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 the operation of a user interface device or insulin pump. In some embodiments, the system, device, or method may use a physiological model that combines glucose history and β-hydroxybutyrate to better predict diabetic ketoacidosis (DKA) compared to predictions based solely on high glucose thresholds. In other embodiments, the system, device, or method may include features for generating an estimate of a patient's medication status and / or knowledge of medication information, such as for a type 1 diabetes (DM) patient taking an SGLT-2 inhibitor.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 406,989, filed September 15, 2022, which is incorporated herein by reference in its entirety for all purposes. [Technical Field]

[0002] The subject matter described herein relates generally to systems, devices, and methods for dual analyte sensors. In particular, the embodiments described herein include using data collected by a glucose sensor in conjunction with data collected from a ketone sensor to control a user interface device or dosage management device to improve control of a patient's glucose levels. [Background technology]

[0003] There is a huge and growing market for monitoring the health and condition of humans and other living animals. Information describing the physical or physiological state of humans can be used in countless ways to assist and improve quality of life, diagnose, and even treat undesirable human conditions.

[0004] Devices typically used to gather such information are physiological sensors, such as biochemical analyte sensors, or devices capable of sensing chemical analytes in biological entities. Biochemical sensors come in many forms and can be used to sense analytes in fluids, tissues, or gases that form part of or are produced by biological entities, such as humans. These analyte sensors can be used on or in the body, such as in the case of transcutaneously implanted analyte sensors, or on biological material already removed from the body. Useful applications of such sensors include blood glucose sensing for health assessment, dosage guidance, and related uses. Summary of the Invention [Problem to be solved by the invention]

[0005] However, blood glucose monitoring alone faces certain limitations. For example, type 1 diabetes patients taking SGLT-2 inhibitors, also known as gliflozin or flozin, are at risk of developing diabetic ketoacidosis (DKA). DKA is an adverse condition for diabetic patients that can lead to hospitalization and even death. It is associated with high ketone levels caused by prolonged high glucose levels. DKA can also be caused by a patient's insufficient insulin levels or high levels of insulin resistance, possibly due to illness, in which case glucose levels may be within or below the target range.

[0006] SGLT-2 is a diabetes medication that helps reduce glucose fluctuations around mealtimes and is used in patients with type 2 diabetes. It also helps manage glucose levels in patients with type 1 diabetes. However, there are concerns about its use in type 1 patients because its use can result in high ketone levels and DKA with normal levels of glucose, referred to herein as euglycemic DKA. Treatment of euglycemic DKA primarily involves administering insulin and offsetting unwanted glucose declines with carbohydrate intake. However, patients may be unsure when to do so and when to seek emergency medical intervention. Specifically, it can be difficult to know what to do when ketones are elevated enough to indicate a euglycemic state.

[0007] Although individual ketone test strips can be used in conjunction with continuous glucose monitoring (CGM), continuous monitoring of ketones can be impractical. Regardless of how a patient's ketones are measured, interpreting the ketone levels along with the patient's blood glucose levels and determining appropriate action is too complicated for most patients and requires input from a healthcare provider (HCP). Therefore, ketone sensing and use of ketone data by patients taking SGLT-2 inhibitors is relatively difficult and cumbersome compared to continuous glucose sensing.

[0008] For these and other reasons, there is a need for improved ketone sensing, analysis, and patient guidance for patients susceptible to euglycemic DKA, such as those taking GLT-2 inhibitors. [Means for solving the problem]

[0009] 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 insulin pump.

[0010] The present disclosure describes mobile app-based systems, devices, and methods for detecting actionable conditions, providing guidance to patients and providing a means to record important context that occurred contemporaneously with the situation, which can later assist HCPs in advising patients on how to avoid such adverse conditions in the future. In embodiments, the systems, devices, or methods may use a physiological model that combines glucose history and β-hydroxybutyrate to better predict diabetic ketoacidosis (DKA) compared to predictions based on a single high glucose threshold. Alternatively, or additionally, the systems, devices, or methods may include features for generating an estimate of a patient's medication status and / or knowledge of medication information, such as for type 1 diabetes (DM) patients taking SGLT-2 inhibitors.

[0011] Additionally or alternatively, an improved system, method, or device may include improving the alerting capabilities of an analyte monitoring sensor (e.g., a glucose sensor) by using context from one or more additional analyte sensors (e.g., a ketone sensor) and / or inferring knowledge of medication status and / or medication information (e.g., a type 1 diabetes mellitus (T1DM) patient taking an SGLT-2 inhibitor). A single analyte sensor system may have a variety of alerts. Examples of alerts include upper threshold alerts and lower predicted threshold alerts. Knowing at least one more analyte and / or medication information may improve alerting by adjusting alert behavior and timing. This may include a dual analyte system (e.g., glucose-ketone) where threshold alerts are based on each analyte value independently of other analyte values ​​and / or medication information. Examples of adjusting alert behavior include the use of lower or upper thresholds. Examples of adjusting timing include varying the time interval between alert issuances if alert conditions remain met. This may improve the clinical relevance of alerts and reduce alert fatigue by minimizing issuances that may be clinically irrelevant.

[0012] The systems, devices, and methods disclosed herein incorporate ketone data with blood glucose data to provide patients and HCPs with more reliable guidance than high glucose threshold detection alone. This may provide improved utility for on-demand or continuous glucose monitoring (CGM) systems. For example, on-demand testing systems that include a built-in ketone measurement-compatible strip port may provide increased utility to patients and help HCPs make more accurate recommendations. Overall, the systems, devices, and methods disclosed herein may better protect patients from the risk of DKA. Algorithmic improvements in the systems, methods, and devices, including the use of rich glucose history from on-demand or CGM systems, the opportunistic use of insulin history (e.g., from a built-in bolus calculator), and ongoing ketone measurements (e.g., from a built-in ketone-compatible strip port or in vivo ketone analyte sensor), may improve future DKA risk estimation and further improve DKA risk assessment. Improved risk assessment algorithms may include, for example, comparing estimated ketone time series values ​​to ketone-specific thresholds instead of comparing point glucose levels to conservative point glucose-specific thresholds, as is traditionally done.

[0013] According to some embodiments, an analyte monitoring system is provided in which a sensor controller is configured to collect first time-correlated data indicative of a glucose level and second time-correlated data indicative of a ketone level. For example, the first data can be from an analyte sensor that is a glucose sensor, and the second data can be from an analyte sensor that is a ketone sensor. In other examples, the second data can be received from a ketone test strip measurement, or the like. In some embodiments, one or both of the first data and the second data are from the analyte sensors.

[0014] According to some embodiments, the sensor control device is operably connected 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 may be stored in one or more memories (e.g., a single or separate memories). 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 may be stored in one or more memories (e.g., a single or separate memories).

[0015] According to some embodiments, at least one of the non-transitory memories includes instructions that, when executed, cause at least one of the sensor controller or the reader's processing circuitry to make a decision based on the first and second time correlation data and output an indication of the decision by the reader. The decision may be at least one of a warning threshold for one or both of the first and second time correlation data, a message for output by the reader, and / or a modification of the analyte status estimate. Determining the warning threshold may include, for example, setting or changing a threshold for blood glucose, ketone bodies, or other analyte that, when exceeded, causes the reader or other system component to output an alarm. Determining the message may include, for example, selecting a predetermined message from a data table in response to results of automated analysis of the first and second time correlation data. Determining the modification of the analyte status estimate may include, for example, calculating a correction factor or value for an initial estimate for blood glucose or other analyte based solely on the first time correlation data.

[0016] Other systems, devices, methods, features, and advantages of the subject matter described herein will be or become apparent to one with skill in the art upon examination of the following figures and detailed description. All such additional systems, methods, features, and advantages are intended to be included herein, be within the scope of the subject matter described herein, and be protected by the accompanying claims. Unless features of the example embodiments are expressly recited in the accompanying claims, they should not be construed as limiting the claims in any way.

[0017] Details of the subject matter presented herein, both in structure and operation, will become apparent upon examination of the accompanying drawings, in which like reference numerals indicate like parts. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the subject matter. Moreover, all figures are intended to convey concepts and may depict relative sizes, shapes, and other detailed attributes in a schematic, rather than literal, manner. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 illustrates an exemplary embodiment of an in vivo analyte monitoring system. [Figure 2] FIG. 2 is a block diagram of an exemplary embodiment of a reading device. [Figure 3] FIG. 2 is a block diagram of an exemplary embodiment of a sensor control device. [Figure 4A] 1 is a multiplot graph showing exemplary analyte concentrations measured over time. [Figure 4B] 1 is a multiplot graph showing exemplary analyte concentrations measured over time. [Figure 4C] 1 is a multiplot graph showing exemplary analyte concentrations measured over time. [Figure 5] 1 is a flow chart illustrating an exemplary embodiment of an analyte monitoring method. [Figure 6] 6 is a flow chart illustrating alternative embodiments and aspects of the method shown in FIG. 5. [Figure 7]6 is a flow chart illustrating alternative embodiments and aspects of the method shown in FIG. 5. [Figure 8] 6 is a flow chart illustrating alternative embodiments and aspects of the method shown in FIG. 5. [Figure 9A] 6 is a flow chart illustrating alternative embodiments and aspects of the method shown in FIG. 5. [Figure 9B] 6 is a flow chart illustrating alternative embodiments and aspects of the method shown in FIG. 5. [Figure 10] 6 is a flow chart illustrating alternative embodiments and aspects of the method shown in FIG. 5. DETAILED DESCRIPTION OF THE INVENTION

[0019] Before describing the present subject matter in detail, it should be understood that the present disclosure is not limited to the particular embodiments described, as such may vary. It should also be understood that the terminology used herein is used merely to describe particular embodiments and is not intended to be limiting. The scope of the present disclosure is limited only by the appended claims.

[0020] The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein should be construed as an admission that the present disclosure is not entitled to antedate such publications by virtue of their prior disclosure. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0021] Generally, embodiments of the present disclosure relate to systems, devices, and methods for detecting at least one analyte, such as glucose, in a bodily fluid (e.g., subcutaneously in interstitial fluid (“ISF”) or blood, dermal fluid in the dermal layer, etc.), along with a feature for ketone analyte sensing that correlates time-series with analyte data from an in vivo glucose sensor. Embodiments may include an in vivo analyte sensor that is structurally configured to acquire information about at least one bodily analyte, such that at least a portion of the sensor is placed or can be placed within a user's body. It should be noted that the embodiments disclosed herein may be used in in vivo analyte monitoring systems that incorporate in vitro capabilities, as well as purely in vitro or ex vivo analyte monitoring systems, including completely noninvasive systems. When used with a single analyte in vivo sensor, ketone test data may be added manually, for example, by using a test strip. Alternatively, embodiments of the present disclosure may be used with dual sensor systems for continuous or near-continuous monitoring of different analytes, such as blood glucose and ketone bodies.

