fit performed in the learning analysis are overlaid with the associated learned parameters. Models may include a P1 zero slope model (no correction) 1406, a P2 piecewise linear function 1408, and a P3 non-linear function 1410. The best fit curve may be highlighted in some way, for example with a bold curve (compare 1406 with 1408 and 1410). DGA may provide four plots (one for each meal and one overall), as previously described.

[0270] In the event that the DGS fails to adequately learn the patient's dosing regimen, instead of further enabling dose guidance, the DGA may provide a means to share a report on the learning status with the HCP. Note also that this method can be used to share any type of report. A method 1420 for facilitating efficient access by HCPs to reports generated by the DGS 100 while protecting the privacy of patient health data is described in FIG. 9F.

[0271] In step 1422, the session with the patient is authenticated by at least one processor of the portable display device. This authentication may be accomplished when the patient logs into the DGA or by other known authentication methods. Most commonly, this function is fulfilled by authentication functionality provided by the patient's smartphone.

[0272] In step 1424, the at least one processor may determine whether input is received from the patient during the session indicating a request to share the EMR with the HCP. The DGA may provide a selectable feature of a user interface that allows the user to indicate a desire to share the report. If it is determined that a request has been made to share the EMR, in step 1426, the at least one processor may generate a report sharing identification code (ID). The ID may be an alphanumeric code, a numeric code, or other suitable format. This code may be displayed by the DGA along with the URL of the remote report access server.

[0273] In step 1428, the at least one processor may provide the ID along with the data required to generate the report to a remote report access server that controls access to the report. In step 1430, the HCP may launch a standard web browser on the PC and enter the URL displayed by the DGA accordingly. The report access server may then cause the browser to display a screen accepting the ID. The HCP may enter the ID, the browser may send this ID to the server, and if the ID matches the ID sent by the DGA and is received before a certain period of time, e.g. 20 minutes, has elapsed since the ID was sent by the DGA, the server may send the report to the browser.

[0274] In another aspect, the method 1420 can also include determining whether the DGS does not meet a match condition between the patient's entered regimen and the learned regimen. If it is determined that the EMR does not meet the match condition, the method can include providing the patient with an option to provide a learned results report to the HCP. In one embodiment, the generating of the report access ID can be conditioned on a determination that the EMR does not meet the match condition.

[0275] In another aspect, the method can include a remote server generating a user interface, e.g., a web page, addressed at least in part by the ID, for displaying a report. The report can include a determination of dosing parameters of medication administered to the patient at times during a defined period of time, and a measure of concordance of the determination with patient-provided medication dosing information, e.g., as seen in Figures 9A-1-9A-2.

[0276] An alternative embodiment includes the DGA providing a user interface means for the patient to directly generate a report for display on the DGA, which may be shown to the HCP. It should be understood that each of these steps is optional and not necessarily required in the method.

[0277] Post-learning titration opportunities During the learning period, insulin doses may not be fully characterized as they are during the guidance period. Thus, initial titrations using data from the learning period may differ from titrations during the guidance period. Specifically, the DGS100 may only titrate fixed doses during the learning period, and the CF may not. Additionally, titration may only be performed if a low pattern is detected by the fixed dose titration algorithm. A detected low pattern may trigger a dose reduction of insulin administration prior to the period, as described in the fixed dose titration section above. The GPA algorithm may be used to evaluate the fixed meal period. Bedtime may be defined as the time 6 hours after the fixed dinner administration time or approximately 6 hours before the fixed breakfast time, whichever occurs first.

[0278] User feedback during the learning period Next, an exemplary embodiment of a method for obtaining user feedback during or after the DGA is learned is described. The user can be prompted for feedback during the initial learning phase with the DGA. The 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 the dose guidance, such as dose administered, analyte history, patient behavior or activity, dosing strategy in general, specific dose type, confirmation that the dose type or strategy determined by the DGA (e.g., learned by the system) is correct.

[0279] During (or after) the learning period, the DGA can output prompts or other indications to the UID 200 requesting user feedback. This feedback can be regarding dosing strategy, e.g., strategy regarding insulin action type (e.g., long-acting and / or short-acting or rapid-acting). If the feedback (or other determination) 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 3 days, to classify each dose or dosing pattern into a single or split dose type and / or characterize dose by time period (e.g., single dose in the morning, single dose in the evening, or split dose (e.g., both morning and evening)). The DGA can also determine trends (e.g., median, mean) and associated dose variation values ​​regarding dosing. From this information, the DGA can develop a predicted basal dose. After the first period of time, if the actual administered dose (eg, that registered automatically by the MDD 152 or that entered by the user) differs from expected, the user may be prompted for feedback.

[0280] According to one aspect of the embodiment, the user may be prompted in many different situations. For example, the DGA may be configured to detect a missed dose, such as when the user does not administer a basal or bolus dose during a time period during which a previous basal or bolus dose is administered. If a missed dose is detected, the DGA may be configured to prompt the user for input regarding whether a basal dose was administered during that time period. According to some embodiments, the DGA may be configured to detect differences in administration timing. For example, the DGA may be configured to detect when the user administers a basal dose at a different time period than when the previous basal dose was administered (e.g., a basal dose that is normally administered in the morning is administered in the evening). If such differences in administration timing are detected, the DGA may be configured to prompt the user for input regarding whether a basal dose was administered during a different time period. In another aspect of the embodiment, the DGA may also be configured to detect when an extra dose has been administered. For example, the DGA may 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 day 1 (e.g., one basal dose was administered) is different from the dosing strategy on day 2 (e.g., two basal doses were administered). If a different dosing strategy is detected, the DGA can be configured to prompt the user for input regarding whether the user adopted the dosing strategy used on day 2 as a new dosing strategy. In yet another aspect of the embodiment, the DGA can also be configured to detect whether a different dosage was administered. For example, the DGA can be configured to detect whether a first amount of a dose administered during a time period is different (smaller or larger) than a preceding dose administered during a time period on the previous day. If a different dosage is detected, the DGA can be configured to prompt the user for input regarding whether the user changed the dosage.

[0281] The user's response to these prompts can allow the DGA to either confirm that it has identified the correct pattern (e.g., the user confirms that they forgot to take a morning basal dose but would normally take one) or provide the user with an opportunity to correct the pattern (e.g., the user informs the DGA that they will adjust the basal dose based on glucose prior to ingestion).

[0282] For fast acting insulin dosing strategies, in addition to the prompts above, the DGA can include prompts for dose classification, which can include, but are not limited to, bolus, correction, split dose, bolus + correction, and bolus + carbohydrate count + correction classifications.

[0283] The user may be prompted in various circumstances regarding the administration of fast-acting insulin. The DGA may be configured to detect whether a dose not associated with a meal was administered. For example, the DGA may be configured to determine whether a dose was taken during a time period in which no meal was identified or detected. If the DGA detects the intake of a dose and no meal was detected within the time period of administration (e.g., within about an hour of administration), the DGA may prompt the user for input regarding why the dose was administered (e.g., because they ate, lowered their glucose, or finished administering a previous meal). The DGA may also be configured to detect that a meal dose does not match a previous meal dose associated with the same meal type. For example, the DGA may be configured to determine whether a bolus dose associated with a first meal type and administered during a time period is not the same as a previous bolus dose associated with the first meal type and administered during that time period on the previous day. If such a difference in the bolus dose is detected, the DGA may be configured to determine the reason for the different dose. For example, the DGA can be configured to determine the difference in pre-prandial glucose values ​​associated with the bolus dose and a previous bolus dose and determine whether the detected difference is a correction. The DGA can also prompt the user for input regarding the reason for the difference in the bolus dose (e.g., eating a small / large meal and / or correcting for hyperglycemia and / or correcting other factors).

