Systems, devices, and methods for improving the accuracy and fault detection of analyte sensors

Abstract

Various embodiments of systems, apparatuses, and methods for improving the accuracy of analyte sensors and for detecting sensor malfunctions are disclosed. According to some embodiments, these systems, apparatuses, and methods may utilize first data collected by a glucose sensor and second data collected by a secondary sensing element. In some embodiments, the secondary sensing element may be one of a lactate sensing element, a ketone sensing element, or a heart rate monitor, etc.

Description

Technical Field

[0001] The subject matter of this application generally relates to systems, apparatus, and methods for improving the accuracy and fault detection of analyte sensors. Specifically, embodiments described in this disclosure relate to validating data collected by a glucose sensor with data collected from a secondary sensing element in order to correct glucose levels or detect suspected adverse conditions, such as suspected sensor malfunction. Background Technology

[0002] There is a huge and growing market for monitoring the health and condition of humans and other living animals. Information describing a human body or physiological condition can be used in countless ways to help and improve quality of life, as well as to diagnose and treat adverse human conditions.

[0003] Common devices used to collect this information are physiological sensors (such as biochemical analyte sensors) or devices capable of sensing chemical analytes from biological entities. Biochemical sensors come in many forms and can be used to sense analytes in liquids, tissues, or gases that form part of or are produced by a biological entity (such as a human). These analyte sensors can be used on or inside the body, such as in the case of percutaneously implanted analyte sensors, or they can be used on biological material that has been removed from the body.

[0004] Although analyte sensors and analyte monitoring systems are typically sophisticated and well-studied designs, they can still fail to function before the end of their intended lifespan. This can lead to an unintended and unexpected decrease in the sensor signal response to actual analyte fluctuations. In many cases, a reduced signal response from an analyte sensor can result in false indications of low analyte levels, or, in the event of a complete sensor failure, inability to indicate any analyte level. Furthermore, an unintended and unexpected decrease in the signal response of an analyte sensor can trigger false alarms regarding low threshold alarms, such as low glucose or hypoglycemia alarms.

[0005] Another potential problem with analyte monitoring systems is "nocturnal glucose dropout," a phenomenon that causes a sudden, brief drop in blood glucose levels overnight while the person wearing the analyte sensor is asleep. When the sensor is used in conjunction with an automated insulin delivery system, these drops in blood glucose levels can trigger false low-threshold alarms or lead to unnecessary insulin adjustments.

[0006] For these and other reasons, there is a need to improve the accuracy of analyte sensors and to detect sensor malfunctions. Summary of the Invention

[0007] This application describes example embodiments of systems, apparatus, and methods for improving the accuracy of analyte sensors and for detecting sensor malfunctions. For example, some embodiments provide detection of suspected random glucose inactivation and / or correction of glucose levels based on glucose and lactate level measurements and calculations. In some embodiments, corrective actions, such as hysteresis correction, glucose sensor termination, or glucose sensor data smoothing, can be performed based on first data indicating glucose levels and second data indicating subphysiological measurements, where subphysiological measurements may include, for example, ketone levels or heart rate measurements. Numerous examples of algorithms and methods for performing one or more of these detection and correction mechanisms, and combinations and / or variations thereof, are provided, along with example embodiments of systems and apparatus for performing these algorithms and methods.

[0008] Other systems, apparatuses, methods, features, and advantages of the subject matter described herein will be or will become apparent to those skilled in the art after studying the following figures and detailed description. All such additional systems, methods, features, and advantages are intended to be included in this specification, within the scope of the subject matter described herein, and protected by the appended claims. Where such features are not expressly recited in the claims, the features of the exemplary embodiments should in no way be construed as limiting the appended claims. Attached Figure Description

[0009] By studying the accompanying drawings, details regarding the structure and operation of the subject matter set forth in this disclosure will become apparent, in which the same reference numerals denote the same parts. The parts in the drawings are not necessarily to scale, but rather the emphasis is on illustrating the principles of the subject matter. Furthermore, all illustrations are intended to convey concepts, wherein relative dimensions, shapes, and other detailed properties may be shown schematically rather than literally or precisely.

[0010] Figure 1 These are illustrative views depicting exemplary embodiments of an in vivo analyte monitoring system.

[0011] Figure 2 This is a block diagram of an example implementation of a reader device.

[0012] Figure 3 This is a block diagram of an example implementation of a sensor control device.

[0013] Figure 4A and Figure 4B It is a multi-plot graph depicting example sensor signals over time.

[0014] Figure 4C and Figure 4D It is a multi-plot graph depicting example sensor signals and their corresponding derivative values ​​over time.

[0015] Figure 5 This is a flowchart depicting an example implementation of a method for detecting suspected random glucose inactivation.

[0016] Figure 6A , Figure 6B and Figure 6C It is a multi-plot graph depicting example sensor signals and corresponding calibrated sensor measurements over time.

[0017] Figure 7A and Figure 7B These are flowcharts illustrating an example implementation of a method for determining a corrected glucose level and an example implementation of a method for determining a suspected sensor malfunction.

[0018] Figure 8 This is a block diagram depicting a system that uses subphysiological measurements to improve the performance of a glucose sensor.

[0019] Figures 9A to 9E This is a block diagram depicting various systems that use subphysiological measurements to improve the performance of glucose sensors.

[0020] Figure 10 This is a flowchart illustrating an example implementation of a method for improving the performance of a glucose sensor using subphysiological measurements.

[0021] Figure 11 This is another flowchart illustrating an example implementation of a method for improving the performance of a glucose sensor using subphysiological measurements. Detailed Implementation

[0022] Before describing this subject matter in detail, it should be understood that this disclosure is not limited to the specific embodiments described, as these embodiments can certainly vary. It should also be understood that the terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of this disclosure will be defined only by the appended claims.

[0023] The publications discussed in this disclosure are intended for disclosure prior to the date of this application. Nothing in this disclosure should be construed as an admission that any content herein is not authorized to precede such publications by any prior disclosure. Furthermore, the publication dates provided may differ from the actual publication dates and may require independent verification.

[0024] Generally, embodiments of this disclosure are used with systems, devices, and methods for detecting at least one analyte (e.g., glucose) in bodily fluids (e.g., subcutaneous tissue fluid (“ISF”) or blood, dermal fluid in the dermis, or others). Therefore, many embodiments include in vivo analyte sensors structurally configured such that at least a portion of the sensor is located or can be located within the user's body to obtain information about at least one analyte in the body. However, embodiments disclosed herein can be used with in vivo analyte monitoring systems incorporating in vitro capabilities, as well as purely in vitro or ex vivo analyte monitoring systems, including those that are entirely non-invasive.

[0025] Furthermore, for each embodiment of the methods disclosed herein, the systems and devices capable of performing each of these embodiments are covered within the scope of this disclosure. For example, embodiments of sensor control devices are disclosed, and these devices may have one or more sensors, analyte monitoring circuitry (e.g., analog circuitry), non-transient memory (e.g., for storing instructions), power supply, communication circuitry, transmitter, receiver, processing circuitry, and / or controller (e.g., for executing instructions), which can perform any and all method steps or facilitate the execution of any and all method steps. These sensor control device embodiments can be used and are capable of being used to implement those steps of any and all methods described herein performed by the sensor control device.

[0026] Similarly, embodiments of reader devices are disclosed, having one or more transmitters, receivers, non-transient memory (e.g., for storing instructions), power supplies, processing circuitry, and / or controllers (e.g., for executing instructions), which can perform any and all method steps or facilitate the execution of any and all method steps. These embodiments of reader devices can be used to implement those steps of any and all methods described herein, performed by the reader device.

[0027] Implementations of trusted computer systems are also disclosed. These trusted computer systems may include one or more processing circuits, controllers, transmitters, receivers, non-transient memory, databases, servers, and / or networks, and may be discretely located or distributed across multiple geographical locations. These implementations of trusted computer systems can be used to implement those steps of any and all methods described herein, performed by the trusted computer systems.

[0028] Various embodiments of systems, apparatuses, and methods for improving the accuracy of analyte sensors and for detecting sensor malfunctions are disclosed. According to some embodiments, these systems, apparatuses, and methods may utilize first data collected by a glucose sensor and second data collected by a secondary sensing element. In some embodiments, the secondary sensing element may be one of a lactate sensing element, a ketone sensing element, or a heart rate monitor, etc.

[0029] Several embodiments of this disclosure are designed to improve the computer implementation capabilities of analyte monitoring systems, for example, detecting random nocturnal glucose inactivation, correcting glucose level measurements, and premature termination of glucose sensors, to name just a few. More specifically, these embodiments can utilize “sub” data (e.g., lactate levels, ketone levels, heart rate measurements, etc.) indicating non-glucose physiological measurements to improve the accuracy of in vivo glucose sensors and determine conditions under which in vivo glucose sensors can or should be terminated or temporarily disabled. Therefore, the embodiments disclosed herein reflect improvements over existing methods and relate to systems, apparatus, and methods for improving the accuracy of analyte monitoring systems by utilizing glucose sensor data in conjunction with non-glucose physiological measurements in specific and unconventional ways. Other features and advantages of the disclosed embodiments are further discussed below.

[0030] However, before describing the implementation details, it is necessary to first describe examples of devices that may exist, such as in vivo analyte monitoring systems, and examples of their operation, all of which can be used in conjunction with the implementations described in this disclosure.

[0031] Example implementation of an analyte monitoring system

[0032] Various types of analyte monitoring systems exist. For example, a "continuous analyte monitoring" system (or "continuous glucose monitoring" system) is an in vivo system that can repeatedly or continuously (e.g., automatically executed according to a schedule) transmit data from a sensor control device to a reader device without prompting. As another example, a "rapid analyte monitoring" system (or "rapid glucose monitoring" system, or simply "rapid" system) is an in vivo system that can transmit data from a sensor control device in response to a reader device scanning or requesting data, for example, using near field communication (NFC) or radio frequency identification (RFID) protocols. In vivo analyte monitoring systems can also operate without requiring manual finger-prick calibration.