[0022] Additionally, systems and devices capable of performing each of the method embodiments disclosed herein are also encompassed within the scope of the present disclosure. For example, sensor control device embodiments are disclosed, which may include one or more sensors, analyte monitoring circuitry (e.g., analog circuitry), non-transitory memory (e.g., for storing instructions), power sources, communication circuitry, transmitters, receivers, processing circuitry, and / or controllers (e.g., for executing instructions) that may perform or facilitate any and all method steps. These sensor control device embodiments may be used to implement the steps performed by the sensor control device from one or more of the methods described herein.

[0023] Similarly, reader device embodiments are disclosed that include one or more transmitters, receivers, non-transitory memory (e.g., for storing instructions), power sources, processing circuits, and / or controllers (e.g., for executing instructions) that may perform or facilitate the execution of any and all method steps. These reader device embodiments may be used to implement those steps performed by the reader device from one or more of the methods described herein.

[0024] Also disclosed are embodiments of trusted computer systems, which may include one or more processing circuits, controllers, transmitters, receivers, non-transitory memory, databases, servers, and / or networks, and may be discretely located or distributed across multiple geographic locations. These trusted computer system embodiments may be used to perform steps from one or more of the methods described herein, performed by the trusted computer system.

[0025] Various embodiments of systems, devices, and methods for improving analyte sensor accuracy and detecting sensor fault conditions are disclosed. According to some embodiments, these systems, devices, and methods may utilize first data collected by a glucose sensor and second data collected by a ketone sensing component.

[0026] Other features and advantages of the disclosed embodiments are further described below.

[0027] However, before describing the embodiments in detail, it is desirable to describe examples of devices that may be placed in an in vivo analyte monitoring system, and examples of their operation, all of which may be used in the embodiments described herein.

[0028] Exemplary Embodiments of an Analyte Monitoring System There are various types of analyte monitoring systems. A "continuous analyte monitoring" system (or "continuous glucose monitoring" system), for example, is an in-vivo system that can transmit data from a sensor controller to a reader device repeatedly or continuously, e.g., automatically, unprompted, on a schedule. As another example, a "flash analyte monitoring" system (or "flash glucose monitoring" system, or simply "flash" system) is an in-vivo system that can transfer data from a sensor controller in response to a scan or request for data by a reader device, such as using near-field communication (NFC) or radio frequency identification (RFID) protocols. In-vivo analyte monitoring systems can operate without the need for fingerstick calibration.

[0029] An in-vivo monitoring system includes a sensor that, when placed in vivo, contacts a user's bodily fluid and senses the level of one or more analytes therein. The sensor is part of a sensor control device located on the user's body and includes electronics and a power source that enable and control analyte sensing. Sensor control devices and variations thereof may also be referred to as, for example, "sensor control units," "on-body electronics" devices or units, "on-body" devices or units, or "sensor data communication" devices or units. As used herein, these terms are not limited to devices with analyte sensors but include devices with other types of sensors, whether biometric or non-biometric. The term "on-body" refers to any device located directly on or near the body, such as a wearable device (e.g., eyeglasses, watches, wristbands or bracelets, neckbands or necklaces, etc.).

[0030] In-vivo monitoring systems may also include one or more reader devices that receive sensed analyte data from the sensor control device. These reader devices may process and / or display the sensed analyte or sensor data to a user in any number of forms. These devices and variations thereof may be referred to as "handheld readers," "readers" (or simply "readers"), "handheld electronic devices" (or handheld devices), "portable data processing" devices or units, "data receivers," "receiver" devices or units (or simply receivers), "relay" devices or units, or "remote" devices or units, to name a few. Other devices, such as personal computers, may also be used with or incorporated into in-vivo and in-vitro monitoring systems.

[0031] In vivo analyte monitoring systems are distinguished from "in vitro" systems that contact a biological sample outside the body (or, rather, "ex vivo"); "in vitro" systems typically include a metering device having a port that receives an analyte test strip carrying a user's bodily fluid, which can be analyzed to determine the user's analyte level. As previously mentioned, the embodiments described herein can be used with in vivo systems, in vitro systems, and combinations thereof.

[0032] 1 illustrates an exemplary embodiment of an in vivo analyte monitoring system 100 having a sensor controller 102 and a reader 120 in communication with each other, which may communicate unidirectionally or bidirectionally over a local communication channel (or link) 140, which may be wired or wireless. In wireless embodiments, communication channel 140 may use a near field communication (NFC) protocol, an RFID protocol, Bluetooth or Bluetooth low energy protocol, Wi-Fi protocol, proprietary protocols, etc., including variations existing as of the filing date of this application or developed thereafter.

[0033] The reading device 120 may communicate with a computer system 170 (e.g., a local or remote computer system) through communication path (or link) 141 and with a network 190, such as the Internet or the cloud, through communication path (or link) 142, either wired, wireless, or a combination thereof. Communication with the network 190 may include communication with a trusted computer system 180 within the network 190 or communication through the network 190 to the computer system 170 via communication link (or channel) 143. Communication paths 141, 142, and 143 may be wireless, wired, or both, 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 communication path. All communications over communication paths 140, 141, 142 are 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 their transmitted and received communications.

[0034] Variations of the devices 102, 120 and other components of an in vivo analyte monitoring system suitable for use with the systems, devices, and methods described herein are disclosed in U.S. Patent Application Publication No. 2011 / 0213225 (hereinafter the '225 publication), the disclosure of which is incorporated herein by reference in its entirety and for all purposes.

[0035] The sensor control device 102 may include a housing 103 that houses an in-vivo analyte monitoring circuit and a power source. In this embodiment, the in-vivo analyte monitoring circuit is electrically connected to one or more analyte sensors 104, 106 that extend through an adhesive patch 105 and protrude from the housing 103. The sensors may include a blood glucose sensor 104 and a ketone sensor 106. Alternatively, a sensor capable of dual analyte sensing may be configured to sense both glucose and ketones. For example, the dual analyte sensor disclosed in U.S. Patent Application Publication No. 2020 / 0237276 (hereinafter the '276 publication), the disclosure of which is incorporated herein by reference in its entirety for all purposes, may be used.

[0036] The adhesive patch 105 may include an adhesive layer (not shown) for attachment to the skin surface of a user's body. Other forms of attachment to the body may be used in addition to or instead of adhesion.

[0037] The glucose sensor 104 and, optionally, the ketone sensor 106 may be adapted for at least partial insertion into a user's body, where they are in fluid contact with the user's bodily fluids (e.g., subcutaneous (subdermal) fluid, skin fluid, or blood) and may be used in conjunction with in-vivo analyte monitoring circuitry to measure data related to the user's analytes. The sensors 104, 106 and any associated sensor control electronics may be applied to the body in any desired manner. For example, an insertion device 150 may be used to position all or a portion of the analyte sensor 104 through the exterior surface of the user's skin and in contact with the user's bodily fluids. In doing so, the insertion device may also position the sensor control device 102 with the adhesive patch 105 on the skin. In other embodiments, the insertion device may first position the sensor 104 and then later couple the associated sensor control electronics to the sensor 104, either manually or with the aid of a mechanical device. Examples of insertion devices are disclosed in U.S. Patent Application Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661, the disclosures of all of which are incorporated by reference in their entirety and for all purposes.

[0038] After collecting raw data from the user's body, the sensor controller 102 may perform analog signal conditioning on the data to convert it into conditioned raw data in a digital format. In some embodiments, the sensor controller 102 may then algorithmically process the digital raw data into a format that represents the user's measured biometric (e.g., analyte level) and / or one or more analyte metrics based thereon. For example, the sensor controller 102 may include processing circuitry to algorithmically perform any of the method steps described herein. The sensor controller 102 may then encode and wirelessly communicate data indicative of glucose levels, ketone levels, a sensor malfunction indication, and / or processed sensor data to the reader 120, which may format or graphically process the received data for digital display to the user. In other embodiments, in addition to or instead of wirelessly communicating the sensor data to another device (e.g., the reader 120), the sensor controller 102 may graphically process the data so that it can be displayed in its final form and display the data on the sensor controller's display. In some embodiments, the biometric data in its final form (before graphical processing) is used by a system (eg, integrated into a diabetes monitoring regimen) without further processing for display to a user.

[0039] In yet other embodiments, the conditioned raw digital data may be encoded for transmission to another device, such as reader 120, and the raw digital data may then be algorithmically processed into a format that represents the user's measured biometric (e.g., a format that may be readily suitable for display to the user) and / or one or more analyte metrics based thereon. Reader 120 may include processing circuitry and may use algorithms to perform any of the method steps described herein, such as, for example, correcting a glucose level measurement, detecting a suspected low glucose condition, or detecting a suspected sensor fault condition, or other action. This algorithmically processed data may then be formatted or graphically processed for digital display to the user.

[0040] In other embodiments, the sensor controller 102 and reader 120 transmit the raw digital data to another computer system for algorithmic processing and display.

[0041] Reading device 120 includes a display 122 for outputting information to a user and / or accepting input from a user, and an optional input 121 (or two or more), such as buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, or a jog wheel, for inputting data, commands, or otherwise controlling the operation of reading device 120. In some embodiments, display 122 and input 121 are integrated into a single component; for example, the display may detect physical contact with the display, such as a touchscreen user interface, and its location. In some embodiments, input 121 of reading device 120 includes a microphone, and reading device 120 may include software configured to analyze voice input received from the microphone, allowing functions and operations of reading device 120 to be controlled by voice commands. In some embodiments, the output of reading device 120 includes a speaker (not shown) for outputting information as an audible signal. A speaker, microphone, and similar audio response components such as software routines for generating, processing, and storing audio signals may be included in the sensor controller 102 .

[0042] The reader 120 may also include one or more data communication ports 123 for wired data communication with external devices, such as the computer system 170 or the 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 suitable data cable. The reader 120 may also include an integrated or attachable in vitro glucose meter, which may include an in vitro test strip port (not shown) for accepting in vitro glucose test strips for performing in vitro blood glucose measurements.

