Systems, apparatus, and methods for improved sample sensor accuracy and fault detection.

Abstract

This invention provides systems, apparatus, and methods for improving the accuracy of sample sensors and for detecting sensor malfunctions. [Solution] The sample monitoring system comprises a sensor control device and a reader. The sensor control device comprises a sample sensor, a first processing circuit, and a first persistent memory. The sample sensor is configured so that at least a portion of it is inserted into the user's body, and the sensor control device is configured to collect first data indicating glucose level and second data indicating lactate level. The reader comprises a second processing circuit and a second persistent memory. At least one of the first or second persistent memory stores a set of instructions, which, when executed, cause at least one of the first or second processing circuits to calculate a corrected glucose level based on a function of the first and second data.

Description

[Technical Field]

[0001] The subject matter described herein generally relates to systems, apparatus, and methods for improving the accuracy and fault detection of sample sensors. In particular, the embodiments described herein relate to corroborating data collected by a glucose sensor with data collected from a secondary sensing element in order to correct glucose levels or detect adverse conditions such as suspected sensor failure. [Background technology]

[0002] There is a huge and growing market for monitoring the health and condition of humans and other living beings. Information describing a person's physical or physiological condition can be used in countless ways to support and improve their quality of life and to diagnose and treat undesirable human conditions.

[0003] The typical devices used to collect such information are physiological sensors, such as biochemical sample sensors or devices capable of detecting chemical samples of biological entities. Biochemical sensors come in many forms and can be used to detect samples within biological entities, such as fluids, tissues, or gases that form or are produced as part of a human body. These sample sensors can be attached to or used inside the body, such as in the case of sample sensors implanted through the skin, or they can be attached to biological objects that have already been removed from the body.

[0004] Sample sensors and monitoring systems often have complex and well-studied designs, but they can be prone to malfunction before their expected lifespan ends. This can result in undesirable and unexpected reductions in the sensor signal response to actual sample fluctuations. In many cases, a reduction in the sample sensor signal response can lead to false indications of low sample levels, or, in the case of complete sensor failure, may result in no indication of any sample level at all. Furthermore, an undesirable and unexpected reduction in the sample sensor signal response can cause false positives regarding low-threshold alarms, such as low glucose or hypoglycemia alarms.

[0005] Another potential issue with sample monitoring systems is "nocturnal glucose drop," a phenomenon that causes a sudden, brief decrease in blood glucose levels while the person wearing the sample sensor is sleeping at night. This drop in blood glucose can lead to false positives regarding low-threshold alarms or, if the sensor is used with an automated insulin delivery system, can cause unnecessary insulin adjustments. [Overview of the project] [Problems that the invention aims to solve]

[0006] For these and other reasons, it is necessary to improve the accuracy of the sample sensor, as well as to detect sensor malfunctions. [Means for solving the problem]

[0007] Embodiments of systems, apparatus, and methods for improving the accuracy of sample sensors and for detecting sensor failure conditions are described in this document. Several embodiments enable, for example, the detection of suspected glucose drops and / or correction of glucose levels based on glucose level and lactate level measurements and calculations. In some embodiments, correction measures, such as delayed correction, glucose sensor termination, or glucose sensor data smoothing, can be performed based on first data showing glucose levels and second data showing secondary physiological measurements. Secondary physiological measurements may be, for example, ketone levels or heart rate measurements. Numerous examples of algorithms and methods for performing one or both of these detection and correction mechanisms, and / or variations thereof, and embodiments of systems and apparatus for performing them are provided.

[0008] Other systems, apparatus, methods, features, and advantages of the subject matter described herein will become apparent to those skilled in the art by considering the following figures and detailed description. All such additional systems, apparatus, methods, features, and advantages are included in the description, are within the scope of the subject matter described herein, and are intended to be protected by the appended claims. Unless there is an express description of the features in the claims, these features of the embodiments should never be construed as limiting the appended claims. [Brief explanation of the drawing]

[0009] Details regarding both the structure and operation of the subject matter described in this book may become clear by examining the accompanying diagrams. In the diagrams, similar symbols refer to similar parts. The parts in the diagrams are not necessarily to a fixed scale, and the emphasis is on illustrating the principles of the subject matter. Furthermore, all diagrams are intended to convey concepts, and relative size, shape, and other detailed attributes may be illustrated in a general manner, not strictly or precisely. [Figure 1] This is an explanatory diagram illustrating an embodiment of an in vivo specimen monitoring system. [Figure 2] This is a block diagram of an embodiment of the reading device. [Figure 3] This is a block diagram of an embodiment of a sensor control device. [Figure 4A] This is a multi-plot graph illustrating an example of a sensor signal that fluctuates over time. [Figure 4B] This is a multi-plot graph illustrating an example of a sensor signal that fluctuates over time. [Figure 4C] This is a multi-plot graph illustrating an example of sensor signals that fluctuate over time and their corresponding derivative values. [Figure 4D] This is a multi-plot graph illustrating an example of sensor signals that fluctuate over time and their corresponding derivative values. [Figure 5] This is a flowchart illustrating an embodiment of a method for detecting a suspicious decrease in glucose levels. [Figure 6A] This is a multi-plot graph illustrating examples of sensor signals that fluctuate over time and their corresponding corrected sensor measurements. [Figure 6B] This is a multi-plot graph illustrating examples of sensor signals that fluctuate over time and their corresponding corrected sensor measurements. [Figure 6C] This is a multi-plot graph illustrating examples of sensor signals that fluctuate over time and their corresponding corrected sensor measurements. [Figure 7A] This is a flowchart illustrating an embodiment of a method for calculating corrected glucose levels. [Figure 7B] A flowchart depicting an embodiment of a method for detecting a suspected sensor failure state. [Figure 8] A block diagram depicting a system for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 9A] A block diagram depicting various systems for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 9B] A block diagram depicting various systems for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 9C] A block diagram depicting various systems for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 9D] A block diagram depicting various systems for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 9E] A block diagram depicting various systems for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 10] A flowchart depicting an embodiment of a method for improving the performance of a glucose sensor using secondary physiological measurements. [Figure 11] Another flowchart depicting an embodiment of a method for improving the performance of a glucose sensor using secondary physiological measurements.

Embodiments for Carrying Out the Invention

[0010] Before explaining the present subject matter in detail, it should be understood that the present disclosure is not limited to the specific embodiments described and, of course, can vary. Also, it should be understood that the terms used in this document are for the purpose of describing only the specific embodiments and are not intended to be limiting. The scope of the present disclosure is limited only by the appended claims.

[0011] The publications described herein are provided solely for disclosure prior to the filing date of this application. This disclosure should not be construed as acknowledging that such disclosure is not eligible to precede any prior disclosure. Furthermore, the provided publication dates may differ from the actual publication dates (which must be independently verified).

[0012] Generally, embodiments of this disclosure are used in conjunction with systems, apparatus, and methods for detecting at least one specimen, such as glucose, in a body fluid (e.g., subcutaneous interstitial fluid (ISF) or blood, dermal fluid of the cortex). Accordingly, many embodiments include in vivo specimen sensors in which at least a portion of the sensor is placed, or can be placed, within the user's body to obtain information on at least one specimen of the body. However, it should be noted that embodiments described herein may be used in conjunction with in vivo specimen monitoring systems having in vitro capabilities and purely in vitro or in vitro specimen monitoring systems (including completely non-invasive systems).

[0013] Furthermore, in the case of each and all embodiments of the methods disclosed herein, systems and apparatus capable of performing each of these embodiments are included within the scope of this disclosure. For example, embodiments of sensor control devices are disclosed, which may include one or more sensors, a sample monitoring circuit (e.g., an analog circuit), persistent memory (e.g., for storing a set of instructions), a power supply, a communication circuit, a transmitter, a receiver, and processing circuits and / or controllers (e.g., for executing a set of instructions) capable of performing or enabling any and all method steps.

[0014] Similarly, embodiments of a reader are disclosed having one or more transmitters, receivers, persistent memory (e.g., for storing instruction sets), power supply, and processing circuits and / or controllers (e.g., for executing instruction sets) capable of or enabling the execution of any and all method steps. These embodiments of the reader may be used to perform any and all method steps described herein that are performed by the reader.

[0015] Embodiments of a reliable computer system are also disclosed. A reliable computer system includes one or more processing circuits, controllers, transmitters, receivers, persistent memory, databases, servers, and / or networks, and may be located as a single entity or distributed across multiple geographical locations. Embodiments of a reliable computer system may be used to perform any and all steps of the methods described herein that are performed by a reliable computer system.

[0016] Various embodiments of systems, apparatus, and methods for improving the accuracy of sample sensors and for detecting sensor failure conditions are disclosed. According to some embodiments, these systems, apparatus, and methods can 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 the following: a lactate sensing element, a ketone sensing element, or a heart rate monitor.