[0033] An in vivo monitoring system may include a sensor that, when located inside the body, comes into contact with a user's bodily fluids and senses the levels of one or more analytes contained therein. The sensor may be part of a sensor control device located on the user's body and including electronics and a power source capable of enabling and controlling analyte sensing. The sensor control device and variations thereof may also be referred to as a "sensor control unit," a "personal electronics" device or unit, a "personal" device or unit, or a "sensor data communication" device or unit, for example only. As used herein, these terms are not limited to devices having analyte sensors and cover devices having other types of sensors, whether biometric or non-biometric. The term "personal" refers to any device located directly on or adjacent to the body, such as wearable devices (e.g., glasses, watches, wristbands or bracelets, neckbands or necklaces, etc.).

[0034] In vivo monitoring systems may also include one or more reader devices that receive sensed analyte data from sensor control devices. These reader devices may process and / or display the sensed analyte data or sensor data to a user in any number of formats. These devices and variations thereof may be referred to as “handheld reader devices,” “reader devices” (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, for example only. Other devices such as personal computers have also been used with or incorporated into in vivo and in vitro monitoring systems.

[0035] In vivo analyte monitoring systems can be distinguished from "in vitro" systems, which contact biological samples outside the body (or more precisely, "ex vivo") and typically include a metrology device with a port for receiving and analyzing analyte test strips containing the user's bodily fluids to determine the user's analyte levels. As described above, the embodiments described in this disclosure can be used with in vivo systems, in vitro systems, and combinations thereof.

[0036] The embodiments described in this disclosure can be used to monitor and / or process information about any number of one or more different analytes. Analytes that can be monitored include, but are not limited to, acetylcholine, amylase, bilirubin, cholesterol, human chorionic gonadotropin (hCG), glycosylated hemoglobin (HbA1c), creatine kinase (e.g., CK-MB), creatine, creatine anhydride, DNA, fructosamine, glucose, glucose derivatives, glutamine, growth hormone, hormones, ketones, ketone bodies, lactate, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone (TSH), and troponin. The concentration of drugs, such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitalis, digoxin, drugs of abuse, theophylline, and warfarin, can also be monitored. In embodiments monitoring more than one analyte, the analytes can be monitored at the same or different times.

[0037] Figure 1 This is an illustrative view depicting an exemplary embodiment of an in vivo analyte monitoring system 100, which includes a sensor control device 102 and a reader device 120. The sensor control device 102 and the reader device 120 communicate with each other via a local communication path (or link) 140, which can be wired or wireless, and can be one-way or two-way. In embodiments where path 140 is wireless, Near Field Communication (NFC) protocol, RFID protocol, Bluetooth or Bluetooth Low Energy protocol, Wi-Fi protocol, proprietary protocols, etc., can be used, including communication protocols existing as of the date of this application or variants thereof that have been subsequently developed.

[0038] The reader device 120 is also capable of wired, wireless, or combined communication with computer system 170 (e.g., a local or remote computer system) via communication path (or link) 141, and with network 190 such as the Internet or a cloud via communication path (or link) 142. Communication with network 190 may involve communication with a trusted computer system 180 within network 190, or communication from network 190 to computer system 170 via communication link (or path) 143. Communication paths 141, 142, and 143 may be wireless, wired, or both, may be unidirectional or bidirectional, and may be part of a telecommunications network such as a Wi-Fi network, local area network (LAN), wide area network (WAN), Internet, or other data network. In some cases, communication paths 141 and 142 may be the same path. All communications on paths 140, 141, and 142 can be encrypted, and each of the sensor control device 102, reader device 120, computer system 170, and trusted computer system 180 can be configured to encrypt and decrypt those communications sent and received.

[0039] Variations of devices 102 and 120, and other components of in vivo analyte monitoring systems suitable for use with embodiments of the systems, devices, and methods described herein, are described in U.S. Patent Publication No. 2011 / 0213225 ('225 Publication), which is incorporated herein by reference in its entirety for all purposes.

[0040] The sensor control device 102 may include a housing 103, which contains an in vivo analyte monitoring circuit and a power supply. In this embodiment, the in vivo analyte monitoring circuit is electrically coupled to one or more analyte sensors 104, which extend through an adhesive patch 105 and protrude away from the housing 103. The adhesive patch 105 includes an adhesive layer (not shown) for attachment to the skin surface of a user's body. Other forms of body attachment may be used in addition to or instead of adhesives.

[0041] Sensor 104 is adapted to be at least partially inserted into a user's body, where it can come into fluid contact with the user's bodily fluids (e.g., subcutaneous tissue (subcutaneous) fluid, dermal fluid, or blood) and, together with in vivo analyte monitoring circuitry, be used to measure the user's analyte-related data. Sensor 104 and any accompanying sensor control electronics can be applied to the body in any desired manner. For example, insertion device 150 can be used to position all or part of the analyte sensor 104 through the outer surface of the user's skin and into contact with the user's bodily fluids. In doing so, insertion device can also position sensor control device 102 with adhesive patch 105 onto the skin. In other embodiments, insertion device can first position sensor 104, and then the accompanying sensor control electronics can be coupled to sensor 104 manually or by means of mechanical means. Examples of insertion devices are described in U.S. Publications Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345 and 2015 / 0173661, the entire contents of which are incorporated herein by reference for all purposes.

[0042] After collecting raw data from the user's body, sensor control device 102 can apply analog signal conditioning to the data and convert it into conditioned raw data in digital form. In some embodiments, sensor control device 102 can then algorithmically process the digital raw data into a form representing a biometric (e.g., analyte level) and / or one or more analyte measures based thereon. For example, sensor control device 102 may include processing circuitry to algorithmically perform any of the method steps described in this disclosure, such as correcting glucose level measurements, detecting suspected random glucose inactivation, or detecting suspected sensor malfunction conditions, to name just one example. Sensor control device 102 can then encode data indicating glucose levels, indications of sensor malfunctions, and / or processed sensor data and wirelessly transmit it to reader device 120, which can then format or graphically process the received data for digital display to the user. In other embodiments, in addition to or instead of wirelessly transmitting sensor data to another device (e.g., reader device 120), sensor control device 102 can graphically process the final form of the data to suit display and display the data on a display of sensor control device 102. In some implementations, the final form of biometrics (before graphical processing) is used by the system (e.g., incorporated into a diabetes monitoring protocol) without undergoing processing for display to the user.

[0043] In other embodiments, the conditioned raw digital data may be encoded for transmission to another device, such as reader device 120, which then algorithmically processes the raw digital data into a form representing the user's measured biometrics (e.g., easily formatted to be displayed to the user) and / or one or more analyte measurements based thereon. Reader device 120 may include processing circuitry to algorithmically perform any of the method steps described in this disclosure, such as correcting glucose level measurements, detecting suspected random glucose inactivation, or detecting suspected sensor malfunctions, to name just a few. The algorithmically processed data can then be formatted or graphically processed for digital display to the user.

[0044] In other embodiments, sensor control device 102 and reader device 120 transmit digital raw data to another computer system for algorithm processing and display.

[0045] The reader device 120 may include a display 122 for outputting information to a user and / or receiving input from the user, and optional input components 121 (or more), such as buttons, actuators, touch-sensitive switches, capacitive switches, pressure-sensitive switches, scroll wheels, etc., for inputting data, commands, or otherwise controlling the operation of the reader device 120. In some embodiments, the display 122 and the input components 121 may be integrated into a single component, for example, where the display can detect the presence and location of physical touches on the display, such as a touchscreen user interface. In some embodiments, the input component 121 of the reader device 120 may include a microphone, and the reader device 120 may include software configured to analyze audio input received from the microphone, such that the functionality and operation of the reader device 120 can be controlled by voice commands. In some embodiments, the output component of the reader device 120 includes a speaker (not shown) for outputting information as an audible signal. A similar voice response component, such as a speaker, microphone, and software routines for generating, processing, and storing voice-driven signals, may be included in the sensor control device 102.

[0046] The reader device 120 may also include one or more data communication ports 123 for wired data communication with external devices, such as computer system 170 or sensor control device 102. Exemplary data communication ports include USB ports, mini USB ports, USB Type-C ports, USB micro-A and / or micro-B ports, RS-232 ports, Ethernet ports, FireWire ports, or other similar data communication ports configured to connect to compatible data cables. The reader device 120 may also include an integrated or attachable in vitro glucose meter, which includes an in vitro test strip port (not shown) for receiving an in vitro glucose test strip for performing in vitro blood glucose measurements.

[0047] The reader device 120 can display measured biometric data wirelessly received from the sensor control device 102, and can also be configured to output alarms, warnings, glucose levels, etc., which can be visual, auditory, tactile, or any combination thereof. Further details and other display implementations can be found, for example, in U.S. Publication No. 2011 / 0193704, which is incorporated herein by reference in its entirety for all purposes.

[0048] The reader device 120 can be used as a data pipeline to transmit measurement data and / or analyte measurements from the sensor control device 102 to the computer system 170 or the trusted computer system 180. In some embodiments, data received from the sensor control device 102 may be stored (permanently or temporarily) in one or more memories of the reader device 120 before being uploaded to the system 170, 180 or network 190.

[0049] Computer system 170 may be a personal computer, server terminal, laptop computer, tablet computer, or other suitable data processing device. Computer system 170 may include (or include) software for data management and analysis, and for communicating with components in 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 transmit biometric data directly to computer system 170 without intermediaries such as reader device 120, or indirectly using an Internet connection (optionally without first sending to reader device 120). The operation and use of computer system 170, further described in '225 disclosure, are incorporated herein by reference. Analyte monitoring system 100 may also be configured to operate in conjunction with a data processing module (not shown), as also described in the incorporated '225 disclosure.

[0050] The trusted computer system 180 may be physically or virtually owned by the manufacturer or distributor of the sensor control device 102 via a secure connection, and may be used to perform authentication of the sensor control device 102, secure storage of user biometric data, and / or as a service for data analysis programs (e.g., accessible via a web browser) to perform analysis on the user's measurement data.

[0051] Example implementation of reader device

[0052] The reader device 120 may be a mobile communication device, such as a dedicated reader device (configured to communicate with the sensor control device 102 and optionally with the computer system 170, but without mobile phone communication capabilities) or a mobile phone, including but not limited to Wi-Fi or Internet-enabled smartphones, tablets, or personal digital assistants (PDAs). Examples of smartphones may include those based on the Windows® operating system, Android™ operating system, iPhone® operating system, Palm® WebOS™, Blackberry® operating system, or Symbian® operating system, which have data network connectivity capabilities for data communication via Internet connection and / or local area network (LAN).

