Systems, devices, and methods for dual analyte sensors

Abstract

Systems, devices, and methods are provided for a dual analyte sensor that uses glucose history from a glucose sensor in combination with data from a ketone sensor to control operation of a user interface device or insulin pump. In some embodiments, the systems, devices, or methods may utilize a combination of glucose history and a beta-hydroxybutyrate physiological model to better predict diabetic ketoacidosis (DKA) as compared to prediction based on a simple high glucose threshold. In other embodiments, systems, devices, or methods may include features for generating patient drug state estimates and / or drug information knowledge, such as T1-type diabetes (DM) patients using an SGLT-2 inhibitor.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 406,989, filed on September 15, 2022, the entire contents of which are expressly incorporated herein by reference for all purposes. Technical Field

[0003] The subject matter described herein generally relates to systems, devices, and methods for dual analyte sensors. Specifically, embodiments described herein relate to using data collected by a glucose sensor and data collected from a ketone sensor to control a user interface device or a dosing device to improve control of a patient's glucose levels. Background Art

[0004] The market for monitoring the health and condition of humans and other living animals is large and growing. Information describing the physical or physiological condition of a human can be used in countless ways to assist and improve quality of life, as well as to diagnose and treat undesirable human conditions.

[0005] Common devices for collecting such information are physiological sensors, such as biochemical analyte sensors, or devices capable of sensing chemical analytes of biological entities. Biochemical sensors come in a variety of forms and can be used to sense analytes in fluids, tissues, or gases that form part of or are produced by a biological entity (such as a human). These analyte sensors can be used on or in the body itself, such as in the case of a percutaneously implanted analyte sensor, or they can be used for biological substances that have been removed from the body. Useful applications for such sensors include blood glucose sensing for health assessment, dosage guidance, and related uses.

[0006] However, blood glucose monitoring alone is subject to certain limitations. For example, people with type 1 diabetes who use SGLT-2 inhibitors (also known as glipizide or fluazines) are at risk for developing euglycemic diabetic ketoacidosis (DKA). DKA is an adverse condition in people with diabetes that can lead to hospitalization or even death. It is associated with high ketone levels caused by chronically high glucose levels. DKA can also be caused by insufficient insulin levels or high levels of insulin resistance in the patient, which may be caused by the disease—in which case glucose levels may be within or below the target range.

[0007] SGLT-2 is a diabetes medication that helps reduce glucose variability during meals and is indicated for patients with type 2 diabetes. It can also help patients with type 1 diabetes manage glucose levels; however, its use in type 1 patients is problematic because it can cause high ketone levels and DKA in the presence of normal glucose levels (referred to herein as euglycemic DKA).

[0008] Treatment for euglycemic DKA is essentially insulin injections and the consumption of carbohydrates to counteract any unwanted glucose-lowering effects. However, patients may be confused about when they should do this and when they should seek emergency medical intervention. Specifically, it can be confusing to know what to do when ketones are elevated enough to represent a euglycemic condition.

[0009] Discrete ketone test strips as well as continuous glucose monitoring (CGM) are available, but may not be suitable for continuous ketone monitoring. Regardless of how a patient's ketones are measured, interpreting ketone levels in conjunction with the patient's blood glucose levels and determining appropriate action is too complex for most patients and requires input from a healthcare provider (HCP). As a result, ketone sensing and use of ketone data in patients taking SGLT-2 inhibitors is relatively difficult and cumbersome compared to continuous glucose sensing.

[0010] For these and other reasons, there is a need for improved ketone sensing, analysis, and guidance in patients susceptible to euglycemic DKA (e.g., patients taking SGLT-2 inhibitors). Summary of the invention

[0011] Described herein are exemplary embodiments of systems, devices, and methods for dual analyte sensors that use glucose history from a glucose sensor in combination with data from a ketone sensor to control the operation of a user interface device or an insulin pump.

[0012] The present disclosure describes mobile application-based systems, devices, and methods for detecting situations in which action should be taken, providing guidance to the patient, and providing methods for recording important context concurrent with the situation, which will help the HCP understand later when advising the patient how to avoid future adverse situations. In one aspect, the system, device, or method can utilize a combination of glucose history and a β-hydroxybutyrate physiological model to better predict diabetic ketoacidosis (DKA) compared to predictions based on a simple high glucose threshold. In the alternative, or in addition, the system, device, or method may include features for generating patient medication status estimates and / or drug information knowledge, such as patients with type 1 diabetes (DM) using SGLT-2 inhibitors.

[0013] Additionally, or in the alternative, the improved system, method or device may include improving the alarm features of an analyte monitoring sensor (e.g., a glucose sensor) by using background and / or drug state estimation and / or drug information knowledge (e.g., T1DM patients using SGLT-2 inhibitors) from one or more additional analyte sensors (e.g., ketone sensors). Single analyte sensor systems may have various alarms. Examples of alarms include high threshold alarms and low expected threshold alarms. If at least one or more analyte information and / or drug information is known, the alarm can be improved by adjusting the alarm behavior and timing. This may include a dual analyte system (e.g., glucose ketone), whose threshold alarm is based on the value of each analyte, without being affected by other analyte values ​​and / or drug-based information. Examples of adjusting alarm behavior include: using lower or higher thresholds. Examples of adjusting timing include: changing the alarm pronunciation time interval when the alarm condition is still met. This improves the clinical relevance of the alarm and may reduce alarm fatigue by minimizing pronunciations that may not be clinically relevant.

[0014] The systems, devices, and methods disclosed herein combine ketone data with blood glucose data to provide more reliable patient and HCP guidance than using high glucose threshold detection alone. Therefore, an on-demand system or continuous glucose monitoring (CGM) system can provide improved practicality. For example, an on-demand testing system including a built-in ketone measurement compatible strip port can provide enhanced practicality for patients and help HCPs make more accurate suggestions. In general, the systems, devices, and methods disclosed herein can better protect patients from DKA risks. Algorithm improvements of systems, methods, and devices may include: utilizing rich glucose history from an on-demand or CGM system, opportunistic use of insulin history (e.g., from a built-in bolus calculator), and continuous ketone measurements (e.g., from a built-in ketone compatible strip port or an in vivo ketone analyte sensor) to improve future DKA risk estimates and improve DKA risk assessment. Improved risk assessment algorithms may include: for example, comparing an estimated ketone time series with a ketone-specific threshold, rather than comparing point glucose levels with conservative point glucose-specific thresholds as in traditional methods.

[0015] According to some embodiments, an analyte monitoring system is provided, wherein a sensor control device is configured to collect first time-related data indicating a glucose level and second time-related data indicating a ketone level. For example, the first data may come from an analyte sensor (the analyte sensor is a glucose sensor), and the second data may come from an analyte sensor (the analyte sensor is also a ketone sensor). In other examples, the second data may be received such as from a ketone test strip measurement. In some embodiments, one or more of the first data and the second data comes from an analyte sensor.

[0016] According to some embodiments, the sensor control device is operatively coupled to at least one first processing circuit and at least one first non-transitory memory. For example, the first data and / or the second data can be stored in one or more memories (e.g., in a single or separate memory). In some embodiments, the reader device includes at least one second processing circuit and at least one second non-transitory memory. For example, the first data and / or the second data can be stored in one or more memories (e.g., in a single or separate memory).

[0017] According to some embodiments, at least one of the non-transitory memories includes instructions that, when executed, cause at least one of the processing circuits in the sensor control device or the reader device to make a determination based on the first time-related data and the second time-related data, and output an indication of the determination by the reader device. The determination may be at least one of: an alarm threshold for one or both of the first time-related data and the second time-related data, a message for output by the reader device, and / or a correction to the analyte state estimate. For example, determining the alarm threshold may include: setting or modifying a threshold for blood glucose, for ketone bodies, or for another analyte, causing the reader device or another system component to output an alarm when the threshold is exceeded. For example, determining the message may include: selecting a predetermined message from a data table in response to the results of the automatic analysis of the first time-related data and the second time-related data. For example, determining the correction to the analyte state estimate may include: calculating a correction factor or correction value for an initial estimate of blood glucose or other analyte based only on the first time-related data.

[0018] Other systems, devices, methods, features, and advantages of the subject matter described herein will be or will become apparent to one skilled in the art upon examination of the following drawings and detailed description. All such additional systems, methods, features, and advantages are included in this specification, are within the scope of the subject matter described herein, and are protected by the appended claims. Features of the exemplary embodiments should in no way be construed as limiting the appended claims without explicit recitation of such features in the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The details of the subject matter described herein (both with respect to its structure and operation) may be apparent from a careful reading of the accompanying drawings, in which like reference numerals refer to like parts. The components in the figures are not necessarily drawn to scale, emphasis instead being placed on illustrating the principles of the subject matter. In addition, all illustrations are intended to convey concepts, in which relative size, shape, and other detailed attributes may be illustrated schematically rather than literally or precisely.

[0020] Figure 1is an illustration depicting an exemplary embodiment of an in vivo analyte monitoring system.

[0021] Figure 2 is a block diagram of an exemplary embodiment of a reader device.

[0022] Figure 3 is a block diagram of an exemplary embodiment of a sensor control device.

[0023] Figure 4A , Figure 4B and Figure 4C are multiple graphs depicting exemplary analyte concentrations measured over time.

[0024] Figure 5 is a flow chart depicting an exemplary embodiment of a method for analyte monitoring.

[0025] Figures 6 to 10 It depicts Figure 5 Flowcharts of alternative embodiments and aspects of the method shown in . DETAILED DESCRIPTION

[0026] Before describing the present subject matter in detail, it should be understood that the present disclosure is not limited to the specific embodiments described, as such may vary. It should also be understood that the terms used herein are only used to describe specific embodiments and are not intended to be limiting. The scope of the present disclosure will be limited only by the appended claims.

[0027] The disclosure discussed herein is only for disclosure prior to the filing date of the present application. Anything herein should not be construed as admitting that the present disclosure is not entitled to be published in advance by virtue of a previous disclosure. In addition, the publication date provided may be different from the actual publication date, which may require independent confirmation.

[0028] In general, embodiments of the present disclosure are used with systems, devices, and methods for detecting at least one analyte (such as glucose) in a body fluid (e.g., injected subcutaneously in interstitial fluid ("ISF") or blood, in dermal fluid of the dermis, or otherwise), and in combination with a ketone analyte sensing feature that is chronologically correlated with the analyte data of an in vivo glucose sensor. Embodiments may include an in vivo analyte sensor that is structurally configured such that at least a portion of the sensor is or can be positioned in the body of a user to obtain information about at least one analyte of the body. However, embodiments disclosed herein may be used with in vivo analyte monitoring systems that incorporate in vitro capabilities as well as purely in vitro or ex vivo analyte monitoring systems (including those that are completely non-invasive). If used with a single analyte in vivo sensor, ketone test data may be manually added, for example by using a test strip. In an alternative, embodiments of the present disclosure may be used with a dual sensor system for continuous or semi-continuous monitoring of different analytes, such as blood glucose and ketone bodies.