[0043] The reader 120 is configured to display the measured biometric data wirelessly received from the sensor control device 102 and also to output alarms, warning notifications, glucose values, etc., which may be visual, audible, tactile, or any combination thereof. Further details and other display embodiments are disclosed, for example, in U.S. Patent Application Publication No. 2011 / 0193704, the disclosure of which is incorporated herein by reference in its entirety and for all purposes.

[0044] The reader 120 may act as a data waveguide, transferring measurement data and / or analyte metrics from the sensor controller 102 to the computer system 170 or the trusted computer system 180. In some embodiments, data received from the sensor controller 102 may be stored (permanently or temporarily) in one or more memories of the reader 120 before being uploaded to the systems 170, 180, or the network 190.

[0045] Computer system 170 may be a personal computer, server terminal, laptop computer, tablet, or other suitable data processing device. Computer system 170 may be (or may include) software for data management and analysis, as well as communication with components of analyte monitoring system 100. Computer system 170 may be used by a user or medical professional to display and / or analyze biometric data measured by sensor control device 102. In some embodiments, sensor control device 102 may communicate biometric data to computer system 170 directly without an intermediary, such as reader 120, or indirectly using an internet connection (and optionally without first transmitting to reader 120). Operation and use of computer system 170 are further disclosed in the incorporated '225 publication, further including method steps for handling ketone data along with blood glucose data. Analyte monitoring system 100 may also be configured to operate using a data processing module (not shown), also disclosed in the incorporated '225 publication.

[0046] The trusted computer system 180 may be physically or virtually owned by the manufacturer or distributor of the sensor control device 102 through a protected network and may be used to authenticate the sensor control device 102 and serve as a secure storage unit for the user's biometric data and / or a server that functions, for example, as a data analysis program (e.g., accessible via a web browser) for analyzing the user's measurement data.

[0047] Exemplary embodiments of a reading device The reader 120 may be a portable communication device such as a dedicated reader (a computer system 170 configured for communication with the sensor control device 102, optionally without cellular communication capabilities) or a mobile phone, including, but not limited to, a Wi-Fi or Internet-enabled smartphone, tablet, or personal digital assistant (PDA). Examples of smartphones include mobile phones based on the Windows® operating system, the Android™ operating system, the iPhone® operating system, the Palm® WebOS™, the Blackberry® operating system, or the Symbian® operating system, and have Internet connectivity and / or data network connectivity for data communication over a local area network (LAN).

[0048] The reading device 120 may also be configured as a portable smart wearable electronics assembly, such as an optical assembly worn on or adjacent to a user's eye (e.g., a smart monocular or smart glasses, such as Google® Glasses, that are portable communication devices). Such an optical assembly may 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, minimizing obstruction to the user's overall field of view. The optical assembly may be capable of wireless communication, similar to a smartphone. Other examples of wearable electronics include devices worn around or near the user's wrist (e.g., a watch), around or near the neck (e.g., a necklace), around or near the head (e.g., a headband, hat), around or near the chest, etc.

[0049] 2 is a block diagram of an exemplary embodiment of reader 120 configured as a smartphone. Here, reader 120 includes input 121, display 122, and processing circuitry 206, which may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be individual chips or distributed across (or portions of) multiple different chips. Here, processing circuitry 206 includes a communications processor 222 having on-board memory 223 and an application processor 224 having on-board memory 225. Reader 120 further includes RF communications circuitry 228 connected to RF antenna 229, memory 230, multifunction circuitry 232 having one or more associated antennas 234, power supply 226, power management circuitry 238, and a clock (not shown). One or both of the memories 223, 225 may hold program instructions that, when executed by one or more processing units 222, 224, may cause the reading device 120 to perform one or more of the operations of the methods described herein. Figure 2 is a schematic diagram illustrating typical hardware and functionality located in a smartphone, and one skilled in the art will readily recognize that other hardware and functionality (e.g., codecs, drivers, glue logic) may also be included.

[0050] The communications processor 222 interfaces with the RF communications circuitry 228 and may perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions to facilitate converting voice, video, and data signals into a format (e.g., in-phase and quadrature) suitable for providing to the RF communications circuitry 228, which may then transmit the signals wirelessly. The communications processor 222 may also interface with the RF communications circuitry 228 and perform the reverse functions necessary to receive wireless communications and convert them into digital data, voice, and video. The RF communications circuitry 228 may include a transmitter and receiver (e.g., integrated as a transceiver) and associated encoder logic.

[0051] The application processor 224 may be adapted to run the operating system and any software applications on the reader 120, process video and graphics, and perform other functions unrelated to processing communications transmitted and received through the RF antenna 229. The smartphone operating system operates in conjunction with numerous applications on the reader 120. Any number of applications (also known as “user interface applications”) may be running on the reader 120 at any one time, which may include one or more applications related to the diabetes monitoring therapies and methods described herein, in addition to other commonly used applications unrelated to such therapies, such as email, calendar, weather, sports, games, etc. For example, data indicative of sensed analyte levels and ex vivo blood analyte measurements received by the reader may be securely communicated to a user interface application resident in the memory 230 of the reader 120. Such communication may be performed securely, for example, through mobile application containerization or wrapping techniques.

[0052] The memory 230 may be shared by one or more of the various functional units within the reader 120, or distributed among two or more of them (e.g., as separate memories in different chips). The memory 230 may also be a separate chip in its own right. The memories 223, 225, 230 may be non-transitory and may further be volatile (e.g., RAM, etc.) and / or non-volatile memory (e.g., ROM, flash memory, F-RAM, etc.).

[0053] The multi-function circuitry 232 may be embodied as one or more chips and / or components (e.g., transmitters, receivers, transceivers, and / or other communication circuitry) that perform, for example, local wireless communication with the sensor controller 102 over an appropriate protocol (e.g., Wi-Fi, Bluetooth, Bluetooth low energy, near field communication (NFC), radio frequency identification (RFID), proprietary protocols, etc.) and other functions, such as determining the geographic location of the reader 120 (e.g., Global Positioning System (GPS) hardware). One or more other antennas 234 may be associated with the functional circuitry 232 as needed to operate with various protocols and circuits.

[0054] The power supply 226 may include one or more batteries, which may be rechargeable or disposable. The power management circuitry 238 may regulate battery charging and power monitoring, boost power, DC conversion, etc.

[0055] The reading device 120 may include or be integrated with a medication (e.g., insulin) delivery device, e.g., sharing a common housing. An example of such a medication delivery device is a medication pump (e.g., a wearable pump for delivering basal and bolus insulin) that is left in the body and has a cannula that allows infusion over hours or days. When combined with a medication pump, the reading device 120 may include a reservoir for storing the medication, a pump connectable to a transfer tube, and an infusion cannula. The pump may push the medication from the reservoir, through the tube, and into the diabetic patient's body through an inserted cannula. Another example of a medication delivery device that may be included in (or integrated with) the reading device 120 includes a portable infusion device (e.g., an insulin pen) that pierces the skin only for each delivery and is then removed. When combined with a portable infusion device, the reading device 120 includes an infusion needle, a cartridge for containing the medication, an interface for controlling the amount of medication to be delivered, and an actuator for causing the infusion. The device may be used repeatedly until the medication is depleted, at which point the combined device may be discarded or the cartridge replaced with a new one, at which point the combined device may be reused repeatedly. The needle may be replaced after each injection.

[0056] The combined device may function as part of a closed-loop system (e.g., an artificial pancreas system that requires no user intervention to operate) or a semi-closed-loop system (e.g., an insulin loop system that requires little user intervention to operate, such as checking for dosage changes). For example, a diabetic patient's analyte level may be repeatedly and automatically monitored by the sensor control device 102, the monitored analyte level may then be communicated to the reader 120, the appropriate medication dosage may be automatically determined to control the analyte level, and the medication may then be delivered to the diabetic patient. Software instructions for controlling the pump and the amount of insulin delivered may be stored in the memory of the reader 120 and executed by the reader's processing circuitry. Furthermore, these instructions may cause the amount and duration of medication delivery (e.g., a bolus infusion and / or a basal infusion profile) to be calculated based on analyte level measurements obtained directly or indirectly from the sensor control device 102. In some embodiments, the sensor control device 102 may determine the medication dosage and communicate it to the reader 120.

[0057] Exemplary Embodiments of a Sensor Control Device FIG. 3 is a block diagram illustrating an exemplary embodiment of a sensor controller 102 having an analyte sensor 104 and sensor electronics 250 (including analyte monitoring circuitry), which may contain most of the processing power suitable for displaying final result data to a user. In FIG. 3, a single semiconductor chip 251 is shown, which may be a custom application-specific integrated circuit (ASIC). Within the ASIC 251, several high-level functional units are shown, including an analog front-end (AFE) 252, a power management (or control) circuit 254, a processing unit 256, and a communications circuit 258 (which may be implemented as a transmitter, a receiver, a transceiver, passive circuitry, or other aspects depending on the communications protocol). In this embodiment, both the AFE 252 and the processing unit 256 are used as analyte monitoring circuitry, although in other embodiments, either circuitry may perform the analyte monitoring function. The processing unit 256 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a separate chip or may be distributed across multiple different chips.

[0058] Memory 253 is also included within ASIC 251 and may be shared by various functional units within ASIC 251 or distributed among two or more processors. Memory 253 may also be a separate chip. Memory 253 may be non-transitory, volatile, and / or non-volatile memory. In this embodiment, ASIC 251 is connected to a power source 260, such as a coin battery. AFE 252 interfaces with one or more in-vivo analyte sensors 104, 106 to receive measurement data therefrom and outputs the data in digital form to processor 256, which may then, in some embodiments, process the data in any manner described herein. This data is then provided to communication circuitry 258 for transmission via antenna 261 to reader 120 (not shown), where it requires minimal further processing by a resident software application to display the data. Antenna 261 may be configured as needed for the application and communication protocol. Antenna 261 may be, for example, a printed circuit board (PCB) trace antenna, a ceramic antenna, or a discrete metal antenna. Antenna 261 may be configured as a monopole antenna, a dipole antenna, an F-antenna, a loop, etc.

[0059] Information may be communicated from the sensor controller 102 to a second device (e.g., the reader 120) at the initiative of the sensor controller 102 or the reader 120. For example, information may be communicated by the sensor controller 102 automatically and / or repeatedly (e.g., continuously) when analyte information is available or on a schedule (e.g., about every minute, about every five minutes, about every ten minutes, etc.), where the information may be stored or logged in the sensor controller 102's memory for later communication. Information may be transmitted from the sensor controller 102 in response to receipt of a request by the second device. This request may be an automatic request, e.g., a request transmitted by the second device on a schedule, or a user-initiated request (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 controller 102 or an "on-demand" data transfer from the device 102. In some embodiments, the second device may send polling signals or data packets to the sensor control device 102, and the device 102 may treat each poll (or polls occurring at certain time intervals) as a data request and transmit such data to the second device when the data is available. While in many embodiments, communication between the sensor control device 102 and the second device is secure (e.g., encrypted and / or between authenticated devices), in some embodiments, data may be broadcast from the sensor control device 102 in an insecure manner, for example, to all devices within range.