[0053] The reader device 120 can also be configured as a mobile smart wearable electronic component, such as an optical component worn above or near the user's eyes (e.g., one or more smart glasses, such as Google Glass as a mobile communication device). The optical component may have a transparent display that shows the user information about the user's analytics level (as described in this disclosure) while allowing the user to view through the display with minimal obstruction to the user's overall vision. The optical component is capable of wireless communication similar to that of a smartphone. Other examples of wearable electronic devices include devices worn around or near the user's wrist (e.g., a watch), around the neck (e.g., a necklace), on the head (e.g., a headband, a hat), or on the chest.

[0054] Figure 2 This is a block diagram of an example embodiment of a reader device 120 configured as a smartphone. Here, the reader device 120 includes an input component 121, a display 122, and processing circuitry 206, which may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed across multiple different chips (and portions thereof). Here, processing circuitry 206 includes a communication processor 222 with on-board memory 223 and an application processor 224 with on-board memory 225. Reader device 120 also includes RF communication circuitry 228 coupled to RF antenna 229, memory 230, a multifunction circuitry 232 with one or more associated antennas 234, a power supply 226, power management circuitry 238, and a clock (not shown). Figure 2 It is a simplified representation of typical hardware and functions residing in a smartphone, and those skilled in the art will readily recognize that it may include other hardware and functions (such as codecs, drivers, glue logic).

[0055] The communication processor 222 can interface with the RF communication circuit 228 and perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that help convert voice, video, and data signals into formats suitable for delivery to the RF communication circuit 228 (e.g., in-phase and quadrature), which the RF communication circuit 228 can then wirelessly transmit the signals. The communication processor 222 can also interface with the RF communication circuit 228 to perform the inverse functions required to receive wireless transmissions and convert them into digital data, voice, and video. The RF communication circuit 228 may include a transmitter and receiver (e.g., integrated as a transceiver) and associated encoder logic.

[0056] Application processor 224 can be adapted to execute an operating system and any software applications residing on reader device 120, process video and graphics, and perform other functions unrelated to the processing of communications transmitted and received via RF antenna 229. The smartphone operating system will operate in conjunction with multiple applications on reader device 120. Any number of applications (also referred to as “user interface applications”) can run on reader device 120 at any given time, and may include one or more applications related to the diabetes monitoring program, in addition to other commonly used applications unrelated to the diabetes monitoring program (e.g., email, calendar, weather, sports, games, etc.). For example, data received by the reader device indicating sensed analyte levels and in vitro blood analyte measurements can be securely transmitted to the user interface application residing in memory 230 of reader device 120. Such communication can be securely performed, for example, using mobile application containerization or packaging technologies.

[0057] Memory 230 may be shared by one or more of the various functional units present in reader device 120, or may be distributed among two or more functional units (e.g., as separate memories existing in different chips). Memory 230 may also be a separate chip. Memories 223, 225, and 230 are non-transient and may be volatile (e.g., RAM, etc.) and / or non-volatile memories (e.g., ROM, flash memory, F-RAM, etc.).

[0058] The multifunction circuit 232 can be implemented as one or more chips and / or components (e.g., transmitters, receivers, transceivers, and / or other communication circuits) that perform other functions, such as local wireless communication with the sensor control device 102 under appropriate protocols (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy, Near Field Communication (NFC), Radio Frequency Identification (RFID), proprietary protocols, etc.), and determining the geographic location of the reader device 120 (e.g., Global Positioning System (GPS) hardware). One or more additional antennas 234 may be associated with the functional circuit 232 as needed to operate with various protocols and circuits.

[0059] The power supply 226 may include one or more batteries, which may be rechargeable or disposable. The power management circuit 238 may regulate battery charging and power monitoring, boost voltage, perform DC conversion, etc.

[0060] The reader device 120 may also include or integrate a drug delivery device (e.g., insulin, etc.) such that they, for example, share a common housing. Examples of such a drug delivery device may include a drug pump with a cannula held in the body to allow infusion over multiple hours or days (e.g., a wearable pump for delivering basal and high-dose insulin). When combined with a drug pump, the reader device 120 may include a reservoir for storing the drug, a pump connectable to a delivery tubing, and an infusion cannula. The pump can force the drug from the reservoir through the tubing and into the body of the diabetic patient via the inserted cannula. Other examples of drug delivery devices that may be included together (or integrated) with the reader device 120 include portable injection devices (e.g., insulin pens) that puncture the skin only once for each dose and are subsequently removed. When combined with a portable injection device, the reader device 120 may include an injection needle, a cartridge for carrying the drug, an interface for controlling the amount of drug to be delivered, and an actuator that causes the injection to occur. The device is reusable until the medication is exhausted, at which point the device can be discarded or replaced with a new cartridge, allowing for reuse. The needle can be replaced after each injection.

[0061] This combined device can be used as part of a closed-loop system (e.g., an artificial pancreas system that does not require user intervention) or a semi-closed-loop system (e.g., an insulin loop system that requires minimal user intervention, such as confirming dose changes). For example, the analyte level of a diabetic patient can be monitored repeatedly and automatically by a sensor control device 102, which can then transmit the monitored analyte level to a reader device 120 and automatically determine the appropriate drug dose for controlling the analyte level in the diabetic patient, and subsequently deliver it to the patient's body. Software instructions for controlling the pump and the amount of insulin delivered can be stored in the memory of the reader device 120 and executed by the processing circuitry of the reader device. These instructions can also cause calculations of drug delivery volume and duration (e.g., bolus infusion and / or basal infusion profiles) based on analyte level measurements obtained directly or indirectly from the sensor control device 102. In some embodiments, the sensor control device 102 can determine the drug dose and transmit it to the reader device 120.

[0062] Example implementation of sensor control device

[0063] Figure 3 This is a block diagram illustrating an example embodiment of a sensor control device 102 having an analyte sensor 104 and sensor electronics 250 (including analyte monitoring circuitry), the sensor electronics 250 potentially having most of the processing power for rendering final result data suitable for display to a user. Figure 3The image depicts a single semiconductor chip 251, which may be a custom application-specific integrated circuit (ASIC). Within the ASIC 251 are shown certain high-level functional units, including an analog front-end (AFE) 252, power management (or control) circuitry 254, a processor 256, and communication circuitry 258 (which may be implemented as a transmitter, receiver, transceiver, passive circuitry, or other circuitry according to a communication protocol). In this embodiment, both the AFE 252 and the processor 256 serve as analyte monitoring circuitry; however, in other embodiments, either circuitry may perform analyte monitoring functions. The processor 256 may include one or more processors, microprocessors, controllers, and / or microcontrollers, each of which may be a discrete chip or distributed across several different chips (and portions of several different chips).

[0064] Memory 253 is also included within ASIC 251 and can be shared by various functional units within ASIC 251, or can be distributed across two or more units. Memory 253 can also be a separate chip. Memory 253 is non-transient and can be volatile and / or non-volatile memory. In this embodiment, ASIC 251 is coupled to power supply 260, which can be a coin cell battery, etc. AFE 252 interfaces with and receives measurement data from in vivo analyte sensor 104, and outputs the data in digital form to processor 256, which in some embodiments can process the data in any manner described elsewhere in this disclosure. The data can then be provided to communication circuitry 258 for transmission via antenna 261 to reader device 120 (not shown), for example, where a resident software application requires minimal further processing to display the data. Antenna 261 can be configured according to the needs of the application and communication protocol. Antenna 261 can be, for example, a printed circuit board (PCB) trace antenna, a ceramic antenna, or a discrete metal antenna. Antenna 261 can be configured as a monopole antenna, dipole antenna, F-type antenna, loop antenna, and others.

[0065] Information can be communicated from sensor control device 102 to a second device (e.g., reader device 120) initiated by sensor control device 102 or reader device 120. For example, when analyte information is available, information can be communicated automatically and / or repeatedly (e.g., continuously) by sensor control device 102, or according to a schedule (e.g., approximately every 1 minute, approximately every 5 minutes, approximately every 10 minutes, or similar), in which case the information can be stored or recorded in the memory of sensor control device 102 for later communication. In response to a request received from the second device, information can be sent from sensor control device 102. This request can be an automatic request, such as a request sent by the second device according to a schedule, or it can be a request generated at the initiation of a user (e.g., self-organized or manual request). In some embodiments, a manual request for data is referred to as a “scan” of sensor control device 102 or “on-demand” data transmission from device 102. In some implementations, the second device may send polling signals or data packets to the sensor control device 102, and the device 102 may treat each polling (or polling occurring at specific time intervals) as a request for data, and if data is available, may send such data to the second device. In many implementations, communication between the sensor control device 102 and the second device is secure (e.g., encrypted and / or between authenticated devices), but in some implementations, data may be transmitted from the sensor control device 102 in an insecure manner, for example, as a broadcast to all listening devices within range.

[0066] Information of different types and / or forms and / or quantities may be sent as part of each communication, including but not limited to one or more current sensor measurements (e.g., the most recently acquired analyte level information corresponding to the time of the initiation of the read), the rate of change of a metric measured over a predetermined time period, the rate of change of the metric (acceleration of the rate of change), or historical metric information corresponding to the metric information acquired and stored in the memory of the sensor control device 102 prior to a given read.

[0067] Partial or complete real-time, historical, rate of change, and rate of change (e.g., acceleration or deceleration) information may be sent to reader device 120 in a given communication or transmission. In some embodiments, the type and / or form and / or quantity of information sent to reader device 120 may be pre-programmed and / or immutable (e.g., preset at manufacturing), or may not be pre-programmed and / or immutable so that it can be selected and / or changed once or multiple times in the field (e.g., by activating a system switch, etc.). Thus, in some embodiments, reader device 120 may output current (real-time) analyte values ​​derived by the sensor (e.g., in digital format), the current rate of change of the analyte (e.g., in the form of an analyte rate indicator, such as an arrow pointing in the direction indicating the current rate), and historical analyte trend data based on sensor readings acquired by sensor control device 102 and stored in the memory of sensor control device 102 (e.g., in the form of a graphical trajectory). Additionally, skin or sensor temperature readings or measurements may be collected by optional temperature sensor 257. These readings or measurements (individually or as aggregated measurements over time) can be transmitted from sensor control device 102 to another device (e.g., reader 120). However, temperature readings or measurements can be used in conjunction with software routines executed by reader device 120 to correct or compensate for analyte measurements output to the user, in lieu of or in addition to the actual temperature measurements displayed to the user.