[0029] In addition, for each embodiment of the method disclosed herein, systems and devices capable of performing each of these embodiments are covered within the scope of the present disclosure. For example, embodiments of sensor control devices are disclosed, and these devices can have one or more sensors, analyte monitoring circuits (e.g., analog circuits), non-transitory memories (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, processing circuits, and / or controllers (e.g., for executing instructions), which can perform any and all method steps or facilitate the performance of any and all method steps. These sensor control device embodiments can be used and can be used to implement those steps performed by the sensor control device according to one or more of the methods described herein.

[0030] Likewise, embodiments of a reader device having one or more transmitters, receivers, non-transitory memory (e.g., for storing instructions), power supplies, processing circuitry, and / or controllers (e.g., for executing instructions) are disclosed that can perform or facilitate the performance of any and all method steps. These embodiments of the reader device can be used to implement those steps performed by the reader device according to one or more of the methods described herein.

[0031] Implementations of trusted computer systems are also disclosed. These trusted computer systems may include one or more processing circuits, controllers, transmitters, receivers, non-transitory memories, databases, servers, and / or networks, and may be dispersedly located or distributed across multiple geographic regions. These implementations of trusted computer systems may be used to implement the steps performed by the trusted computer system according to one or more of the methods described herein.

[0032] Various embodiments of systems, devices, and methods for improving the accuracy of analyte sensors and for detecting sensor failure conditions are disclosed. According to some embodiments, these systems, devices, and methods can utilize first data collected by a glucose sensor and second data collected by a ketone sensing element.

[0033] Other features and advantages of the disclosed embodiments are discussed further below.

[0034] However, before describing the embodiments in detail, it is first necessary to describe examples of devices that may be present, for example, within an in vivo analyte monitoring system, as well as examples of their operation, all of which may be used with the embodiments described herein.

[0035] Exemplary Embodiments of Analyte Monitoring Systems

[0036] There are various types of analyte monitoring systems. For example, a "continuous analyte monitoring" system (or "continuous glucose monitoring" system) is an in vivo system that can repeatedly or continuously transmit data from a sensor control device to a reader device without prompting (e.g., automatically according to a plan). As another example, a "transient analyte monitoring" system (or "transient glucose monitoring" system or simply "transient" system) can respond to a scan or data request from a reader device, such as by near field communication (NFC) or radio frequency identification (RFID) protocol, to transmit data from the sensor control device. The in vivo analyte monitoring system can also operate without the need for fingertip calibration.

[0037] In vivo monitoring system may include a sensor, which contacts the user's body fluids when positioned in the body and senses one or more analyte levels contained therein. The sensor may be a part of a sensor control device located on the user's body, and includes an electronic device and a power supply that can realize and control analyte sensing. For example, a sensor control device and its variants may also be referred to as a "sensor control unit", "on-body electronic device" device or unit, "on-body" device or unit, or "sensor data communication" device or unit. As used herein, these terms are not limited to devices with analyte sensors, and encompass devices with sensors of other types (whether biometric or non-biometric). The term "on-body" refers to any device directly located on or close to the body, such as a wearable device (e.g., glasses, watches, wristbands or bracelets, neckbands or necklaces, etc.).

[0038] In vivo analyte monitoring system can also include one or more reader devices, which receive sensed analyte data from the sensor control device. These reader devices can process and / or display sensed analyte data or sensor data to the user in any number of forms. These devices and variations thereof can be referred to as "handheld reader device", "reader device" (or simply "reader"), "handheld electronic device" (or handheld device), "portable data processing" device or unit, "data receiver", "receiver" device or unit (or simply receiver), "relay" device or unit, or "remote" device or unit, just to name a few. Other devices (such as personal computers) have also been used with in vivo and in vitro monitoring systems or incorporated into in vivo and in vitro monitoring systems.

[0039] In vivo analyte monitoring systems can be distinguished from "in vitro" systems that contact an in vitro (or more precisely "ex vivo") biological sample and generally include a meter device having a port for receiving an analyte test strip carrying a user's bodily fluid, which can be analyzed to determine the user's analyte level. As described above, the embodiments described herein can be used with in vivo systems, in vitro systems, and combinations thereof.

[0040] Figure 1 1 is an illustration depicting an exemplary embodiment of an in vivo analyte monitoring system 100 having a sensor control device 102 and a reader device 120 communicating with each other via a local communication path (or link) 140, which may be wired or wireless and unidirectional or bidirectional. In embodiments where path 140 is wireless, near field communication (NFC) protocols, RFID protocols, Bluetooth or Bluetooth low energy protocols, WiFi protocols, proprietary protocols, etc. may be used, including those communication protocols existing as of the date of this application or later developed variants thereof.

[0041] Reader device 120 is also capable of wired, wireless, or combined communication with computer system 170 (e.g., a local or remote computer system) via communication path (or link) 141, and communication with network 190 (such as the Internet or the cloud) via communication path (or link) 142. Communication with network 190 may involve communication with a trusted computer system 180 within network 190, or communication with computer system 170 via network 190 via communication link (or path) 143. Communication paths 141, 142, and 143 may be wireless, wired, or both, may be unidirectional or bidirectional, and may be part of a telecommunications network, such as a Wi-Fi network, a local area network (LAN), a wide area network (WAN), the Internet, or other data network. In some cases, communication paths 141 and 142 may be the same path. All communications on paths 140, 141, and 142 may be encrypted, and sensor control device 102, reader device 120, computer system 170, and trusted computer system 180 may each be configured to encrypt and decrypt those communications that are sent and received.

[0042] Variations of devices 102 and 120 and other components of in vivo based analyte monitoring systems suitable for use with the system, device and method embodiments described herein are described in U.S. Patent Publication No. 2011 / 0213225 (the '225 publication), the entire contents of which are incorporated herein by reference for all purposes.

[0043] The sensor control device 102 may include a housing 103, which includes an in vivo analyte monitoring circuit and a power supply. In this embodiment, the in vivo analyte monitoring circuit is electrically coupled to one or more analyte sensors 104, 106, which extend through an adhesive patch 105 and extend from the housing 103. The sensor may include a blood glucose sensor 104 and a ketone sensor 106. In an alternative, a sensor capable of dual analyte sensing may be configured to sense glucose and ketones. For example, a dual analyte sensor disclosed in U.S. Patent Publication No. 2020 / 0237276 ('276 Publication) may be used, the entire contents of which are incorporated herein by reference for all purposes.

[0044] The adhesive patch 105 may include an adhesive layer (not shown) for attachment to a skin surface of a user's body. Other forms of body attachment to the body may be used in addition to or instead of adhesives.

[0045] The glucose sensor 104 (and optionally the ketone sensor 106) can be adapted to be at least partially inserted into the user's body, wherein it can be in fluid contact with the user's body fluids (e.g., subcutaneous (subcutaneous) fluid, dermal fluid, or blood), and used together with the in vivo analyte monitoring circuit to measure the user's analyte-related data. The sensors 104, 106 and any accompanying sensor control electronics can be applied to the body in any desired manner. For example, the insertion device 150 can be used to position all or a portion of the analyte sensor 104 through the outer surface of the user's skin and in contact with the user's body fluids. In doing so, the insertion device can also position the sensor control device 102 and the adhesive patch 105 on the skin. In other embodiments, the insertion device can first position the sensor 104, and then, the accompanying sensor control electronics can be manually or mechanically coupled to the sensor 104. Examples of insertion devices are described in U.S. Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661, all of which are incorporated herein by reference in their entirety for all purposes.

[0046] After collecting raw data from the user's body, the sensor control device 102 can apply analog signal conditioning to the data and convert the data into conditioned raw data in digital form. In some embodiments, the sensor control device 102 can then process the digital raw data into a form representing the measured biometrics (e.g., analyte level) of the user and / or one or more analyte metrics based thereon by an algorithm. For example, the sensor control device 102 may include a processing circuit to perform any method steps described herein by an algorithm. Then, the sensor control device 102 can encode and wirelessly transmit the data of the sensor data indicating the glucose level, ketone level, sensor fault indication and / or processing to the reader device 120, which can then format or graphically process the received data to display digitally to the user. In other embodiments, in addition to wirelessly transmitting the sensor data to another device (e.g., reader device 120), or as a substitute thereof, the sensor control device 102 can graphically process the data in the final form so that it is ready for display and displays the data on the display of the sensor control device 102. In some embodiments, the biometric data is used in its final form (before graphical processing) by the system (eg, for incorporation into a diabetes monitoring regimen) without being processed for display to a user.

[0047] In yet other embodiments, the conditioned raw digital data may be encoded for transmission to another device (e.g., a reader device 120), which then processes the digital raw data by an algorithm into a final form representing the user's measured biometric characteristic (e.g., a form that is easily made suitable for display to the user) and / or one or more analyte metrics based thereon. The reader device 120 may include processing circuitry to perform any of the method steps described herein by an algorithm, such as correcting glucose level measurements, detecting a suspected glucose drop, or detecting a suspected sensor failure condition, or other operations. The algorithm-processed data may then be formatted or graphically processed for digital display to the user.

[0048] In other embodiments, the sensor control device 102 and the reader device 120 transmit the digital raw data to another computer system for algorithmic processing and display.

[0049] The reader device 120 may include: a display 122 for outputting information to a user and / or accepting input from a user; and an optional input component 121 (or more), such as a button, an actuator, a touch-sensitive switch, a capacitive switch, a pressure-sensitive switch, a jog wheel, etc., to input data, commands, or otherwise control the operation of the reader device 120. In some embodiments, the display 122 and the input component 121 may be integrated into a single component, for example, where the display can measure the presence and location of physical contact touches on the display, such as a touch screen user interface. In some embodiments, the input component 121 of the reader device 120 may include a microphone, and the reader device 120 may include software configured to analyze audio input received from the microphone so that the functions and operations of the reader device 120 can be controlled by voice commands. In some embodiments, the output component of the reader device 120 includes a speaker (not shown) for outputting information as an audible signal. Similar voice response components (such as a speaker, a microphone, and software routines for generating, processing, and storing voice-driven signals) may be included in the sensor control device 102.

[0050] The reader device 120 may also include one or more data communication ports 123 for wired data communication with an external device, such as a computer system 170 or a sensor control device 102. Exemplary data communication ports include a USB port, a mini-USB port, a USB Type-C port, a USB micro-A and / or micro-B port, an RS-232 port, an Ethernet port, a FireWire port, or other similar data communication ports configured to connect to a compatible data cable. The reader device 120 may also include an integrated or attachable in vitro glucose meter, including an in vitro test strip port (not shown) to receive an in vitro glucose test strip for performing in vitro blood glucose measurements.

[0051] The display device 120 can display the measured biometric data wirelessly received from the sensor control device 102, and can also be configured to output alarms, alarm notifications, glucose values, etc., which can be visual, auditory, tactile, or any combination thereof. Additional details and other display embodiments can be found, for example, in U.S. Publication No. 2011 / 0193704, the entire contents of which are incorporated herein by reference for all purposes.