[0284] In addition to allowing the DGA to determine what type of immediate-acting doses are being taken throughout the day, it can facilitate the timing of expected doses. After a learning period during which no prompts are provided, the DGA can provide these prompts to the user when the dose differs from the expected dose in order to improve the DGA's model of the user's medication-taking strategy.

[0285] For both long-acting and fast-acting treatments, DGAs can aim to minimize the number of prompts over time and as the user responds, focusing on frequent prompts in the early stages and tapering off if repetitive patterns are observed.

[0286] Glucose pattern analysis and meal bolus titration for MDI insulin therapy. Next, an exemplary embodiment of a method for determining meal bolus titration will be described. Once the system learns (or is configured for) the patient's current dosing strategy, it can provide titration guidance for multiple injection (MDI) dosing therapy. For patients using fixed meal dosing, fixed doses (e.g., breakfast, lunch, dinner, snacks, etc.) can be titrated. For patients using carbohydrate counting, carbohydrate ratios for the same meal or different times can be adjusted. For patients using empirical dosing, dose titration can be performed for each meal. Titration guidance by DGA can provide recommendations to change the dose or carbohydrate ratio in a particular direction. The change can be an appropriate percentage change, such as, for example, 5%, 10%, 15%, etc. Dose guidance can also include the initiation of meal dosing. For example, if a patient is using basal+1 (e.g., lunch dosing regimen) and breakfast shows a high pattern, DGA can provide a recommendation to administer RA insulin at breakfast.

[0287] The DGA may require defining dosing categories such as time of day (TOD) period, meal type (e.g., breakfast), and meal composition (e.g., cereal with milk). For example, a dosing category may be a time of day period defined by the duration associated with a prandial insulin dosing for the time of day period. In a further example, a post-breakfast time period may be defined as starting when a prandial insulin dose is taken during a defined time period, e.g., 5:00 AM-10:00 AM, and ending after a defined post-prandial period (e.g., 6 hours later) or when the next prandial insulin dose is taken, whichever occurs first. One or more indices may be required to define whether a post-prandial glycemic response is nominal or in need of correction, or to rank one post-prandial glycemic pattern more favorably or unfavorably than another. A low glucose likelihood (LLG) index and median glucose value may be used to quantify the degree of hypoglycemic and hyperglycemic risk, respectively.

[0288] U.S. Patent Application Publication No. 2018 / 0188400 ('400), the entire contents of which are incorporated herein by reference for all purposes, describes an example embodiment for deriving and determining a risk index that can be utilized in glucose pattern analysis (GPA) of a DGA embodiment. This embodiment utilizes, among other things, central tendency (e.g., mean, median, etc.) and variance data from a multi-day period to determine a risk index that corresponds 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.

[0289] An alternative to the embodiment described in the '400 publication is described in U.S. Patent Application Publication No. 2014 / 0350369, which is also incorporated by reference in its entirety for all purposes. For example, instead of using the median and variance, the method can employ any two statistical measures that define the distribution of the data. As described in the '369 publication, the statistical measures can be used to estimate the 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 target range (TIR), time above target (t AT ), and the time to fall short of the target (t BT If the glucose data is modeled as a distribution (e.g., a gamma distribution), then a predefined threshold G LOW and G HIGH About t AT and t BT For the threshold, the algorithm calculates t BT_HYPO We can also define t BT If the patient is at high risk for hypoglycemia, for example, if the patient is at high risk for hypoglycemia, the patient may be considered at high risk for hypoglycemia. LOW = 70 mg / dL, t BT can be defined as when t exceeds 5%. AT_HYPER We can define an index called t AT If the blood sugar level exceeds 100 mg / kg, the patient can be judged to be at high risk for hyperglycemia. The degree of risk for hypoglycemia and hyperglycemia is determined by the following formula: LOW or t BT_HYPO , G HIGH or t AT_HYPER The TIR can be adjusted by adjusting either BT and t AT Any two of the three measures can be used to define the control grid. These options (and others) can be used to determine the risk index of the DGA embodiments described herein.

[0290] The DGA embodiments described herein may operate based on a quantitative evaluation of a user's analyte data during a TOD period. This quantitative evaluation may be performed in a variety of ways. For example, the embodiments described herein may evaluate analyte data over multiple days to determine one or more indicators that describe the associated risk that the analyte data indicates for the corresponding TOD. These indicators may be used to classify the analyte data during the TOD period into one of a number of patterns. For example, the patterns may indicate glucose behaviors or trends that are common or generalized to the TOD. Any number of two or more patterns may be utilized by the DGA embodiments. For ease of reference, these patterns are referred to herein as glucose pattern types, and the embodiments described herein refer to implementations that utilize three glucose pattern types (e.g., low, high / low, and high), although other embodiments may utilize only two types, or more than two types, and these types may differ from those described herein.

[0291] Taking fixed meal dose as an example, once the DGA has learned the dosing strategy and the dose or carbohydrate ratio amount, it can begin 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 two indicators mentioned above (LLG and median glucose) to four logical "pattern" variables by the GPA method described below. FIG. 8A shows an exemplary method 400 operation by the DGA for evaluating meal bolus titration for multiple injection (MDI) dosing therapy. The method 400 can include, at 402, determining at least one TOD analyte pattern type by the DGA by executing a glucose pattern analysis (GPA) algorithm that receives as input time-correlated analyte data originating from a sensor control device worn by the patient during the analysis period. The method 400 can also further include, at 404, selecting an MDI dosing guidance value based on the analyte pattern type and the patient's prescribed dosing strategy during the analysis period by the DGA by executing a recommendation algorithm. Method 400 may further include, at 406, storing in computer memory a recommended course of action by the DGA for output to at least one of the UID 200 or MDD 152 which administers the medication to the patient. The UID 200 may use the recommended course of action to control a user interface, for example, by causing a human readable representation of the guideline to appear on a display or by creating an audio output of the guideline in a human language. The MDD 152 may use the guideline to adjust or maintain the next associated dose administration. Further details of method 400 are provided below.

[0292] 8B is a flow chart illustrating an exemplary embodiment of a GPA method 410 that may be implemented as the GPA algorithm referenced at 402. The method 410 may be performed for a particular TOD period, which may be an entire day (e.g., a 24-hour period), or a time block (e.g., three 8-hour periods) or a portion of a day defined by a user's activities (e.g., eating, exercising, sleeping, etc.). In many embodiments, the TOD periods may correspond to meals (e.g., after breakfast, after lunch, after dinner) and sleep (e.g., overnight). These TOD periods may correspond to fixed times of the day when activities would normally occur (e.g., from 5:00 a.m. after breakfast to 10:00 a.m.), and such time blocks may be set by the user or may be contingent on a meal or activity having actually occurred, as determined by automatic detection of a meal or activity, or by a user indication of such (e.g., using UID 200).