[0068] Furthermore, despite Figure 3 A single analyte sensor 104 has been described, but according to many embodiments of this disclosure, the sensor control device 102 may be configured to collect data indicating multiple physiological measurements, including, but not limited to, data indicating glucose levels, lactate levels, ketone levels, or heart rate measurements, to name just a few. For example, in some embodiments, the sensor 104 may be a dual analyte sensor configured to sense glucose levels and the concentration of another analyte (e.g., lactate, ketones, etc.). Further details regarding dual analyte sensors are described, for example, in U.S. Publication No. 2019 / 0320947 A1, which is incorporated herein by reference for all purposes. In some embodiments, the sensor control device 102 may include a plurality of discrete sensors, each capable of collecting data indicating any of the aforementioned physiological measurements.

[0069] Implementation of systems, apparatus, and methods for detecting suspected random glucose inactivation

[0070] Example characterization of glucose and lactate levels during random glucose inactivation at night

[0071] Random nocturnal glucose inactivation is a phenomenon observed in analyte monitoring systems where the glucose concentration measured by the glucose sensor may suddenly drop within a short period of time during the night while the user wearing the glucose sensor is asleep. If the analyte monitoring system is used in conjunction with an automated drug delivery system (e.g., an automated insulin pump), random nocturnal glucose inactivation can trigger false low glucose alarms or lead to unnecessary drug delivery adjustments.

[0072] Previous research has proposed that nocturnal random glucose inactivation is a result of stress-induced sensor degradation. However, recent studies using dual glucose / lactate sensors (e.g., glucose and lactate sensing elements in a single analyte sensor) suggest that nocturnal random glucose inactivation is actually a physiological phenomenon that can lead to a decrease in interstitial glucose concentration at specific sensing sites. This study also suggests that this physiological phenomenon results in an increase in lactate concentration alongside the decrease in glucose concentration.

[0073] Figure 4A and Figure 4B These are multi-plot curves (400, 410), each depicting a 24 (24)-hour curve of the first and second glucose / lactate sensors worn by the same patient, with the first and second glucose / lactate sensors less than two inches apart. First reference... Figure 4A The multi-plot curve 400 of the first dual glucose / lactic acid sensor includes an upper curve 402 indicating glucose concentration over time and a lower curve 404 indicating lactate concentration over time. As can be seen from the multi-plot curve 400, the first dual glucose / lactic acid sensor did not exhibit random glucose inactivation at night.

[0074] Next reference Figure 4B The multi-plot curve 410 of the second dual glucose / lactic acid sensor includes an upper curve 412 indicating glucose concentration over time and a lower curve 414 indicating lactate concentration over time. As shown by the dashed ellipse, the second dual glucose / lactic acid sensor experienced random glucose inactivation around 5:00 AM, characterized by a sharp drop in glucose concentration at data point 418, while simultaneously, a sharp rise in lactate concentration at data point 416. Apart from the nocturnal random glucose inactivation event, the glucose and lactate concentration trend lines in multi-plot curves 400 and 410 are relatively similar.

[0075] Based on the curves 400 and 410, it can be inferred that both the first and second diglucose / lactic acid sensors were operating normally. However, the analyte at the sensing site of the second diglucose / lactic acid sensor underwent random glucose inactivation at night due to physiological changes. Figure 4B As reflected in the multi-graph curve 410.

[0076] Figure 4C and4D Further characterization of glucose and lactate concentration levels during random glucose inactivation at night, as measured by a dual glucose / lactate sensor, is described. (First reference...) Figure 4C ,and Figure 4A As shown, the multi-plot graph 420 includes an upper curve 402 representing glucose concentration over time and a lower curve 404 representing lactate concentration over time. Below the lactate concentration curve 404, plot 420 also includes additional curves 426 and 428, which respectively describe the derivative values ​​of glucose concentration and lactate concentration with respect to time. Furthermore, a pair of predetermined glucose and lactate derivative thresholds are shown as dashed lines 430 and 432, respectively. According to one aspect of the embodiment, the predetermined thresholds may include a predetermined negative glucose derivative threshold and a predetermined positive lactate derivative threshold, wherein the predetermined thresholds are close to or approximately zero. The absence of random nocturnal glucose inactivation in the first dual glucose / lactic acid sensor is characterized by glucose and lactate derivative values ​​not simultaneously crossing their respective predetermined derivative thresholds.

[0077] Next reference Figure 4D Similar to Figure 4B As shown, the multi-plot curve 440 includes an upper curve 412 and a lower curve 414. The upper curve 412 indicates the glucose concentration over time, and the lower curve 414 indicates the lactate concentration over time. As shown below the lactate concentration curve 414, the curve 440 also includes additional curves 446 and 448, which respectively depict the derivative values ​​of glucose concentration and lactate concentration with respect to time. Furthermore, a pair of predetermined glucose derivative thresholds and lactate derivative thresholds are shown as dashed lines 430 and 432, respectively. As shown by the dashed ellipse, random glucose inactivation at night can be characterized as follows: the glucose derivative value 450 decreases below the predetermined negative glucose derivative threshold 432, while simultaneously or almost simultaneously, the lactate derivative value 352 rises above the predetermined positive lactate derivative threshold 430.

[0078] Exemplary methods for detecting suspected random glucose inactivation

[0079] An example implementation of a method for detecting suspected random glucose inactivation in an analyte monitoring system based on glucose and lactate concentration measurements will now be described. Prior to this, those skilled in the art will understand that any one or more steps of the example methods described herein can be stored as software instructions in a sensor control device, reader device, remote computer, or trusted computer system (e.g., regarding...). Figure 1The instructions stored are in non-transient memory (as described herein). When executed, the stored instructions can cause the processing circuitry of an associated device or computing system to perform any one or more steps of the example methods described herein. Those skilled in the art will also understand that in many embodiments, any one or more method steps described herein can be performed using real-time or near-real-time sensor data. In other embodiments, any one or more method steps can be performed retrospectively against stored sensor data, including sensor data from sensors previously worn by the same user. In some embodiments, the method steps described herein can be performed periodically, according to a predetermined schedule, and / or in batches as a retrospective process.

[0080] Those skilled in the art will also understand that instructions may be stored in non-transient memory on a single device (e.g., a sensor control device or a reader device), or alternatively, may be distributed across multiple discrete devices located in geographically dispersed locations (e.g., a cloud platform). For example, in some embodiments, the collection of data indicating analyte levels (e.g., glucose, lactate) may be performed on the sensor control device, while the calculation of analyte measures (e.g., glucose yield, lactate yield) and the comparison of analyte measures with predetermined thresholds may be performed on the reader device, a remote computing system, or a trusted computing system. In some embodiments, the collection of analyte data and the comparison with predetermined thresholds may be performed separately on the sensor control device. Similarly, those skilled in the art will recognize that the representation of computing devices in the embodiments disclosed herein, such as… Figure 1 Those shown are intended to cover both physical devices and virtual devices (or “virtual machines”).

[0081] Figure 5 This is a flowchart of an example implementation of a method 500 for detecting suspected random glucose inactivation. In step 510, first data indicating glucose levels, such as information about glucose levels, are collected by an analyte sensor. Figure 1 and Figure 3Those described. In step 520, second data indicating lactate levels is collected by a lactate sensing element. According to some embodiments, steps 510 and 520 may be performed by a sensor control unit including an analyte sensor having a portion configured to be inserted into the user's body at an insertion site, wherein the portion includes a first sensing element configured to sense glucose levels in bodily fluids and a second sensing element configured to sense lactate levels in bodily fluids at the same insertion site. In other embodiments, steps 510 and 520 may be performed by a sensor control unit including a first analyte sensor and a second analyte sensor, wherein the first analyte sensor is configured to sense glucose levels in bodily fluids, the second analyte sensor is configured to sense lactate levels in bodily fluids, and wherein the first and second analyte sensors are configured to sense analyte levels at the same local insertion site.

[0082] Still referencing Figure 5 In step 530, a first analyte measure is calculated based on first data, and a second analyte measure is calculated based on second data. According to many embodiments, the first analyte measure is the glucose derivative, and the second analyte measure is the lactate derivative. In step 540, the first analyte measure is compared with a first threshold, and the second analyte measure is compared with a second threshold. According to many embodiments, the first threshold may be a predetermined glucose derivative threshold, and the second threshold may be a predetermined lactate derivative threshold. Furthermore, according to some embodiments, the first threshold may be a negative threshold, and the second threshold may be a positive threshold.

[0083] In step 550, based on the comparison in the previous step 540, it is determined whether both the first and second thresholds have been reached or exceeded. For example, according to some embodiments, the first threshold may be a predetermined negative glucose derivative threshold, and the second threshold may be a predetermined positive lactate derivative threshold. In this case, when the first analyte measure (e.g., glucose derivative) is less than or equal to the first threshold, the first threshold (e.g., the predetermined glucose derivative threshold) is met and / or exceeded; when the second analyte measure (e.g., lactate derivative) is greater than or equal to the second threshold, the second threshold (e.g., the predetermined lactate derivative threshold) is met and / or exceeded.

[0084] According to another aspect of the implementation, determining whether a threshold has been met or exceeded may further include assessing whether the threshold has been met or exceeded simultaneously or nearly simultaneously. In some implementations, for example, the first and second analyte measures may be derived from analyte level data collected within the same time period, for example, using a sliding window, wherein the sliding window may be defined by a predetermined number of data points (e.g., the last five glucose yield values, the last five lactate yield values) or a predetermined duration (e.g., a 5, 10, or 15-minute window). In other implementations, determining whether a threshold has been met or exceeded may include comparing the average glucose yield value within a first predetermined time period with the average lactate yield value within a second predetermined time period. Those skilled in the art will recognize that other methods can be used to assess whether two analyte level measures meet or exceed their respective thresholds, and these methods are fully within the scope of this disclosure.