[0052] The reader device 120 may serve as a data conduit for transmitting measurement data from the sensor control device 102 to the computer system 170 or the trusted computer system 180. In certain embodiments, data received from the sensor control device 102 may be stored (permanently or temporarily) in one or more memories of the reader device 120 before being uploaded to the systems 170, 180 or the network 190.

[0053] The computer system 170 may be a personal computer, a server terminal, a laptop computer, a tablet computer, or other suitable data processing device. The computer system 170 may be (or include) software for data management and analysis and for communicating with components in the analyte monitoring system 100. The computer system 170 may be used by a user or a medical professional to display and / or analyze biometric data measured by the sensor control device 102. In some embodiments, the sensor control device 102 may transmit the biometric data directly to the computer system 170 without an intermediary such as a reader device 120, or indirectly using an Internet connection (also optionally without first sending to the reader device 120). The operation and use of the computer system 170 may be as further described in the '225 disclosure incorporated herein, with additional method steps for processing ketone data in conjunction with blood glucose data. The analyte monitoring system 100 may also be configured to operate with a data processing module (not shown), also as described in the incorporated '225 disclosure.

[0054] The trusted computer system 180 can be physically or virtually owned by a manufacturer or distributor of the sensor control device 102 via a secure connection, and can be used to perform authentication of the sensor control device 102, to securely store the user's biometric data, and / or to serve as a server for a data analysis program (e.g., accessible via a web browser) for performing analysis on the user's measurement data.

[0055] Exemplary Embodiments of Reader Devices

[0056] The reader device 120 may be a mobile communication device, such as a dedicated reader device (configured to communicate with the sensor control device 102 and optionally the computer system 170, but without mobile phone communication capabilities) or a mobile phone, including but not limited to a Wi-Fi or Internet-enabled smart phone, tablet computer, or personal digital assistant (PDA). Examples of smart phones may include a Operating system, Android TM operating system, operating system, WebOS TM , Operating system or Those mobile phones that operate the system and have data network connection capabilities for data communications via an Internet connection and / or a local area network (LAN).

[0057] The reader device 120 can also be configured as a mobile smart wearable electronic device assembly, such as an optical assembly worn above or near the user's eyes (e.g., smart glass or smart glasses, such as Google Glass, which is a mobile communication device). The optical assembly can have a transparent display that displays information about the user's analyte level (as described herein) to the user while allowing the user to see through the display so that the user's overall vision is minimally obstructed. The optical assembly can be capable of wireless communication similar to a smart phone. Other examples of wearable electronic devices include devices worn around or near a user's wrist (e.g., a watch, etc.), neck (e.g., a necklace, etc.), head (e.g., a headband, a hat, etc.), chest, etc.

[0058] Figure 2 1 is a block diagram of an exemplary embodiment of a reader device 120 configured as a smart phone. Here, the reader device 120 includes an input component 121, a display 122, and a processing circuit 206, which may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip, or distributed between multiple different chips (and portions thereof). Here, the processing circuit 206 includes a communication processor 222 with an on-board memory 223 and an application processor 224 with an on-board memory 225. The reader device 120 also includes an RF communication circuit 228 coupled to an RF antenna 229, a memory 230, a multifunction circuit 232 with one or more associated antennas 234, a power supply 226, a power management circuit 238, and a clock (not shown). One or more of the memories 223, 225 may store program instructions that, when executed by one or more of the processors 222, 224, cause the reader device 120 to perform one or more operations of the methods described herein. Figure 2is an abbreviated representation of typical hardware and functionality residing within a smartphone, and one of ordinary skill in the art will readily recognize that other hardware and functionality (eg, codecs, drivers, glue logic) may also be included.

[0059] The communication processor 222 may interface with the RF communication circuit 228 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that help convert voice, video, and data signals into a format suitable for providing to the RF communication circuit 228 (e.g., in-phase and quadrature), which may then transmit the signals wirelessly. The communication processor 222 may also interface with the RF communication circuit 228 to perform the inverse functions required to receive wireless transmissions and convert them into digital data, voice, and video. The RF communication circuit 228 may include a transmitter and a receiver (e.g., integrated as a transceiver) and associated encoder logic.

[0060] The application processor 224 may be adapted to execute an operating system and any software applications resident on the reader device 120, process video and graphics, and perform other functions unrelated to processing communications sent and received via the RF antenna 229. The smartphone operating system will operate in conjunction with multiple applications on the reader device 120. Any number of applications (also referred to as "user interface applications") may be running on the reader device 120 at any time, and may include one or more applications related to the diabetes monitoring regimen and methods described herein, in addition to other commonly used applications unrelated to the diabetes monitoring regimen (e.g., email, calendar, weather, sports, games, etc.). For example, data indicative of sensed analyte levels and in vitro blood analyte measurements received by the reader device may be securely communicated to a user interface application resident in the memory 230 of the reader device 120. Such communications may be securely performed, for example, through mobile application containerization or packaging techniques.

[0061] The memory 230 may be shared by one or more of the various functional units present within the reader device 120, or may be distributed among two or more of them (e.g., as separate memories present within different chips). The memory 230 may also be its own separate chip. The memories 223, 225, and 230 are non-transitory and may be volatile memory (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0062] The multi-function circuit 232 may be implemented as one or more chips and / or components (e.g., transmitters, receivers, transceivers, and / or other communication circuits) that perform other functions, such as local wireless communication with the sensor control device 102 under an appropriate protocol (e.g., Wi-Fi, Bluetooth, Bluetooth low energy, near field communication (NFC), radio frequency identification (RFID), proprietary protocols, etc.), and determining the geographic location of the reader device 120 (e.g., global positioning system (GPS) hardware). One or more other antennas 234 are associated with the functional circuit 232 as needed to operate using various protocols and circuits.

[0063] The power supply 226 may include one or more batteries, which may be rechargeable or disposable batteries for single use. The power management circuit 238 may regulate battery charging and power supply monitoring, boost voltage, perform DC conversion, and the like.

[0064] Reader device 120 can also include drug (for example, insulin, etc.) delivery device or be integrated with drug delivery device so that they, for example, share a common housing. The example of such drug delivery device can include a drug pump with a cannula remaining in the body to allow infusion (for example, a wearable pump for delivering basal insulin and bolus insulin) in a few hours or days. When combined with a drug pump, reader device 120 can include a reservoir for storing drugs, a pump that can be connected to a transmission tube, and an infusion cannula. The pump can force the drug to pass through the pipeline from the reservoir and enter the body of the diabetic patient by inserting the cannula therein. Other examples of drug delivery devices that can be included (or integrated) with reader device 120 include only a portable injection device (for example, an insulin pen) that pierces the skin and is subsequently removed at each delivery. When combined with a portable injection device, reader device 120 can include an injection needle, a cartridge for carrying drugs, an interface for controlling the amount of drug to be delivered, and an actuator that causes injection to occur. The device can be reused until the drug is exhausted, at which time the combination device can be discarded, or the cartridge can be replaced with a new cartridge, at which time the combination device can be reused. The needle can be replaced after each injection.

[0065] The combined device can be used as a closed-loop system (e.g., an artificial pancreas system that does not require user intervention to operate) or a semi-closed-loop system (e.g., an insulin circuit system that rarely requires user intervention to operate, such as confirming a dose change). For example, the analyte level of a diabetic patient can be monitored in a repeated automatic manner by the sensor control device 102, and then the sensor control device can transmit the monitored analyte level to the reader device 120, and can automatically determine the appropriate drug dose to control the analyte level, and then deliver it to the diabetic patient. Software instructions for controlling the amount of insulin delivered by the pump and can be stored in the memory of the reader device 120 and executed by the processing circuit of the reader device. These instructions can also calculate the drug delivery amount and duration (e.g., a single infusion and / or a basic infusion curve) based on the analyte level measurement value obtained directly or indirectly from the sensor control device 102. In some embodiments, the sensor control device 102 can determine the drug dose and transmit it to the reader device 120.

[0066] Exemplary Embodiments of a Sensor Control Device

[0067] Figure 3 is a block diagram depicting an exemplary embodiment of a sensor control device 102 having an analyte sensor 104 and sensor electronics 250 (including analyte monitoring circuitry) that may have most of the processing capabilities for presenting final result data suitable for display to a user. Figure 3 , a single semiconductor chip 251 is depicted, which may be a customized application specific integrated circuit (ASIC). Shown within ASIC 251 are certain advanced functional units, including an analog front end (AFE) 252, a power management (or control) circuit 254, a processor 256, and a communication circuit 258 (which may be implemented as a transmitter, a receiver, a transceiver, a passive circuit, or otherwise implemented according to a communication protocol). In this embodiment, both AFE 252 and processor 256 are used as analyte monitoring circuits, but in other embodiments, either circuit may perform analyte monitoring functions. Processor 256 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed between several different chips.

[0068] Memory 253 is also included in ASIC 251 and can be shared by various functional units present in ASIC 251, or can be distributed between two or more processors. Memory 253 can also be a separate chip. Memory 253 is non-temporary and can be volatile and / or non-volatile memory. In this embodiment, ASIC 251 is coupled to power supply device 260, which can be a coin-shaped battery or the like. AFE 252 interfaces with one or more in vivo analyte sensors 104, 106, receives measurement data therefrom, and outputs the data to processor 256 in digital form, which in some embodiments can then be processed in any manner described elsewhere herein. The data can then be provided to communication circuit 258 for transmission to reader device 120 (not shown) via antenna 261, for example, in which a resident software application requires minimal further processing to display the data. Antenna 261 can be configured as required by the application and communication protocol. Antenna 261 can be, for example, a printed circuit board (PCB) trace antenna, a ceramic antenna, or a discrete metal antenna. The antenna 261 may be configured as a monopole antenna, a dipole antenna, an F-type antenna, a loop antenna, or the like.

[0069] Information can be transmitted from the sensor control device 102 to the second device (e.g., the reader device 120) at the initiative of the sensor control device 102 or the reader device 120. For example, the sensor control device 102 can automatically and / or repeatedly (e.g., continuously) transmit information when analyte information is available, or according to a schedule (e.g., approximately every 1 minute, approximately every 5 minutes, approximately every 10 minutes, etc.), in which case the information can be stored or recorded in the memory of the sensor control device 102 for later communication. Information can be transmitted from the sensor control device 102 in response to receiving a request by the second device. The request can be an automatic request (e.g., a request transmitted by the second device according to a schedule), or can be a request actively generated by a user (e.g., an ad hoc or manual request). In some embodiments, a manual request for data is referred to as a "scan" of the sensor control device 102 or an "on-demand" data transmission from the device 102. In some embodiments, the second device may transmit a polling signal or data packet to the sensor control device 102, and the device 102 may treat each poll (or polls occurring at specific time intervals) as a request for data and, if data is available, may transmit such data to the second device. In many embodiments, the communication between the sensor control device 102 and the second device is secure (e.g., encrypted and / or between authenticated devices), but in some embodiments, data may be transmitted from the sensor control device 102 in an unsecured manner, for example, as a broadcast to all listening devices within range.