[0017] Multiple embodiments of this disclosure are configured to improve the computer execution capabilities of a sample monitoring system, for example, with respect to detecting nocturnal glucose drops, correcting glucose level measurements, and early termination of glucose sensors. More specifically, these embodiments can utilize “secondary” data showing non-glucose physiological measurements (e.g., lactate levels, ketone levels, heart rate measurements, etc.) to improve the accuracy of the in vivo glucose sensor and to determine conditions under which the in vivo glucose sensor can or should be terminated or temporarily masked. Accordingly, the embodiments disclosed herein are directed toward systems, apparatus, and methods that reflect improvements over conventional methods and can improve the accuracy of a sample monitoring system by utilizing non-glucose physiological measurements and glucose sensor data combined in a particular novel way. Other features and advantages of the disclosed embodiments are further described below.

[0018] However, before describing the embodiments in detail, it is desirable to describe examples of devices that may be present in an in vivo sample monitoring system and examples of their operation, all of which can be used in conjunction with the embodiments described herein.

[0019] Embodiment of a specimen monitoring system Various types of sample monitoring systems exist. A "continuous sample monitoring" system (or "continuous glucose monitoring" system) is an in vivo system that can automatically transmit data from a sensor control device to a reader device, for example, according to a schedule, without repeated or continuous prompting. Another example is a "flash sample monitoring" system (or "flash glucose monitoring" system or simply a "flash" system), an in vivo system that can transfer data from a sensor control device in response to scanning or data requests by a reader device, for example, using a Near Field Communication (NFC) or Radio Frequency Identification (RFID) protocol. Furthermore, in vivo sample monitoring systems can operate without the need for finger puncture calibration.

[0020] An in vivo monitoring system may include sensors located within the body that come into contact with the user's bodily fluids and detect one or more sample levels within them. The sensors may be part of a sensor control unit attached to the user's body, including electronic circuits and a power supply that enable and control sample detection. Sensor control units and their variations may also be referred to as “sensor control units,” “body-attached electronic circuits” devices or units, “body-attached” devices or units, or “sensor data communication” devices or units. As used in this document, these terms are not limited to devices having sample sensors but encompass devices having other types of sensors (whether biological or non-biological). The term “body-attached” refers to any device that is directly attached to the body or located very close to the body, such as wearable devices (e.g., glasses, watches, wristbands or bracelets, neckbands or necklaces).

[0021] An in vivo monitoring system may also include one or more readers that receive sample data detected by a sensor control unit. These readers may process the detected sample or sensor data and / or display it to the user in any number of formats. These devices and their variations may be called “handheld readers,” “readers” (or simply “readers”), “handheld electronic devices” (or handhelds), “portable data processing” devices or units, “data receivers,” “receiving” devices or units (or simply receivers), “relay” devices or units, or “remote” devices or units, etc. Other devices, such as personal computers, have also been used with or incorporated into in vivo and in vivo monitoring systems.

[0022] In vivo sample monitoring systems can be distinguished from extra vivo systems that come into contact with biological samples outside the body, typically including instruments with ports for receiving sample test strips containing the user's bodily fluids that can be analyzed to measure the user's sample level. As described above, the embodiments described herein can be used with in vivo systems, extra vivo systems, and combinations thereof.

[0023] The embodiments described herein may be used to monitor and / or process information on any number of different samples. Samples that may be monitored include, but are not limited to, acetylcholine, amylase, bilirubin, cholesterol, chorionic gonadotropin, glycosylated hemoglobin (HbA1c), creatine kinase (e.g., CK-MB), creatine, creatinine, DNA, fructosamine, glucose, glucose derivatives, glutamine, growth hormone, hormones, ketones, ketone bodies, lactate, peroxides, prostate-specific antigen, prothrombin, RNA, thyroid-stimulating hormone, and troponin. The concentrations of drugs such as antibiotics (e.g., gentamicin, vancomycin, etc.), digitoxin, digoxin, drugs of abuse, theophylline, and warfarin may also be monitored. In embodiments where two or more samples are monitored, the samples may be monitored simultaneously or at different times.

[0024] Figure 1 is an explanatory diagram illustrating an embodiment of a sample monitoring system 100, which comprises a sensor control device 102 and a reader 120 communicating with each other via a local communication channel (or link) 140 that can be wired or wireless, unidirectional or bidirectional. In embodiments where the communication channel 140 is wireless, a Near Field Communication (NFC) protocol, RFID protocol, Bluetooth or Bluetooth Low Energy protocol, Wi-Fi protocol, private protocol, etc. (including existing communication protocols as of the filing date or modified protocols developed thereafter) may be used.

[0025] The reader 120 can also communicate with a computer system 170 (e.g., a local or remote computer system) via a communication channel (or link) 141, and with a network 190 such as the Internet or the cloud via a communication channel (or link) 142, either wired, wireless, or combined. Communication with the network 190 may include communication with a trusted computer system 180 within the network 190, or communication with a computer system 170 via a communication link (or channel) 143 through the network 190. Communication channels 141, 142, and 143 may be wireless, wired, or both; they may be one-way or two-way; and they may be part of a telecommunications network, such as a Wi-Fi network, a local area network (LAN), a wide area network (WAN), the Internet, or other data network. In some cases, communication channels 141 and 142 may be the same communication channel. All communications through communication channels 140, 141, and 142 are encrypted, and the sensor control device 102, the reader 120, the computer system 170, and the trusted computer system 180 may be configured to encrypt and decrypt those communications being transmitted and received, respectively.

[0026] Variations of apparatus 102 and 120 and other components of an in vivo specimen monitoring system suitable for use with the system, apparatus, and method embodiments described herein are described in U.S. Patent Application Publication No. 2011 / 0213225 (Published 225), which is incorporated herein by reference.

[0027] The sensor control device 102 may include a housing 103 that houses an in vivo sample monitoring circuit and a power supply. In this embodiment, the in vivo sample monitoring circuit is electrically coupled to one or more sample sensors 104 that extend through an adhesive patch 105 and protrude from the housing 103. The adhesive patch 105 includes an adhesive layer (not shown) for attachment to the skin surface of the user's body. In addition to or instead of the adhesive, other forms of attachment to the body may be used.

[0028] The sensor 104 is adapted to be inserted at least partially into the user's body and come into contact with the user's bodily fluids (e.g., subcutaneous fluid, skin fluid, or blood) within the body, and to be used in conjunction with an in vivo sample monitoring circuit to measure the user's sample-related data. The sensor 104 and any accompanying sensor control electronic circuitry can be attached to the body in any desired manner. For example, an inserter 150 may be used to position all or part of the sample sensor 104 through the outer surface of the user's skin to come into contact with the user's bodily fluids. In doing so, the inserter may also attach a sensor control device 102 having an adhesive patch 105 to the skin. In other embodiments, the inserter may first position the sensor 104, and then the accompanying sensor control electronic circuitry may be coupled to the sensor 104 by hand or using a mechanical device. Examples of inserters are described in U.S. Patent Publication Nos. 2008 / 0009692, 2011 / 0319729, 2015 / 0018639, 2015 / 0025345, and 2015 / 0173661, all of which are incorporated herein by reference.

[0029] After collecting raw data from the user's body, the sensor control device 102 performs analog signal adjustment on the data and converts it into adjusted raw data in digital format. In some embodiments, the sensor control device 102 can then algorithmically process the digital raw data into a format that represents the user's measured biological metrics (e.g., sample levels) and / or one or more sample metrics based thereon. For example, the sensor control device 102 may include processing circuits that algorithmically perform any of the method steps described herein, such as correcting glucose level measurements, detecting suspicious glucose drops, or detecting suspicious sensor malfunctions. The sensor control device 102 can then encode the data indicating glucose levels, sensor malfunction indications, and / or processed sensor data and transmit it wirelessly to the reader 120, which can format or graphically process the received data for digital display to the user. In other embodiments, in addition to or instead of wirelessly transmitting the sensor data to another device (e.g., the reader 120), the sensor control device 102 can graphically process the data so that it can be displayed in its final format and display the data on the sensor control device 102's display. In some embodiments, the final form of the biometric data (before graphical processing) is used by the system (e.g., integrated into a diabetes monitoring system) without further processing for display to the user.

[0030] In yet another embodiment, the adjusted raw digital data may be encoded for transmission to another device, such as a reader 120. The reader 120 algorithmically processes the raw digital data into a format representing the user's measured biometric (e.g., in a readily available format suitable for display to the user) and / or one or more specimen metrics based thereon. The reader 120 may include processing circuits that algorithmically perform any of the method steps described herein, for example, correcting glucose level measurements, detecting suspicious glucose drops, or detecting suspicious sensor malfunctions. The algorithmically processed data may then be formatted or graphically processed for digital display to the user.

[0031] In other embodiments, the sensor control device 102 and the reading device 120 transmit the raw digital data to another computer system for algorithmic processing and display.

[0032] The reader 120 may include a display 122 for outputting information to and / or receiving input from the user, and an optional input component 121, such as a button, actuator, touch sensor switch, capacitive switch, pressure-sensitive switch, jog wheel, etc., for inputting data, commands, or otherwise controlling the operation of the reader 120. In some embodiments, the display 122 and the input component 121 may be integrated into a single component, for example, the display may be able to detect the presence and location of physical contact to the display, such as a touch screen user interface. In some embodiments, the input component 121 of the reader 120 may include a microphone, and the reader 120 may include software configured to analyze the audio input received from the microphone and allow voice commands to control the functions and operation of the reader 120. In some embodiments, the output component of the reader 120 may include a speaker (not shown) for outputting information as an audible signal. Similar audio response components, 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.