[0085] Similarly, those skilled in the art will further understand that variations of the first and second thresholds can be utilized. In some embodiments, for example, the first and second analyte measures can be the absolute values ​​of the glucose derivative and the lactate derivative, respectively. Therefore, the first and second thresholds can also be predetermined absolute values ​​of the glucose derivative threshold and the lactate derivative threshold.

[0086] Refer again Figure 5 If neither of the two thresholds is met and / or exceeded, method 500 returns to step 510. However, if both thresholds are met and / or exceeded, in step 560, an indication of suspected random glucose inactivation is generated. In some embodiments, the indication of suspected random glucose inactivation may include visual output to a display of a reader device, a remote computer, or a trusted computer system, such as regarding... Figure 1 Those described herein. For example, in some embodiments, generating an indication of suspected random glucose inactivation may cause a notification or message to be displayed on a sensor results screen of a software application running on a user's mobile device. Similarly, in some embodiments, an indication of suspected random glucose inactivation may include one or more of a visual, audio, or vibration alarm or alert output to a display of a reader device, a remote computer, or a trusted computer system. Subsequently, in step 570, a remedial action may optionally be performed in response to or instead of the indication of suspected random glucose inactivation. In some embodiments, for example, the remedial action may be suppressing a low glucose alarm. In other embodiments, the remedial action may be preventing the issuance of a command to alter or cause the delivery of a drug (e.g., insulin) via an automated drug delivery system (e.g., an insulin pump).

[0087] Implementation of systems, devices, and methods for lactate-corrected glucose levels

[0088] Example characterization of lactate concentration during later sensor decay.

[0089] Late sensor attenuation (“LSA,” also known as “drooping”) is a phenomenon in which partially implanted (e.g., subcutaneous, percutaneous) or fully implanted glucose sensors may experience a decrease in sensitivity later in the sensor’s intended wear life. LSA occurs in a relatively small percentage of sensors and typically begins, for example, around day 10 to 12 in glucose sensors with a 14-day wear life.

[0090] Studies using dual glucose / lactic acid sensors (e.g., glucose sensing elements and lactate sensing elements in a single analyte sensor) have suggested a relationship between LSA and lactate concentration levels measured at the sensor's insertion site. Specifically, data obtained using dual glucose / lactic acid sensors have demonstrated a correlation between a decrease in LSA or glucose sensitivity and an increase in baseline lactate levels over the same time period.

[0091] According to one aspect of the implementation, the aforementioned relationship between the increase in LSA and baseline lactate levels can be used to correct one or more spuriously suppressed glucose measurements. Specifically, the following equation can be used:

[0092] i 葡萄糖（校正） = i 葡萄糖（原始） + K c (i 乳酸 - i 乳酸（基线） ),in:

[0093] i 葡萄糖（原始） This is the glucose current before correction;

[0094] i 乳酸 It is the lactic acid current during calibration;

[0095] i 乳酸（基线） It is the baseline lactate current; and

[0096] K c It is the batch constant of the sensor.

[0097] According to some implementation methods, i 乳酸 It is a smoothing value, such as a one-hour smoothing lactate current, to remove instantaneous changes in lactate value. Those skilled in the art will recognize that other smoothing lactate values ​​(e.g., over 30 minutes, 2 hours, 5 hours) can be used, and are fully within the scope of this disclosure.

[0098] According to another aspect of some implementation methods, i 乳酸（基线） This could be the baseline lactate current over a predetermined period of time during which the glucose sensor is unlikely to be affected by LSA. In some implementations, for example, i 乳酸（基线）The average lactic acid current could be the average lactic acid current over days 5 to 8 of the sensor wear period. Those skilled in the art will understand that other predetermined time periods (e.g., days 4 to 7, days 6 to 8, etc.) could also be used to calculate the baseline lactic acid current, and this is entirely within the scope of this disclosure.

[0099] According to another aspect of the implementation method, K c This can be an empirically determined constant assigned to a given batch of sensors. Figures 6A to 6C In the following description, the sensor batch constant K c = 3. Those skilled in the art will understand that other sensor batch constants can be used and are included within the scope of this disclosure.

[0100] Figure 6A This is a multi-plot graph 600, which depicts various analyte measurements taken by a dual glucose / lactic acid sensor over a 20-day period. The multi-plot graph 600 includes an uncorrected glucose current curve 602 at the top. At the bottom of the multi-plot graph 600, an unfiltered lactate current curve 604 and a one-hour filtered lactate value curve 604 are also shown. According to one aspect of the multi-plot graph 600, a corrected glucose level curve 608, based on the aforementioned lactate-based glucose correction equation, is plotted adjacent to the uncorrected glucose current curve 602. Furthermore, it can be seen in plot 600 that the LSA begins at or near day 12, which can be evidenced by the gradual decrease in the uncorrected glucose current curve 602 (i.e., indicating a gradual decrease in the sensitivity of the glucose sensor), while the unfiltered lactate current curve 604 and the one-hour filtered lactate value curve 606 gradually increase over the same time period.

[0101] Figure 6BAnother multi-plot graph 610 depicts various analyte measurements taken by a dual glucose / lactic acid sensor over a 20-day period. Similar to the previous plot 600, multi-plot 610 includes an uncorrected glucose current curve 612 at the top. At the bottom of multi-plot 610, an unfiltered lactate current curve 614 and a one-hour filtered lactate value curve 616 are also shown. According to one aspect of plot 610, a corrected glucose level curve 618 based on the aforementioned lactate-based glucose correction equation is depicted adjacent to the uncorrected glucose current curve 612. A relatively more significant LSA compared to multi-plot 600 can be seen in plot 610 starting at or near day 15, at which point the uncorrected glucose current curve 612 drops sharply (i.e., indicating a significant decrease in the sensitivity of the glucose sensor), while the unfiltered lactate current curve 614 and the one-hour filtered lactate value curve 616 increase sharply within the same time period. According to one aspect of the multi-graph curve 610, the steep increase in lactate curves 614 and 616 during the later stages of wear (e.g., days 18 to 20) demonstrates that LSA is more significant, and there is a large divergence between the uncorrected glucose current curve 612 and the corrected glucose value curve 618.

[0102] Figure 6C Another multi-plot graph 620 depicts various analyte measurements taken by a dual glucose / lactic acid sensor over a 20-day period. Multi-plot 620 includes an uncorrected glucose current curve 622 at the top. At the bottom of multi-plot 620, an unfiltered lactate current curve 624 and a one-hour filtered lactate value curve 626 are also shown. According to one aspect of plot 620, a corrected glucose level curve 628, based on the aforementioned lactate-based glucose correction equation, is depicted adjacent to the uncorrected glucose current curve 622. No LSA is present in the sensor depicted in multi-plot 620, as illustrated by the relatively stable pair of lactate measurements 624 and 626 from day 15 to day 20. Therefore, the corrected and uncorrected glucose curves 622 and 628 are nearly identical during the same time period.

[0103] Example methods for LSA calibration and sensor fault detection

[0104] Exemplary implementations of methods for correcting spurious inhibition of glucose values ​​using lactate values ​​and methods for sensor fault detection will now be described.

[0105] As with the previous embodiments, those skilled in the art will understand that any one or more steps of the exemplary methods described herein can be stored as software instructions in the non-transient memory of a sensor control device, reader device, remote computer, or trusted computer system, for example, regarding Figure 1 The stored instructions, when executed, can cause the processing circuitry of an associated device or computing system to perform any one or more steps of the example methods described herein. Those skilled in the art will also understand that in many embodiments, real-time or near-real-time sensor data can be used to perform any one or more of the method steps described herein. In other embodiments, any one or more method steps can be performed retrospectively with respect to stored sensor data, including sensor data from sensors previously worn by the same user. In some embodiments, the method steps described herein can be performed retrospectively as a periodic process, according to a predetermined schedule, and / or in batches.

[0106] Those skilled in the art will also understand that instructions may be stored in non-transient memory on a single device (e.g., a sensor control device or a reader device), or, alternatively, may be distributed across multiple discrete devices located in geographically dispersed locations (e.g., the cloud). For example, in some embodiments, the collection of data indicating analyte levels (e.g., glucose, lactate) may be performed on the sensor control device, while the correction of analyte measures, the calculation of analyte measures (e.g., baseline lactate values), and the comparison of analyte measures with predetermined thresholds may be performed on the reader device, a remote computing system, or a trusted computing system. In some embodiments, the collection of analyte level data and the correction of analyte values ​​may be performed separately on the sensor control device. Similarly, those skilled in the art will recognize that the representation of computing devices in the embodiments disclosed herein, such as… Figure 1 Those shown are intended to cover both physical devices and virtual devices (or “virtual machines”).

[0107] Figure 7A This is a flowchart depicting an exemplary implementation of a method 700 for correcting spuriously low glucose level measurements (e.g., those caused by LSA). In step 705, first data indicating glucose levels, such as information about..., are collected by an analyte sensor. Figure 1 and Figure 3Those described. In step 710, second data indicating lactate levels is collected by a lactate sensing element. According to some embodiments, steps 705 and 710 may be performed by a sensor control unit including an analyte sensor having a portion configured to be inserted into the user's body at the insertion site, wherein the portion includes a first sensing element configured to sense glucose levels in bodily fluids and a second sensing element configured to sense lactate levels in bodily fluids at the same insertion site. In other embodiments, steps 705 and 710 may be performed by a sensor control unit including a first analyte sensor and a second analyte sensor, the first analyte sensor being configured to sense glucose levels in bodily fluids and the second analyte sensor being configured to sense lactate levels in bodily fluids, wherein the first and second analyte sensors are configured to sense analyte levels at the same local insertion site.

[0108] Still referencing Figure 7A In step 715, a corrected glucose level is determined based on a function of the first and second data. According to many embodiments, the function of the first and second data may include the measured glucose level, the measured lactate level, and the baseline lactate level. Furthermore, in many embodiments, the function may include a sensor batch constant, wherein the sensor batch constant is associated with a sensor batch including the analyte sensor.