[0070] Different types and / or forms and / or amounts of information may be sent as part of each communication, including, but not limited to, one or more of the following: current sensor measurements (e.g., most recently obtained analyte level information corresponding in time to the time when the reading was initiated), the rate of change of a metric measured over a predetermined time period, the rate of change of a metric (acceleration of the rate of change), or historical metric information corresponding to metric information obtained prior to a given reading and stored in a memory of the sensor control device 102.

[0071] Some or all of the real-time, historical, rate of change, rate of change (such as acceleration or deceleration) information may be sent to the reader device 120 in a given communication or transmission. In certain embodiments, the type and / or form and / or amount of information sent to the reader device 120 may be pre-programmed and / or unchangeable (e.g., preset at the time of manufacture), or may not be pre-programmed or unchangeable, such that it may be selected and / or changed one or more times in the field (e.g., by activating a switch of the system, etc.). Thus, in certain embodiments, the reader device 120 may output the current (real-time) sensor-derived analyte value (e.g., in digital format), the current rate of change of the analyte (e.g., in the form of an analyte rate indicator, such as an arrow pointing in a direction indicating the current rate), and analyte trend historical data (e.g., in the form of a graphical trace) based on sensor readings acquired by the sensor control device 102 and stored in its memory. Additionally, skin or sensor temperature readings or measurements may be collected by an optional temperature sensor 257. These readings or measurements may be communicated from the sensor control device 102 to another device (e.g., the reader 120) (either individually or as aggregated measurements over time). However, the temperature readings or measurements may be used in conjunction with a software routine executed by the reader device 120 to correct or compensate the analyte measurements output to the user, rather than or in addition to actually displaying the temperature measurements to the user.

[0072] In addition, although Figure 3Dual analyte sensors 104, 106 are depicted, but according to many embodiments of the present disclosure, the sensor control device 102 can be configured to collect data indicating multiple physiological measurements, including but not limited to data indicating glucose levels, lactate levels, ketone levels, or heart rate measurements, to name a few. In some embodiments, for example, the sensor 104 can be a dual analyte sensor configured to sense glucose levels and the concentration of another analyte (e.g., lactate, ketones, etc.). For example, additional details about dual analyte sensors are described in the '276 disclosure cited above. In some embodiments, the sensor control device 102 may include multiple discrete sensors, each of which is capable of collecting data indicating any of the above physiological measurements. For example, in some embodiments, the first analyte sensor 104 can be used to sense blood glucose, and the second analyte sensor 106 can be used to sense ketones.

[0073] Embodiments of systems, devices, and methods for combined use of blood glucose and ketone data

[0074] Ketone monitoring systems can provide ketone test alerts based on whether the most recent discrete self-monitored blood glucose (SMBG) measurement exceeds a certain predetermined high glucose threshold. However, the connection between ketones and BG is not static. Instead, a long-term lack of insulin triggers elevated glucagon, which in turn increases glucose release from the liver. Additionally, the absence or very low levels of insulin cause adipose tissue to release free fatty acids, which are converted to ketone bodies (including beta-hydroxybutyrate) in the liver.

[0075] FIG. 4A to FIG. 4C An example of how ketone levels are related to BG levels, but not to each other, is shown. The illustration is based on an insulin pump pause study protocol [MJ Castillo et al., "The degree / rapidity of the metabolic deterioration following interruption of a continuous subcutaneous insulin infusion is influenced by the prevailing blood glucose Level," Journal of Clinical Endocrinology & Metabolism, Vol. 81, pp. 1975-8, May 1, 1996], in which the study participants were patients with type 1 diabetes. FIG. 4A to FIG. 4CThe two study groups shown in were started with different conditions (i.e., with a low basal rate, and with a sufficient basal rate in the insulin pump system). In this study, insulin pump delivery was paused, and analyte levels were measured every hour during the hours after the pump was paused. The numbers are aligned relative to the start of the pause, marked as 0 hours. The analytes measured were insulin ( Figure 4A ),glucose( Figure 4B ) and ketone ( Figure 4C ) concentration levels, in all charts 401, 404, 406, a common key 408 represents data from the first study group 400 and the second study group 410. Specifically, the ketone measurement selected is β-hydroxybutyrate. Since the first study group 400 started with a low basal rate, the insulin concentration 1 hour before and at the beginning of the insulin pump pause (see insulin chart 402) was lower than the insulin concentration of the study group 410 that started with a sufficient basal rate. Therefore, as shown in the glucose chart 404, the glucose concentration of the first study group 400 was higher than the glucose concentration of the second study group 410. In both study groups, as shown in the ketone chart 406, as the insulin pump pause continues, the ketone concentration level gradually increases because the release of free fatty acids is associated with an increase in ketone bodies. Since glucose monitoring is more common than ketone monitoring, the recommendation to check ketones is usually based on the observation of high glucose levels (e.g., the threshold is approximately 240 mg / dL to 250 mg / dL). For example, in the first study group, glucose exceeded 250 mg / dL (about 13.9 mMol / L) about 3 hours after the insulin pump was suspended. The corresponding ketone concentration was slightly higher than 0.6 mMol / L, which is just equal to or higher than the value that is generally considered not to involve DKA. Therefore, based on the data from the first study group, it seems reasonable to use high glucose values ​​as an indicator for detecting DKA risk. However, the same high glucose standard was applied to the second study group, and glucose exceeded 250 mg / dL at about 6 hours, where the ketone measurement value had reached a higher value of about 0.9 mMol / L. Using a lower glucose threshold (such as 150 mg / dL (about 8.3 mMol / L)) will quickly capture the ketones of the second study group exceeding 0.6 mMol / L. However, before the study of the first study group, this lower glucose threshold had been exceeded and therefore could not be correctly used to infer DKA risk.

[0076] Unlike SMBG blood glucose meters, glucose sensor-based blood glucose meters (such as on-demand systems and continuous glucose monitoring (CGM) systems) have access to a much longer historical glucose stream relative to any time point at which a patient queries a glucose measurement. Model-based in vivo ketone estimation using a glucose time series can significantly improve the specificity of DKA risk and thereby provide more reliable reminders for patients to test for ketones. Since DKA risk is directly related to ketone history (in a similar concept to in vivo insulin), ketone test reminders based on this approach can be adjusted relative to reminders based on spot glucose thresholds to improve specificity while maintaining the same or lower false positives and false negatives.

[0077] For manual ketone testing devices, the approach can be used to generate recommendations for ketone measurements to better protect patients from DKA risk. The model can also improve the specificity of in vivo ketone estimates when patients provide adequate insulin use information (by using a built-in insulin calculator).

[0078] The system for implementing the method may include two components. The first is a front end, which is associated with the system user interface (UI). The second is a back end, which performs the calculations required for ketone estimation in the body.

[0079] The front end provides a recommendation to the patient to perform a ketone measurement when a higher risk of DKA is predicted based on estimated body ketones. A brief explanation may be provided on the screen and / or in the user guide explaining that recent glucose history indicates it is a good time to test for ketones.

[0080] The backend updates the estimated in vivo ketone model based on the available glucose time series from the most recent relevant scan or periodic data collection. The model can include a single glucose compartment, a single effective insulin compartment, a single plasma insulin compartment, and a single β-hydroxybutyrate compartment. The glucose compartment, effective insulin compartment, and ketone compartment are represented as model states x g 、x e and x k , the rate of change of these compartments at any consecutive time point t can be described as follows: x iT (t) = x i (t)-x iB (t). Additional variables and inputs are represented as follows: x NHGB is the rate of glucose appearance from the net hepatic glucose balance, u M is the rate of glucose appearance from the meal, x iT is x i The transition element, f iT is the function describing the accumulation of ketones due to insufficient circulating insulin, x iB is x iThe baseline element, and x kB is x k . Parameter p1 is the glucose effectiveness index that controls the insulin-independent glucose clearance rate, p2 is the insulin clearance rate in the effective insulin compartment, p3 is the insulin activation rate constant, p4 is the ketone clearance rate, and p5 is the accumulation rate constant due to insufficient circulating insulin. Variants of this model or other models can be used to provide a way to describe the interconnections between glucose, insulin, and ketones. In general, the dynamic model at any continuous time instance t can be described as a function of the current compartment value represented by the state x and the known or estimated external inputs: or can be described as a function of the current compartment value at any sampling time instance k: g (k+1)=f g2 (k),x e (k+1)=f e2 (k),x k (k+1)=f k2 (k). The dynamic model is updated over time during each scan instance or periodic time interval, where each instance calculates the best estimate of these states (i.e., glucose, effective insulin, plasma insulin, and β-hydroxybutyrate compartments) and their variances. The primary source of measurement is the glucose time series provided by the glucose sensor. Any closed-loop state observer form (such as a Kalman filter) can be used to reconcile the measured values ​​at each time step with the predicted values ​​of the state to obtain a corrected state estimate. For example, the model discussed previously can be used to generate equations for the process model to calculate the average estimate of the predicted state and the covariance of the predicted state. The measurements from one or more glucose sensors can then be used as the primary measurements in the measurement equations to perform state correction or state update in the context of a Kalman filter structure (such as an extended Kalman filter). Each time a patient takes a ketone measurement, the filter uses the additional information to refine the estimate of one or more parameters related to the body ketone estimate based on the glucose data. Therefore, the body ketones are continuously recalculated as a measure of continued exposure to β-hydroxybutyrate levels. This may be a simple integral of the β-hydroxybutyrate level exceeding a minimum threshold, with an assumed decay rate. Whenever beta-hydroxybutyrate exposure levels exceed a predetermined level, the DKA risk is considered high enough to warrant an actual ketone test.

[0081] The state observer framework allows for other additional information to be provided to better refine the DKA risk estimate. In one example, for systems with built-in ketone strip compatibility, measured ketones are used to feed back into the state observer to correct the state estimate of beta-hydroxybutyrate. In a second example, for systems where the patient provides insulin usage information, insulin dosage and timing are used to feed back into the state observer to correct the plasma insulin state estimate. One way to obtain this data is to perform a manual test when the user invokes the built-in insulin calculator on a system with built-in ketone strip compatibility.

[0082] In one embodiment, it is considered to use a ketone sensor to improve the high ketone alarm of patients with type 1 diabetes (T1DM), by using information of other analytes besides ketones to reduce false high ketone alarms that are not related to the risk of diabetic ketoacidosis (DKA). In a single analyte design, a high ketone threshold (which can be pre-set or user-settable) reminds the user of high ketones to avoid the risk of DKA. In a first improved embodiment, information from a glucose sensor is used to distinguish between dangerous high ketone levels caused by insufficient insulin delivery (which may cause DKA) and high ketone levels caused by a successful keto diet. The latter case is accompanied by a specific glucose pattern with very little glucose variability and a very short time in a high glucose state. Then, the first case will issue a high ketone alarm, while the second case will not trigger a high ketone alarm. Typically, this embodiment distinguishes when a threshold of an analyte (such as ketones) is reached, and determines whether the nature of the threshold crossing is worrying (such as the possibility of DKA) or encouraging (such as a successful keto diet).