[0293] The DGA can perform method 410 independently for each TOD period to obtain an individual pattern assessment for that period. At 412, the DGA can determine a central tendency and variability value from the user's analyte data for a particular TOD period. The user's analyte data can be available from the user's own records or the user's medical professional's records, or the user's analyte data can be collected, for example, by the DGS 100. The analyte data preferably spans a multi-day period (e.g., 2 days, 2 weeks, 1 month, etc.) such that there is enough data within the TOD period to make a reliable determination. In other embodiments, the method can be performed in real time on limited data. The DGA can use any type of central tendency measure that correlates to the central tendency of the data, including but not limited to the median or mean. Any variability index can also be used, including, but not limited to, a variability range that spans the entire data set (e.g., minimum to maximum), a variability range that spans the majority of the data but less than the entire data set to reduce the significance of outliers (e.g., 90th to 10th percentile, 75th to 25th percentile), or a variability range that covers a particular asymmetric range (e.g., low range variability, which can span, for example, from a central tendency value to a lower value of the data, for example, the 25th percentile, 10th percentile, or minimum). The choice of indices of central tendency and variability can vary depending on the implementation.

[0294] At 414, the DGA can assess a hypoglycemia risk ("hypo risk") index based on the central tendency and variability values. One such methodology for determining hypoglycemia risk is described with respect to FIG. 8C, which shows an exemplary embodiment of a framework for determining hypoglycemia risk and other indexes. 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 mathematical formula, a set of if-else statements, etc.), a lookup table, firmware, combinations thereof, etc.

[0295] 8C is a graph of central tendency versus variability (e.g., low range variability) that can be used to evaluate or identify an area or zone that holds or corresponds to the determined central tendency and variability data pair for a particular TOD. Any number of two or more zones can be used. In this embodiment, the data pair can correspond to a target zone 425 or one of three hypoglycemia risk areas: low 426, medium 428, or high 430. A first hypoglycemia risk function (e.g., a curve or linear boundary), referred to as the medium risk function 422, distinguishes between the low 426 and medium 428. A second hypoglycemia risk function, referred to as the high risk function 424, distinguishes between the medium 428 and high 430. The central tendency versus variability data pair can be evaluated or compared to the zones to determine a hypoglycemia risk index for the corresponding TOD period.

[0296] The hypoglycemia risk functions 422 and 424 may be explicitly implemented in the DGA as mathematical functions (e.g., polynomials) or may be implemented implicitly by defining each zone by the pairs involved, by using lookup tables, by a set of if-else statements, by threshold comparisons, etc. The hypoglycemia risk functions 422 and 424 may be preloaded into the DGA, downloaded from a trusted computer system 480, or set by another party such as an HCP. Once implemented in the DGA, the hypoglycemia risk functions 422 and 424 may be treated as fixed or may be adjusted by the user or HCP. An exemplary methodology for determining the hypoglycemia risk functions is described in the '400 publication.

[0297] At 416, the DGA can assess a hyperglycemic risk index ("hyper risk") based on the central tendency value. In this embodiment, hyperglycemic risk can be assessed 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 of the central tendency value from the target 432 can identify the amount of hyperglycemic risk. For example, a low hyperglycemic risk may exist if the central tendency value is less than the target 432 (e.g., a negative value). A moderate hyperglycemic risk may exist if the central tendency value is less than a threshold (e.g., 5 percent, 10 percent, etc.) and exceeds the target 432 (e.g., a positive value). A high hyperglycemic risk may exist if the central tendency value exceeds the target 432 by a value greater than the threshold. The use of three distinct groupings for hyperglycemic risk (e.g., low, medium, high) is an example, and any number of two or more groupings may be used.

[0298] In other embodiments, the DGA can assess the hyperglycemic risk indicator at 416 prior to assessing the hypoglycemic risk at 414. Alternatively, in another embodiment, the assessment of hypoglycemic risk at 414 and the assessment of hyperglycemic risk at 416 can be performed simultaneously in parallel.

[0299] Other indicators such as variability risk may also be evaluated. For example, a variability value less than a first variability threshold 434 may indicate a low variability risk, a variability value greater than the first variability threshold 434 and less than a second variability threshold 436 may indicate a medium variability risk, and a variability value greater than the second variability threshold 436 may indicate a high variability risk. Again, the use of three separate groupings for variability risk is an example. The DGA may use any number of two or more groupings.

[0300] In step 418, the DGA can determine a pattern type for the TOD period based on one or more risk indices evaluated. In an exemplary embodiment, the pattern determination can be evaluated using a hypoglycemia risk index and a hyperglycemia risk index. If the hypoglycemia risk index is high, the pattern can be set as a low value pattern (or a "low" pattern). Otherwise, if the hypoglycemia risk is moderate and the hyperglycemia risk is high or moderate, the pattern can be set as a high / low (or moderate) pattern (or "low and some high values" or "high values ​​with some low values"). Otherwise, if the hyperglycemia risk is high or moderate and the hypoglycemia risk is low, the pattern can be set as a high value pattern (or a "high" pattern). If both the hyperglycemia risk and the hypoglycemia risk are low, the identified pattern can be No Problem (e.g., an "OK" message is displayed and output) (or "No Pattern").

[0301] Thus, method 410 is one example of how the DGA may output one of a number of pattern types for each TOD period. The number of pattern types themselves may vary from those described in this embodiment (e.g., low, high / low, high). Once the pattern type for a TOD period has been determined, the DGA may store an indication of the pattern type in a memory location for use in determining a titration recommendation. Referring again to FIG. 8A, once the DGA has completed the GPA for each associated TOD period at 404, it may proceed to determine a titration recommendation.

[0302] The recommendation methodology can branch depending on other factors such as pattern type (e.g., low, high / low, high), TOD period, dosing strategy, adherence status of the strategy (e.g., underdosed or not), whether sufficient data is available to make the evaluation, etc. The DGA will not make a titration recommendation until sufficient data is available for the corresponding TOD period, for example, the DGA can omit the evaluation and generate an error message if the amount of available data is below a threshold, e.g., if the number of distinct days with more than a minimum portion of available data (e.g., 90%) is below a threshold (e.g., 5).

[0303] 8D-8H show examples of branching of recommendation algorithms or methods for determining dose titration recommendations based on the input information described above. Other branching may also be useful. FIG. 8D shows a recommendation method branch 440 for TOD where sufficient data is available and has a possible cause of low value pattern type including one or more of basal dose, prandial dose, pre-prandial correction dose or post-prandial dose higher than optimal amount. At 442, the DGA evaluates whether the pattern type of the night TOD period is low. If the pattern is low, at 444, the DGA generates a recommendation to reduce all associated doses including at least the basal dose and, optionally, one or more of the prandial dose, pre-prandial correction dose or post-prandial dose by the same amount, for example, 10%. Titration recommendation rules for low value patterns may include generating a recommendation to reduce the long-acting insulin dose or basal rate for the night TOD period at 444. If other TOD periods are low value patterns at 446, the DGA may generate a recommendation to reduce the fixed prandial dose only for the associated TOD period at 448.

[0304] In this embodiment, if there is at least one low pattern, no titration guidance is provided for the high pattern TOD period. The idea here is to emphasize hypoglycemia prevention and only increase the dose if the risk of hypoglycemia is low for all TOD periods. Also, in some cases, if a TOD period has a high pattern, this may be due to a previous TOD period having a low pattern and the patient overeating to compensate, so addressing the low pattern itself may help address the subsequent high pattern. If the pattern is not high at 449, the process 440 waits or ends without generating a recommendation or moves to high pattern evaluation 450.

[0305] Therefore, in the case of a high / low pattern, the DGA will not generate titration guidance. If there are no TOD periods for which titration guidance can be given and data for all time periods is sufficient, the DGA can provide a message to the patient indicating that glucose excursions need to be addressed before further titration guidance can be given. The DGA can also provide a report to the patient's HCP to consider alternative medications or therapies that can address glucose excursions.