[0109] According to another aspect of the embodiments, the measured glucose level may indicate the glucose level sensed in a first time period, and the measured lactate level may indicate the lactate level sensed in a second time period. In many embodiments, the lactate level sensed in the second time period may include a smoothed lactate value over a one-hour time period. Those skilled in the art will recognize that other time periods (e.g., 30 minutes, 2 hours, 5 hours, etc.) may also be used, and are fully within the scope of this disclosure. Furthermore, in some embodiments, the first time period may overlap with or fall within the second time period.

[0110] According to another aspect of the implementation, the baseline lactate value can be the average lactate value over one or more days (e.g., two days, three days, etc.). In some implementations, for example, one or more days can occur during the middle portion of the analyte sensor's sensor lifespan.

[0111] Still referencing Figure 7A In step 720, the corrected glucose level can be visually output to a display. In many embodiments, for example, the corrected glucose level can be output to the display of a reader device, a remote computing device, and / or a trusted computer system, as per [reference to...]. Figure 1 As described.

[0112] Figure 7BThis is a flowchart illustrating an exemplary implementation of a method 750 for detecting suspected sensor malfunction using lactate level measurements. In step 755, first data indicating glucose levels, such as information about..., are collected by the analyte sensor. Figure 1 and Figure 3 Those described. In step 760, second data indicating lactate levels is collected by a lactate sensing element. According to some embodiments, steps 755 and 760 may be performed by a sensor control unit including an analyte sensor having a portion configured to be inserted into the user's body at an insertion site, wherein the portion includes a first sensing element configured to sense glucose levels in bodily fluids and a second sensing element configured to sense lactate levels in bodily fluids at the same insertion site. In other embodiments, steps 755 and 760 may be performed by a sensor control unit including a first analyte sensor and a second analyte sensor, wherein the first analyte sensor is configured to sense glucose levels in bodily fluids, the second analyte sensor is configured to sense lactate levels in bodily fluids, and wherein the first and second analyte sensors are configured to sense analyte levels at the same local insertion site.

[0113] Still referencing Figure 7B In step 765, a baseline lactate value is calculated using the second data. In many embodiments, the baseline lactate value may include an average lactate value over one or more days. In step 770, the baseline lactate value is compared with a predetermined baseline lactate value threshold. Subsequently, in step 775, it is determined whether the baseline lactate value reaches or exceeds the predetermined baseline lactate value threshold. If not, method 750 returns to step 755. If the predetermined baseline lactate value threshold is reached or exceeded, in step 780, an indication of suspected sensor failure is generated. According to many embodiments, the indication of suspected sensor failure may also include terminating the analyte sensor; shielding or discarding the measured glucose level; and / or causing one or more commands to display a notification, warning, or alarm on a reader device, remote computing system, or trusted computer system.

[0114] Implementation of systems, devices, and methods for improving glucose sensor performance through the use of subphysiological measurements. Mode

[0115] Several factors, including calibration variations between sensors and time-related variations (e.g., ESA, LSA, and random overnight inactivation), can adversely affect the low-end performance of glucose sensors. Furthermore, the uncertainties associated with these factors can limit the amount of hysteresis correction that can be applied to glucose level readings. Therefore, it is beneficial to be able to distinguish between true high / low glucose conditions (e.g., hypoglycemia, hyperglycemia) and false high / low glucose conditions in order to determine the optimal amount of hysteresis correction, thereby improving the sensitivity and specificity of sensor fault detection and enhancing the overall low-end accuracy of the glucose sensor.

[0116] The increasing adoption of wearable devices capable of quantifying an individual's health status offers opportunities to improve the low-end performance of glucose sensors by utilizing information from non-glucose sensors (also known as "subsensors"). Examples of non-glucose sensors or "subsensors" include, but are not limited to, heart rate monitors, insertable cardiac monitors, implantable electrocardiogram (ECG) devices, implantable electroencephalogram (EEG) devices, ketone sensors, continuous ketone monitors, ketone test strip readers, and the like. These non-glucose or "subsensors" can provide subphysiological measurements, which can then be analyzed together with glucose level readings from a glucose sensor to confirm or refute high / low glucose conditions detected by the glucose sensor.

[0117] For example, when blood glucose levels remain in the hyperglycemic range for an extended period, ketone levels have been shown to gradually increase. Therefore, ketone level measurements from test strip-based or continuous ketone monitors can be used in conjunction with glucose-based fault detection modules to determine whether persistently low glucose sensor readings are physiologically possible.

[0118] As another example, prolonged periods of low blood glucose have demonstrated that hypoglycemia can have pathophysiological effects on cardiac workload, QT interval, and other factors. Many of these factors (e.g., heart rate, ECG, and EEG) can be measured using wearable devices and other similar medical devices. For instance, studies have shown that arrhythmias can occur during hypoglycemia. Similarly, other studies have demonstrated the use of EEG to infer hypoglycemia. Therefore, data from subsensors (e.g., heart rate monitors, ECG, EEG, etc.) can be used to distinguish between genuine hypoglycemia and pseudo-hypoglycemia.

[0119] In addition to the benefits mentioned above, fusing subphysiological measurements from non-glucose or "sub" sensors with data from glucose sensors can improve low-end glucose sensor performance in at least two other ways. First, more aggressive hysteresis correction can be applied at the lower end of the glucose range because the likelihood of erroneous low glucose readings (e.g., due to ESA, LSA, or nocturnal random inactivation) is reduced. Second, non-glucose or "sub"physiological measurements can be combined with glucose sensor data to better detect sensor malfunctions, thereby temporarily masking glucose readings, adjusting glucose readings, or prematurely terminating the glucose sensor.

[0120] Before discussing in detail exemplary implementations of the method for fusing glucose sensor data and subsensor data, it is desirable to first describe examples of systems and devices that can be used to perform the methods described in this disclosure, as well as examples of their operation. Figure 8 This is a logic diagram depicting one aspect of the exemplary implementation described herein. According to Figure 8In one aspect of the embodiment shown, glucose sensor 104 collects data indicating glucose levels and provides that data to sensor data fusion and analysis module 825. Similarly, secondary sensing element 804 collects data indicating secondary physiological measurements and provides that data to sensor data fusion and analysis module 825. Secondary sensing element 804 may include one or more of a heart rate monitor 806, ECG 808, EEG 812, ketone monitor 814, or ketone test strip reader 816. Furthermore, those skilled in the art will understand that other secondary sensing elements 804 (e.g., implantable or insertable cardiac monitors, lactate sensors, etc.) may be utilized and are fully within the scope of this disclosure. Sensor data fusion and analysis module 825 then analyzes the first and second data to determine: whether a true high / low glucose condition exists (e.g., hyperglycemia, hypoglycemia), whether hysteresis correction is applied to the glucose level readings, whether data smoothing is applied to the glucose level readings, the degree of hysteresis correction and / or data smoothing applied to the glucose level readings, whether certain glucose level readings are masked, whether the glucose sensor is terminated, or whether a notification, alarm, or warning is generated related to any of the aforementioned actions. Additional details regarding the specific methodological steps for performing these steps are provided below. Figure 10 describe.

[0121] Figures 9A to 9E A system overview diagram is depicting various example systems and devices that can be used to perform the methods described in this disclosure. Figure 9A This is a system overview diagram of a single sensor control device 102 including a glucose sensor 104 and a secondary sensing element 804, such as regarding Figure 8 Those described. According to some embodiments, glucose sensor 104 may be a dual analyte sensor (as shown by the dashed rectangle) including a secondary sensing element 804 or integrated with the secondary sensing element 804, wherein the secondary sensing element 804 is configured to collect data indicating subphysiological measurements (such as, for example, ketone levels or lactate levels).

[0122] According to other embodiments, the glucose sensor 104 and the secondary sensing element 804 may include two discrete sensors configured to measure glucose levels and secondary physiological measurements (e.g., ketone levels) in or around the same insertion site, respectively. According to one aspect of the embodiment, the sensor control device 102 may include processing circuitry coupled to a non-transient memory, wherein the non-transient memory stores software and / or firmware instructions (e.g., sensor data fusion and analysis module 825) that, when executed by the processing circuitry of the sensor control device 102, cause the processing circuitry to perform the following method steps.

[0123] Figure 9BA system overview diagram is depicted, showing a first sensor control device 102 having a glucose sensor 104 and a second sensor control device 902 having a secondary sensing element 804. According to... Figure 9B In the illustrated embodiment, data can communicate between the two sensor control devices, and the sensor data fusion and analysis module 825 can reside in the non-transient memory of either of the sensor control devices 102 and 902. Those skilled in the art will recognize that, although... Figure 9B Bidirectional arrows are depicted indicating bidirectional communication between sensor control devices 102 and 902, but some implementations may utilize only unidirectional data transmission (e.g., where sensor control device 902 transmits data to sensor control device 102, and where sensor data fusion and analysis module 825 resides in non-transient memory of sensor control device 102).

[0124] Figure 9C A system overview diagram is depicted, showing a first sensor control device 102 having a glucose sensor 104 and a second sensor control device 902 having a secondary sensing element 804. According to... Figure 9C In the illustrated implementation, data is transmitted from each sensor control device (102, 902) to a reader device 120, which may have a mobile software application (“app”) 903 configured to receive both types of data and also perform a sensor data fusion and analysis module 825. Figure 9D Similarly, a system overview diagram is depicted showing a first sensor control device 102 with a glucose sensor 104 and a second sensor control device 902 with a secondary sensing element 803, wherein each sensor control device 102, 902 is configured to communicate with a reader device 120. Figure 9D In the illustrated embodiment, the first sensor control device 102 is configured to transmit data indicating glucose levels to an application 904 residing in the non-transient memory of the reader device 120, and the second sensor control device 902 is configured to transmit data indicating subphysiological measurements to an application 905 also residing in the non-transient memory of the reader device 120. According to... Figure 9D In another aspect of the implementation, applications 904 and 905 are configured to communicate with each other in one or both directions; and sensor data fusion and analysis module 825 may be integrated into either or both of applications 904 and 905.