[0083] In a second embodiment, different types of high ketone notifications / alarms are created. For example, for the first situation described in the first embodiment, a more urgent type of pronunciation is triggered, while for the second situation described in the first embodiment, a more aggressive notification is presented. In other words, the ketone alarm can be modified based on a combined analysis of glucose data and ketone data (e.g., to distinguish between potential DKA and a successful keto diet). For example, the ketone alarm can be modified by adjusting the severity of the alarm, such as changing the alarm type (e.g., sound instead of just vibration), changing the volume, displaying different colors of alarms on the display, whether to repeat the alarm, and the repetition timing, etc. The alarm may also include information associated with whether the alarm is associated with the first situation (e.g., potential DKA) or the second situation (e.g., a successful keto diet).

[0084] In a third embodiment, a high ketone alert may be accompanied by a notification to a healthcare professional (HCP) who consults with the patient when a specific condition is suspected. For example, a person with T1DM may use a class of drugs called SGLT-2 inhibitors (SGLT-2i). This class of drugs was originally developed for managing glucose in people with T2DM, and in some regions, the use of SGLT-2i in people with T1DM is labeled and in other regions it is unlabeled. One effect of taking SGLT-2i is to lower the renal clearance threshold, resulting in high glucose concentrations in the blood being released in the urine. As a result, insulin-dependent cells may be starved of glucose, triggering a process that ultimately leads to DKA, while the measured glucose remains in a good range, known as euglycemic DKA. In order to ensure that the modified alerts described in the first and second embodiments do not appear in error when a person with T1DM takes SGLT-2i, this third embodiment estimates whether a person is taking SGLT-2i by using the following method. When not taking SGLT-2i, a dynamic relationship can be established to estimate ketones based on glucose history. Using data from a glucose sensor and this dynamic relationship, a ketone estimate can be calculated. This ketone estimate can then be compared to the ketone readings from the ketone sensor. If a person with T1DM is also taking an SGLT-2i, the estimated ketones may be consistently lower than the measured ketones. The comparison may conclude that this person may be taking an SGLT-2i, leading to a particular condition. In this case, certain high ketone alerts may have a warning or disclaimer to consult their HCP regarding the co-use of an SGLT-2i. In other words, the ketone alert can be modified based on an assessment of whether the patient is taking a medication associated with renal clearance, such as an SGLT-2i.

[0085] In a fourth embodiment, when a comparison between ketone and glucose data indicates potential DKA rather than keto diet-induced high ketones, the high ketone alarm re-emerges with increased frequency when the high threshold is reached.

[0086] In a fifth embodiment, the predicted high ketones alarm is resolved when the suspected cause of high ketones is from the keto diet rather than underlying DKA.

[0087] In a sixth embodiment, when high ketones are suspected to be caused by a keto diet, alternative information is provided, wherein the alert may provide useful information such as the number of hours that high ketones were achieved, rather than the fact that the most recent ketones exceeded a high threshold. Another alternative information may include a trend of the number of hours or percentage of time that high ketones were achieved per week, a daily trend, or a temporal breakdown of the trend.

[0088] In a seventh embodiment, when use of a drug that may alter renal clearance thresholds (such as an SGLT-2i) is suspected, use of a high glucose alarm is accompanied by a notification in the user interface that the absence of a high glucose alarm may not achieve the intended purpose and to consult the user's HCP for guidance.

[0089] How to use BG and ketone data together

[0090] An exemplary embodiment of a method for using combined glucose and ketone data in the operation of a medical device or system will now be described. Before doing so, one skilled in the art will appreciate that any one or more of the steps of the exemplary methods described herein may be stored as software instructions in a non-transitory memory of a sensor control device, a reader device, a remote computer, or a trusted computer system, such as with reference to Figure 1 Those described. The stored instructions, when executed, may cause the processing circuitry of an associated device or computing system to perform any one or more steps of the exemplary methods described herein. Those skilled in the art will also appreciate that in many embodiments, any one or more of the method steps described herein may be performed using real-time or near real-time sensor data. In other embodiments, any one or more of the method steps may be performed retrospectively with respect to stored sensor data, including sensor data from sensors previously worn by the same user. In some embodiments, the method steps described herein may be performed periodically according to a predetermined schedule, and / or in batches.

[0091] Those skilled in the art will also understand that instructions may be stored in non-transitory memory on a single device (e.g., a sensor control device or a reader device), or, in the alternative, may be distributed across multiple discrete devices that may be located in geographically dispersed locations (e.g., a cloud platform). For example, in some embodiments, data collection indicative of analyte levels (e.g., glucose, ketones) may be performed on a sensor control device, while calculation of analyte metrics (e.g., glucose derivative values, ketone derivative values) and comparison of the analyte metrics to predetermined thresholds may be performed on a reader device, a remote computing system, or a trusted computing system. In some embodiments, collection of analyte data and comparison to predetermined thresholds may be performed only on the sensor control device. Likewise, those skilled in the art will recognize that representations of computing devices in the embodiments disclosed herein (e.g., Figure 1 ) is intended to cover both physical devices and virtual devices (or "virtual machines").

[0092] Figure 5It is a flowchart of an exemplary embodiment of a method 500 for analyte monitoring. The steps of method 500 can be performed by a system including a sensor control unit, the sensor control unit including an analyte sensor having a portion configured to be inserted into a user's body at an insertion site, wherein the portion includes a first sensing element configured to sense a glucose level in a body fluid and a second sensing element configured to sense an analyte of a ketone level (e.g., β-hydroxybutyrate) in a body fluid indicating the same insertion site. In other embodiments, steps 510 and 520 can be performed by a sensor control unit, the sensor control unit including a first analyte sensor and a second analyte sensor, wherein the first analyte sensor is configured to sense a glucose level in a body fluid, and the second analyte sensor is configured to sense an analyte indicating a ketone level in a body fluid, and wherein the first analyte sensor and the second analyte sensor are configured to sense analyte levels at the same local insertion site. In another example, the analyte sensor can be a glucose sensor, and the sensor control unit can be configured to receive data indicating ketone levels such as from a ketone test strip.

[0093] refer to Figure 5 , at step 510, method 500 may include: collecting, by a sensor control device, first time-related data indicating a glucose level and second time-related data indicating a ketone level, wherein the sensor control device includes an analyte sensor, at least a portion of which is inserted into the user's body. According to many embodiments, the first analyte measurement is a glucose derivative, and the second analyte measurement is a ketone derivative. At step 520, a processor of the system (e.g., a processor of a reader device in communication with the sensor control device) may make a determination for controlling the output of a system component. The determination may be based on the first time-related data and the second time-related data. The determination may include at least one of the following: an alarm threshold for one or both of the first time-related data and the second time-related data, a message for output by a reader device in communication with the sensor control device, or a correction of an analyte state estimate in a system memory. The determination may include one or more of these, and each may be provided in combination or separately. For example, the determination may include: determining an alarm threshold, but not determining a message for output.

[0094] For example, determining an alarm threshold may include setting or modifying a threshold for blood glucose, for ketone bodies, or for another analyte, so that the reader device or another system component outputs an alarm when the threshold is exceeded. Under normal conditions, a high glucose alarm may be set to 250 mg / dL, and the reader device or another system component may be configured to output an alarm when the data indicating the glucose level exceeds the first threshold from a lower value. Similarly, a high ketone alarm may be set to 1.3 mMol / L, and the reader device or another system component may be configured to output an alarm when the data indicating the ketone level exceeds the second threshold from a lower value. In one example, the second threshold is modified based on distinguishing between dangerous high ketone levels due to insufficient insulin delivery (which may cause DKA) and high ketone levels due to a successful keto diet. In the former case, the second threshold remains unchanged or is slightly reduced (e.g., to 1.0 mMol / L) to allow for earlier intervention. In the latter case, the second threshold may be increased (e.g., to 1.5 mMol / L) to prevent false high ketone alarms. In another example, the estimate of the amount of diet-related ketones is used to adjust the second threshold, for example, by dynamically adding a fixed fraction of the estimated diet-related ketone value to the second threshold. If the latest diet-related ketone value is estimated to be 0.6 mMol / L, and the fixed fraction is set to 0.9, the second threshold is set so that the threshold is 0.9*0.6 mMol / L higher than the nominal value of the second threshold (previously set to 1.3 mMol / L).

[0095] For example, determining a message may include: selecting a predetermined message from a data table in response to the results of the automatic analysis of the first time-related data and the second time-related data. Under normal conditions, the high glucose alarm may be set to 250 mg / dL, and the reader device or another system component may be configured to output an alarm when the data indicating the glucose level exceeds the first threshold from a lower value. Similarly, the high ketone alarm may be set to 1.3 mMol / L, and the reader device or another system component may be configured to output an alarm when the data indicating the ketone level exceeds the second threshold from a lower value. In one example, if a ketone threshold crossing relative to the second threshold occurs, and most or all of the ketone levels are estimated to be due to insufficient insulin delivery (which may cause DKA) based on the first time-related data and the second time-related data, the message content may convey the need for urgency and corrective action. The pronunciation of the sound alarm and the visual aspects of the message may also correspond to a higher urgency, a louder sound alarm, a higher frequency of re-pronunciation when the alarm is snoozed, and a warmer color on the message. In another example, if a ketone threshold crossing relative to a second threshold occurs, and it is estimated based on the first time-related data and the second time-related data that no or only a very small amount of ketone levels are due to insufficient insulin delivery (which may lead to DKA), the message content may be congratulatory and may not affect more urgent messages (such as impending hypoglycemia or hyperglycemia). In another example, if a ketone threshold crossing relative to a second threshold occurs, and it is estimated based on the first time-related data and the second time-related data that no or only a very small amount of ketone levels are due to a successful keto diet, the message content may convey urgency and the need for corrective action. The pronunciation of the sound alarm and the visual aspects of the message may also correspond to a higher urgency, a louder sound alarm, a higher frequency of re-sounding when the alarm is snoozed, and a warmer color on the message. In another example, if a ketone threshold crossing relative to a second threshold occurs, and it is estimated based on the first time-related data and the second time-related data that most or all of the ketone levels are due to a successful keto diet, the message content may be congratulatory and may not affect more urgent messages (such as impending hypoglycemia or hyperglycemia).

[0096] For example, determining a correction to an analyte state estimate may include calculating a correction factor or correction value for an initial estimate of blood glucose or other analyte based only on first time-related data. For example, time-related data indicating glucose levels may contain slowly varying errors. Prior to calculating the correction factor, the estimated state associated with glucose may also be affected by the error. Conversely, the correction factor may be applied to adjust the state associated with glucose to improve the estimated blood glucose value.

[0097] Combination Figures 6 to 10A more detailed description of these determinations and related operations is provided above.