[0060] Different types and / or formats and / or amounts of information may be transmitted as part of each communication, including, but not limited to, one or more of the current sensor measurement value (e.g., most recently acquired test substance level information, corresponding in time to the start time of the reading), the rate of change of the measured metric over a predetermined period of time, the metric rate of change velocity (acceleration of the rate of change), or historical metric information corresponding to metric information acquired prior to the predetermined reading and stored in the memory of the sensor control device 102.

[0061] Some or all of the real-time, historical, rate-of-change, and (acceleration or deceleration) rate-of-change velocity information may be sent to reader 120 in a given communication or transmission. In some embodiments, the type and / or format and / or amount of information sent to reader 120 may be preprogrammed and / or non-alterable (e.g., preset at the time of manufacture), or may not be preprogrammed and / or immutable, but may be selectable and / or alterable one or more times in the field (e.g., by actuation of a switch in the system, etc.). Thus, in some embodiments, reader 120 may output the current (real-time) analyte value from the sensor (e.g., in numeric form), the current analyte rate of change (e.g., in the form of an analyte rate indicator, such as an arrow pointing in the direction indicating the current rate), and historical analyte trend data (e.g., in the form of a graphical trace) based on sensor readings obtained by sensor controller 102 and stored in its memory. Additionally, on-skin or sensor temperature readings or measurements may be collected by optional temperature sensor 257. These readings or measurements may be communicated (individually or as aggregate measurements over time) from sensor control device 102 to other devices (e.g., reader 120). Note that instead of, or in addition to, actually displaying the temperature measurements to the user, the temperature readings or measurements may be used in conjunction with software routines executed by reader 120 to modify or correct the analyte measurements output to the user.

[0062] Further, while FIG. 3 illustrates dual analyte sensors 104, 106 according to numerous embodiments of the present disclosure, the sensor control device 102 may be configured to collect data indicative of multiple physiological measurements, which may include, but are not limited to, measurements of glucose level, lactate level, ketone level, or heart rate, to name a few. In some embodiments, for example, the sensor 104 may be a dual analyte sensor configured to sense glucose level and the concentration of another analyte (e.g., lactate, ketone, etc.). Further details regarding dual analyte sensors are described, for example, in the '276 publication previously referenced herein. In some embodiments, the sensor control device 102 may include multiple individual sensors, each capable of collecting data indicative of any of the above physiological measurements. For example, in some embodiments, a first analyte sensor 104 may be used to sense blood glucose, and a second analyte sensor 106 may be used to sense ketones.

[0063] Embodiments of a system, device and method for using combined blood glucose and ketone data Ketone monitoring systems can provide ketone test alerts based on whether a recent individual self-monitoring blood glucose (SMBG) measurement exceeds a predefined high glucose threshold. However, the relationship between ketones and SMBG is not static. Rather, prolonged insulin deficiency triggers a rise in glucagon, which in turn increases glucose release from the liver. Furthermore, absent or very low levels of insulin lead to the release of free fatty acids from adipose tissue, which are converted into ketone bodies (including beta-hydroxybutyrate) in the liver.

[0064] Figures 4A-4C show examples of how ketone levels correlate with blood glucose levels, but do not have a static correlation with each other. The figures are based on the insulin pump cessation 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. 1975-1978, May 1, 1996]. Study participants were patients with type 1 diabetes. The two study arms shown in Figures 4A-4C began under different conditions: a low basal rate and a sufficient basal rate in the insulin pump system. In this study, insulin pump delivery was stopped, and test substance levels were measured hourly for several hours after the pump was stopped. The figures are aligned to the start of the cessation period, labeled as time 0. The analytes measured were concentration levels of insulin ( FIG. 4A ), glucose ( FIG. 4B ), and ketones ( FIG. 4C ), and a common key 408 across all charts 402, 404, 406 shows data from the first and second study arms 400, 410. Specifically, the ketone measurement selected was that of beta-hydroxybutyrate. Because the first study arm 400 begins with a low basal rate, the insulin concentrations (see insulin chart 402) one hour before and at the start of insulin pump cessation are lower than those in study arm 410, which begins with an adequate basal rate. Consequently, as shown by glucose chart 404, the glucose concentrations in the first study arm 400 are higher than those in the second study arm 410. In both study arms, because the release of free fatty acids is associated with an increase in ketone bodies, ketone concentration levels gradually increase as insulin pump cessation continues, as shown by ketone chart 406.Because glucose monitoring is more common than ketone monitoring, advice to check for ketones is generally based on the observation of high glucose levels (e.g., a threshold of approximately 240 mg / dL to 250 mg / dL). For example, in the first study arm, glucose levels exceeding 250 mg / dL (approximately 13.9 mMol / L) occurred approximately 3 hours after insulin pump cessation began. The corresponding ketone levels were slightly higher than 0.6 mMol / L, generally at or just above a level considered safe for DKA. As a result, using high glucose levels as a proxy for DKA risk detection seems reasonable based on data from this first study arm. However, when the same high glucose criteria were applied to the second study arm, glucose exceeded 250 mg / dL at approximately 6 hours, which would already result in very high ketone readings of approximately 0.9 mMol / L. Using a lower glucose threshold, such as 150 mg / dL (approximately 8.3 mMol / L), would capture ketones in the second study arm that quickly exceeded 0.6 mMol / L, but this lower glucose threshold was already exceeded prior to the study for the first study arm and therefore cannot be used correctly to estimate DKA risk.

[0065] Unlike SMBG meters, glucose sensor-based meters, such as on-demand and continuous glucose monitoring (CGM) systems, have access to longer historical glucose trends for any given time when a patient inquires about a glucose measurement. Model-based ketone-on-board estimation using glucose time series values ​​significantly improves DKA risk identification and therefore provides more reliable ketone testing reminders to patients. Because DKA risk is directly linked to ketone history (a concept similar to insulin-on-board), ketone testing reminders based on this method are tailored compared to reminders based on point glucose thresholds and can be highly specific while maintaining lower false positives and false negatives.

[0066] For manual ketone testing devices, this method can be used to generate suggestions to perform ketone measurements to better protect patients from DKA risk. If patients provide sufficient insulin usage information (by using the built-in insulin calculator), the model can further improve the specificity of ketone-on-board estimation.

[0067] A system for implementing this method may include two components: the first, a front-end, which is responsible for the system user interface (UI), and the second, a back-end, which performs the necessary calculations for ketone-on-board estimation.

[0068] Whenever the backend predicts a high DKA risk based on estimated ketone-on-board, the frontend will suggest to the patient to perform a ketone test, and a short explanation will be provided on-screen and / or in the user guide explaining that recent glucose history suggests it would be a good time to perform a ketone test.

[0069] The backend updates the estimated ketone-on-board model based on available glucose time series values ​​from relevant recent scans or periodic data collection. The model may include a single glucose compartment, a single effective insulin compartment, a single plasma insulin compartment, and a single β-hydroxybutyrate compartment. The model may map the glucose, effective insulin, and ketone compartments to x g , x e , and ,x k , then the rate of change of each of these compartments at any consecutive time instance t may be written as:

[0070]

number

[0071] Further variables and input values ​​are expressed as: x NHGBis the rate of glucose emergence from the net hepatic glucose balance, u M is the rate of glucose appearance from the meal, x iT is x i The temporal element of f it is a function describing ketone increases from insufficient circulating insulin, x iB is x i The reference element of, and,x kB is x k is a reference element of the model. Parameter p1 is a glucose availability index governing the insulin-independent glucose clearance rate, p2 is the insulin clearance rate in the active 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. Variations of this or other models may be used to provide a way to describe the interrelationships between glucose, insulin, and ketones. In general, the dynamic model at any continuous time instance t may be described as a function of state x and current compartment values ​​represented by known or estimated external inputs:

[0072]

number

[0073] , or at any sampled time instance k, it can be written as a function of the current compartment value:

[0074]

number

[0075] The dynamic model is updated over each scan instance or periodic time interval, where each instance calculates the best estimates and variances of these states (i.e., glucose, effective insulin, plasma insulin, and β-hydroxybutyrate compartments). The primary source of measurements is from the glucose time series values ​​made available by the glucose sensor. Any closed-loop state observer type, such as a Kalman filter, may be used to reconcile the measurements at each time step with predicted values ​​of the states to obtain a corrected state estimate. For example, the previously described models may be used to generate processing model equations to calculate the mean estimate of the predicted states and the covariance of the predicted states. Measurements from one or more glucose sensors may then be used as the first measurement in the measurement equations to perform state correction or state updates in the context of a Kalman filter structure, such as an extended Kalman filter. Each time a ketone measurement is made by the patient, additional information is used by the filter to improve the estimate of one or more parameters related to the ketone-on-board estimate based on the glucose data. As a result, ketones-on-board are then repeatedly recalculated as a measure of continuous exposure to β-hydroxybutyrate. This can take the form of a simple integration of β-hydroxybutyrate levels above a minimum threshold, with an assumed decay rate. Each time β-hydroxybutyrate exposure exceeds a predetermined level, the risk of DKA is considered high enough to warrant actual ketone testing.

[0076] The state observer framework considers additional information to further refine the DKA risk estimate. In one example, for systems with built-in ketone fragment compatibility, measured ketone levels are used to provide feedback to the state observer to correct the beta-hydroxybutyrate state estimate. In a second example, for systems in which the patient provides insulin use information, insulin dose and timing are used to provide feedback to the state observer to correct the plasma insulin state estimate. One way to obtain this data is to allow manual inspection when the user invokes the built-in insulin calculator on a system with built-in ketone fragment compatibility.