[0125] Figure 9EA system overview diagram is depicted, showing a first sensor control device 102 having a glucose sensor 104 and a second sensor control device 902 having a secondary sensing element 804, wherein each sensor control device 102, 902 is configured to communicate with a reader device 120. According to Figure 9E In the embodiment shown, the first sensor control device 102 is configured to transmit data indicating glucose levels to an application 904 residing in the non-transient memory of the reader device 120, and the second sensor control device 902 is configured to transmit data indicating subphysiological measurements to an application 905 also residing in the non-transient memory of the reader device 120. Figure 9E In another aspect of the implementation described herein, each of applications 904 and 905 is configured to communicate unidirectionally or bidirectionally with one or both of the local computer system 170 or the trusted computer system 180 via network 190. In some implementations, network 190 may include a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a virtual private network (VPN), a cellular network, or the Internet. In some implementations, the trusted computer system 180 may include a cloud-based platform, a server cluster, a server farm, etc. Figure 9E In another aspect of the implementation described herein, the sensor data fusion and analysis module 825 may reside partially or entirely in one or more of the application 904, the non-transient memory of the application 905, the local computer system 170, and the trusted computer system 180. According to one aspect of some implementations, data from the secondary sensing element 804, or information processed by 170 or 180 based on the fused data, may be transmitted to the application 904 to provide regulation of the glucose sensor 104.

[0126] Example methods for improving glucose sensor performance using subsensor data

[0127] An exemplary implementation of a method for improving the performance of a glucose sensor using subphysiological measurements from a subsensing element will now be described. As with the previous implementations, those skilled in the art will understand that any one or more steps of the exemplary method described herein can be stored as software instructions in the non-transient memory of a sensor control device, reader device, remote computer, or trusted computer system, for example, regarding… Figure 1The stored instructions, when executed, can cause the processing circuitry of an associated device or computing system to perform any one or more steps of the example methods described herein. Those skilled in the art will also understand that in many embodiments, real-time or near-real-time sensor data can be used to perform any one or more of the method steps of this disclosure. In other embodiments, any one or more method steps can be performed retrospectively with respect to stored sensor data, including sensor data from sensors previously worn by the same user. For example, in some embodiments, the method steps of this disclosure can be performed periodically, according to a predetermined schedule, and / or in batches as a retrospective process.

[0128] Those skilled in the art will also understand that instructions may be stored in non-transient memory on a single device (e.g., a sensor control device or a reader device), or alternatively, may be distributed across multiple discrete devices located in geographically dispersed locations (e.g., the cloud). For example, in some embodiments, the collection of data indicating analyte levels (e.g., glucose, lactate), the identification of suspected pseudoglucose states and related physiological conditions, the application of hysteresis correction and / or data smoothing, and the termination of the glucose sensor may all be performed individually on the sensor control device. In some embodiments, the application of identifying suspected pseudoglucose states and related physiological conditions, hysteresis correction, and / or data smoothing may be performed on the reader device or via a trusted computer system. Similarly, those skilled in the art will recognize that the representation of computing devices in the embodiments disclosed herein, such as… Figure 1 Those shown are intended to cover both physical devices and virtual devices (or “virtual machines”).

[0129] Typically, in cases of fixed-degree hysteresis correction, such as assuming a fixed hysteresis time constant in a first-order differential equation model that realizes the hysteresis from blood glucose to interstitial glucose, the degree of hysteresis correction is a trade-off between the positive impact of hysteresis correction on overall performance and the negative impact of excessive hysteresis correction in uncertain regions (e.g., with suspected false hypoglycemia). According to one aspect of the implementation, by using subphysiological measurements, the certainty of a specific glucose condition, such as low glucose concentration, can be distinguished from erroneous hypoglycemia; and the entire range of fluctuations in the glucose sensor can be assumed to be physiological. As a result, it is possible and therefore more advantageous to implement more aggressive hysteresis correction by using subphysiological measurements compared to a degree determined solely by trade-off considerations.

[0130] As a non-limiting example, a compromise analysis might conclude that, according to the aforementioned compromise, a hysteresis correction equivalent to compensating for a nine (9) minute lag is the optimal approach. However, since it can improve the determinism of a particular glucose condition, a more aggressive hysteresis correction equivalent to compensating for a twenty (20) minute lag could lead to performance improvements without increasing the likelihood of spurious hysteresis corrections. For those skilled in the art, determining a more aggressive hysteresis correction can involve models more complex than first-order differential equations with more than one parameter, and does not necessarily imply increasing the values ​​of all parameters in the hysteresis correction model used.

[0131] Figure 10 This is a flowchart depicting an example embodiment of a method 1000 for improving the accuracy of glucose sensor data by using subphysiological measurements. In step 1002, a sensor control device including an analyte sensor, processing circuitry, and memory collects first data indicating glucose levels. In step 1004, a subsensing element collects second data indicating subphysiological measurements. As previously discussed... Figures 9A to 9E As described, according to one aspect of the implementation, the secondary sensing element may include one or more of a heart rate monitor, an insertable cardiac monitor, an implantable ECG device, or an implantable EEG device, and the secondary physiological measurement may be one or more of heart rate, QT interval, ECG, or EEG. According to some implementations, the secondary sensing element may include one or more of a ketone sensor, a continuous ketone monitor, or a ketone test strip sensor (e.g., as part of a reader device), and the secondary physiological measurement may be ketone levels.

[0132] In step 1006, based on the first data, it is determined whether there is a suspected false glucose condition.

[0133] According to one aspect of the implementation, a suspected false glucose condition can be a suspected false hypoglycemic condition, such as a suspected false hypoglycemic condition. In some implementations, the absence or presence of a suspected false hypoglycemic condition can be determined by using one or more tests with first data, including but not limited to determining whether:

[0134] i) Data quality checks of one or more glucose sensors indicate a suspected false low glucose condition;

[0135] ii) Glucose levels are below a first predetermined low glucose threshold;

[0136] iii) The area under the curve (“AUC”) is calculated (which may be based on a first most recent predetermined time window with a value below the second predetermined low glucose threshold) exceeding the predetermined low glucose AUC threshold;

[0137] iv) A glucose percentage measure (e.g., from a second most recent predetermined time window having a value below a third predetermined low glucose threshold) exceeds a predetermined low glucose percentage threshold; or

[0138] v) The average glucose level in the most recent predetermined time window (e.g., the third most recent predetermined time window) exceeds the third predetermined low glucose threshold.

[0139] According to another aspect of the implementation, a suspected false glucose condition can be a suspected false hyperglycemia condition, such as a suspected false hyperglycemia condition. In some implementations, the presence or absence of a suspected false hyperglycemia condition can be determined using first data through one or more tests, including but not limited to determining whether:

[0140] i) Data quality checks of one or more glucose sensors indicate a suspected false high glucose status;

[0141] ii) Glucose levels are higher than a first predetermined high glucose threshold;

[0142] iii) The AUC calculation (which may be based on a fourth most recent predetermined time window with a value higher than the second predetermined high glucose threshold) exceeds the predetermined high glucose AUC threshold;

[0143] iv) A glucose percentage measure (e.g., from a fifth most recent predetermined time window having a value higher than a third predetermined high glucose threshold) exceeds a predetermined high glucose percentage threshold; or

[0144] v) The average glucose level in the most recent predetermined time window (e.g., the sixth most recent predetermined time window) exceeds the third predetermined high glucose threshold.

[0145] Return to reference Figure 10 In step 1008, second data (e.g., a secondary physiological measurement) is analyzed to determine the presence of a relevant physiological condition. According to one aspect of the implementation, the relevant physiological condition can be one of the following: inferred absence of high glucose, inferred presence of high glucose, inferred absence of low glucose, or inferred presence of low glucose. According to some implementations, for example, the presence of a relevant physiological condition can be identified by comparing a sensed ketone level (e.g., using a continuous ketone monitor or ketone test strip reader) with a predetermined ketone threshold. A high ketone level exceeding the predetermined ketone threshold can indicate the absence of low glucose (or conversely, the presence of high glucose). Similarly, according to some implementations, the presence of a relevant physiological condition can be identified by comparing a heart rate measurement (e.g., using a heart rate monitor) with a predetermined heart rate threshold. A high or increasing heart rate exceeding the predetermined heart rate threshold can indicate the absence of high glucose (or conversely, the presence of low glucose).

[0146] According to one aspect of the implementation, if it has been determined (from step 1006) that there is no suspected false glucose condition (e.g., a suspected false hypoglycemia, such as a suspected false hypoglycemia), and if it has been determined (from step 1008) that there is a relevant physiological condition (e.g., a ketone level above a predetermined ketone threshold, indicating that hypoglycemia is inferred), a first correction action can be performed in step 1010. In some implementations, the first correction action may include aggressive hysteresis correction. For example, according to some implementations, a more aggressive hysteresis correction may be applied when no conflicting information is presented in the seventh most recent predetermined time window—for example, where: (1) it is determined that the suspected hypoglycemia condition is not present, and (2) it is determined that a relevant physiological measurement is present, wherein the relevant physiological measurement may be a ketone level above a predetermined ketone threshold, indicating that hypoglycemia is inferred to be absent.

[0147] Those skilled in the art will also understand that a suspected false glucose condition can be a suspected false hyperglycemic condition (e.g., suspected false hyperglycemia), and related physiological conditions (such as, for example, a heart rate higher than a predetermined heart rate threshold) can lead to the conclusion that hyperglycemia does not exist.

[0148] Those skilled in the art will also understand that the step of determining that there is no suspected false glucose condition (step 1006) may include determining that neither the suspected false hypoglycemia condition nor the suspected false hyperglycemia condition exists, and further, based on second data, determining whether there is a related physiological condition for both of the non-existent suspected false glucose conditions (step 1008).

[0149] According to another aspect of some implementations, if it has been determined (from step 1006) that there is no suspected false glucose condition, but also no related physiological condition, a second correction action (not shown) can be performed, wherein the second correction action includes one or more of moderate hysteresis correction or increased glucose sensor signal smoothing.