[0098] At step 530, method 500 may include: outputting a determined indication by a reader device in communication with a sensor control device. For example, the reader device may output an audible and / or visual alarm, display an alarm message, display an information message, or correct one or more analyte values ​​stored in a memory and set an indicator indicating that the value is corrected. In some embodiments, the generation of an alarm indication may include: outputting a notification or message for display by a mobile device of a user running a reader application. In an alternative, or in addition, the alarm indication may include one or more of a visual, audio, or vibration alarm or an alarm output to a display of a reader device, a remote computer, or a trusted computer system. Remedial measures may optionally be performed in response to or in place of the alarm. For example, in some embodiments, the remedial measure may be an indication of suppressing or modifying a low glucose alarm. In other embodiments, the remedial measure may include: preventing a command from being issued to change or cause a drug (e.g., insulin) to be delivered by an automatic drug delivery system (e.g., an insulin pump).

[0099] In some embodiments, determining at step 520 of method 500 may include: Figure 6 600. At step 610, making the determination may include reconciling the measured values ​​at each time step with the predicted values ​​of the estimated state of the analyte of interest using a closed-loop state observer to obtain a corrected state estimate of the analyte of interest. An example of a closed-loop state observer is a Kalman filter or a variant thereof. The process model and the measurement model may be constructed based on a dynamic model involving available measurements such as glucose, effective insulin, and ketones. There may be model states corresponding to these quantities, denoted by x and y, respectively. g 、x e and x k represents the glucose, effective insulin, and ketone related states. In general, the dynamic model at any continuous time instance t can be described as a function of the current compartment value x and known or estimated external inputs: or can be described as a function of the current compartment value at any sampling time instance k: g (k+1)=f g2 (k),x e (k+1)=f e2 (k),x k (k+1)=f k2 (k). At each time step, the process model will predict the possible g 、x e or x kThe value of the latest state of . A closed-loop state observer (such as a Kalman filter) then uses the available measurements to obtain a corrected state estimate for the analyte of interest. In one aspect of operation 600, at 620, the second time-related data may be or may include data from a sensor for beta-hydroxybutyrate. At step 630, making a determination may also include: periodically recalculating ketones in the body based on sensor data indicating beta-hydroxybutyrate using known correlation factors. The processor of the reader device may perform periodic recalculations at each time step, for example, once every tenth of a second, once every second, once every ten seconds, or once every minute, etc. In some embodiments, frequent measurements of beta-hydroxybutyrate may not be available, and the corrector of the closed-loop state observer may only be able to access ketone measurements when using ketone test strip data. At step 640, making a determination may also include: based on the value of the ketone-related state exceeding a predetermined level, selecting a message for output indicating that a patient wearing the sensor control device should undergo a ketone test. Return FIG. 4A to FIG. 4C , meaning that the ketone-related state replaces frequent β-hydroxybutyrate measurements, and the threshold can be the same as that used if frequent β-hydroxybutyrate was available for measurement, or some confidence interval across the threshold is used to determine when to issue an output message indicating that a patient wearing the sensor-controlled device should undergo a ketone test.

[0100] In other embodiments, determining at step 520 of method 500 may include: Figure 7 Certain additional operations and aspects shown in 700. At 710, the second time-related data can be or can include data from a sensor for beta-hydroxybutyrate. In this embodiment, as shown at 720, the input device is operatively coupled to at least one of the reader device or the sensor control device and is configured to receive a ketone test result. For example, the sensor control device can be configured with a reader for a ketone test strip. At step 730, determining also includes: correcting the estimate of beta-hydroxybutyrate based on the ketone test result. For example, using a closed-loop state observer such as a Kalman filter, when additional ketone test results are available, a state correction process can be performed using a standard Kalman filter framework.

[0101] In other embodiments, determining at step 520 of method 500 may include: Figure 8800. In these embodiments, as shown in 810, the input device can be operatively coupled to at least one of the reader device or sensor control device configured to receive information defining insulin dosage by the patient wearing the sensor control device. For example, the reader device can prompt the user to input insulin dosage information at a specific time or in response to user input. At step 820, method 500 can also include: correcting the estimation of the patient's plasma insulin state. For example, using a closed-loop state observer like a Kalman filter, when additional insulin results are available, a state correction process can be performed using a standard Kalman filter framework. In fact, the patient's plasma insulin state can be in the form of insulin type and amount. The amount can be the delivery rate of an insulin delivery device (e.g., an insulin pump), or the form of an insulin bolus dose of an insulin delivery device (e.g., an insulin pump or a connected insulin pen).

[0102] In an alternative, or in addition, as shown at 830, an input device can be operatively coupled to at least one of the reader device or the sensor control device, configured to receive the ketone test result. For example, the sensor control device can be configured with a reader for a ketone test strip, or the reader device can prompt a user to enter the ketone test result. At step 840, method 500 can include automatically executing an insulin dose calculation algorithm in response to receiving the ketone test result.

[0103] In other embodiments, determining at step 520 of method 500 may include: FIG. 9A to FIG. 9B900. At step 910, the operation may include: distinguishing dangerous high ketone levels due to insufficient insulin delivery and high ketone levels due to a successful keto diet based at least in part on the first time-related data indicating the glucose level. For example, at step 920, the reader device may determine whether the first time-related data indicates a condition characterized by a glucose variability below a threshold, a high glucose less than a maximum time threshold, and a ketone level greater than a predetermined threshold. At step 925, if the condition is detected, the system may perform at least one of the following: suppressing the high ketone alarm, or reducing the urgency of the high ketone alarm. For example, at step 930, the system may suppress the high ketone alarm until the indicated condition is no longer met, or may attach a notification to the alarm indicating that the ketone condition is not indicative or urgent, or may indicate ketosis caused by a low-carbohydrate diet. In an alternative, or in addition, at step 940, the system may output a message of interest to a user interested in achieving intentional dietary ketosis, for example, causing the message to indicate a time period for achieving a high ketone level, i.e., how long the ketosis state lasts. In general, the embodiments distinguish when a threshold for an analyte (e.g., ketones) is reached, determining whether the nature of the threshold crossing is worrisome (e.g., possibility of DKA) or encouraging (e.g., successful keto diet). Depending on the determination, the content of the message sent (i.e., what is conveyed to the user, whether it is informative or encouraging) and the timing of the message sending (i.e., whether the message is sent upon a trigger or at a predetermined notification time, whether the tone reoccurs periodically until the condition disappears, or whether the tone occurs only upon a trigger) may vary.

[0104] In the alternative, or in addition, if the system determines at step 925 that the condition is not satisfied, or if the system does not perform test step 920, the system may determine at step 960 whether the first time-related data indicates a condition characterized by glucose variability above a threshold, high glucose greater than a maximum time threshold, and ketone levels greater than a predetermined threshold. If at 965 the system determines that the condition is satisfied, then at step 970 the system may increase at least one of the frequency or urgency of messages indicating that euglycemic DKA may occur in the patient wearing the sensor control device.

[0105] In other embodiments, determining at step 520 of method 500 may include: Fig.10, and certain additional operations 1000 shown in . At step 1010, method 500 may include: estimating, by the system processor, whether the patient wearing the sensor control device is a type 1 diabetes patient taking an SGLT-2 inhibitor based on comparing the ketone estimate based on the first time-related data with the ketone level indicated by the second time-related data. At step 1020, the method may include: determining whether the ketone estimate is consistently below the indicated ketone level, and if so, determining a message including an indication that the patient should consult his or her healthcare provider regarding the use of the SGLT-2 inhibitor. Additionally, or in the alternative, at step 1030, method 500 may include: causing the message to include an indication that the lack of a high glucose alarm may not achieve the intended purpose of glucose monitoring.

[0106] Additionally, in some embodiments, any of the method steps described herein may be performed on a remote monitoring device or a cloud-based server communicatively coupled to the remote monitoring device, including but not limited to making a determination (step 520) and / or outputting an indication of the determination (step 530). In some embodiments, the remote monitoring device may include, for example, an auxiliary reader device (e.g., a second smart phone) configured to be used by a third-party caregiver (e.g., a parent of a child wearing a sensor, an adult child of an elderly parent wearing a sensor, or a healthcare professional responsible for monitoring a patient wearing a sensor). In many embodiments, the auxiliary reader device may include one or more processors coupled to a memory for storing a remote analyte monitoring program configured to perform any one or more of the method steps described herein. For example, in certain embodiments, the remote analyte monitoring program on the auxiliary reader device may output an audible and / or visual alarm, display an alarm message, display an information message, or correct one or more analyte values ​​stored in a storage set and correct an indicator indicating the value. In some embodiments, the generation of an alarm indication may include outputting a notification or message for display by an auxiliary reader device running the remote analyte monitoring program. In an alternative, or in addition, the alarm indication may include one or more of a visual, audio, or vibration alarm or alarm output to a display of the auxiliary reader device. According to another aspect of some embodiments, the remote analyte monitoring program may allow an administrator to configure their own alarm settings, such as enabling or disabling certain alarms, or changing certain analyte thresholds. Additional details of the remote analyte monitoring program are described in the following disclosures: U.S. Publication No. 2022 / 0248988 and U.S. Publication No. 2022 / 0240819, the entire contents of which are incorporated herein by reference for all purposes.

[0107] For each embodiment of the method disclosed herein, the system and device capable of executing each of these embodiments are covered within the scope of the present disclosure. For example, an embodiment of a sensor control device is disclosed, and these devices may have one or more analyte sensors, analyte monitoring circuits (e.g., analog circuits), memories (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, clocks, counters, time, temperature sensors, processors (e.g., for executing instructions), which may perform any and all method steps or facilitate the execution of any and all method steps. These sensor control device embodiments may be used for and may be used to implement those steps performed by the sensor control device according to any and all methods described herein. Similarly, an embodiment of a reader device is disclosed, and these devices may have one or more memories (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, clocks, counters, time, processors (e.g., for executing instructions), which may perform any and all method steps or facilitate the execution of any and all method steps. These reader device embodiments may be used for and may be used to implement those steps performed by the reader device according to any and all methods described herein. Computer device and server embodiments are disclosed, and these devices may have one or more memories (e.g., for storing instructions), power supplies, communication circuits, transmitters, receivers, clocks, counters, times, and processors (e.g., for executing instructions) that may perform any and all method steps or facilitate the performance of any and all method steps. These reader device embodiments may be used and can be used to implement those steps performed by the reader device according to any and all methods described herein.

[0108] Computer program instructions for performing operations according to the described subject matter may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, JavaScript, Smalltalk, C++, C#, Transact-SQL, XML, PHP, and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program instructions may be executed entirely on the user's computing device, partially on the user's computing device as a separate software package, partially on the user's computing device, and partially on a remote computing device, or entirely on a remote computing device or server. In the latter case, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0109] It should be noted that all features, elements, components, functions and steps described with respect to any embodiment provided herein are intended to be freely combined and replaced with those from any other embodiment. If a feature, element, component, function or step is described only for one embodiment, it should be understood that the feature, element, component, function or step can be used with each other embodiment described herein unless otherwise explicitly stated. Therefore, this paragraph serves as a pre-condition and written support for the introduction of claims at any time, which combine the features, elements, components, functions and steps of different embodiments, or replace those of one embodiment with the features, elements, components, functions and steps of another embodiment, even if the foregoing description does not explicitly state, but in certain circumstances, such a combination or replacement is possible. It is explicitly acknowledged that the description of each possible combination and replacement is too cumbersome, especially considering that the permissibility of each such combination and replacement will be easily recognized by a person of ordinary skill in the art. Aspects of the invention are set forth in the independent claims. Preferred features are set forth in the dependent claims and can be implemented in combination with each aspect set forth in the independent claims. A device including a device for implementing each method is also provided. Features of one aspect may be applied to each aspect alone or in combination with other features.