[0033] The reader 120 may also include one or more data communication ports 123 for wired data communication with external devices such as a computer system 170 or a sensor control device 102. Examples of data communication ports include USB ports, mini USB ports, USB Type-C ports, USB Micro-A and / or Micro-B ports, RS232 ports, Ethernet ports, FireWire ports, or other similar data communication ports configured to connect to a compatible data cable. The reader 120 may also include an in vitro test strip port (not shown) for receiving in vitro glucose test strips to perform in vitro blood glucose measurement.

[0034] The reader 120 can display measured biometric data received wirelessly from the sensor control device 102 and may be configured to output alarms, warnings, glucose values, etc. (visual, audible, tactile, or a combination thereof). Further details and other display embodiments can be found, for example, in U.S. Patent Application Publication No. 2011 / 0193704, which is incorporated herein by reference.

[0035] The reader 120 may function as a data conduit for transferring measured data and / or sample metrics from the sensor control device 102 to the computer system 170 or a trusted computer system 180. In one embodiment, the data received from the sensor control device 102 may be stored (permanently or temporarily) in one or more memories of the reader 120 before being uploaded to the systems 170, 180, or the network 190.

[0036] The computer system 170 may be a personal computer, server terminal, laptop computer, tablet, or other suitable data processing device. The computer system 170 may be (or may include) software for data management and analysis and communication with components within the specimen monitoring system 100. The computer system 170 may be used by a user or healthcare professional to display and / or analyze biometric data measured by the sensor control device 102. In some embodiments, the sensor control device 102 can communicate biometric data directly to the computer system 170 without the mediation of a reader 120 or similar device, or indirectly using an internet connection (optionally without first transmitting to the reader 120). The operation and use of the computer system 170 are further described in Publication 225 cited herein. The specimen monitoring system 100 may also be configured to operate with a data processing module (not shown) (as described in Publication 225 cited herein).

[0037] A trusted computer system 180 may be physically or substantially owned by the manufacturer or distributor of the sensor control unit 102 through a secure connection and may be used as a server to perform authentication of the sensor control unit 102 for the secure storage of the user's biometric data and / or to run a data analysis program (e.g., accessible via a web browser) to analyze the user's measured data.

[0038] Embodiment of a reading device The reader 120 may be a mobile communication device, such as a dedicated reader (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 a Wi-Fi or internet-enabled smartphone, tablet, or personal digital assistant (PDA), but not limited to these. Examples of smartphones may include mobile phones 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 functionality for internet connectivity and / or data communication via a local area network (LAN).

[0039] The reader 120 may also be configured as a portable smart wearable electronic assembly, such as an optical assembly worn over or close to the user's eyes (e.g., smart glasses such as Google Glass, which are a mobile communication device). This optical assembly may have a transparent display that shows the user information about the user's sample level and at the same time allows the user to see through the display (slightly obstructing the user's overall field of view). The optical assembly may be wirelessly wireless, similar to a smartphone. Other examples of wearable electronic devices include devices worn around or near the user's wrist (e.g., a wristwatch), devices worn around the neck (e.g., a necklace), devices worn around the head (e.g., a headband, a hat), and devices worn near the chest.

[0040] Figure 2 is a block diagram of an embodiment of a reader 120 configured as a smartphone. Here, the reader 120 includes an input component 121, a display 122, and a processing circuit 206, the processing circuit 206 may include one or more processors, microprocessors, controllers, and / or microcontrollers (each of which may be distributed (and partially) across individual chips or multiple different chips). The processing circuit 206 includes a communication processor 222 having an internal memory 223 and an application processor 224 having an internal memory 225. The reader 120 further includes an RF communication circuit 228 coupled with an RF antenna 229, a memory 230, a multifunction circuit 232 having one or more associated antennas 234, a power supply 226, a power management module 238, and a clock (not shown). Figure 2 is a simplified diagram of typical hardware and functions present in a smartphone, and those skilled in the art will readily recognize that other hardware and functions (e.g., encoders / decoders, drive circuits, glue logic) may also be included.

[0041] The communication processor 222 connects to the RF communication circuit 228 and may perform analog-to-digital conversion, encoding and decoding, digital signal processing, and other functions that enable the conversion of voice, video, and data signals into a format suitable for supply to the RF communication circuit 228 (e.g., in-phase and quadrature), which the RF communication circuit 228 then wirelessly transmits. The communication processor 222 may also connect to the RF communication circuit 228 and perform the inverse functions necessary to receive the wireless transmission and convert it 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 encoding logic.

[0042] The application processor 224 may be configured to run an operating system and any software applications residing on the reader 120, process images and graphics, and perform other functions not related to the processing of communications transmitted and received through the RF antenna 229. The smartphone operating system operates with multiple applications on the reader 120. Any number of applications (also known as user interface applications) may run on the reader 120 at any time and may include one or more applications related to the diabetes monitoring regime and other commonly used applications not related to such a regime, such as email, calendar, weather, sports, and games. For example, data indicating detected sample levels and in vitro blood sample measurements received by the reader 120 may be securely communicated to a user interface application residing in the reader 120's memory 230. Such communication may be securely performed using mobile application containerization or wrapping technology.

[0043] Memory 230 may be shared by one or more of the various functional units present in the reader 120, or distributed among two or more of them (for example, as separate memories on different chips). Memory 230 may be a separate chip itself. Memories 223, 225, and 230 may be persistent and volatile (e.g., RAM) and / or non-volatile memory (e.g., ROM, flash memory, FRAM®, etc.).

[0044] The multifunction circuit 232 may be implemented as one or more chips and / or components (e.g., transmitters, receivers, transceivers, and / or other communication circuits) that perform local wireless communication with the sensor control device 102 and other functions such as determining the geographic location of the reader device 120 (e.g., Global Positioning System (GPS) hardware) under an appropriate protocol (e.g., Wi-Fi, Bluetooth, Bluetooth Low Energy, Near Field Communication (NFC), Radio Frequency Identification (RFID), private protocol, etc.). One or more other antennas 234 are associated with the multifunction circuit 232, which need to operate with various protocols and circuits.

[0045] The power supply 226 may include one or more batteries, which may be rechargeable or disposable batteries. The power management module 238 may regulate battery charging, monitor the power supply, boost voltage, perform DC conversion, etc.

[0046] The reader 120 may include or be integrated with a drug delivery device (e.g., insulin), which may share a common housing. Examples of such drug delivery devices may include a drug delivery pump (e.g., a wearable pump for the delivery of basal and bolus insulin) having a cannula that remains in the body and allows for infusion over multiple hours or days. When the reader 120 is coupled with a drug delivery pump, it may include a tank for storing the drug, a pump connectable to a transfer tube, and an infusion cannula. The pump can deliver the drug from the tank through the tube and into the body of a diabetic patient through the inserted cannula. Other examples of drug delivery devices that may be included in (or integrated with) the reader 120 include a portable infusion device (e.g., an insulin pen) that is inserted into the skin for each delivery and later removed. When the reader 120 is coupled with a portable infusion device, it may include a needle, a cartridge for containing the drug, an interface for controlling the amount of drug to be infused, and an actuator for inducing the infusion. The device is used repeatedly until the medication runs out. Once the medication is depleted, the combined device is discarded, or the cartridge is replaced with a new one, and the combined device can be reused repeatedly. The needle may be replaced after each injection.

[0047] The coupled device can function as part of a closed-loop system (e.g., an artificial pancreas system that does not require user intervention to operate) or a semi-closed-loop system (e.g., an insulin loop system that requires occasional user intervention, such as checking for dose changes, to operate). For example, the sensor control device 102 may repeatedly and automatically monitor the sample level of a diabetic patient and communicate that sample level to the reader 120, and an appropriate drug dose to control the diabetic patient's sample level may be automatically determined and delivered to the diabetic patient's body. Software instructions for controlling the pump and the amount of insulin delivered may be stored in the memory of the reader 120 and executed by the reader's processing circuit. These instructions may also cause the calculation of drug delivery amount and duration (e.g., bolus infusion and / or basal infusion profile) based on sample level measurements obtained directly or indirectly from the sensor control device 102. In some embodiments, the sensor control device 102 may determine the drug dose and communicate it to the reader 120.

[0048] Embodiment of a sensor control device Figure 3 is a block diagram illustrating an embodiment of a sensor control device 102 having a sample sensor 104 and a sensor electronic circuit 250 (including a sample monitoring circuit) that may have most of the processing power to prepare the final result data for display to the user. Figure 3 depicts a single semiconductor chip 251 which may be a custom application-specific integrated circuit (ASIC). A group of high-level functional units including an analog front-end (AFE) 252, a power management (or control) circuit 254, a processor 256, and a communication circuit 258 (which may be a transmitter, receiver, transceiver, passive circuit, etc., depending on the communication protocol) are shown within the ASIC 251. In this embodiment, both the AFE 252 and the processor 256 are used as sample monitoring circuits, but in other embodiments, either circuit may perform the sample monitoring function. The processor 256 may include one or more processors, microprocessors, controllers, and / or microcontrollers (each of which may be distributed (and partially) across individual chips or multiple different chips).