[0077] In one embodiment, we contemplate improving high ketone alerts for type 1 diabetes mellitus (T1DM) patients using a ketone sensor to reduce false high ketone alerts unrelated to the risk of diabetic ketoacidosis (DKA) by using information from other analytes in addition to ketones. In a single-analyte design, a high ketone threshold (preset or user-configurable) alerts the user to high ketone levels to prevent the risk of DKA. In a first improved embodiment, information from a glucose sensor is used to distinguish dangerously high ketone levels due to insufficient insulin delivery (which can lead to DKA) from high ketone levels due to successful ketogenic dieting. The latter case involves a specific glucose pattern with very small glucose fluctuations and very short durations of high glucose. Then, in the first case, a high ketone alert is issued, but in the second case, no high ketone alert is triggered. Generally, this embodiment identifies when a threshold is reached for a test substance (e.g., ketones) and determines whether exceeding the threshold is cause for concern (e.g., potential DKA) or encouraging (e.g., successful ketogenic diet).

[0078] In a second embodiment, different types of high ketone notifications / alerts are generated. For example, a more urgent type of announcement is triggered for the first case described in the first embodiment, and a more positive notification is indicated for the second case described in the first embodiment. In other words, the ketone alert may be modified based on the combined analysis of glucose and ketone data (e.g., distinguishing between potential DKA and successful ketogenic dieting). For example, the ketone alert may be modified by adjusting the severity of the alert, e.g., by changing the alert type (e.g., sound instead of just vibration), changing the volume, displaying a different color alert on the display screen, repeating the alert, and even changing the recurrence timing. The alert may also include information corresponding to whether the alert is associated with a first case (e.g., potential DKA) or a second case (e.g., successful ketogenic dieting).

[0079] In a third embodiment, the high ketone alert may be accompanied by a notification that the patient's healthcare provider (HCP) should be consulted if a particular condition is suspected. For example, a patient with T1DM may be taking a class of medications called SGLT-2 inhibitors (SGLT-2i). This class of medications was originally developed to manage glucose in patients with T2DM, and the use of SGLT-2i in patients with T1DM is on-label in some regions but not in others. One effect of taking SGLT-2i is to lower the renal clearance threshold, resulting in high glucose concentrations in the blood being released into the urine. This can lead to glucose starvation in insulin-dependent cells, ultimately triggering a process known as euglycemic DKA, which can lead to DKA even when measured glucose remains in a healthy range. To ensure that modified alerts, as described in the first and second embodiments, do not misbehave if a patient with T1DM is taking an SGLT-2i, this third embodiment uses the following method to estimate whether the patient is taking an SGLT-2i: If the patient is not taking an SGLT-2i, a dynamic relationship may be established to estimate ketones based on glucose history. Data from the glucose sensor and this dynamic relationship may be used to calculate a ketone estimate. This ketone estimate may then be compared to the ketone reading from the ketone sensor. If the patient with T1DM is also taking an SGLT-2i, the estimated ketones may always be lower than the measured ketones. This comparison may lead to the conclusion that the patient is taking an SGLT-2i and is experiencing this particular condition. In this case, a high ketone alert may include a warning or caution to consult an HCP about concurrent use of an SGLT-2i. In other words, the ketone warning may be modified based on an assessment of whether the patient is taking a medication associated with renal clearance (such as an SGLT-2i).

[0080] In a fourth embodiment, if a comparison of ketone and glucose data suggests the possibility of DKA rather than high ketones caused by a ketogenic diet, the high ketone alert is reasserted more frequently when an upper threshold is reached.

[0081] In a fifth embodiment, if the reason for suspected high ketones is due to a ketogenic diet rather than possible DKA, the asserted high ketone warning is silenced.

[0082] In a sixth embodiment, alternative information is provided when high ketones are suspected to be caused by a ketogenic diet, in which case the alert may provide useful information such as the length of time high ketones have been reached instead of the fact that ketones have most recently exceeded the upper threshold. Other alternative information may include the length of time high ketones have been reached per week, or a trend in percent of time, or a trend in percent, day to day, or a trend in hours per day.

[0083] In a seventh embodiment, when the use of a drug that may alter renal clearance thresholds, such as an SGLT-2i, is suspected, the use of a high glucose alert is accompanied by a notification in the user interface that the absence of the high glucose alert may not achieve its intended purpose and that the user's HSP should be consulted. Methods for using BG and ketone data in combination We now describe exemplary embodiments of methods for using combined glucose and ketone data in the operation of a medical device or system. Prior to this, those 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 non-transitory memory of a sensor control device, reader, remote computer, or trusted computer system, such as those described with respect to FIG. 1 . The stored instructions, when executed, may cause the processing circuitry of the associated device or computing system to perform any one or more of the steps of the exemplary methods described herein. Furthermore, those skilled in the art will appreciate that in many embodiments, any one or more of the steps of the methods 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 on stored sensor data, including sensor data from previous sensor wears by the same user. In some embodiments, the steps of the methods described herein may be performed periodically, according to a predetermined schedule and / or in a batch retrospective process.

[0084] Further, those skilled in the art will appreciate that instructions may be stored in non-transitory memory on a single device (e.g., sensor controller or reader) or, alternatively, distributed across multiple discrete devices at geographically dispersed locations (e.g., a cloud platform). For example, in some embodiments, collection of data indicative of analyte levels (e.g., glucose, ketone) may occur on the sensor controller, while calculation of analyte metrics (e.g., glucose derivatives, ketone derivatives) and comparison of the analyte metrics to predetermined thresholds may occur on the reader, a remote computing system, or a trusted computing system. In some embodiments, collection of analyte data and comparison to predetermined thresholds may occur solely on the sensor controller. Similarly, those skilled in the art will appreciate that computing devices in the embodiments disclosed herein, such as those shown in FIG. 1, are intended to encompass both physical and virtual devices (or “virtual machines”).

[0085] 5 is a flow diagram of an exemplary embodiment of an analyte monitoring method 500. The steps of method 500 may be performed by a system including a sensor control unit including an analyte sensor having a portion configured to be inserted into a user's body at an insertion site, the portion including a first sensing portion configured to sense glucose levels in a bodily fluid and a second sensing portion configured to sense an analyte (e.g., beta-hydroxybutyrate) indicative of ketone levels in the bodily fluid at the same insertion site. In another embodiment, steps 510 and 520 may be performed by a sensor control unit including a first analyte sensor and a second analyte sensor, where the first analyte sensor is configured to sense glucose levels in the bodily fluid and the second analyte sensor is configured to sense an analyte indicative of ketone levels in the bodily fluid, and the first and second analyte sensors are configured to sense analyte levels at the same local insertion site. In another example, the analyte sensor is a glucose sensor and the sensor control unit can be configured to accept data indicative of ketone levels, such as from a ketone test strip.

[0086] Referring to FIG. 5 , at step 510, method 500 includes collecting first time-correlated data indicative of glucose levels and second time-correlated data indicative of ketone levels by a sensor control device, the sensor control device including an analyte sensor at least partially inserted into the user's body. According to various embodiments, the first analyte metric is a glucose derivative and the second analyte metric is a ketone derivative. At step 520, a processing unit of the system, e.g., a processing unit of a reader device in communication with the sensor control device, may make a decision to control output from a system component. The decision may be based on the first and second time-correlated data. The decision may include determining at least one of an alert threshold for one or both of the first and second time-correlated data, a message for output by the reader device in communication with the sensor control device, or a modification of the analyte status estimate in system memory. The decision may include one or more of these, each of which may be provided in combination or separately. For example, the decision may include determining an alert threshold but not a message for output.

[0087] Determining a warning threshold may involve, for example, setting or modifying a threshold for blood glucose, ketones, or other analytes that, when exceeded, causes the reader or other system component to output an alarm. Under nominal conditions, a high glucose warning may be set at 250 mg / dL, and the reader or other system component may be configured to output an alarm whenever data indicating a glucose level exceeds this first threshold from a lower value. Similarly, a high ketone warning may be set at 1.3 mMol / L, and the reader or other system component may be configured to output an alarm when data indicating a ketone level exceeds this second threshold from a lower value. In one example, the second threshold is modified based on distinguishing between dangerously high ketone levels due to insufficient insulin delivery (which may lead to DKA) and high ketone levels due to successful ketogenic dieting. In the former case, the second threshold may be left unchanged or slightly lowered (e.g., to 1.0 mMol / L) to allow for earlier intervention. In the latter case, the second threshold is 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 percentage of the estimated diet-related ketone value to the second threshold. If the recent diet-related ketone value is estimated to be 0.6 mMol / L and the fixed percentage is set to 0.9, the second threshold is set such that the threshold is 0.9 x 0.6 mMol / L above the nominal value of the second threshold (previously set to 1.3 mMol / L).

[0088] Determining the message may include, for example, selecting from a data table of predetermined messages in response to the results of an automated analysis of the first and second time-correlated data. Under nominal conditions, the high glucose alert may be set to 250 mg / dL, and the reader or other system component may be configured to output an alarm whenever data indicating a glucose level exceeds this first threshold from a lower value. Similarly, the high ketone alert may be set to 1.3 mMol / L, and the reader or other system component may be configured to output an alarm whenever data indicating a ketone level exceeds this second threshold from a lower value. In one example, if ketones exceed the second threshold and most or all of the ketone levels are estimated to be due to insufficient insulin delivery (which may lead to DKA) based on the first and second time-correlated data, the message content may convey a level of urgency and the need for corrective action. The presentation of the auditory alert and the visual aspects of the message may also correspond to a higher level of urgency, such as a louder auditory alert, more frequent re-presentation when the alert is snoozed, and a warmer color on the message. In another example, if ketones exceed a second threshold and the first and second time correlation data indicate that the ketone levels are not due to insufficient insulin delivery (which may lead to DKA) or are very low, the message content may be congratulatory and less assertive than a more urgent message, such as an impending hypoglycemia or hyperglycemia. In another example, if ketones exceed a second threshold and the first and second time correlation data indicate that the ketone levels are not due to successful ketogenic dieting or are very low, the message content may convey a level of urgency and a need for corrective action. The presentation of the audible alert and the visual appearance of the message also correspond to higher urgency, with louder audible alerts, more frequent re-announcements when the alert is snoozed, and warmer colors on the message being selected.In another example, if ketones exceed a second threshold and based on the first time correlation data and the second time correlation data, it is estimated that most or all of the ketone levels are due to successful implementation of the ketogenic diet, the content of the message may be congratulatory and less assertive than a more urgent message, such as an impending hypoglycemia or hyperglycemia.

[0089] Determining a correction to the analyte state estimate may include, for example, calculating a correction factor or value for an initial estimate of blood glucose or other analyte made based solely on the first time-correlated data. For example, the time-correlated data indicating glucose levels may contain slowly varying errors. Prior to calculating the correction factor, the estimated glucose-related state may also be affected by this error. Instead, the correction factor may be applied to adjust the glucose-related state to improve the estimated blood glucose level.