[0110] To the extent that the embodiments disclosed herein include or operate in association with memory, storage, and / or computer-readable media, then the memory, storage, and / or computer-readable media are non-transitory. Thus, to the extent that memory, storage, and / or computer-readable media are covered by one or more claims, then the memory, storage, and / or computer-readable media are only non-transitory.

[0111] As used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise.

[0112] Although the embodiments are susceptible to various modifications and alternative forms, specific examples thereof have been shown in the drawings and described in detail herein. However, it should be understood that these embodiments are not limited to the specific forms disclosed, but rather, these embodiments will cover all modifications, equivalents, and alternatives that fall within the spirit of the present disclosure. In addition, any features, functions, steps, or elements of the embodiments may be listed in or added to the claims, as well as negative limitations on the scope of the invention of the claims by defining features, functions, steps, or elements that are not within the scope.

[0113] Systems, devices, and methods for dual analyte sensors are provided that use glucose history from a glucose sensor in combination with data from a ketone sensor to control the operation of a user interface device or an insulin pump. In some embodiments, the system, device, or method can utilize a combination of glucose history and a β-hydroxybutyrate physiological model to better predict diabetic ketoacidosis (DKA) than predictions based on a simple high glucose threshold. In other embodiments, the system, device, or method can include features for generating patient medication state estimates and / or medication information knowledge, such as T1 type 1 diabetes (DM) patients using SGLT-2 inhibitors.

[0114] The disclosure of this application also includes the following numbered clauses:

[0115] 1. An analyte monitoring system comprising:

[0116] a sensor control device including an analyte sensor, wherein the analyte sensor includes at least a portion configured to be inserted into a user, wherein the sensor control device is configured to collect first time-dependent data indicative of a glucose level and second time-dependent data indicative of a ketone level, and wherein the sensor control device is operatively coupled to a first processing circuit and a first non-transitory memory; and

[0117] a reader device comprising a second processing circuit and a second non-transitory memory,

[0118] Wherein, at least one of the first non-transitory memory or the second non-transitory memory includes an instruction, which, when executed, causes at least one of the first processing circuit or the second processing circuit to execute:

[0119] Based on the first time-related data and the second time-related data, at least one of the following is determined:

[0120] an alarm threshold for one or both of the first time-related data and the second time-related data,

[0121] for messages output by a reader device, or

[0122] corrections to analyte state estimates; and

[0123] An indication of the determination is output by the reader device.

[0124] 2. The analyte monitoring system of clause 1, wherein the instructions for determining further cause the measured value at each time step to be reconciled with a predicted value of the estimated state of the analyte of interest using a closed-loop state observer formulation to obtain a corrected state estimate of the analyte of interest.

[0125] 3. An analyte monitoring system according to clause 1 or 2, wherein the second time-related data comprises data from a ketone sensor.

[0126] 4. An analyte monitoring system according to clause 3, wherein the second time-related data includes data indicative of beta-hydroxybutyrate.

[0127] 5. The analyte monitoring system of clause 4, wherein the instructions for making the determination further cause periodic recalculation of ketones in the body based on the data indicative of beta-hydroxybutyrate.

[0128] 6. An analyte monitoring system according to clause 4 or 5, wherein the instructions for making the determination cause selection of a message for output indicating that a patient wearing the sensor control device should undergo a ketone test based on data from the sensor exceeding a predetermined level of beta-hydroxybutyrate.

[0129] 7. An analyte monitoring system according to any of the preceding clauses, wherein the second time-related data includes data of beta-hydroxybutyrate from the sensor, and further comprising an input device operatively coupled to at least one of a reader device or a sensor control device configured to receive a ketone test result, wherein the instructions for making the determination cause the estimate of beta-hydroxybutyrate to be corrected based on the ketone test result.

[0130] 8. An analyte monitoring system according to any of the preceding clauses, further comprising an input device operatively coupled to at least one of a reader device or a sensor control device configured to receive information defining an insulin dose by a patient wearing the sensor control device, wherein the instructions for making the determination cause a correction to be made to the estimate of the patient's plasma insulin state.

[0131] 9. An analyte monitoring system according to any of the preceding clauses, further comprising an input device operatively coupled to at least one of a reader device or a sensor control device configured to receive a ketone test result, wherein the instructions further cause an insulin dosage calculation algorithm to be automatically executed in response to receiving the ketone test result.

[0132] 10. The analyte monitoring system according to any preceding clause, wherein the determined indication is an alarm condition for one analyte, and wherein the alarm condition is based on more than one analyte.

[0133] 11. An analyte monitoring system according to clause 10, wherein the instructions for distinguishing cause determining whether the first time-related data indicates conditions including glucose variability below a threshold, high glucose less than a maximum time threshold, and ketone levels greater than a predetermined threshold.

[0134] 12. The analyte monitoring system of clause 11, wherein the instructions for determining cause at least one of: suppressing a high ketone alarm, or reducing the urgency of a high ketone alarm.

[0135] 13. The analyte monitoring system of clause 11 or 12, wherein the instructions for determining cause a message to indicate a time period during which the high ketone level was reached.

[0136] 14. An analyte monitoring system according to any of clauses 10 to 13, wherein the instructions for distinguishing cause a determination as to whether the first time-related data indicates a condition including glucose variability above a threshold, high glucose greater than a maximum time threshold, and ketone levels greater than a predetermined threshold, and if the condition is detected, at least one of increasing the frequency or urgency of messages indicating that euglycemic DKA may occur in a patient wearing the sensor control device is performed.

[0137] 15. An analyte monitoring system according to any of the preceding clauses, wherein the instructions for making the determination further provide for estimating whether a patient wearing the sensor-controlled device is a type 1 diabetic patient taking an SGLT-2 inhibitor based on comparing a ketone estimate based on the first time-related data with a ketone level indicated by the second time-related data.

[0138] 16. An analyte monitoring system according to clause 15, wherein the instructions for making the determination are to determine whether the ketone estimate is consistently below the indicated ketone level, and if so, to determine a message including an indication that the patient should consult their healthcare provider regarding use of an SGLT-2 inhibitor.

[0139] 17. The analyte monitoring system of clause 16, wherein the instructions for determining further cause determining a message including an indication that the absence of a high glucose alarm may not achieve an intended purpose.

[0140] 18. An analyte monitoring system according to any preceding clause, wherein the instructions are stored on a second non-transitory memory.

[0141] 19. An analyte monitoring system according to any preceding clause, wherein the instructions are stored on a first non-transitory memory.

[0142] 20. An analyte monitoring system according to any preceding clause, wherein the sensor control device further comprises wireless communication circuitry configured to transmit the first data and the second data to a reader device.

[0143] 21. The analyte monitoring system of clause 20, wherein the wireless communication circuit is configured to transmit the first data and the second data according to a Bluetooth protocol.

[0144] 22. An analyte monitoring system according to any of the preceding clauses, wherein the analyte sensor is a first analyte sensor, wherein the sensor control device further comprises a second analyte sensor, wherein the first analyte sensor is configured to sense glucose levels in a body fluid, and wherein the second analyte sensor is configured to sense ketone levels in a body fluid.

[0145] 23. An analyte monitoring system according to any preceding clause, further comprising a drug delivery device.

[0146] 24. The analyte monitoring system of clause 23, wherein the drug delivery device comprises an insulin pump.

[0147] 25. A computer-implemented method for detecting suspected glucose loss, the method comprising:

[0148] collecting, by a sensor control device, first time-dependent data indicative of a glucose level and second time-dependent data indicative of a ketone level, wherein the sensor control device includes an analyte sensor at least a portion of which is inserted into a user;

[0149] Based on the first time-related data and the second time-related data, at least one of the following is determined:

[0150] an alarm threshold for one or both of the first time-related data and the second time-related data,

[0151] for a message output by a reader device communicating with a sensor control device, or

[0152] corrections to analyte state estimates; and

[0153] The determined indication is output by a reader device in communication with the sensor control device.

[0154] 26. The method of clause 25, wherein determining further comprises: reconciling the measured value at each time step with the predicted value of the estimated state of the analyte of interest using a closed-loop state observer formulation to obtain a corrected state estimate of the analyte of interest.

[0155] 27. A method according to clause 24 or 25, wherein the second time-related data comprises data from a glucose sensor.

[0156] 28. The analyte monitoring system of clause 27, wherein the second time-related data comprises data indicative of beta-hydroxybutyrate.

[0157] 29. The method of clause 28, wherein determining further comprises periodically recalculating ketones in the body based on data indicative of beta-hydroxybutyrate.

[0158] 30. The method of clause 28 or 29, wherein making the determination further comprises selecting a message for output indicating that a patient wearing the sensor control device should perform a ketone test based on data from the sensor exceeding a predetermined level of beta-hydroxybutyrate.

[0159] 31. A method according to any of clauses 25 to 29, wherein the second time-related data includes data of beta-hydroxybutyrate from a sensor, wherein the input device is operatively coupled to at least one of a reader device or a sensor control device configured to receive a ketone test result, and wherein determining further includes: correcting the estimate of beta-hydroxybutyrate based on the ketone test result.

[0160] 32. A method according to any of the preceding clauses, wherein the input device is operatively coupled to at least one of a reader device or a sensor control device configured to receive information defining an insulin dose by a patient wearing the sensor control device, and determining further comprises: correcting an estimate of the patient's plasma insulin state.

[0161] 33. A method according to any preceding clause, wherein the input device is operatively coupled to at least one of a reader device or a sensor control device configured to receive a ketone test result, further comprising: automatically executing an insulin dosage calculation algorithm in response to receiving the ketone test result.

[0162] 34. The method according to any preceding clause, wherein the determined indication is an alarm condition for one analyte, and wherein the alarm condition is based on more than one analyte.

[0163] 35. The method of clause 34, wherein distinguishing further comprises determining whether the first time-related data indicates a condition characterized by glucose variability below a threshold, high glucose less than a maximum time threshold, and ketone levels greater than a predetermined threshold.

[0164] 36. The method of clause 35, wherein determining comprises at least one of: suppressing a high ketone alarm, or reducing the urgency of a high ketone alarm.

[0165] 37. The method of clause 35 or 36, wherein determining comprises causing a message to indicate a time period during which high ketone levels are reached.