[0306] FIG. 8E illustrates operations by the DGA to generate titration recommendations for high patterns in the absence of TOD periods with low patterns. If the nighttime period has a high pattern at 452 and there are no other periods with moderate risk of hypoglycemia, the DGA can increase the long-acting insulin dose or basal rate recommendation at 454. If the nighttime period has a high pattern at 456 and there is at least one other non-dinner period with moderate risk of hypoglycemia, the DGA can decrease the prandial insulin dose associated with any period with moderate risk of hypoglycemia at 458. If the nighttime TOD period does not have a moderate risk of hypoglycemia and does not have a high pattern at 460, the DGA can generate a recommendation to increase the prandial insulin amount associated with the first TOD period with a high pattern at 462. If the nighttime period has a moderate risk of hypoglycemia and the only post-prandial period with a high pattern at 464 is the dinner, the DGA can generate a recommendation to increase the long-acting insulin dose or basal rate at 466. If the nighttime period has a moderate risk of hypoglycemia and the post-dinner period does not, then at 462 the DGA can generate a recommendation to increase the meal insulin dose associated with the first TOD period with a high value pattern.

[0307] In alternative embodiments, pre-meal glucose may be higher or lower than the target glucose (e.g., 120 mg / dL). The glucose data for each meal that contributes to the calculation of the hypoglycemia and hyperglycemia risk index may be modified to compensate for effects from previous meals or conditions that affect glucose not attributable to the current meal. The DGA may modify these data by subtracting an offset so that the resulting starting glucose is at the target level. Alternatively, the DGA may modify these data with a "triangular" function, where the difference between the meal start glucose and the target glucose is subtracted for the start time of the meal, but this correction is linearly reduced over a defined period (e.g., 3 hours) or reduced by another decay function.

[0308] Alternatively, the function may itself be a function of the glucose level or glucose trend at the start of a meal and / or when the previous meal dose was taken.

[0309] According to another aspect of the embodiment, the algorithm for generating meal bolus titration recommendations may become more complex when considering additional aspects such as, for example, missed meal doses, missed basal doses, post-prandial corrections, and pre-prandial corrections, etc. An algorithm for providing an appropriate recommendation when these factors are present may need to exclude some data while still meeting a data sufficiency threshold after excluding data to provide guidance.

[0310] For example, referring to FIG. 8F, if a high value pattern is detected at 461 and there are several days with missed meal doses, the days with missed meal doses are excluded at 463 and the GPA analysis 410 is repeated. Then, if a high value pattern is detected at 465, the dose can be increased 467 based on the patterns identified in the other TODs or await further input or return at 469. Alternatively, the system can evaluate only high value patterns using data that excludes the days with missed meal doses. An algorithm 470 with this branching pattern is shown in FIG. 8F. If the system detects a low value pattern at 473, it can execute the low value pattern algorithm 472 described in the next paragraph. If the system does not detect a high value pattern or a low value pattern, it may return to block 469 for further input or return.

[0311] At 472, if a meal administration is missed, the DGA can detect a pattern of low values ​​during the TOD period and generate a recommendation to reduce the dose if a meal administration is missed on any days during the TOD. The recommendation can include, for example, reducing the fixed dose or compensation dose portion.

[0312] Regarding missed basal doses, if the DGA detects a low pattern 473 on a nighttime TOD, the missed basal dose should not impact the dose titration logic. Similarly, if a low pattern is detected on a non-nighttime TOD, the missed basal dose should not impact the dose titration logic.

[0313] If the DGA detects a high pattern 461 in TODs using data that includes at least one day (or TOD) with a missed basal dose, the data for any day (or TOD) with a missed basal dose can be excluded 463 and the pattern analysis 410 can be repeated. Subsequent actions can depend on the particular TOD for which the high pattern was detected. For example, if the DGA detects a high pattern in overnight TODs for data that includes at least one day with a missed basal dose, the data for any day with a missed basal dose can be excluded and the pattern analysis can be repeated. If a high pattern is detected in overnight TODs when the days with missed basal doses are excluded, the results of the overnight TODs can be used as a guide to adjust the basal dose and increase the basal dose. If a high pattern is detected in non-night TODs, the days with missed basal doses can be excluded and the pattern analysis can be repeated. If a high pattern is detected when the days with missed basal doses are excluded, the meal dose associated with the TOD with the high pattern can be analyzed and titrated as described herein. In either case, the logic flow 470 is as illustrated in FIG. 8F.

[0314] FIG. 8G shows an example of a logic flow 474 for developing a recommendation with a post-prandial correction. If, after GPA 410, the DGA detects a pattern of low values ​​479 of TODs over several days that include a post-prandial correction, the following analysis can be used to perform a correction or titration of the meal dose. If the DGA first detects a low value pattern 479 at 475, it can first test that sufficient data is available at 487 to exclude data from days without a post-prandial correction. If sufficient data is not available, the DGA can perform an error recovery routine 489, e.g., display an error message. If sufficient data is available, the DGA can repeat the pattern analysis 410. If the DGA subsequently detects a low value pattern, it can decrease the post-prandial correction dose (i.e., increase the correction factor) at 476, contingent on the pattern analysis results at other TODs. If the DGA subsequently does not detect a low value pattern, it can decrease the meal dose at 477.

[0315] In all embodiments described herein, a correction dose modification (e.g., titration) in one direction can be achieved by a correction factor modification in the opposite direction. The 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 a correction by either a correction factor modification or a correction dose modification. Thus, to the extent that a correction factor modification or titration is described herein, the embodiment can be configured to achieve the same effect by an inverse correction dose modification, and conversely, to the extent that a correction dose modification or titration is described herein, the embodiment can be configured to achieve the same effect by an inverse correction factor modification. Given this compatibility, both options are available in all embodiments described herein, although not all embodiments are described solely for ease of explanation.

[0316] Additionally or alternatively, starting with the original data set 491, the DGA can exclude days where meal administrations were missed at 478. After finding sufficient data at 487, if pattern analysis 410 of these data excluding days where post-prandial corrections were made at 490 does not show a low value pattern, the DGA can recommend reducing the post-prandial correction dose at 476. Alternatively, the DGA can implement logic 510 of FIG. 10B of U.S. Patent Application No. 16 / 944,736, published as U.S. Patent Application Publication No. 2021 / 0050085, the entire contents of which are incorporated herein by reference for all purposes, thereby recommending reducing either the prandial insulin or pre-prandial correction portion 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 value pattern at 492, process 480 may be implemented at block 471 (FIG. 8H).

[0317] Referring to FIG. 8H, if the DGA detects a high value pattern 493 for a TOD with some days including post-prandial corrections, the following procedure 480 can be performed to develop a recommendation for correction and titration of the meal dose. At 481, the DGA can repeat the pattern analysis 410, including data from missed dose days and days where post-prandial corrections were made. If the DGA then detects a high value pattern at 494, it can increase the post-prandial correction dose (i.e., decrease the correction factor) at 482, contingent on the pattern analysis results at other TODs. If it does not detect a high value pattern at 494, it can check for a low value pattern at 495, and if it does detect a low value pattern, it can return to 474 of FIG. 8G, or else wait for further input or return at 469. Although not shown in FIG. 8H, after excluding any data in the GPA 410, and before running the GPA, the DGA can test for data sufficiency and perform an error recovery routine if insufficient data is available.