[0049] Memory 253 is also included within the ASIC 251 and may be shared by various functional units present within the ASIC 251, or distributed among two or more of them. Memory 253 may also be a separate chip. Memory 253 may be persistent, volatile, and / or non-volatile memory. In this embodiment, the ASIC 251 is coupled to a power supply 260 (which may be a coin cell battery, for example). The AFE 252 connects to the in vivo sample sensor 104 and receives measurement data from it, outputs the data in digital format to the processor 256, which may process the data in any of the ways described herein. The data may be provided to the communication circuit 258 for transmission to a reader 120 (not shown) via the antenna 261. The reader 120 requires minimal additional processing by a resident software application to display the data. The antenna 261 may be configured as required by the application and communication protocol. The antenna 261 may be, for example, a printed circuit board (PCB) wiring antenna, a ceramic antenna, or a discrete metal antenna. Antenna 261 can be configured as a unipolar antenna, dipolar antenna, F-type antenna, loop antenna, etc.

[0050] Information may be communicated from the sensor control device 102 to a second device (e.g., a reader 120) at the initiative of either the sensor control device 102 or the reader 120. For example, the sensor control device 102 may communicate information automatically and / or repeatedly (e.g., continuously) when sample information is available or according to a schedule (e.g., approximately every minute, every five minutes, every ten minutes, etc.). In this case, the information may be stored or recorded in the memory of the sensor control device 102 for later communication. Information may be transmitted from the sensor control device 102 in response to a request received by the second device. This request may be an automated request, such as a request transmitted by the second device according to a schedule, or a user-initiated request (e.g., an ad-hoc or manually entered request). In some embodiments, a manually entered request for data is referred to as a “scan” by the sensor control device 102, or an “on-demand” data transfer from the sensor control device 102. In some embodiments, the second device transmits a polling signal or data packet to the sensor control device 102, which treats each pole (or poles occurring at a certain time interval) as a data request and, if data is available, can transmit such data to the second device. In many embodiments, communication between the sensor control device 102 and the second device is protected (e.g., encrypted and / or authenticated between devices), but in some embodiments, data may be transmitted from the sensor control device 102 in an unprotected manner, for example, by simultaneous transmission to all devices within range.

[0051] Information of different types and / or formats and / or quantities, including, but not limited to, current sensor measurements (e.g., the most recently obtained sample level information corresponding in time when reading begins), the rate of change of a measured metric over a predetermined period, the percentage of the rate of change of the metric (acceleration of the rate of change), or historical metric information corresponding to metric information obtained before a particular reading and stored in the memory of the sensor control device 102, may be transmitted as part of each communication.

[0052] Some or all of the real-time, historical, rate of change, and rate of change information (such as acceleration or deceleration) may be sent to the reader 120 by specific communication or transmission. In some embodiments, the type and / or format and / or amount of information sent to the reader 120 may not be pre-programmed and / or modifiable (e.g., set at manufacturing), or it may not be pre-programmed and / or modifiable and may be selectable and / or changed on-site one or more times (e.g., by activating a system switch). Thus, in some embodiments, the reader 120 may output the current (real-time) sensor-generated sample value (e.g., in numerical format), the current rate of change of the sample (e.g., in the form of a sample rate indicator such as an arrow pointing in direction to indicate the current rate), and sample trend history data (e.g., in graphic line format) based on sensor readings acquired by the sensor control device 102 and stored in memory. Skin surface or sensor temperature readings or measurements may also be collected by an optional temperature sensor 257. These readings or measurements may be communicated from the sensor control device 102 to another device (e.g., the reader 120) individually or as aggregated measurements over time. However, instead of displaying the temperature measurement to the user, or in addition to doing so, the temperature readings or measurements may be used in conjunction with a software routine performed by the reader 120 to correct or compensate for the sample measurement output to the user.

[0053] Furthermore, while Figure 3 depicts a single sample sensor 104, according to many embodiments of this disclosure, the sensor control unit 102 may be configured to collect data indicating multiple physiological measurements, including, but not limited to, glucose levels, lactate levels, ketone levels, or heart rate measurements. In some embodiments, for example, sensor 104 may be a dual sample sensor configured to detect glucose levels and the concentration of another sample (e.g., lactate, ketone, etc.). Additional details relating to dual sample sensors are described, for example, in U.S. Patent Application Publication 2019 / 0320947, which is incorporated herein by reference in its entirety. In some embodiments, the sensor control unit 102 may include multiple individual sensors, each capable of collecting data indicating any of the above physiological measurements.

[0054] Embodiments of a system, apparatus, and method for detecting suspicious glucose depression Examples of characterizing glucose and lactate levels during nocturnal glucose decline Nocturnal glucose drop is a phenomenon observed in sample monitoring systems, where the glucose concentration measured by the glucose sensor may suddenly decrease for a short period while the user wearing the glucose sensor is sleeping at night. Nocturnal glucose drop can trigger false low glucose alarms or, if the sample monitoring system is used with an automated drug delivery system, such as an automated insulin pump, it can cause unnecessary drug delivery adjustments.

[0055] Previously, researchers theorized that nocturnal glucose decline was a result of pressure-induced sensor attenuation. However, recent studies using dual glucose / lactate sensors (e.g., glucose and lactate sensing elements in a single sample sensor) suggest that nocturnal glucose decline is actually a physiological phenomenon that can decrease interstitial glucose concentration at specific detection sites. This study also suggests that this physiological phenomenon increases lactate concentration simultaneously with the decrease in glucose concentration.

[0056] Figures 4A and 4B show multi-plot graphs 400 and 410, respectively, which plot 24-hour data from the same patient with first and second glucose / lactate sensors placed less than 2 inches (5.08 cm) apart. Referring first to Figure 4A, multi-plot graph 400 for the first dual glucose / lactate sensor includes an upper plot 402 showing glucose concentration over time and a lower plot 404 showing lactate concentration over time. As can be seen from multi-plot graph 400, the first dual glucose / lactate sensor did not experience nocturnal glucose decline.

[0057] Referring to Figure 4B, the multi-plot graph 410 for the second dual glucose / lactate sensor includes an upper plot 412 showing glucose concentration over time and a lower plot 414 showing lactate concentration over time. As indicated by the dotted ellipse, the second dual glucose / lactate sensor experienced a glucose drop around 5 a.m. This drop can be characterized by a sharp decrease in glucose concentration at data point 418 and a sharp increase in lactate concentration at data point 416. Except for the nocturnal glucose drop event, the glucose and lactate concentration trend lines in multi-plot graphs 400 and 410 are relatively similar.

[0058] Based on the multiple plot graphs 400 and 410, both the first and second dual glucose / lactate sensors functioned properly, but it can be inferred that at the detection point of the second dual glucose / lactate sensor, the sample experienced a nocturnal glucose drop as a result of physiological changes (reflected in the multiple plot graph 410 in Figure 4B).

[0059] Figures 4C and 4D illustrate further characterization of glucose and lactate concentration levels during nocturnal glucose decline measured by a dual glucose / lactate sensor. Referring first to Figure 4C, the multi-plot graph 420 includes an upper plot 402 showing glucose concentration over the same time period as shown in Figure 4A, and a lower plot 404 showing lactate concentration over time. As shown below lactate concentration plot 404, graph 420 further includes additional plots 426 and 428, which depict the differential values ​​of glucose concentration and lactate concentration over time, respectively. Also, a pair of predetermined glucose and lactate differential thresholds are shown as dotted lines 430 and 432, respectively. According to one aspect of the embodiment, the predetermined thresholds may include a predetermined negative glucose differential threshold and a predetermined positive lactate differential threshold, and the predetermined thresholds are approximately or nearly zero. The absence of nocturnal glucose decline in the first dual glucose / lactate sensor is characterized by glucose and lactate differential values ​​that do not simultaneously cross their respective predetermined differential thresholds.

[0060] Referring next to Figure 4D, the multi-plot graph 440 includes an upper plot 412 showing glucose concentration over time and a lower plot 414 showing lactat concentration over time, the same as shown in Figure 4B. As shown below the lactat concentration plot 414, graph 440 further includes additional plots 446 and 448, plotting the differential values ​​of glucose concentration and lactat concentration over time, respectively. A pair of predetermined glucose and lactat differential thresholds are also shown as dotted lines 430 and 432, respectively. As shown by the dotted ellipse, nocturnal glucose decline can be characterized by a glucose differential value 450 that falls below a predetermined negative glucose differential threshold 432 around the time the lactat differential value 352 exceeds a predetermined positive lactat differential threshold 430 or simultaneously.