[0166] 38. A method according to any of clauses 34 to 37, wherein the distinguishing comprises: determining whether the first time-related data indicates a condition characterized by glucose variability above a threshold, high glucose greater than a maximum time threshold, and ketone levels greater than a predetermined threshold, and if the condition is detected, performing at least one of increasing the frequency or urgency of messages indicating that normoglycemic DKA may occur in a patient wearing the sensor control device.

[0167] 39. A method according to any of the preceding clauses, wherein determining further comprises estimating whether a patient wearing the sensor-controlled device is a type 1 diabetes patient taking an SGLT-2 inhibitor based on comparing a ketone estimate based on the first time-related data with a ketone level indicated by the second time-related data.

[0168] 40. The method of clause 39, wherein making the determination comprises determining whether the ketone estimate is consistently below the indicated ketone level, and if so, determining a message including an indication that the patient should consult their healthcare provider regarding use of the SGLT-2 inhibitor.

[0169] 41. The method of clause 40, wherein the determining is further made such that the message includes an indication that the absence of the high glucose alarm may not achieve the intended purpose.

[0170] 42. The method according to any preceding clause, further comprising: wirelessly transmitting the first data and the second data from the sensor control device to the reader device.

[0171] 43. A computer program, computer program product or computer readable medium comprising instructions which, when executed by a processor, cause the processor to perform the method of any of clauses 25 to 42.

[0172] 44. An apparatus comprising:

[0173] means for collecting, by a sensor control device, first time-dependent data indicative of glucose levels and second time-dependent data indicative of ketone levels, wherein the sensor control device includes an analyte sensor at least a portion of which is inserted into a user;

[0174] means for determining, based on the first time-related data and the second time-related data, at least one of:

[0175] an alarm threshold for one or both of the first time-related data and the second time-related data,

[0176] for a message output by a reader device communicating with a sensor control device, or

[0177] corrections to analyte state estimates; and

[0178] Means for outputting the determined indication by a reader device in communication with the sensor control device.

[0179] 45. The apparatus according to clause 44, further comprising means for implementing the method of any one of clauses 25 to 42.

Claims

1. An analyte monitoring system comprising: a sensor control device comprising an analyte sensor, wherein the analyte sensor comprises at least a portion configured to be inserted into a body of a user, wherein the sensor control device is configured to collect first time-dependent data indicative of a glucose level and second time-dependent data indicative of a ketone level, and wherein the sensor control device is operatively coupled to a first processing circuit and a first non-transitory memory; and a reader device comprising a second processing circuit and a second non-transitory memory, Wherein, at least one of the first non-transitory memory or the second non-transitory memory includes instructions, which, when executed, cause at least one of the first processing circuit or the second processing circuit to perform: Based on the first time-related data and the second time-related data, at least one of the following is determined: an alarm threshold for one or both of the first time-related data and the second time-related data, for a message output by said reader device, or corrections to analyte state estimates; and An indication of the determination is output by the reader device.

2. The analyte monitoring system of claim 1, wherein: The instructions for making the determination further cause reconciling the measured value at each time step with a predicted value of the estimated state of the analyte of interest using a closed-loop state observer formulation to obtain a corrected state estimate of the analyte of interest.

3. The analyte monitoring system of claim 2, wherein: The second time-related data includes data from a ketone sensor.

4. The analyte monitoring system of claim 3, wherein: The second time-related data includes data indicative of beta-hydroxybutyrate.

5. The analyte monitoring system of claim 4, wherein: The instructions for making the determination further cause periodic recalculation of ketones in the body based on the data indicative of beta-hydroxybutyrate.

6. The analyte monitoring system of claim 4, wherein: The instructions for making the determination cause a message to be selected for output indicating that a patient wearing the sensor control device should undergo a ketone test based on data from the sensor exceeding a predetermined level of beta-hydroxybutyrate.

7. The analyte monitoring system of claim 1, wherein: The second time-related data includes data of β-hydroxybutyrate from a sensor, and also includes an input device operatively coupled to at least one of the reader device or the sensor control device configured to receive a ketone test result, wherein the instructions for making the determination cause the estimate of β-hydroxybutyrate to be corrected based on the ketone test result.

8. The analyte monitoring system of claim 1, further comprising an input device operatively coupled to at least one of the reader device or the sensor control device configured to receive information defining an insulin dose by a patient wearing the sensor control device, wherein The instructions for making the determination cause an estimate of the patient's plasma insulin state to be corrected.

9. The analyte monitoring system of claim 8, further comprising an input device operatively coupled to at least one of the reader device or the sensor control device configured to receive a ketone test result, wherein The instructions also cause automatically executing an insulin dosage calculation algorithm in response to receiving the ketone test result.

10. The analyte monitoring system of claim 1, wherein: The determined indication is an alarm condition for one analyte, and wherein the alarm condition is based on more than one analyte.

11. The analyte monitoring system of claim 10, wherein: The instructions for distinguishing cause determining whether the first time-related data indicates conditions including glucose variability below a threshold, high glucose less than a maximum time threshold, and ketone levels greater than a predetermined threshold.

12. The analyte monitoring system of claim 11, wherein: The instructions for determining cause at least one of: suppressing a high ketone alarm, or reducing the urgency of a high ketone alarm.

13. The analyte monitoring system of claim 11, wherein: The instructions for the determining cause the message to indicate a time period during which the high ketone level was reached.

14. The analyte monitoring system of claim 10, wherein: The instructions for distinguishing cause a determination as to whether the first time-related data indicates conditions including glucose variability above a threshold, high glucose greater than a maximum time threshold, and ketone levels greater than a predetermined threshold, and if the conditions are detected, at least one of increasing the frequency or urgency of messages indicating that euglycemic DKA may occur in a patient wearing the sensor control device is performed.

15. The analyte monitoring system of claim 1, wherein: The instructions for making the determination further cause estimating whether the patient wearing the sensor-controlled device is a type 1 diabetes patient taking an SGLT-2 inhibitor based on comparing the ketone estimate based on the first time-related data with the ketone level indicated by the second time-related data.

16. The analyte monitoring system of claim 15, wherein: The instructions for making the determination provide for determining whether the ketone estimate is consistently below the indicated ketone level, and if so, determining a message including an indication that the patient should consult the patient's healthcare provider regarding use of the SGLT-2 inhibitor.

17. The analyte monitoring system of claim 16, wherein: The instructions for making the determination further cause determining a message including an indication that the absence of a high glucose alarm may not achieve an intended purpose.

18. The analyte monitoring system of claim 1, wherein: The instructions are stored on the second non-transitory memory.

19. The analyte monitoring system of claim 1, wherein: The instructions are stored on the first non-transitory memory.

20. The analyte monitoring system of claim 1, wherein: The sensor control device also includes a wireless communication circuit configured to transmit first data and second data to the reader device.

21. The analyte monitoring system of claim 20, wherein: The wireless communication circuit is configured to transmit the first data and the second data according to a Bluetooth protocol.

22. The analyte monitoring system of claim 1, wherein: The analyte sensor is a first analyte sensor, wherein the sensor control device further comprises a second analyte sensor, wherein the first analyte sensor is configured to sense a glucose level in a body fluid, and wherein the second analyte sensor is configured to sense a ketone level in the body fluid.

23. The analyte monitoring system of claim 1, further comprising a drug delivery device.

24. The analyte monitoring system of claim 23, wherein: The drug delivery device comprises an insulin pump.

25. A computer-implemented method for detecting suspected glucose loss, the method comprising: collecting, by a sensor control device, first time-dependent data indicative of a glucose level and second time-dependent data indicative of a ketone level, wherein the sensor control device includes an analyte sensor, at least a portion of the analyte sensor being inserted into a user; Based on the first time-related data and the second time-related data, at least one of the following is determined: an alarm threshold for one or both of the first time-related data and the second time-related data, for a message output by a reader device in communication with the sensor control device, or corrections to analyte state estimates; and An indication of the determination is output by a reader device in communication with the sensor control device.

26. The method according to claim 25, wherein: Making the determination further includes reconciling the measured value at each time step with a predicted value of the estimated state of the analyte of interest using a closed-loop state observer formulation to obtain a corrected state estimate of the analyte of interest.

27. The method according to claim 26, wherein: The second time-related data includes data from a ketone sensor.

28. The method according to claim 27, wherein: The second time-related data includes data indicative of beta-hydroxybutyrate.

29. The method according to claim 28, wherein: Making the determination also includes periodically recalculating ketones in the body based on data indicative of beta-hydroxybutyrate.

30. The method of claim 28, wherein: Making the determination also includes selecting for output a message indicating that a patient wearing the sensor control device should undergo a ketone test based on data from the sensor exceeding a predetermined level of beta-hydroxybutyrate.

31. The method of claim 25, wherein: The second time-related data includes data of β-hydroxybutyrate from a sensor, wherein an input device is operatively coupled to at least one of the reader device or the sensor control device configured to receive a ketone test result, wherein making the determination further includes correcting an estimate of β-hydroxybutyrate based on the ketone test result.

32. The method of claim 25, wherein: An input device is operatively coupled to at least one of the reader device or the sensor control device configured to receive information defining an insulin dose by a patient wearing the sensor control device, and making the determination further comprises correcting an estimate of the patient's plasma insulin state.

33. The method of claim 25, wherein: An input device is operatively coupled to at least one of the reader device or the sensor control device configured to receive a ketone test result, further comprising automatically executing an insulin dosage calculation algorithm in response to receiving the ketone test result.

34. The method of claim 25, wherein: The determined indication is an alarm condition for one analyte, and wherein the alarm condition is based on more than one analyte.

35. The method of claim 34, wherein: Differentiating further includes determining whether the first time-related data indicates a condition characterized by glucose variability below a threshold, high glucose less than a maximum time threshold, and ketone levels greater than a predetermined threshold.

36. The method of claim 35, wherein: The determining includes at least one of: suppressing a high ketone alarm, or reducing the urgency of a high ketone alarm.

37. The method of claim 35, wherein: The determining includes causing the message to indicate a time period during which high ketone levels were achieved.

38. The method of claim 34, wherein: The distinguishing includes determining whether the first time-related data indicates a condition characterized by glucose variability above a threshold, high glucose greater than a maximum time threshold, and ketone levels greater than a predetermined threshold, and if the condition is detected, performing at least one of increasing the frequency or urgency of messages indicating that euglycemic DKA may occur in a patient wearing the sensor control device.

39. The method of claim 25, wherein: Making the determination further comprises estimating whether the patient wearing the sensor controlled device is a type 1 diabetic patient taking an SGLT-2 inhibitor based on comparing the ketone estimate based on the first time-related data with the ketone level indicated by the second time-related data.

40. The method of claim 39, wherein: Making the determination includes determining whether the ketone estimate is consistently below the indicated ketone level, and if so, determining a message including an indication that the patient should consult the patient's healthcare provider regarding use of the SGLT-2 inhibitor.

41. The method of claim 40, wherein: The determination is also made such that the message includes an indication that the absence of a high glucose alarm may not achieve its intended purpose.

42. The method of claim 25, further comprising: First data and second data are wirelessly transmitted from the sensor control device to the reader device.

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