[0318] Alternatively, or additionally, starting with the original data set at 493, if the pattern analysis of the data excluding days with missed meal administrations at 483 indicates a high value pattern along either one of branches 2.1 or 2.2, the DGA can continue with procedure 480 as follows: For branch 2.1, if the pattern analysis 410 of the data excluding days with missed meal administrations at 484 (i.e., bolus-only data) does not indicate a high value pattern at 497, the DGA can generate a recommendation to increase the post-meal correction dose at 482, subject to the pattern analysis of the other TODs. Otherwise, the DGA can generate a recommendation to increase either the prandial insulin or the pre-meal correction portion according to procedure 550 of FIG. 10C of U.S. Patent Application No. 16 / 944,736, published as U.S. Patent Application Publication No. 2021 / 0050085, previously incorporated by reference in its entirety.

[0319] For branch 2.2, if the pattern analysis of data for only days that include post-prandial corrections at 485 does not indicate a high pattern at 496, the DGA may increase either the prandial insulin or the pre-prandial correction portion according to step 550 of FIG. 10C of U.S. Patent Application No. 16 / 944,736, published as U.S. Patent Application Publication No. 2021 / 0050085, previously incorporated by reference in its entirety. If no high pattern is detected at 496, the DGA may return to block 484.

[0320] If the correction factor titration recommendations from different TODs are conflicting and the patient is currently using the same correction factor in all TODs, the DGA can increase the correction factor. Procedure 480 can increase the meal dose first if all three components, meal dose, pre-meal correction, and post-meal correction, are not optimal. The pre-meal correction can be titrated and increased after the meal dose titration has been performed. The post-meal correction can be titrated and increased after the meal dose and pre-meal correction have been completed.

[0321] During subsequent analysis, any TOD for which the DGA generated a recommendation to “increase the correction factor” using the method described above will now have a recommendation to “keep the correction factor unchanged.” Conversely, if a TOD for which the DGA generated a recommendation to “decrease the correction factor” using the method described above now has a recommendation to “decrease the correction factor,” it is considered likely that different TODs are optimized using different correction factors.

[0322] 8D-8H illustrate various embodiments of recommendation algorithms 404 for use in the method 400, but it should be understood that these are exemplary. Various other algorithms may also be suitable.

[0323] Guidance Period Algorithm Description During the guidance period, the DGS100 can (1) provide the user with insulin dose recommendations for meals and post-prandial corrections and (2) titrate the dose settings initially learned from the learning period to improve glycemic control.

[0324] Before starting the guidance period, the insulin dose settings may need to be initialized. Initialization may be accomplished if learning was successful, or by manually entering initialized values ​​if learning was not successful. Once the insulin dose settings are initialized, the user may receive insulin dose advice in multiple ways. The user may request a specific meal dose through the DGA. Alternatively, the DGS 100 may detect a missed meal dose event and notify the user. If the user confirms a missed meal dose event, the DGS 100 may provide a recommendation for a "late meal dose" to account for the fact that insulin dosing is occurring after the start of a meal, instead of a prescribed dose at or before the start of the meal. Alternatively, the DGS 100 may detect and notify the user if glucose between meal doses is too high. If the user confirms that glucose between meal doses is too high, the DGS 100 may suggest a "post-meal correction dose" to bring the user's glucose to the target value or range before the next meal dose.

[0325] The frequency with which the subroutines for dose suggestions are processed may vary. Those subroutines associated with the detection of missed meal doses and high post-prandial glucose may be run continuously, while those subroutines associated with dose calculation may be run only when requested by the user. The implementation of when mealtime or post-prandial dose suggestions may be called may be governed by the state transition diagram shown in FIG. 10. Periodically, the DGS 100 may titrate fixed doses and correction factor values ​​to improve the user's blood glucose level. These titration algorithms may be processed daily and may operate independently of the dosing algorithms described above.

[0326] Customization Settings In some embodiments, the user can customize certain settings. For example, in some embodiments, the user can indicate that certain data should be ignored by the algorithm. Or, if the user is sick or taking a new medication, the user may want to ignore certain periods of time. The DGS can include a "vacation mode," where the user can inform the DGS that the DGA should ignore certain past or future days or time periods where their behavior may be or may have been anomalous. For example, if the user goes on a cruise or wants to exclude Labor Day weekend, the user can select the periods of time that the DGS should ignore when determining titration.

[0327] In some embodiments, the user can also adjust previously selected dosage amounts and time periods if there are changes in the user's routine: in some embodiments, if the time the user normally eats a meal has changed, the user can input the new time period for that meal so that the DGS can properly record the meal dose.

[0328] In some embodiments, the user may also input a dose adjustment as a result of the new medication. In some embodiments, this change may only be made if the user's HCP advises them to input the change. For example, the HCP may have prescribed a new oral diabetes medication, which the DGS must take into account. For example, it may be necessary to input a 10% increase in a glucose-elevating agent (e.g., a steroid) or a 10% decrease in a glucose-lowering agent (e.g., metformin). If such a change occurs, the DGS will immediately adjust the prescribed dose, and the DFA may continue to learn and adjust based on the user's glucose results even after the dosage change.

[0329] State transition diagram of the dosing algorithm The DGS 100 can recommend two doses of fast-acting insulin to the user: a meal dose and a correction dose. The meal dose is the dose the user requests to address the glucose rise in response to a meal. The correction dose is the dose recommended by the DGS 100 to correct high glucose between meal doses.

[0330] When these meal and correction dose calculation functions are called is determined by a state transition diagram. The state transition diagram for managing insulin dosing, known collectively as the Dose Guidance State Machine (DGSM) 2000, is presented in FIG.

[0331] The current state of the dose guidance is determined by two factors: the time since the dose was delivered and the classification of the recent past dose. The insulin administration time is defined by the timestamp the system receives from the connected insulin pen. The classification depends on the rules for defining insulin doses in other parts of the application that describe real-time dose classification.

[0332] Meal Dose State Machine 2002 - A waiting state shown as "Meal Dose Guidance Available" 2004 may be defined as a state where more than two hours have passed since the initial meal insulin administration. In the Meal Dose Guidance Available state 2004, the user may receive meal dose guidance by querying the DGS 100 for a dose recommendation. Once an insulin dose (including timestamp and amount) is received by the DGS 100 and correctly classified as an initial meal dose, it transitions to a "Post-Meal Dose State" 2006 where the DGS 100 remains for a period of time following the timestamp reported by the connected pen, e.g., two hours. In this "Post-Meal Dose State" 2006, a meal administration may not be recommended to the user. Additional doses received by the DGS 100 while in the Post-Meal Dose State 2006 may also be considered meal doses and may not impact the time spent in this state.

[0333] Correction Dose State Machine 2010 - Criteria for triggering a correction dose notification are defined elsewhere in this application. When any non-prime dose is received and classified as a correction dose, a transition occurs from the Waiting (Correction-Only Dose Guidance Available) state 2014 to the Post-Correction Dose state 2016, where correction-only dose guidance may not be available. The DGS 100 may remain in this state for a period of time, e.g., 2 hours, from the timestamp reported by the connected pen. While in the Post-Correction Dose state 2016, a correction dose may not be recommended. If any non-prime insulin dose is recorded in the Post-Correction Dose state 2016, the period of time, e.g., 2 hours, timer is restarted at the timestamp of the new dose. Priming before administration of an insulin dose removes air from the needle and cartridge to ensure the correct amount of insulin is administered in full. The prime dose is part of a safety test that sprays a small amount of insulin (e.g., 2 units) into the air, which the user can watch to see as it comes out of the tip of the needle.