[0061] Example of a method for detecting suspicious glucose levels Embodiments of a method for detecting a suspected glucose drop based on glucose and lactate concentration measurements in a sample monitoring system are described below. Before that, those skilled in the art will understand that any one or more of the steps of the method described herein may be stored as a set of software instructions in the persistent memory of a sensor control device, reader, remote computer, or trusted computer system, as described with respect to Figure 1. When the stored instructions are executed, they can cause the processing circuit of the associated device or computing system to execute any one or more of the steps of the method described herein. Furthermore, those skilled in the art will understand that in many embodiments, any one or more of the steps of the method described herein may be executed using real-time or near real-time sensor data. In other embodiments, any one or more of the steps of the method may be executed retrospectively with respect to stored sensor data (including sensor data from previous sensors attached by the same user). In some embodiments, the steps of the method described herein may be executed periodically according to a predetermined schedule and / or in groups of retrospective processing.

[0062] Those skilled in the art will understand that the set of instructions may be stored in the persistent memory of a single device (e.g., a sensor control device or a reader) or distributed across multiple individual devices that may be geographically scattered (e.g., a cloud platform). For example, in some embodiments, the collection of data indicating sample levels (e.g., glucose, lactate) may be performed by the sensor control device, while the calculation of sample metrics (e.g., glucose derivative, lactate derivative) and comparison of the sample metrics with predetermined thresholds may be performed by a reader, a remote computer system, or a trusted computer system. In some embodiments, the collection of sample data and comparison with predetermined thresholds may be performed solely by the sensor control device. Similarly, those skilled in the art will recognize that representations of computing devices, such as those shown in Figure 1 of the embodiments described herein, are intended to include both physical and virtual devices (or virtual machines).

[0063] Figure 5 is a flowchart of an embodiment of method 500 for detecting a suspected glucose drop. In step 510, a sample sensor, as described with respect to Figures 1 and 3, collects first data indicating a glucose level. In step 520, a lactate sensing element collects second data indicating a lactate level. According to some embodiments, steps 510 and 520 may be performed by a sensor control device comprising a sample sensor. The sample sensor is configured such that a portion is inserted into the user's body at an insertion site, and the portion includes a first sensing element configured to detect a glucose level in a body fluid and a second sensing element configured to detect a lactate level in a body fluid at the same insertion site. In other embodiments, steps 510 and 520 may be performed by a sensor control device comprising a first sample sensor and a second sample sensor. The first sample sensor is configured to detect a glucose level in a body fluid, and the second sample sensor is configured to detect a lactate level in a body fluid, and the first and second sample sensors are configured to detect sample levels at the same insertion site.

[0064] Referring further to Figure 5, in step 530, the first sample metric is calculated based on the first data, and the second sample metric is calculated based on the second data. According to many embodiments, the first sample metric is the glucose derivative, and the second sample metric is the lactat derivative. In step 540, the first sample metric is compared to the first threshold, and the second sample metric is compared to the second threshold. According to many embodiments, the first threshold may be a predetermined glucose derivative threshold, and the second threshold may be a predetermined lactat derivative threshold. Also, according to some embodiments, the first threshold may be a negative threshold, and the second threshold may be a positive threshold.

[0065] In step 550, it is determined, based on the comparison in step 540, 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 lactat derivative threshold. In such cases, when the first sample metric (e.g., glucose derivative) is less than or equal to the first threshold, the first threshold (e.g., a predetermined glucose derivative threshold) is reached and / or exceeded, and when the second sample metric (e.g., lactat derivative) is greater than or equal to the second threshold, the second threshold (e.g., a predetermined lactat derivative threshold) is reached and / or exceeded.

[0066] In another embodiment, the determination of whether both thresholds have been reached or exceeded may further include an evaluation of whether both thresholds were reached or exceeded simultaneously or nearly simultaneously. In some embodiments, for example, the first and second sample metrics may be obtained from sample-level data collected over the same period using a slide window. The slide window may be defined by a predetermined number of data points (e.g., the five most recent glucose derivatives, the five most recent lactat derivatives) or a predetermined period (e.g., a 5, 10, or 15-minute window). In other embodiments, the determination of whether both thresholds have been reached or exceeded may include a comparison of the average glucose derivative over a predetermined first period with the average lactat derivative over a predetermined second period. Those skilled in the art will recognize that other methods are available for evaluating whether two sample-level metrics have reached or exceeded their corresponding thresholds, and these are entirely within the scope of the present disclosure.

[0067] Similarly, those skilled in the art will understand that variations of the first and second thresholds can be utilized. In some embodiments, for example, the first and second sample metrics may be the absolute value of the glucose derivative and the absolute value of the lactat derivative, respectively. Thus, the first and second thresholds may be predetermined absolute values ​​of the glucose derivative threshold and the lactat derivative threshold.

[0068] Referring again to Figure 5, if neither threshold is reached and / or exceeded, method 500 returns to step 510. However, if both thresholds are reached and / or exceeded, a suspicious glucose drop indicator is generated in step 560. In some embodiments, the suspicious glucose drop indicator may consist of a visual output to a display on a reader, remote computer, or trusted computer system as described with respect to Figure 1. For example, in some embodiments, the generation of a suspicious glucose drop indicator may result in a notification or message being displayed on the sensor results screen of a software application running on the user's portable device. Similarly, in some embodiments, the suspicious glucose drop indicator may consist of one or more visual, audible, or vibratory warnings or alarms output to a display on a reader, remote computer, or trusted computer system.

[0069] Next, in step 570, a corrective action may be optionally performed in response to or instead of the indication of a suspicious glucose drop. In some embodiments, the corrective action may be, for example, suppressing a low glucose alarm. In other embodiments, the corrective action may be preventing the issuance of a command that alters or causes the delivery of a drug (e.g., insulin) by an automated drug delivery system (e.g., an insulin pump).

[0070] Embodiments of a system, apparatus, and method for lactate-based correction of glucose levels Characterization example of lactate concentration during slow sensor decay Slow sensor decay (LSA, also known as "sagging") is a phenomenon in which partially implanted (e.g., subcutaneous, transdermal) or fully implanted glucose sensors may experience a decrease in sensitivity later in the sensor's predetermined service life. LSA occurs in a relatively small number of sensors and typically begins around day 10-12 in glucose sensors with a 14-day service life.

[0071] Studies using dual glucose / lactate sensors (e.g., a glucose sensing element and a lactate sensing element of one sample sensor) have suggested a relationship between LSA and lactate concentration levels measured at the sensor insertion site. In particular, data obtained using dual glucose / lactate sensors show a correlation between a decrease in LSA or glucose sensitivity and an increase in baseline lactate values during the same period.

[0072] According to one aspect of an embodiment, the above relationship between LSA and the increase in baseline lactate values can be used to correct one or more seemingly decreased glucose measurement values. In particular, the following equation can be used: i ラクタート(基線) , ラクタート(基線) = i グルコース(未処理) + K C (i ラクタート - i ラクタート(基線) ) i グルコース(未処理) is the glucose current before correction, i ラクタート is the lactate current during correction, i ラクタート(基線) is the baseline lactate current, K C is the sensor batch constant.

[0073] According to some embodiments, i ラクタート is a smoothed value that removes transient fluctuations in lactate values, e.g., a 1-hour smoothed lactate current. Other smoothed lactate values (e.g., over 30 minutes, 2 hours, 5 hours) can be utilized, and those skilled in the art will recognize that they are fully within the scope of the present disclosure.

[0074] According to another aspect of an embodiment, i ラクタート(基線) can be the baseline lactate current over a predetermined period during which the glucose sensor is unlikely to be affected by LSA. In some embodiments, for example, i ラクタート(基線)This could be the average lactate current over the 5th to 8th day of the sensor's service life. Those skilled in the art will understand that other predetermined periods (e.g., 4th to 7th day, 6th to 8th day, etc.) can be used to calculate the baseline lactate current, and that these are entirely within the scope of this disclosure.

[0075] According to another embodiment, K C This is an empirically determined constant assigned to a specific group (batch) of sensors. In Figures 6A to 6C below, the sensor batch constant K is shown. C It is 3. Those skilled in the art will understand that other sensor batch constants can be used and are within the scope of this disclosure.

[0076] Figure 6A is a multi-plot graph 600 depicting various sample measurements taken by a dual glucose / lactate sensor over 20 days. The multi-plot graph 600 includes a plot 602 of uncorrected glucose current at the top. At the bottom of the multi-plot graph 600, a plot 604 of unfiltered lactate current and a plot 606 of 1-hour filtered lactate values ​​are also shown. In one aspect of the multi-plot graph 600, a plot 608 of glucose levels corrected based on the lactate-based glucose correction formula described above is plotted near the uncorrected glucose current plot 602. Also, in graph 600, the gradual decrease in the uncorrected glucose current plot 602 (i.e., indicating a gradual decrease in the sensitivity of the glucose sensor) and the gradual increase in the unfiltered lactate current plot 604 and the 1-hour filtered lactate value plot 606 over the same period indicate that the LSA began around day 12.