[0090] These decisions and related actions are described in more detail herein with reference to Figures 6-10.

[0091] At step 530, method 500 may include outputting an indication of the determination by a reading device in communication with the sensor control device. For example, the reading device may output an audible and / or visual alarm, display an alarm message, display an informational message, or modify one or more analyte values ​​stored in memory and set an indicator indicating that the values ​​have been modified. In some embodiments, generating an alert indication may include outputting a notice or message for display by the user's portable device running the reading application. Alternatively, or additionally, the alert indication may include one or more of a visual, audio, or vibration alert or alarm output on a display of the reading device, a remote computer, or a trusted computer system. A therapeutic action may optionally be taken in response to or instead of the alarm. In some embodiments, for example, the therapeutic action may suppress or modify the display of a low glucose alarm. In other embodiments, the therapeutic action may include preventing an automated drug delivery system (e.g., an insulin pump) from issuing a command to alter or cause drug (e.g., insulin) delivery.

[0092] In some embodiments, making a determination at step 520 of method 500 may include an additional operation 600, as shown in FIG. 6. Making a determination at step 610 may include using a closed-loop state observer to reconcile the measurements at each time step with a predicted value for the estimated state of the analyte of interest to obtain a corrected state estimate for the analyte of interest. An example of a closed-loop state observer is a Kalman filter or a variation thereof. The process and measurement models may be constructed based on dynamic models involving available measurements, such as glucose, effective insulin, and ketones. There may be model states corresponding to these quantities, where the states related to glucose, effective insulin, and ketones are represented as x, ... g , x e , x kIn general, we can define a dynamic model at any successive time instance t as a function of the current compartment values ​​x and known or estimated external inputs.

[0093]

number

[0094] or express the dynamic model at any sampled time instance k as a function of the current compartment values

[0095]

number

[0096] At each time step, the processing model can be expressed as x g , x e , or x kThe available measurements may then be used by a closed-loop state observer (e.g., a Kalman filter) to obtain a corrected state estimate for the analyte of interest. In an aspect of operation 600, in step 620, the second time-correlated data may be or include data from a β-hydroxybutyrate sensor. Making a determination in step 630 may further include periodically recalculating ketone-on-board based on the sensor data indicative of β-hydroxybutyrate using a known correlation factor. The periodic recalculation may be performed by the processing unit of the reader at each time step, e.g., once every tenth of a second, once every second, once every ten seconds, once every minute, etc. In some embodiments, frequent β-hydroxybutyrate measurements may not be available, and the corrective portion of the closed-loop state observer may only have access to ketone measurements when using ketone test strip data. Making a decision at step 640 may further include selecting a message for output indicating that the patient wearing the sensor-controlled device should undergo ketone testing based on the value of the ketone-related condition exceeding a predetermined level. Returning to Figure 4, this means that the ketone-related condition may substitute for frequent beta-hydroxybutyrate measurements, with the threshold being the same as the threshold if frequent beta-hydroxybutyrate measurements were available, or a confidence interval for exceeding the threshold may be used to determine when to assert a message for output indicating that the patient wearing the sensor-controlled device should undergo ketone testing.

[0097] In other embodiments, making a decision at step 520 of method 500 may include additional operations and aspects, such as those shown in FIG. 7 . At step 710, the second time-correlated data may be or include data from a sensor for beta-hydroxybutyrate. In this embodiment, as shown at step 720, an input device is operably connected to at least one of a reading device or a sensor control device and configured to receive ketone test results. For example, the sensor control device may be configured with a ketone test strip reader. Making a decision at step 720 may further include modifying the beta-hydroxybutyrate estimate based on the ketone test results. For example, a closed-loop state observer, such as a Kalman filter, may be used to perform state modification processing when additional ketone test results are available, using a standard Kalman filter framework.

[0098] In other embodiments, making a determination at step 520 of method 500 may include an additional operation 800, as shown in FIG. 8 . In these embodiments, as shown in step 810, an input device may be operatively connected to at least one of a reader or sensor control device configured to receive information defining insulin doses by a patient wearing the sensor control device. For example, the reader may prompt a user to input current insulin dose information or may respond to user input. At step 820, method 500 may further include revising the estimate of the patient's plasma insulin status. For example, a closed-loop state observer, such as a Kalman filter, may be used to perform state revision processing when additional insulin status information is available, using a standard Kalman filter framework. In practice, the patient's plasma insulin status may be in the form of insulin type and amount. The amount may be expressed as a delivery rate from an insulin delivery device (e.g., an insulin pump) or an additional insulin dose from an insulin delivery device (e.g., an insulin pump or a connected insulin pen).

[0099] Alternatively, or additionally, the input device may be operably connected to at least one of a reader or a sensor control device configured to receive the ketone test results, as shown in step 830. For example, the sensor control device may be configured with a reader for ketone test strips, or the reader may prompt the user to input the ketone test results. At step 840, method 500 may include automatically executing an insulin dose calculation algorithm in response to receiving the ketone test results.

[0100] In another embodiment, making a determination at step 520 of method 500 may include further operations 900, as shown in FIGS. 9A and 9B. The operations may include, at step 910, distinguishing dangerously high ketone levels due to insufficient insulin delivery from high ketone levels due to successful ketogenic dieting based, at least in part, on first time correlation data indicative of glucose levels. For example, at step 920, the reader may determine whether the first time correlation data indicates a condition characterized by glucose excursions below a threshold, a period of high glucose below a maximum time threshold, and ketone levels above a predetermined threshold. If such a condition is detected, at step 925, the system may at least one of suppressing a high ketone alert and / or reducing the urgency of the high ketone alert. For example, at step 930, the system may suppress the high ketone alert until the indicated conditions are no longer met, or accompany the alert with a notice indicating that the ketotic state is not indicative of an emergency or is not an emergency, or indicate that ketosis is caused by a low-carbohydrate diet. Alternatively, or additionally, in step 940, the system may output a message of interest to a user interested in achieving ketosis through intentional dieting. For example, the message may indicate the duration of high ketone levels, i.e., the length of time the ketosis state has lasted. Generally, this embodiment determines, when a threshold for a test substance (e.g., ketones) is reached, whether the exceedance of the threshold is concerning (e.g., potential DKA) or encouraging (e.g., successful ketogenic diet). Depending on the determination, the content of the message (i.e., what the user is told about the concerning or encouraging information) and the timing of the message (i.e., whether the message is issued immediately upon a trigger, at a predetermined notification time, periodically re-notifying until the condition disappears, or only upon a trigger) may vary.

[0101] Alternatively, or additionally, if the system determines in step 925 that the condition is not met or if the system does not perform testing step 920, the system may determine in step 960 whether the first time correlation data indicates a condition characterized by glucose excursions above a threshold, a period of high glucose greater than a maximum time threshold, and ketone levels above a predetermined threshold. If the system determines in step 965 that the condition is met, then in step 970 the system may increase the frequency or urgency of at least one of messages indicating the patient wearing the sensor control device is at risk for euglycemic DKA.

[0102] In another embodiment, the determination at step 520 of method 500 may further include an additional operation 1000, as shown in FIG. 10 . At step 1010, method 500 may include, by a system processor, inferring whether a patient wearing the sensor control device is a type 1 diabetes patient taking an SGLT-2 inhibitor based on a comparison of a ketone estimate based on the first time correlation data to a ketone level indicated by the second time correlation data. At step 1020, the method may include determining whether the ketone estimate is consistently lower than the displayed ketone level, and if so, determining a message including an indication that the patient should consult a healthcare provider about taking an SGLT-2 inhibitor. Additionally or alternatively, method 500 may include, at step 1030, including in the message an indication that the absence of a high glucose warning may not achieve the intended purpose of glucose monitoring.

[0103] Additionally, in some embodiments, any method steps described herein, including, but not limited to, making the determination (step 520) and / or outputting an indication of the determination (step 530), may be performed on the remote monitoring device or on a cloud-based server communicatively connected to the remote monitoring device. In some embodiments, the remote monitoring device may include a secondary reader device (e.g., a second smartphone) configured for use by, for example, 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 provider responsible for monitoring a patient wearing a sensor). In many embodiments, the secondary reader device may include one or more processors connected to a memory storing a remote analyte monitoring program configured to perform any one or more of the method steps described herein. For example, in some embodiments, the remote analyte monitoring program on the secondary reader device may output an audible and / or visual alarm, display an alarm message, display an informational message, or modify one or more analyte values ​​stored in memory and set an indicator indicating that the values ​​have been modified. In some embodiments, generating an alert indication may include outputting a notice or message for display by a secondary reader device running the remote analyte monitoring program. Alternatively, or additionally, the alert indication may include one or more of a visual, audio, or vibratory warning or alarm output on a display of the secondary reader device. According to another aspect of some embodiments, the remote analyte monitoring program may be configured to allow a caregiver to configure their own alarm settings, for example, to enable or disable an alarm or change an analyte threshold value. Further details of the remote analyte monitoring program are described in U.S. Patent Application Publication Nos. 2022 / 0248988 and 2022 / 0240819, the disclosures of which are incorporated herein by reference in their entireties for all purposes.

[0104] For each and every embodiment of the methods disclosed herein, systems and devices capable of performing each embodiment are also encompassed within the scope of the present disclosure. For example, sensor control device embodiments are disclosed, which may include one or more analyte sensors, analyte monitoring circuitry (e.g., analog circuitry), memory (e.g., for storing instructions), power supplies, communication circuitry, transmitters, receivers, clocks, counters, timers, temperature sensors, and processors (e.g., for executing instructions) that perform or facilitate any and all method steps. These sensor control device embodiments may be used or be capable of being used to implement any and all methods described herein. Similarly, reader device embodiments are disclosed, which may include one or more memory (e.g., for storing instructions), power supplies, communication circuitry, transmitters, receivers, clocks, counters, timers, and processors (e.g., for executing instructions) that perform or facilitate any and all method steps. These reader embodiments may be used or capable of being used to implement the steps performed by the reader from any and all of the methods described herein. Computer device and server embodiments are disclosed, which may include one or more memory (e.g., for storing instructions), power supplies, communications circuitry, transmitters, receivers, clocks, counters, timers, temperature sensors, and processors (e.g., for executing instructions) that perform or facilitate the execution of any and all method steps. These reader embodiments may be used or capable of being used to implement the steps performed by the reader from any and all methods described herein.

[0105] Computer program instructions for performing operations in accordance with the subject matter described herein 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, etc., and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program instructions may execute entirely on the user's computing device, partially on the user's computing device as a standalone 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 scenario, the remote computing device may be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be to an external computer (e.g., through the Internet using an Internet Service Provider).