[0334] 11, beginning at step 2022, in an exemplary method 2020, the DGS 100 may receive or otherwise access the subject's insulin data (e.g., from the MDD 152). For example, the DGA may check the latest insulin delivery information by requesting delivery information from various sources, including, but not limited to, the MDD 152, an MDD-related application, or an interface that stores the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information.

[0335] In step 2024, the DGS 100 may classify the most recently administered drug dose as one of certain dose categories. For example, the most recently administered dose may be a meal dose, a correction dose, or a prime dose. The most recently administered dose may be classified automatically by the DGS 100 or manually by the user, as described elsewhere in this application. If the most recently administered dose is a prime dose, no further action is taken. If the most recently administered dose is a meal dose or a correction dose, in step 2026, the DGS 100 enters a period during which no additional drug dose recommendations may be displayed. This period may be from about 1 hour to about 3 hours, alternatively from about 1.5 hours to about 2.5 hours, alternatively from about 1.5 hours, alternatively from about 2 hours, alternatively from about 2.5 hours, alternatively from about 3 hours.

[0336] In step 2028, the DGS 100 may determine whether at least one additional drug dose has been administered, i.e., administered after the time the previous "last drug dose" was administered. If at least one additional drug dose has been administered, in step 2030, the DGS 100 may classify the at least one additional drug dose as either a meal dose or a correction dose. If the at least one additional drug dose was a meal dose, in step 2032, no change is made to the period during which a recommendation for an additional drug dose may not be displayed. However, if the at least one additional drug dose was a correction dose, in step 2034, the start of the period during which a recommendation for an additional drug dose may not be displayed may be restarted and set to start from the time the at least one additional drug dose was administered.

[0337] Dietary Dosage Calculation Algorithm Interacting with the user interface The DGS 100 can display the dose guidance screen only if the dose guidance state machine (DGSM) and the dose classification state machine DCSM are up to date. As seen in FIG. 12, in an exemplary method 2040, beginning at step 2042, the DGS 100 can receive or otherwise access the subject's insulin data (e.g., from the MDD 152). For example, the DGA can check the latest insulin delivery information by requesting delivery information from different sources, including, but not limited to, the MDD 152, an MDD-related application, or an interface that stores the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information. The DGA can record the dose when the DGA electronically receives the insulin dose information (e.g., dose amount and time of administration).

[0338] In step 2044, the DGS 100 may determine whether the received data includes the most recently administered drug dose data, i.e., whether the data is "up-to-date." To be up-to-date, the DGS 100 may determine whether the most recently recorded insulin dose is confirmed by the user and / or whether the most recently recorded insulin dose is updated based on the time of the reset (t reset) may require all doses recorded since the last recorded dose to be classified automatically by the system or manually by the user. The confirmation may be done manually, for example, the DGS 100 may prompt the user to confirm that the last dose recorded was indeed the last dose. Alternatively, the confirmation may be performed by the DGA interrogating the connected pen to confirm that it has the latest dose information. In some embodiments, the display device 120 may be configured to send a request (e.g., a query) for data to the MDD 152 via a wired or wireless communication link. In response to the received request, the MDD 152 may transmit data to the display device 120. In some embodiments, for example, the display device 120 may be configured to communicate with the insulin pen according to an NFC (near field communication) protocol. In other embodiments, the MDD 152 may transmit data autonomously to the reader device via a wired or wireless communication link. The MDD 152 may be configured to transmit according to a schedule, based on a triggering event or condition, and / or upon entering wireless communication range of the reading device. In some embodiments, the MDD 152 may be configured to communicate with the display device 120 according to a Bluetooth or Bluetooth Low Energy networking protocol. However, those skilled in the art will recognize that other wireless communication protocols may be implemented (e.g., infrared, UHF, 802.11x, etc.). The DCSM may reset It may be reset at the reset time (t reset ) may be set to midnight, or may be a different TOD depending on the timing parameters of the dosing regimen. It should be understood that each of these conditions is optional and not necessarily required.

[0339] If, in step 2046, it is determined that the DGS 100 has data related to the most recent dose of drug administered, a screen may be displayed. This screen may be the DGA home screen, including the dose guidance recommendation. This screen may only be displayed for a predetermined period of time after the most recent dose has been confirmed by the user. The predetermined period of time may be at least about 10 minutes, alternatively at least about 15 minutes, alternatively at least about 20 minutes, alternatively about 15 minutes. If a bolus record is recorded while the home screen or any of its subordinate flow screens are active, the DGA may exit these screens and require a new user confirmation (if the dose is not automatically classified) and subsequent flow and logic so that the state machine is updated before the home screen is displayed again.

[0340] Dose Classification State Machine The DCSM 2000 has the necessary information to determine how to display the meal carousel icon in the DGA home screen and how to function when the icon is selected. As seen in FIG. 13, in an exemplary method 2050, beginning at step 2052, the DGS 100 can receive or otherwise access the subject's insulin data (e.g., from the MDD 152). For example, the DGA can check the latest insulin delivery information by requesting delivery information from different sources, including but not limited to the MDD 152, an MDD-related application, or an interface that stores the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information. The insulin dose data may relate to meal doses administered since a reset time.

[0341] In step 2054, the DGS 100 may determine whether meal doses received since the reset time have been classified. The meal doses may be classified as associated with a particular meal, i.e., breakfast, lunch, or dinner. As described with respect to other methods and embodiments described herein, the meal doses may be classified automatically by the DGS 100 or manually by a user.

[0342] If, in step 2056, it is determined that all meal doses received since the reset time have been classified, the DGS 100 may display a screen including a plurality of meal icons. The plurality of meal icons may include breakfast, lunch, and dinner icons. Each meal icon may have a first appearance or indication associated with a first state and a second appearance or indication associated with a second state. The first appearance or indication may be associated with a first state in which meal doses administered since the reset time have been classified as a meal type corresponding to the breakfast, lunch, or dinner icon. The second appearance or indication may be associated with a second state in which meal doses administered since the reset time have not been classified as a meal type corresponding to the breakfast, lunch, or dinner icon. For example, a meal icon (one each for breakfast, lunch, or dinner) may indicate that the associated meal record has not yet been classified for that meal. reset If present later, it can be "shaded" (first state). When selected, the shaded meal icon can display recorded dose information and provide an option to display meal dose guidance calculations, if available. reset If there is no meal record associated with time t (second state), the icon is not shaded and, if selected, can display a meal dose calculation if available. reset After this time, all icons are reset and unshaded (second state) and meal dose calculations can be displayed.

[0343] Variable t resetcan be determined by the regimen parameters, dinner dose time range and breakfast dose time range. If the dinner dose time range is the last meal dose time range before midnight, t reset is equal to midnight (this is the most likely case). Otherwise, t reset should be set to the midpoint between the end of the dinner dose time range and the beginning of the breakfast dose time range.

[0344] Dietary Dosage Calculation If the meal icon is selected and a meal dose calculation is displayed, the DGA can determine whether the regular meal dose calculation or the late dose calculation should be used to determine the appropriate dose recommendation. To determine which meal dose calculation is appropriate and obtain the associated calculation result, the DGA can determine whether a missed dose alert, as described elsewhere in this application, is enabled.