[0077] Figure 6B is another multiplot graph 610 depicting various sample measurements taken by a dual glucose / lactate sensor over 20 days. Similar to graph 600, multiplot graph 610 includes a plot 612 of uncorrected glucose current at the top. At the bottom of multiplot graph 610, a plot 614 of unfiltered lactate current and a plot 616 of 1-hour filtered lactate values ​​are also shown. In one aspect of graph 610, a plot 618 of glucose levels corrected based on the lactate-based glucose correction formula described above is plotted near the uncorrected glucose current plot 612. In graph 610, a relatively more pronounced LSA than in multiplot graph 600 can be seen that the uncorrected glucose current plot 612 began to decrease sharply (i.e., indicating a significant decrease in the sensitivity of the glucose sensor), while the unfiltered lactate current plot 614 and the 1-hour filtered lactate value plot 616 increased sharply during the same period, starting around day 15. According to one aspect of the multi-plot graph 610, a more pronounced LSA is evidenced by the greater increase in lactate plots 614, 616 in the later stages of endurance (e.g., days 18-20) and the greater difference between the uncorrected glucose current plot 612 and the corrected glucose value plot 618.

[0078] Figure 6C is another multiplot graph 620 depicting various sample measurements taken by a dual glucose / lactate sensor over 20 days. The multiplot graph 620 includes a plot 622 of uncorrected glucose current at the top. At the bottom of the multiplot graph 620, a plot 624 of unfiltered lactate current and a plot 626 of 1-hour filtered lactate values ​​are also shown. According to one aspect of graph 620, a plot 628 of glucose levels corrected based on the lactate-based glucose correction formula described above is plotted near the uncorrected glucose current plot 622. As indicated by the relatively stable pair of lactate measurements 624, 626 from day 15 to day 20, LSA is not present in the sensor depicted in the multiplot graph 620. Thus, the corrected and uncorrected glucose plots 622, 628 are nearly identical over the same period.

[0079] Example of a method for LSA correction and sensor failure detection Embodiments of a method for correcting artificially reduced glucose levels using lactate values ​​and a method for detecting sensor malfunctions are described.

[0080] As with previous embodiments, those skilled in the art will understand that any one or more steps of the methods described herein may be stored as a set of software instructions in the persistent memory of the sensor control device, reader, remote computer, or trusted computer system described with reference to Figure 1. When the stored instructions are executed, they can cause the processing circuits of the associated device or computing system to execute any one or more steps of the methods described herein. Furthermore, those skilled in the art will understand that in many embodiments, any one or more steps of the methods described herein may be executed using real-time or near-real-time sensor data. In other embodiments, any one or more steps of the methods may be executed retrospectively with respect to stored sensor data (including sensor data from previous sensors attached by the same user). In some embodiments, the steps of the methods described herein may be executed periodically according to a predetermined schedule and / or in batches of retrospective processing.

[0081] Those skilled in the art will understand that the set of instructions may be stored in the persistent memory of a single device (e.g., a sensor control device or a reader) or distributed across multiple separate devices that may be geographically scattered (e.g., a cloud). For example, in some embodiments, the collection of data indicating sample levels (e.g., glucose, lactate) may be performed by the sensor control device, while the correction of sample values, the calculation of sample metrics (e.g., baseline lactate values), and the comparison of sample metrics with predetermined thresholds may be performed by a reader, a remote computer system, or a trusted computer system. In some embodiments, the collection of sample data and the correction of sample values ​​may be performed solely by the sensor control device. Similarly, those skilled in the art will recognize that the representation of computing devices, such as that shown in Figure 1 of the embodiments described herein, is intended to include both physical and virtual devices (or virtual machines).

[0082] Figure 7A is a flowchart illustrating an embodiment of method 700 for correcting a falsely reduced glucose level measurement, such as that resulting from an LSA, using a lactate level measurement. In step 705, a sample sensor, as described with respect to Figures 1 and 3, collects first data indicating the glucose level. In step 710, a lactate sensing element collects second data indicating the lactate level. According to some embodiments, steps 705 and 710 may be performed by a sensor control device comprising a sample sensor. The sample sensor is configured such that a portion is inserted into the user's body at an insertion site, and the portion includes a first sensing element configured to detect the glucose level in the body fluid and a second sensing element configured to detect the lactate level in the body fluid at the same insertion site. In other embodiments, steps 705 and 710 may be performed by a sensor control device comprising a first sample sensor and a second sample sensor. The first sample sensor is configured to detect the glucose level in the body fluid, and the second sample sensor is configured to detect the lactate level in the body fluid, and the first and second sample sensors are configured to detect sample levels at the same insertion site.

[0083] Referring further to Figure 7A, in step 715, the corrected glucose level is calculated based on a function of the first and second data. In 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. In many embodiments, the function may also include a sensor batch constant, which is associated with a group (batch) of sensors including the sample sensor.

[0084] In another embodiment, the measured glucose level may represent the glucose level detected in a first period, and the measured lactate level may represent the lactate level detected in a second period. In many embodiments, the lactate level detected in the second period may consist of lactate values ​​smoothed over one hour. Other periods (e.g., 30 minutes, 2 hours, 5 hours, etc.) may be used and will be recognized as being entirely within the scope of the disclosure by those skilled in the art. In some embodiments, the first period may partially overlap with or fall within the second period.

[0085] According to another embodiment, the baseline lactate value may be the average lactate value over a period of one day or more (e.g., two days, three days, etc.). In some embodiments, for example, one day or more may be within the median period of the sensor life of the sample sensor.

[0086] Referring further to Figure 7A, in step 720, the corrected glucose level may be visually output to a display. In many embodiments, for example, the corrected glucose level may be output to a display of a reading device, remote computer device, and / or reliable computer system as described with respect to Figure 1.

[0087] Figure 7B is a flowchart illustrating an embodiment of method 750 for detecting a suspected sensor malfunction using lactate level measurements. In step 755, a sample sensor, as described with respect to Figures 1 and 3, collects first data indicating a glucose level. In step 760, a lactate sensing element collects second data indicating a lactate level. According to some embodiments, steps 755 and 760 may be performed by a sensor control device comprising a sample sensor. The sample sensor is configured such that a portion is inserted into the user's body at an insertion site, and the portion includes a first sensing element configured to detect glucose levels in body fluids and a second sensing element configured to detect lactate levels in body fluids at the same insertion site. In other embodiments, steps 755 and 760 may be performed by a sensor control device comprising a first sample sensor and a second sample sensor. The first sample sensor is configured to detect glucose levels in body fluids, and the second sample sensor is configured to detect lactate levels in body fluids, and the first and second sample sensors are configured to detect sample levels at the same insertion site.

[0088] Referring further to Figure 7B, in step 765, the baseline lactate value is calculated using the second data. In many embodiments, the baseline lactate value may be the average lactate value over a period of one day or more. In step 770, the baseline lactate value is compared to a predetermined baseline lactate value threshold. Subsequently, in step 775, it is determined whether the baseline lactate value has reached or exceeded the predetermined baseline lactate value threshold. If not, method 750 returns to step 755. If the predetermined baseline lactate value threshold has been reached or exceeded, in step 780, a suspicious sensor failure indication is generated. According to many embodiments, the suspicious sensor failure indication may further include one or more commands to terminate the sample sensor, a command to mask or discard the measured glucose level, and / or a command to display a notification, warning, or alarm on a reader, remote computer system, or trusted computer system.

[0089] Embodiments of a system, apparatus, and method for improving glucose sensor performance using secondary physiological measurements Several factors, including calibration and time differences between sensors (e.g., ESA, LSA, and nocturnal drops), can negatively impact the low-end performance of glucose sensors. Furthermore, uncertainties associated with these factors can limit the amount of delay compensation that can be applied to glucose level readings. Therefore, it would be 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 delay compensation, improve the sensitivity and specificity of sensor fault detection, and enhance the overall low-end accuracy of the glucose sensor.

[0090] The increasing adoption of wearable devices that can quantify a person's health status provides an opportunity to leverage information from non-glucose sensors (also known as secondary sensors) to improve the low-end performance of glucose sensors. Examples of non-glucose or secondary sensors include, but are not limited to, heart rate monitors, implantable cardiac monitors, implantable electrocardiogram (ECG) devices, implantable electroencephalogram (EEG) devices, ketone sensors, continuous ketone monitors, and ketone fragment readers. These non-glucose or secondary sensors may provide secondary physiological measurements, which may be analyzed together with glucose level readings from glucose sensors to confirm or deny high / low glucose states detected by glucose sensors.

[0091] For example, it has been shown that ketone levels will gradually increase when blood glucose levels remain in the hyperglycemic range for an extended period. Therefore, ketone level measurements from fragment-based or continuous ketone monitors can be used, along with glucose-based fault detection modules, to determine whether persistent low glucose sensor readings are physiologically plausible.