[0106] 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 combinable and interchangeable with any other embodiment. When a feature, element, component, function, or step is described with respect to only one embodiment, it should be understood that the feature, element, component, function, or step can be used with all other embodiments unless expressly stated otherwise. Therefore, this paragraph serves as a precursor and supporting description for introducing claims that combine features, elements, components, functions, and steps from different embodiments or replace features, elements, components, functions, and steps from one embodiment with those from other embodiments, even if the preceding description does not explicitly state that such combinations and substitutions are possible in a particular case. It is expressly stated that explicitly listing all possible combinations and substitutions would be an excessive burden, especially given that those skilled in the art would readily recognize that such combinations and substitutions are possible. Aspects of the invention are set forth in independent claims. Preferred features are set out in the dependent claims, which may be implemented in combination with the respective aspects set out in the independent claims. Apparatuses comprising means for implementing the respective methods are also provided. Features of one aspect may be applied to each aspect alone or in combination with other features.

[0107] To the extent that an embodiment disclosed herein includes or operates with memory, storage, and / or computer-readable medium, then that memory, storage, and / or computer-readable medium is non-transitory. Thus, to the extent that memory, storage, and / or computer-readable medium is covered by one or more claims, then the memory, storage, and / or computer-readable medium is only non-transitory.

[0108] As used in this specification and the appended claims, indefinite and definite articles indicating the singular include the plural as well, unless the context clearly indicates otherwise.

[0109] While the embodiments are susceptible to various modifications and alternative forms, specific examples have been shown in the drawings and are described in detail herein. However, it should be understood that these embodiments are not limited to the particular forms disclosed; rather, these embodiments are intended to cover all modifications, equivalents, and alternatives falling within the spirit of the disclosure. Furthermore, any feature, function, step, or element of the embodiments may be recited or added to the claims, and negative limitations may be added to the claims that define the scope of the claims by any feature, function, step, or element not included in the scope of the claims.

[0110] Systems, devices, and methods are provided 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 insulin pump. In some embodiments, the system, device, or method may use a physiological model that combines glucose history and β-hydroxybutyrate to better predict diabetic ketoacidosis (DKA) compared to predictions based on a single high glucose threshold. In other embodiments, the system, device, or method may include features for generating an estimate of a patient's medication status and / or knowledge of medication information, such as for a type 1 diabetes (DM) patient taking an SGLT-2 inhibitor.

[0111] The present disclosure also includes the following bullet points: 1. In the test substance monitoring system, a sensor control device including an analyte sensor, the analyte sensor including at least a portion configured to be inserted into a body of a user, the sensor control device configured to collect first time-correlated data indicative of a glucose level and second time-correlated data indicative of a ketone level, the sensor control device operably connected to a first processing circuit and a first non-transitory memory; a reader including a second processing circuit and a second non-transitory memory; Including, At least one of the first or second non-transitory memories includes instructions that, when executed, cause at least one of the first or second processing circuit to make a decision based on the first and second time correlation data and output an indication of the decision by a reader, the decision comprising: an alert threshold for one or both of the first and second time-correlated data; a message for output by a reader, or Correction of test substance status estimates A test substance monitoring system that is at least one determination of. 2. The test substance monitoring system of paragraph 1, wherein the instructions for making the decision further include using a closed-loop state observer to reconcile the measurements at each time step against predicted values ​​of the estimated state of the test substance of interest to obtain a corrected state estimate of the test substance of interest. 3. The analyte monitoring system of claim 1 or 2, wherein the second time-correlated data includes data from a ketone sensor. 4. The test substance monitoring system of claim 3, wherein the second time-correlated data includes data indicative of beta-hydroxybutyrate. 5. The test substance monitoring system of claim 4, wherein the instructions for making a decision further cause the ketones-on-board to be periodically recalculated based on the data indicative of beta-hydroxybutyrate. 6. The test substance monitoring system of claim 4 or 5, wherein the instruction to make a decision selects a message for output indicating that a patient wearing the sensor control device should undergo a ketone test based on data from the sensor that indicates beta-hydroxybutyrate exceeds a predetermined level. 7. The second time-correlated data includes data from a sensor for β-hydroxybutyrate; The system includes an input device operably connected to at least one of a reader or a sensor controller configured to receive a ketone test result; Further including, 7. The test substance monitoring system of any one of paragraphs 1 to 6, wherein the instruction to make a decision is to revise the estimated value of beta-hydroxybutyrate based on the ketone test results. 8. An input device operatively connected to at least one of the reader or the sensor control device configured to receive information defining insulin administration by a patient wearing the sensor control device; Further including, 8. The analyte monitoring system of any one of paragraphs 1 to 7, wherein the instruction to make a decision causes a correction to the estimate of the patient's plasma insulin status. 9. An input device operably connected to at least one of the reader or sensor control device configured to receive a ketone test result; Further including, 9. The test substance monitoring system of any one of paragraphs 1 to 8, wherein the instructions further cause an insulin dosage calculation algorithm to be automatically executed in response to receiving the ketone test result. 10. The test substance monitoring system of any one of paragraphs 1 to 9, wherein the indication of the decision is an alert status for one test substance, and the alert status is based on more than one test substance. 11. The test substance monitoring system of claim 10, wherein the instructions for making a determination determine whether the first time correlation data indicates conditions including glucose excursion being below a threshold, a period of high glucose being less than a maximum time threshold, and ketone levels being above a predetermined threshold. 12. The test substance monitoring system described in paragraph 11, wherein the instruction for the decision causes at least one of suppressing a high ketone alert or reducing the urgency of a high ketone alert. 13. The test substance monitoring system of claim 11 or 12, wherein the instruction for the decision causes the message to indicate the period during which high ketone levels have been reached. 14. A test substance monitoring system as described in any one of paragraphs 10 to 13, wherein the instruction to make a decision determines whether the first time correlation data indicates conditions including glucose excursions higher than a threshold, a period of high glucose longer than a maximum time threshold, and ketone levels higher than a predetermined threshold, and if the conditions are detected, increases at least one of the frequency or urgency of messages indicating that a patient wearing the sensor control device is at risk of developing euglycemic DKA. 15. The test substance monitoring system of any one of paragraphs 1 to 14, wherein the instructions to make a determination further include determining whether a patient wearing the sensor control device is a person with type 1 diabetes taking an SGLT-2 inhibitor based on a comparison of a ketone estimate based on the first time correlation data to a ketone level indicated by the second time correlation data. 16. The test substance monitoring system of paragraph 15, wherein the instructions for making a decision include determining whether the ketone estimate is consistently lower than the indicated ketone level, and if so, determining a message including an indication that the patient should consult with his or her healthcare provider about taking an SGLT-2 inhibitor. 17. The test substance monitoring system of claim 16, wherein the instructions to make a decision further cause a message to be determined that includes an indication that the absence of a high glucose warning may not achieve its intended purpose. 18. The analyte monitoring system of any one of paragraphs 1 to 17, wherein the instructions are stored in a second non-transitory memory. 19. The analyte monitoring system of any one of paragraphs 1 to 18, wherein the instructions are stored in a first non-transitory memory. 20. The test substance monitoring system of any one of paragraphs 1 to 19, wherein the sensor control device further includes wireless communication circuitry configured to transmit the first and second time-correlated data to the reader device. 21. The analyte monitoring system of paragraph 20, wherein the wireless communication circuitry is configured to transmit the first and second time-correlated data via a Bluetooth protocol. 22. The analyte monitoring system of any one of paragraphs 1 to 21, wherein the analyte sensor is a first analyte sensor, and the sensor control device further includes a second analyte sensor, wherein the first analyte sensor is configured to sense a glucose level in the bodily fluid, and the second analyte sensor is configured to sense a ketone level in the bodily fluid. 23. A drug delivery device comprising: 23. The test substance monitoring system of any one of paragraphs 1 to 22, further comprising: 24. The analyte monitoring system of paragraph 23, wherein the drug delivery device includes an insulin pump. 25. A computer-based method for detecting suspected glucose hypotension, comprising: collecting first time-correlated data indicative of glucose levels and second time-correlated data indicative of ketone levels with a sensor control device, the sensor control device including an analyte sensor at least partially inserted into the user's body; making a determination based on the first and second time correlation data, the determination comprising: an alert threshold for one or both of the first and second time-correlated data; a message for output by a reader in communication with the sensor controller; or Correction of test substance status estimates determining at least one of outputting an indication of the determination by a reader in communication with the sensor controller; A method comprising: 26. The method of claim 25, wherein the step of making a decision further includes using a closed-loop state observer to reconcile the measurements at each time step against a predicted value of the estimated state of the analyte of interest to obtain a corrected state estimate of the analyte of interest. 27. The method of claim 25 or 26, wherein the second time-correlated data includes data from a ketone sensor. 28. The method of claim 27, wherein the second time-correlated data includes data indicative of β-hydroxybutyrate. 29. The method of claim 28, wherein the determining step further comprises periodically recalculating ketones-on-board based on data indicative of beta-hydroxybutyrate. 30. The method of claim 28 or 29, wherein the step of making a decision further includes selecting a message for output indicating that a patient wearing the sensor control device should undergo a ketone test based on data from the sensor of beta-hydroxybutyrate exceeding a predetermined level. 31. The method of any one of paragraphs 25 to 29, wherein the second time-correlated data includes data from a sensor for beta-hydroxybutyrate, the input device is operably connected to at least one of a reading device or a sensor control device configured to receive ketone test results, and the determining step includes a step of revising the beta-hydroxybutyrate estimate based on the ketone test results. 32. An input device operatively connected to at least one of a reader or the sensor control device configured to receive information defining insulin administration by a patient wearing the sensor control device; 32. The method of any one of clauses 25 to 31, wherein making a determination further comprises revising the estimate of the patient's plasma insulin status. 33. An input device operably connected to at least one of a reader or a sensor controller configured to receive a ketone test result; automatically executing an insulin dosage calculation algorithm in response to receiving the ketone test result; 33. The method of any one of paragraphs 25 to 32, further comprising: 34. The method of any one of paragraphs 25 to 33, wherein the indication of the decision is an alert status for one test substance, and the alert status is based on more than one test substance. 35. The method of claim 34, wherein the determining further comprises determining whether the first time correlation data indicates a condition comprising glucose excursion being below a threshold, a period of high glucose being less than a maximum time threshold, and ketone levels being above a predetermined threshold. 36. The method of claim 35, wherein the decision includes at least one of suppressing the high ketone alert or reducing the urgency of the high ketone alert. 37. The method of claim 35 or 36, wherein determining comprises causing a message to indicate the period during which high ketone levels have been reached. 38. A test substance monitoring system as described in any one of paragraphs 34 to 37, wherein the determination includes determining whether the first time correlation data indicates a condition characterized by glucose excursions higher than a threshold, a period of high glucose longer than a maximum time threshold, and ketone levels higher than a predetermined threshold, and, if the condition is detected, increasing at least one of the frequency or urgency of messages indicating that a patient wearing the sensor control device is at risk of developing euglycemic DKA. 39. The test substance monitoring system of any one of paragraphs 25 to 38, wherein the determining step further includes a step of inferring whether the patient wearing the sensor control device is a person with type 1 diabetes taking an SGLT-2 inhibitor based on a comparison of a ketone estimate based on the first time correlation data to a ketone level indicated by the second time correlation data. 40. The method of claim 39, wherein making a determination includes determining whether the ketone estimate is consistently lower than the indicated ketone level, and if so, determining a message including an indication that the patient should consult with their healthcare provider about taking an SGLT-2 inhibitor. 41. The method of paragraph 40, wherein making a determination further comprises including in the message an indication that the absence of a high glucose warning may not achieve its intended purpose. 42. Wirelessly transmitting the first and second data from the sensor control device to the reader device; 42. The method of any one of clauses 25 to 41, further comprising: 43. A computer program, computer program product or computer readable medium comprising instructions which, when executed by a processing unit, cause the processing unit to perform a method according to any one of clauses 25 to 42. 44. In the device, means for collecting first time-correlated data indicative of glucose levels and second time-correlated data indicative of ketone levels by a sensor control device, the sensor control device including an analyte sensor at least partially inserted into the body of a user; means for making a decision based on the first and second time correlation data, the decision comprising: an alert threshold for one or both of the first and second time-correlated data; a message for output by a reader in communication with the sensor controller; or Correction of test substance status estimates and a determination of at least one of: means for outputting an indication of the determination by a reading device in communication with the sensor control device; An apparatus comprising: 45. Means for carrying out the method according to any one of paragraphs 25 to 42, 45. The apparatus of paragraph 44, further comprising: [Explanation of symbols]