[0345] The dose guidance displayed may be based on a late meal dose calculation if a missed meal dose alert was issued. Additionally, no insulin doses may have been recorded in the past two hours. It is understood that each of these conditions is optional and not necessarily required. Note that the DGA may ensure that all alerts are cleared before dose guidance is provided to avoid race conditions. For example, no dose may have been received and the alerts may not have been cleared by the time the user is prompted to connect or scan the MDD 152. If the above conditions are not met, the dose displayed by the DGA may be based on a regular meal dose calculation.

[0346] 14, in an exemplary method 2060, beginning at step 2062, the DGS 100 may receive or otherwise access the subject's insulin data (e.g., from the MDD 152). For example, the DGA may check the latest insulin delivery information by requesting delivery information from different sources, including, but not limited to, the MDD 152, an MDD-related application, or an interface that stores the latest insulin delivery information (e.g., an MDD application web server), or by checking the memory of various applications for the latest insulin delivery information.

[0347] In step 2064, the DGA may determine whether insulin doses have been recorded for a period of time, for example, two hours. If insulin doses have been recorded in the past two hours, as seen in step 2066, the DGA may not display a dose recommendation.

[0348] If no insulin dose has been recorded in the last two hours, in step 2068, the DGA may determine whether a missed dose alert is enabled. If a missed dose alert is enabled or has been triggered, in step 2070, the DGA may display a dose recommendation based on a calculation of a late meal dose. If a missed dose alert is not enabled or has not been triggered, in step 2072, the DGA may display a dose recommendation based on a regular meal dose calculation.

[0349] The calculation of a regular meal dose (i.e., not a calculation of a late meal dose) may be based on recent glucose levels (e.g., scanned glucose or streaming glucose) and may be represented by the following logic and equation: If G prm ≦G T I guide =I fixed -IOB(Equation 1) Else I guide =I fixed -IOB+CorrectionAdj+TrendAdj(Equation 2) During the ceremony, G prm = Current scanned glucose value G T = Target Glucose (Regimen Parameter Lookup) I guide =Insulin dose calculation result I fixed = the fixed insulin dose associated with the applicable breakfast, lunch or dinner (regimen parameter lookup) IOB = Insulin On Board (calculated by the Insulin On Board module) CF prm = Pre-meal correction factor (regimen parameter lookup) CorrectionAdj=(G prm -G T ) / CF prm TrendAdj = Trend adjustment value (based on table lookup in Kudva, et al.), C F =CF prm Based on.

[0350] Glucose Trend Definition and CF prm TrendAdj by Kudva et al. is described in the following table from Kudva, et al. "Approach to Using Trend Arrows in the FreeStyle Libre Flash Glucose Monitoring Systems in Adults." J Endocr Soc. 2018;2(12):1320-1337, the entire contents of which are incorporated herein by reference for all purposes.

[0351] I guideThe calculation of may be rounded to the nearest unit of insulin according to standard rounding rules. If the calculated dose is negative, the displayed dose may be set to zero. In some embodiments, CorrectionAdj may be negative if pre-meal glucose is less than target glucose. Note that in alternative embodiments, the IOB value is only subtracted from the correction and trend adjustment values. For example, if there are no correction and trend adjustment values, Iguide=Ifixed. If there is an adjustment value, IOB is subtracted from that value before being added to Ifixed. If adjustment minus IOB is less than zero, Iguide=Ifixed.

[0352] As an additional safety measure against the timeouts mentioned above (see, e.g., step 2046 of FIG. 12 ), the DGS may use the glucose data received by the DGS on a schedule, based on a triggering event or condition, and / or when in wireless communication with the reading device to determine whether to calculate and present a meal dose recommendation to the user. In some embodiments, if the user's current glucose at the time of the meal dose calculation request is below a threshold, the DGS may not present the calculated meal dose suggestion to the user. Instead, the DGS may present the user with an alert regarding the current glucose level and suggest that the current glucose value be increased before administering an insulin dose. In some embodiments, if the current glucose value is determined to be below the threshold, no dose guidance is displayed. This additional safety measure is to avoid hypoglycemia caused by administering insulin at glucose values ​​that are too low. This threshold may be configurable based on the user's tolerance to hypoglycemia.

[0353] IOB (insulin on board) is a measure of the active insulin remaining in the user's bloodstream after an injection. IOB is subtracted from the currently calculated dose to account for active insulin from previous injections to avoid insulin-induced hypoglycemia. It is calculated by multiplying the previous dose by a fraction that represents the fraction of insulin remaining at that point after injection. IOB has insulin units (U).

[0354] [Table 2]

[0355] [Table 3A]

[0356] [Table 3B]

[0357] The calculation of a meal dose may be triggered by a user request for a meal dose in the DGA. The input data stream may include on-demand scan glucose, on-demand scan glucose trend data, on-demand IOB, and check for late dose. The input parameters may be the requested meal fixed dose, mealtime CF, and target glucose. The output may be a mealtime insulin dosing suggestion.

[0358] Late meal dose calculation Both the regular meal dose and the late meal dose may be calculated when a person selects a meal dose within the DGA. The presentation of the late meal dose value may depend on the DGS 100 detecting a missed meal administration event, as described elsewhere in this application.

[0359] The calculation of the later meal dose is G prmFor the late meal dose calculation, G, and TrendAdj may be calculated using continuous streaming glucose data, e.g., streaming glucose once per minute, rather than scanned glucose. prm may be the glucose value at the estimated meal start time, and TrendAdj may be determined using the glucose trend at the estimated meal start time. The estimated meal start time may be calculated based on streaming data once per minute and may be output from a missed meal dose detection algorithm described elsewhere in this application. Additionally, for the calculation of a late meal dose, the IOB may be calculated according to the time the late meal dose was requested by the user, rather than the estimated meal start time. If the calculation of the late meal dose is less than the calculation of the normal meal dose, the DGS100 may display the lower value.

[0360] As an additional safety measure against the timeout mentioned above (see, e.g., step 2046 of FIG. 12 ), the DGS may use the glucose data received by the DGS on a schedule, or based on a triggering event or condition, and / or when in wireless communication with the reading device to determine whether to calculate and present a late meal dose recommendation to the user. In some embodiments, if the user's glucose at the planned meal time is below a threshold, the DGS may not present a calculated dose suggestion to the user. Instead, instead of a dose recommendation, the DGS may present a warning to the user to correct low glucose before administering insulin. This additional safety measure is to avoid hypoglycemia due to administering insulin at glucose values ​​that are too low. This threshold may be configurable based on the user's tolerance to hypoglycemia.

[0361] The calculation of the late meal dose may be triggered by the user responding to a missed meal administration notification, selecting a meal and scanning glucose. The input data stream may include estimated meal start time, historical glucose at estimated meal start time, and IOB at the time of the requested late dose. The input parameters may be a fixed dose, mealtime CF, and target glucose for the requested missed meal. The output may be a suggested prandial insulin dose at estimated meal start time.

[0362] Dose calculation instructions When the user selects a displayed dose, a pop-up screen may display an explanation of how the dose was calculated. fixed and I guide -I fixed In addition, explanatory text may be displayed according to Table 4.

[0363] [Table 4]

[0364] Meal tracking and tagging In some embodiments, a user may be able to tag meals that resulted in fluctuating glucose levels, e.g., glucose levels above or below a target range. In some embodiments, based on the tagged meal and associated post-prandial glucose data from multiple tagged meals, the DGS can provide a new suggested dose specific to the tagged meal.