[0092] As another example, it has been shown that when blood glucose levels remain within the hypoglycemic range for an extended period, hypoglycemia can have abnormal physiological effects on cardiac work, QT interval, and other factors. Many of these factors (e.g., heart rate, ECG, and EEG) can be measured by wearable devices and other similar medical devices. For example, studies have shown that arrhythmias can occur during periods of hypoglycemia. Similarly, other studies have demonstrated the use of EEG to predict hypoglycemia. Therefore, data from secondary sensors (e.g., heart rate monitors, ECG, EEG, etc.) can be used to determine whether a patient is experiencing true hypoglycemia or a false hypoglucose state.

[0093] In addition to the above benefits, fusing secondary physiological measurements from non-glucose or secondary sensors with data from glucose sensors can improve low-end glucose sensor performance in at least two other ways. First, the possibility of false low glucose readings (e.g., due to ESA, LSA, or nocturnal drop) is reduced, allowing for more aggressive delay correction at the lower end of the glucose range. Second, using non-glucose or secondary physiological measurements together with glucose sensor data allows for better detection of sensor failures, such as temporarily masking glucose readings, adjusting glucose readings, or terminating the glucose sensor earlier.

[0094] Before describing in detail the embodiments of the method for fusing glucose sensor data and secondary sensor data, it is desirable to first describe examples of systems and apparatus that may be used to carry out the method described herein, as well as examples of their operation. Figure 8 is a logical diagram illustrating one aspect of the embodiment described herein. According to one aspect of the embodiment shown in Figure 8, a glucose sensor 104 collects data indicating glucose levels and provides it to a sensor data fusing and analysis module 825. Similarly, a secondary sensing element 804 collects data indicating secondary physiological measurements and provides it to the sensor data fusing and analysis module 825. The secondary sensing element 804 may include one or more of a heart rate monitor 806, ECG 808, EEG 812, ketone monitor 814, or ketone fragment reader 816. Other secondary sensing elements 804 (e.g., implantable or insertable cardiac monitors, lactate sensors, etc.) may be used and will be understood by those skilled in the art to be entirely within the scope of this disclosure. The sensor data fusion and analysis module 825 analyzes the first and second data to determine whether a true high / low glucose state exists (e.g., hyperglycemia, hypoglycemia), whether delayed correction should be applied to the glucose level readings, whether data smoothing should be applied to the glucose level readings, the degree of delayed correction and / or data smoothing to be applied to the glucose level readings, whether certain glucose level readings should be masked, whether the glucose sensor should be terminated, or whether a notification, alarm, or warning related to any of the above actions should be generated. Additional details regarding the method steps for making these decisions are described below with reference to Figure 10.

[0095] Figures 9A–9E illustrate schematic system diagrams showing various system and apparatus examples that may be used to carry out embodiments of the methods described herein. Figure 9A is a schematic system diagram of a single sensor control device 102 comprising a glucose sensor 104 and a secondary detection element 804 as described in relation to Figure 8. According to some embodiments, the glucose sensor 104 may be a dual sample sensor (shown by a dashed rectangle) with or integrated with the secondary detection element 804. The secondary detection element 804 is configured to collect data indicating secondary physiological measurements, such as ketone levels or lactate levels.

[0096] In other embodiments, the glucose sensor 104 and the secondary sensing element 804 may be two separate sensors located near or around the same insertion site, configured to measure glucose levels and secondary physiological measurements (e.g., ketone levels), respectively. In one aspect of the embodiment, the sensor control device 102 may include a processing circuit coupled with persistent memory. The persistent memory stores a set of software and / or firmware instructions (e.g., a sensor data fusion and analysis module 825) that, when executed by the processing circuit of the sensor control device 102, cause the processing circuit to perform the method steps described below.

[0097] Figure 9B shows a schematic system diagram of 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 the embodiment shown in Figure 9B, data can be communicated between the two sensor control devices, and the sensor data fusion and analysis module 825 can reside in the persistent memory of either sensor control device 102 or 902. Figure 9B shows bidirectional arrows indicating bidirectional communication between sensor control devices 102 and 902, but those skilled in the art will recognize that some embodiments may use only unidirectional data transmission (for example, sensor control device 902 transmits data to sensor control device 102, and sensor data fusion and analysis module 825 resides in the persistent memory of sensor control device 102).

[0098] Figure 9C shows a schematic system diagram of 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 the embodiment shown in Figure 9C, data is communicated from each sensor control device 102, 902 to a reader 120. The reader 120 may have a portable software application (app) 903 configured to receive both types of data and execute a sensor data fusion and analysis module 825. Figure 9D similarly shows a schematic system diagram of a first sensor control device 102 having a glucose sensor 104 and a second sensor control device 902 having a secondary sensing element 803. Each sensor control device 102, 902 is configured to communicate with the reader 120. According to the embodiment shown in Figure 9D, the first sensor control device 102 is configured to transmit data indicating glucose levels to an app 904 residing in the persistent memory of the reader 120, and the second sensor control device 902 is configured to transmit data indicating secondary physiological measurements to an app 905 also residing in the persistent memory of the reader 120. According to another embodiment of the design shown in Figure 9D, apps 904 and 905 are configured to communicate with each other in one direction or bidirectionally, and the sensor data fusion and analysis module 825 may be integrated with either or both apps 904 and 905.

[0099] Figure 9E shows a schematic system diagram illustrating a first sensor control unit 102 having a glucose sensor 104 and a second sensor control unit 902 having a secondary sensing element 804. Each sensor control unit 102, 902 is configured to communicate with a reader 120. According to the embodiment shown in Figure 9E, the first sensor control unit 102 is configured to transmit data indicating glucose levels to an application 904 residing in the persistent memory of the reader 120, and the second sensor control unit 902 is configured to transmit data indicating secondary physiological measurements to an application 905 also residing in the persistent memory of the reader 120. According to another aspect of the embodiment depicted in Figure 9E, each of the applications 904 and 905 is configured to communicate unidirectionally or bidirectionally with one or both of the local computer systems 170 or trusted computer systems 180 via a network 190. In some embodiments, the network 190 may consist of a local area network, a wide area network, a metropolitan area network, a virtual private network, a cellular network, or the Internet. In some embodiments, the trusted computer system 180 may consist of a cloud-based platform, a server cluster, a server farm, and the like. According to another aspect of the embodiment depicted in Figure 9E, the sensor data fusion and analysis module 825 may reside partially or integrally in the persistent memory of one or more of the apps 904, 905, local computer system 170, and trusted computer system 180. According to one aspect of some embodiments, information processed by 170 or 180 based on data from the secondary sensing element 804 or fused data may be communicated to app 904 to adjust the glucose sensor 104.

[0100] Example of a method for improving glucose sensor performance using secondary sensor data Embodiments of a method for improving glucose sensor performance using secondary physiological measurements from a secondary sensing element are described. As with previous embodiments, those skilled in the art will understand that any one or more of the steps of the method described herein may be stored as a set of software instructions in the persistent memory of the sensor control device, reader, remote computer, or trusted computer system described with reference to Figure 1. When the stored instructions are executed, they can cause the processing circuit of the associated device or computing system to execute any one or more of the steps of the method described herein. Furthermore, those skilled in the art will understand that in many embodiments, any one or more of the steps of the method described herein may be executed using real-time or near real-time sensor data. In other embodiments, any one or more of the steps of the method may be executed retrospectively with respect to stored sensor data (including sensor data from previous sensors attached by the same user). In some embodiments, the steps of the method described herein may be executed periodically according to a predetermined schedule and / or in batches of retrospective processing.

[0101] Those skilled in the art will understand that the set of instructions may be stored in the persistent memory of a single device (e.g., a sensor control device or a reader) or distributed across multiple separate devices that may be geographically scattered (e.g., a cloud). For example, in some embodiments, the collection of data indicating sample levels (e.g., glucose, lactate), the determination of suspicious false glucose states and correlated physiological states, the application of delay correction and / or data smoothing, and the termination of the glucose sensor may all be performed solely by the sensor control device. In some embodiments, the determination of suspicious false glucose states and correlated physiological states, the application of delay correction and / or data smoothing may be performed by the reader or by a trusted computer system. Similarly, those skilled in the art will recognize that representations of computing devices, such as those shown in Figure 1 of the embodiments described herein, are intended to include both physical and virtual devices (or virtual machines).

[0102] Generally, when running a first-order differential equation model of blood glucose levels on interstitial glucose lag with a certain degree of lag correction, assuming a constant lag time constant, the degree of lag correction is a trade-off between the positive effect on the overall performance of the lag correction and the negative effect of excessive lag correction in uncertain regions, such as those involving questionable false low glucose states. According to one embodiment, secondary physiological measurements can be used to distinguish the certainty of a specific glucose state, such as low glucose concentration, from false low glucose states, and the entire range of glucose sensor fluctuations can be assumed to be physiological. As a result, it is possible to perform more aggressive lag correction using secondary physiological measurements, which is consequently more advantageous than a degree determined by considering only the trade-off.

[0103] As an unrestricted example, trade-off analysis has concluded that a delay compensation equivalent to compensating for a 9-minute delay is the optimal measure following the above trade-off. However, a more aggressive delay compensation equivalent to compensating for a 20-minute delay may yield improved performance without increasing the number of false delay compensations, as it can improve the certainty of specific glucose states. For those skilled in the art, determining a more aggressive delay compensation may require a model more complex than a first-order differential equation with two or more parameters, and does not necessarily suggest increasing the values ​​of all parameters in the delay compensation model used.