[0112] 102 Sensor control device 104, 106 Test substance sensor 120 Reading Device

Claims

1. In the test substance monitoring system, a sensor control device including an analyte sensor, the analyte sensor including at least a portion configured to be inserted into a body of a user, the sensor control device configured to collect first time-correlated data indicative of a glucose level and second time-correlated data indicative of a ketone level, the sensor control device operably connected to a first processing circuit and a first non-transitory memory; a reader including a second processing circuit and a second non-transitory memory; Including, At least one of the first or second non-transitory memories includes instructions that, when executed, cause at least one of the first or second processing circuit to make a decision based on the first and second time correlation data and output an indication of the decision by the reading device, the decision comprising: an alert threshold for one or both of the first and second time correlation data; a message for output by said reading device, or Correction of test substance status estimates The analyte monitoring system is at least one determination of.

2. 2. The test substance monitoring system of claim 1, wherein the instructions for making the decision further include using a closed-loop state observer to reconcile the measurements at each time step against a predicted value of the estimated state of the test substance of interest to obtain a corrected state estimate for the test substance of interest.

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

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

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

6. 5. The test substance monitoring system of claim 4, wherein the instructions for making the decision include selecting a message for the output indicating that a patient wearing the sensor control device should undergo a ketone test based on data from the sensor that the beta-hydroxybutyrate exceeds a predetermined level.

7. the second time-correlated data includes data from a sensor for beta-hydroxybutyrate; The system includes an input device operably connected to at least one of the reader device or the sensor control device configured to receive a ketone test result; Further including, 10. The analyte monitoring system of claim 1, wherein the instructions for making the decision include modifying the beta-hydroxybutyrate estimate based on the ketone test results.

8. an input device operatively connected to at least one of the reader or the sensor control device, the input device configured to receive information defining insulin administration by a patient wearing the sensor control device; Further including, 10. The analyte monitoring system of claim 1, wherein the instruction to make the decision causes a revision of the estimate of the patient's plasma insulin status.

9. an input device operably connected to at least one of the reader device or the sensor control device configured to receive a ketone test result; Further including, 10. The analyte monitoring system of claim 8, wherein the instructions further cause an insulin dosage calculation algorithm to be automatically executed in response to receiving the ketone test result.

10. 10. The analyte monitoring system of claim 1, wherein the indication of the determination is an alert condition for one analyte, the alert condition being based on more than one analyte.

11. 11. The analyte monitoring system of claim 10, wherein the instructions for making the determination determine whether the first time correlation data indicates conditions including glucose excursion being below a threshold, a period of high glucose being less than a maximum time threshold, and ketone levels being above a predetermined threshold.

12. 12. The analyte monitoring system of claim 11, wherein the instruction for the decision is to at least one of suppress a high ketone alert or reduce the urgency of a high ketone alert.

13. 12. The analyte monitoring system of claim 11, wherein the instructions for the determination cause the message to indicate a period of time during which high ketone levels have been reached.

14. 11. The test substance monitoring system of claim 10, wherein the instructions for making the determination determine whether the first time correlation data indicates conditions including glucose excursions higher than a threshold, a period of high glucose longer than a maximum time threshold, and ketone levels higher than a predetermined threshold, and, if such conditions are detected, increase at least one of the frequency or urgency of messages indicating that the patient wearing the sensor control device is at risk of developing euglycemic DKA.

15. 2. The test substance monitoring system of claim 1, wherein the instructions for making the determination further include estimating whether a patient wearing the sensor control device is a type 1 diabetic taking an SGLT-2 inhibitor based on a comparison of a ketone estimate based on the first time correlation data to a ketone level indicated by the second time correlation data.

16. 16. The analyte monitoring system of claim 15, wherein the instructions for making the decision determine whether the ketone estimate is consistently lower than the indicated ketone level, and if so, the message includes an indication that the patient should consult his or her healthcare provider about taking the SGLT-2 inhibitor.

17. 17. The test substance monitoring system of claim 16, wherein the instructions for making the decision further cause the message to be determined to include an indication that the absence of a high glucose warning may not achieve its intended purpose.

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

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

20. 10. The analyte monitoring system of claim 1, wherein the sensor control device further includes wireless communication circuitry configured to transmit the first and second time-correlated data to the reader device.

21. 21. The analyte monitoring system of claim 20, wherein the wireless communication circuitry is configured to transmit the first and second time correlation data via a Bluetooth protocol.

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

23. A drug delivery device The analyte monitoring system of claim 1 further comprising:

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

25. 1. A computer-implemented method for detecting suspected glucose hypotension, comprising: collecting first time-correlated data indicative of glucose levels and second time-correlated data indicative of ketone levels with a sensor control device, the sensor control device including an analyte sensor at least partially inserted into the user's body; making a determination based on the first and second time correlation data, the determination comprising: an alert threshold for one or both of the first and second time correlation data; a message for output by a reader in communication with said sensor controller; or Correction of test substance status estimates determining at least one of: outputting an indication of said determination by a reading device in communication with said sensor control device; A method comprising:

26. 26. The method of claim 25, wherein the determining step further comprises using a closed-loop state observer to reconcile the measurements at each time step against a predicted value of the estimated state of the analyte of interest to obtain a corrected state estimate for the analyte of interest.

27. 27. The method of claim 26, wherein the second time-correlated data comprises data from a ketone sensor.

28. 28. The method of claim 27, wherein the second time-correlated data comprises data indicative of beta-hydroxybutyrate.

29. 30. The method of claim 28, wherein the determining step further comprises periodically recalculating ketones-on-board based on the data indicative of beta-hydroxybutyrate.

30. 30. The method of claim 28, wherein making the decision further comprises selecting a message for the output indicating that a patient wearing the sensor control device should undergo ketone testing based on data from the sensor of the beta-hydroxybutyrate exceeding a predetermined level.

31. 26. The method of claim 25, wherein the second time-correlated data includes data from a sensor for beta-hydroxybutyrate, an input device is operably connected to at least one of the reading device or the sensor control device configured to receive ketone test results, and the determining step includes modifying the beta-hydroxybutyrate estimate based on the ketone test results.

32. an input device operatively connected to at least one of the reader or the sensor control device configured to receive information defining insulin administration by a patient wearing the sensor control device; 26. The method of claim 25, wherein the determining step further comprises revising an estimate of the patient's plasma insulin status.

33. an input device operably connected to at least one of the reader device or the sensor controller configured to receive a ketone test result; automatically executing an insulin dosage calculation algorithm in response to receiving the ketone test result; 26. The method of claim 25, further comprising:

34. 26. The method of claim 25, wherein the indication of the determination is an alert status for one analyte, the alert status being based on more than one analyte.

35. 35. The method of claim 34, wherein the determining further comprises determining whether the first time correlation data indicates conditions including glucose excursion below a threshold, a period of high glucose less than a maximum time threshold, and ketone levels above a predetermined threshold.

36. 36. The method of claim 35, wherein the decision comprises at least one of suppressing a high ketone alert or reducing the urgency of a high ketone alert.

37. 36. The method of claim 35, wherein said determining comprises causing said message to indicate a period of time during which high ketone levels have been reached.

38. 35. The test substance monitoring system of claim 34, wherein the determining step includes determining whether the first time correlation data indicates a condition characterized by glucose excursions greater than a threshold, a period of high glucose greater than a maximum time threshold, and ketone levels greater than a predetermined threshold, and, if the condition is detected, increasing at least one of the frequency or urgency of messages indicating that the patient wearing the sensor control device is at risk of developing euglycemic DKA.

39. 26. The test substance monitoring system of claim 25, wherein the determining step further comprises the step of inferring whether the patient wearing the sensor control device is a type 1 diabetic taking an SGLT-2 inhibitor based on a comparison of a ketone estimate based on the first time correlation data to a ketone level indicated by the second time correlation data.

40. 40. The method of claim 39, wherein said determining step comprises determining if the ketone estimate is consistently lower than the indicated ketone level, and if so, determining that the message includes an indication that the patient should consult his or her healthcare provider about taking the SGLT-2 inhibitor.

41. 41. The method of claim 40, wherein making the determination further comprises including in the message an indication that the absence of a high glucose warning may not serve an intended purpose.

42. wirelessly transmitting the first and second data from the sensor control device to the reader; 26. The method of claim 25, further comprising:

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