[0365] In some embodiments, a user may eat the same pancake and egg breakfast at a local restaurant once a week. However, each time they eat the pancake and egg breakfast, their post-meal glucose levels fall outside of the target range. In some embodiments, the DGS can inform the user that the meal dose did not bring the user back on target after the meal and prompt the user to tag or track this meal. The user can enter a descriptive tag for this meal as a favorite.

[0366] Subsequently, after the user has eaten or consumed the same meal, the user may tag the meal, and the DGS may associate the meal dose administered in association with the tagged meal and the post-prandial glucose dataset associated with the meal to this tag. Once the DGS has acquired a sufficient amount of dataset, the DGS may determine a specific dose recommendation for this tagged meal based at least on the previously administered doses and the post-prandial glucose dataset associated with the tagged meal. The DGS may also consider the user's current glucose level at the time the request for the dose recommendation is made in determining the dose recommendation for the tagged meal. The DGS may require data for at least three meals, or at least four meals, or at least five meals before calculating a recommended dose for the tagged pancakes and eggs meal. The tag may be any food and / or beverage consumed at breakfast, lunch, dinner or snack. The dose recommendation may include multiple components, such as a base dose and a correction dose. The base dose is the user's standard or typical meal dose, and the correction dose may consider the user's previous response to the same tagged meal. In some embodiments, the correction dose may take into account the user's current glucose level at the time the request for the dose recommendation is made.

[0367] In one exemplary embodiment, as seen in Figure 23A, in a first step 2402 of method 2400, the DGS may prompt the user to enter tags associated with a meal type. For example, a meal type may be a description of a particular food and / or beverage consumed by the user during a meal or snack, such as "cheeseburger and fries," "two slices of cheese pizza," "eggs and pancakes," or "apple and diet soda."

[0368] In step 2404, the DGS may receive an entered tag for an instance of a meal type. The user may enter this meal's descriptive tag as a favorite, or if the meal type has already been entered into the DGS, the user may select the tag from, for example, a list of favorite meal types.

[0369] In step 2406, the DGS may determine whether a threshold number of instances of a meal type tag have been received by the DGS. For example, the DGS may require that a minimum of three tags for a particular meal type are required. If the DGS determines that only two instances of a meal type tag have been received, the DGS may not calculate a recommended drug dose for that meal type. However, if the DGS determines that three or more instances of a meal type tag have been received, the DGS may calculate a recommended drug dose for that meal type, as shown in step 2408.

[0370] In some embodiments, after a user tags a meal, the DGS can detect that the administered drug dose is different from the drug dose administered during previous consumption of the same meal. If the DGS detects a significant difference in drug dose, such as a difference of 1 unit, or a difference of 2 units, or a difference of 3 units, or a difference of 4 units, or a difference of 5 units, or a difference of about 1 unit to about 5 units, or a difference of about 2 units to about 5 units, or a difference of about 3 units to about 5 units, the DGS can prompt the user. In some embodiments, the DGS can prompt the user to create a new tag. The new tag can include an explanation in the tag to account for the different drug dosage. For example, the new tag can include the amount of the meal or another reason why the drug dosage is different (e.g., exercised before the meal). In some embodiments, the DGS can prompt the user in real time, i.e., within 5 minutes, or within 4 minutes, or within 3 minutes, or within 2 minutes, or within 1 minute after the user tags the meal type.

[0371] 23B, in a first step 2422 of method 2420, the DGS may prompt the user to enter tags associated with a meal type. For example, a meal type may be a description of a particular food and / or beverage consumed by the user during a meal or snack, such as "cheeseburger and fries," "two slices of cheese pizza," "eggs and pancakes," or "apple and diet soda."

[0372] In step 2424, the DGS may receive a first entered tag for an instance of a first meal ...

Claims

1. 1. A system for providing dosage guidance to a subject, comprising: an input configured to receive dosage data from a medication delivery device; a display configured to visually present the dosage guidance; and one or more processors coupled to the input, the display, and a memory for storing instructions; Equipped with The instructions, when executed by the one or more processors, cause the one or more processors to: Determine whether the missed dose alert is enabled; in response to determining that the missed dose alert is not enabled, displaying a dose guidance recommendation based on a calculation of a normal meal dose; in response to determining that the missed dose alert is enabled, displaying a dose guidance recommendation based on a calculation of the late meal dose; system.

2. 1. A method for providing dosage guidance, comprising: receiving, by the electronic device, user drug dosage data from a medication delivery device; determining whether a missed dose alert is enabled; in response to determining that the missed dose alert is not enabled, displaying a dose guidance recommendation based on a calculation of a normal meal dose; in response to determining that the missed dose alert is enabled, displaying a dose guidance recommendation based on a calculation of the late meal dose; A method comprising:

3. The instructions further include, determining whether the dosage data received from the medication delivery device includes a dosage administered within a time period from a current time; in response to determining that the dosage data received from the medication delivery device includes a dosage administered within a time period from a current time, displaying a dosage guidance recommendation based on the calculation of the later meal dosage. The system of claim 1 .

4. The instructions are for the one or more processors: in response to determining that the missed dose alert is enabled and determining that the dose data received from the medication delivery device includes a dose administered within a time period from a current time, displaying a dose guidance recommendation based on the calculation of the later meal dose. The system of claim 3.

5. The system of claim 3, wherein the period is approximately two hours.

6. The system of claim 1, wherein the calculation of the normal meal dose is based on a fixed insulin dose associated with a meal and an on-board amount of insulin when the user's current glucose level is below a target glucose value.

7. The system of claim 1, wherein each of the regular meal dose calculation and the later meal dose calculation is based on a fixed insulin dose associated with a meal, an insulin on-board amount, a correction adjustment and a trend adjustment when the user's current glucose level is above a target glucose value.

8. The system of claim 7, wherein the calculation of the normal meal dose is based on a current glucose level determined from scanned glucose data.

9. The system of claim 7, wherein the calculation of the later meal dose is based on a current glucose level determined from streaming glucose data.

10. The system of claim 9, wherein the calculation of the later meal dose is further based on a trend adjustment determined from streaming glucose data.

11. The system of claim 7, wherein the calculation of the later meal dose is based on an on-board amount of insulin calculated according to the time of the user's request for the dose guidance recommendation.

12. The system of claim 7, further comprising a drug delivery device configured to deliver at least one dose of drug to a subject. determining whether the drug dosage data includes data on doses administered within a certain period of time; displaying the dose guidance recommendation based on the later meal dose calculation in response to determining that the drug dosage data does not include data for a dose administered within a period of time; The method of claim 2 , further comprising:

14. The method of claim 13, wherein the period of time is about 2 hours.

15. The method of claim 2, wherein the calculation of the normal meal dose is based on a fixed insulin dose associated with a meal and an amount of insulin onboard when the user's current glucose level is below a target glucose value.

16. The method of claim 2, wherein each of the regular meal dose calculation and the later meal dose calculation is based on a fixed insulin dose associated with a meal, an insulin on-board amount, a correction adjustment and a trend adjustment when the user's current glucose level is above a target glucose value.

17. The method of claim 16, wherein the calculation of the normal meal dose is based on a current glucose level determined from scanned glucose data.

18. The method of claim 16, wherein the calculation of the later meal dose is based on a current glucose level determined from streaming glucose data.

19. The method of claim 18, wherein the calculation of the later meal dose is further based on a trend adjustment determined from streaming glucose data.

20. The method of claim 16, wherein the calculation of the later meal dose is based on an on-board amount of insulin calculated according to the time of the user's request for the dose guidance recommendation.

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