[0150] In some embodiments, the step of determining the presence of a relevant physiological condition (step 1008) may further include determining the degree of correlation between the relevant physiological condition and the suspected false glucose condition. According to these embodiments, if neither the suspected false glucose condition nor the relevant physiological condition exists, a second correction action (not shown) may be performed, wherein the second correction action includes one or both of variable hysteresis correction and variable glucose sensor signal smoothing. According to another aspect of these embodiments, variable hysteresis correction may be a function of the degree of correlation between the relevant physiological condition and the suspected false glucose condition. Conversely, variable glucose sensor signal smoothing may be an inverse function of the degree of correlation between the relevant physiological condition and the suspected false glucose condition. Those skilled in the art will also understand that other types of variable correction actions (e.g., filtering, masking, etc.) may be performed, wherein the magnitude of the correction action may be a function or an inverse function of the degree of correlation between the relevant physiological condition and the suspected false glucose condition.

[0151] Furthermore, according to some implementations, if it has been determined that there is no suspected false glucose condition (e.g., no suspected false hypoglycemia) and there is a relevant physiological condition that suggests the absence of hypoglycemia (e.g., high ketone levels above a predetermined ketone level threshold), a third correction action, including prematurely terminating the glucose sensor, can be taken.

[0152] According to another aspect of some implementations, determining a conflict between a first data point indicating glucose levels and a second data point indicating a subphysiological measurement can be the basis for taking one or more corrective actions. Figure 11 This is a flowchart illustrating an example implementation of a method 1100 for terminating a sensor or blocking sensor data from a glucose sensor based on a conflict between detected glucose data and a secondary physiological measurement. In step 1102, a sensor control device including an analyte sensor, processing circuitry, and memory collects first data indicating glucose levels. In step 1104, a secondary sensing element collects second data indicating a secondary physiological measurement. As previously described, the secondary sensing element may include one or more of a heart rate monitor, an insertable cardiac monitor, an implantable ECG device, or an implantable EEG device, and the secondary physiological measurement may be one or more of heart rate, QT interval, ECG, or EEG. According to some implementations, the secondary sensing element may include one or more of a ketone sensor, a continuous ketone monitor, or a ketone test strip sensor (e.g., as part of a reader device), and the secondary physiological measurement may be a ketone level.

[0153] In step 1106, it is determined whether there is a conflict or inconsistency between the first data and the second data. According to many embodiments, a conflict can be defined as an inconsistency between a glucose measurement on one hand (indicating a hyperglycemic state, a hypoglycemic state, a suspected false hyperglycemic state, or a suspected false hypoglycemic state) and a related physiological condition on the other hand (such as an inferred hyperglycemic state (e.g., high ketone levels) or an inferred hypoglycemic state (e.g., increased or higher heart rate)).

[0154] In step 1108, if such a conflict has been detected, the glucose sensor can be terminated, or alternatively, sensor data can be discarded and / or temporarily blocked. Furthermore, terminating the glucose sensor or temporarily blocking sensor data from the glucose sensor may also include displaying a notification, warning, or alarm indicating that the sensor has been terminated or temporarily blocked by a reader device, remote computing system, or trusted computer system.

[0155] For each embodiment of the methods disclosed in this disclosure, systems and devices capable of performing each of these embodiments are covered within the scope of this disclosure. For example, embodiments of sensor control devices are disclosed, and these devices may have one or more analyte sensors, analyte monitoring circuitry (e.g., analog circuitry), memory (e.g., for storing instructions), power supply, communication circuitry, transmitter, receiver, clock, counter, timer, temperature sensor, and processor (e.g., for executing instructions), which can perform any and all method steps or facilitate the execution of any and all method steps. These sensor control device embodiments can be used and are capable of being used to implement those steps performed by the sensor control device according to any and all methods described in this disclosure. Similarly, embodiments of reader devices are disclosed, and these devices may have one or more memories (e.g., for storing instructions), power supply, communication circuitry, transmitter, receiver, clock, counter, timer, and processor (e.g., for executing instructions), which can perform any and all method steps or facilitate the execution of any and all method steps. These reader device embodiments can be used and are capable of being used to implement those steps performed by the reader device according to any and all methods described in this disclosure. Implementations of computer devices and servers are disclosed, and these devices may have one or more memories (e.g., for storing instructions), a power supply, communication circuitry, a transmitter, a receiver, a clock, a counter, a timer, and a processor (e.g., for executing instructions), which can perform any and all method steps or facilitate the execution of any and all method steps. These reader device implementations can be used and are capable of implementing those steps performed by the reader device according to any and all methods described in this disclosure.

[0156] Computer program instructions for performing operations according to the subject matter can 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 can 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 case, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0157] It should be noted that all features, elements, components, functions, and steps described with respect to any embodiment provided in this disclosure are intended to be freely combined and substituted with features, elements, components, functions, and steps from any other embodiment. If a feature, element, component, function, or step is described with respect to only one embodiment, it should be understood that such feature, element, component, function, or step may be used with every other embodiment described in this disclosure, unless otherwise expressly stated. Therefore, this paragraph serves as a prior basis and written support for introducing claims at any time that combine features, elements, components, functions, and steps from different embodiments, or replace features, elements, components, functions, and steps from one embodiment with features, elements, components, functions, and steps from another embodiment, even if such combinations or substitutions are possible in certain circumstances. It is expressly recognized that, particularly considering that those skilled in the art will readily recognize the permissibility of every and every such combination and substitution, it would be overly cumbersome to explicitly describe every possible combination and substitution. Aspects are set forth in independent claims 1, 21, 41, 56, 61, 76, 81, 102, 103, and 124. Preferred features are set forth in the dependent claims and can be implemented in combination with each aspect set forth in the independent claim. Apparatus including means for implementing each method is also provided.

[0158] As the embodiments disclosed in this disclosure include or operate in association with a memory, storage device, and / or computer-readable medium, the memory, storage device, and / or computer-readable medium is non-transient. Therefore, to the extent that one or more claims cover the memory, storage device, and / or computer-readable medium, it is merely non-transient.

[0159] As used in this disclosure and the appended claims, the singular forms “a,” “an,” and “the” include plural indications unless the context clearly indicates otherwise.

[0160] While the embodiments are readily available in various modifications and alternatives, specific examples have been shown in the drawings and described in detail in this disclosure. However, it should be understood that these embodiments are not limited to the specific forms disclosed, but rather, they will cover all modifications, equivalents, and substitutions falling within the spirit of this disclosure. Furthermore, any feature, function, step, or element of the embodiments may be recited in or added to the claims, and the scope of the claims may be negatively limited by features, functions, steps, or elements not within that scope.

Claims

1. An analyte monitoring system, comprising: A sensor control device includes an analyte sensor, a first processing circuit, and a first non-transient memory. The analyte sensor includes at least a portion configured for insertion into a user's body and includes a first sensing element and a second sensing element. The first sensing element is configured to detect glucose levels in subcutaneous tissue fluid at the insertion site, and the second sensing element is configured to detect lactate levels in the subcutaneous tissue fluid. The sensor control device is configured to collect first data indicating glucose levels and second data indicating lactate levels. The reader device includes a second processing circuit and a second non-transient memory. Wherein, at least one of the first non-transient memory or the second non-transient memory includes instructions, which, when executed, cause at least one of the first processing circuit or the second processing circuit to: Calculate the first analyte measurement based on the first data; Calculate the second analyte measure based on the second data; The first analyte measure is compared with a first threshold, and the second analyte measure is compared with a second threshold; In response to determining that the first analyte measure exceeds the first threshold and the second analyte measure exceeds the second threshold, an indication of suspected nocturnal random glucose inactivation is generated, and In response to the indication of suspected random nocturnal glucose inactivation, the output of low glucose alarms and / or commands to alter drug delivery are automatically suppressed.

2. The analyte monitoring system according to claim 1, wherein, The drug includes insulin.

3. The analyte monitoring system according to claim 1, wherein, The first data indicating glucose levels and the second data indicating lactate levels are associated with the insertion site on the user's body.

4. The analyte monitoring system according to claim 1, wherein, The instructions are stored in the second non-transient memory.

5. The analyte monitoring system according to claim 1, wherein, The instructions are stored in the first non-transient memory.

6. The analyte monitoring system according to claim 1, wherein, The sensor control device further includes a wireless communication circuit configured to transmit the first data and the second data to the reader device.

7. The analyte monitoring system according to claim 6, wherein, The wireless communication circuit is configured to transmit the first data and the second data according to the Bluetooth protocol.

8. The analyte monitoring system according to claim 1 further includes a drug delivery device.

9. The analyte monitoring system according to claim 8, wherein, The drug delivery device includes an insulin pump.

10. A computer-implemented method for detecting suspected random glucose inactivation, the method comprising: A sensor control device collects first data indicating glucose levels and second data indicating lactate levels, wherein the sensor control device includes an analyte sensor, at least a portion of which is inserted into the user's body, wherein the analyte sensor includes a first sensing element and a second sensing element, the first sensing element being configured to detect glucose levels in subcutaneous tissue fluid at the insertion site, and the second sensing element being configured to detect lactate levels in the subcutaneous tissue fluid. Calculate the first analyte measurement based on the first data; Calculate the second analyte measure based on the second data; The first analyte measure is compared with a first threshold, and the second analyte measure is compared with a second threshold; In response to determining that during a predetermined nighttime period when the user is asleep, both the first analyte measure and the second analyte measure exceed the second threshold, an indication of suspected nocturnal random glucose inactivation is generated. In response to the indication of suspected random nocturnal glucose inactivation, the output of low glucose alarms and / or commands to alter drug delivery are automatically suppressed.

11. The method of claim 10, further comprising: The indication of suspected random glucose inactivation is received by the drug delivery device.

12. The method according to claim 10, wherein, The first data indicating glucose levels and the second data indicating lactate levels are associated with the insertion site on the user's body.

13. The method according to claim 10, wherein, The reader device, which is wirelessly connected to the sensor control device, performs at least the step of generating an indication of suspected random glucose inactivation in response to determining that the first analyte measure exceeds the first threshold and the second analyte measure exceeds the second threshold.

14. The method of claim 10, wherein, The sensor-controlled device performs the step of generating an indication of suspected random glucose inactivation in response to determining that the first analyte measure exceeds the first threshold and the second analyte measure exceeds the second threshold.

15. The method of claim 10, further comprising: The wireless communication circuit of the sensor control device transmits the first data and the second data to the reader device.

16. The method according to claim 15, wherein, The wireless communication circuit is configured to transmit the first data and the second data according to the Bluetooth protocol.

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