[0104] Figure 10 is a flowchart illustrating an embodiment of Method 1000 for improving the accuracy of glucose sensor data by using secondary physiological measurements. In step 1002, a sensor control device comprising a sample sensor, a processing circuit, and a memory collects first data indicating glucose levels. In step 1004, a secondary detection element collects second data indicating secondary physiological measurements. As described with respect to Figures 9A-9E, according to one aspect of the embodiment, the secondary detection element may consist of one or more of a heart rate monitor, an implantable 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 embodiments, the secondary detection element may consist of one or more of a ketone sensor, a continuous ketone monitor, or a ketone fragment sensor (e.g., as part of a reader), and the secondary physiological measurement may be a ketone level.

[0105] In step 1006, determine whether there are any suspicious false glucose states based on the initial data.

[0106] According to one embodiment, a suspect false glucose state may be a suspect false low glucose state, such as a suspect false hypoglycemic state. In some embodiments, the absence or presence of a suspect false low glucose state may be confirmed by one or more tests using the first data. The tests include, but are not limited to, the following determinations.

[0107] 1) One or more glucose sensor data quality tests suggest a suspicious false low glucose state, 2) The glucose level is below a predetermined first low glucose threshold, 3) Whether the area under the curve (AUC) calculation (which may be based on a first recent predetermined time window with a value below a predetermined second low glucose threshold) exceeds a predetermined low glucose AUC threshold, 4) The glucose percentile metric (e.g., from a second recent predetermined time window with a value below a predetermined third low glucose threshold) exceeds a predetermined low glucose percentile threshold, or 5) Determine whether the average glucose level within a recent predetermined time window (e.g., a third recent predetermined time window) exceeds a predetermined third low glucose threshold.

[0108] According to another embodiment, a suspect false glucose state may be a suspect false hyperglucose state, such as a suspect false hyperglycemic state. In some embodiments, the absence or presence of a suspect false hyperglucose state may be confirmed by one or more tests using the first data. The tests include, but are not limited to, the following determinations.

[0109] 1) If one or more glucose sensor data quality tests suggest a suspicious false high glucose state, 2) Whether the glucose level exceeds a predetermined first high glucose threshold, 3) Whether the AUC calculation (which may be based on a fourth recent predetermined time window with a value exceeding a predetermined second high glucose threshold) exceeds a predetermined high glucose AUC threshold, 4) The glucose percentile metric (e.g., from a fifth recent predetermined time window with a value exceeding a predetermined third high glucose threshold) exceeds a predetermined high glucose percentile threshold, or 5) Determine whether the average glucose level within a recent predetermined time window (e.g., the sixth most recent predetermined time window) exceeds a predetermined third high glucose threshold.

[0110] Returning to Figure 10, in step 1008, the second data (e.g., secondary physiological measurements) is analyzed to determine whether a correlated physiological state exists. According to one embodiment, the correlated physiological state may be one of the following: the presumed absence of high glucose, the presumed presence of high glucose, the presumed absence of low glucose, or the presumed presence of low glucose. According to some embodiments, the presence of a correlated physiological state can be confirmed, for example, by comparing detected ketone levels (e.g., using a continuous ketone monitor or ketone fragment reader) with a predetermined ketone threshold. High ketone levels exceeding a predetermined ketone threshold may indicate the presumed absence of low glucose (or conversely, the presumed presence of high glucose). Similarly, according to some embodiments, the presence of a correlated physiological state can be confirmed by comparing heart rate measurements (e.g., using a heart rate monitor) with a predetermined heart rate threshold. High or increased heart rates exceeding a predetermined heart rate threshold may indicate the presumed absence of high glucose (or conversely, the presumed presence of low glucose).

[0111] According to one embodiment, if it is determined in step 1006 that there is no suspicious false glucose state (e.g., a suspicious false hypoglycemia) and in step 1008 that a correlative physiological state exists (e.g., a ketone level above a predetermined ketone threshold indicating the presumed absence of low glucose), then a first corrective action may be performed in step 1010. In some embodiments, the first corrective action may be a bold delayed correction. For example, according to some embodiments, a seventh recent predetermined time window may not present contradictory information, for example, if (1) the absence of a suspicious low glucose state is determined, and (2) the presence of a correlative physiological measurement is determined, and the correlative physiological measurement may be a ketone level above a predetermined ketone threshold indicating the presumed absence of low glucose.

[0112] Those skilled in the art will also understand that a questionable false glucose state may be a questionable false hyperglucose state (e.g., questionable false hyperglycemia), and a correlated physiological state may be the presumed absence of hyperglucose (e.g., a heart rate exceeding a given heart rate threshold).

[0113] Those skilled in the art will also understand that the step of determining the absence of a suspicious false glucose state (step 1006) may include determining the absence of both a suspicious false low glucose state and a suspicious false high glucose state, and further determining, based on second data, whether there are any correlative physiological states for both of the non-existent suspicious false glucose states (step 1008).

[0114] According to another aspect of several embodiments, if it is determined that there is no suspicious false glucose state and no corresponding physiological state, a second corrective measure (not shown) may be performed, which may consist of one or more of mild delay correction or increased glucose sensor signal smoothing.

[0115] In some embodiments, the step of determining whether a correlated physiological state exists (step 1008) may also include determining the degree of correlation between the correlated physiological state and a suspected false glucose state. According to these embodiments, if neither a suspected false glucose state nor a correlated physiological state is present, a second correction measure (not shown) may be performed, which consists of one or both of variable delay correction and / or variable glucose sensor signal smoothing. According to another aspect of these embodiments, the variable delay correction may be a function of the degree of correlation between the correlated physiological state and / or the suspected false glucose state. Conversely, variable glucose sensor signal smoothing may be an inverse function of the degree of correlation between the correlated physiological state and / or the suspected false glucose state. Those skilled in the art will also understand that other types of variable correction measures (e.g., filtering, masking, etc.) may be performed, and the magnitude of the correction measure may be a function of or an inverse function of the degree of correlation between the correlated physiological state and / or the suspected false glucose state.

[0116] Furthermore, according to some embodiments, if it is determined that there is no suspicious false glucose state (e.g., no suspicious false hypoglycemia) and there is a correlative physiological state (e.g., high ketone levels exceeding a predetermined ketone level threshold) that suggests the absence of an estimated low glucose state, a third corrective measure consisting of early termination of the glucose sensor may be performed.

[0117] According to other aspects of several embodiments, determining a discrepancy between first data indicating glucose levels and second data indicating secondary physiological measurements may be the basis for taking one or more corrective actions. Figure 11 is a flowchart illustrating an embodiment of method 1100 for terminating the sensor or masking sensor data from a glucose sensor based on a detected discrepancy between glucose data and secondary physiological measurements. In step 1102, a sensor control device including a sample sensor, processing circuitry, and memory collects first data indicating glucose levels. In step 1104, a secondary sensing element collects second data indicating secondary physiological measurements. As described above, the secondary sensing element may consist of one or more of a heart rate monitor, an implantable 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 several embodiments, the secondary sensing element may consist of one or more of a ketone sensor, a continuous ketone monitor, or a ketone fragment sensor (e.g., as part of a reader), and the secondary physiological measurement may be a ketone level.

[0118] In step 1106, it is determined whether there is a contradiction or inconsistency between the first data and the second data. According to many embodiments, a contradiction may be defined as an inconsistency between a glucose measurement suggesting a high glucose state, a low glucose state, a questionable false high glucose state, or a questionable false low glucose state, and a correlated physiological state, such as an estimated high glucose state (e.g., high ketone levels) or an estimated low glucose state (e.g., increased or high heart rate).

[0119] If such an inconsistency is detected in step 1108, the glucose sensor may be terminated or the sensor data may be discarded and / or temporarily masked. The termination of the glucose sensor or the temporary masking of sensor data from the glucose sensor may also include displaying a notification, warning, or alarm on the reader, remote computer system, or trusted computer system indicating that the sensor has been terminated or masking has occurred.

[0120] For each and all embodiments of the methods disclosed herein, systems and apparatus capable of performing each of these embodiments are included within the scope of this disclosure. For example, embodiments of sensor control devices are disclosed, which may include one or more sample sensors, sample monitoring circuits (e.g., analog circuits), memory (e.g., for storing instruction sets), a power supply, a communication circuit, a transmitter, a receiver, a clock, a counter, a time, a temperature sensor, and a processor (e.g., for executing instruction sets) capable of performing or enabling any and all method steps. These sensor control device embodiments may be used and may be available to perform steps performed by sensor control devices of any and all methods described herein. Similarly, embodiments of reader devices are disclosed, which may include one or more memory (e.g., for storing instruction sets), a power supply, a communication circuit, a transmitter, a receiver, a clock, a counter, a time, and a processor (e.g., for executing instruction sets) capable of performing or enabling any and all method steps. These reader device embodiments may be used and may be available to perform steps performed by reader devices of any and all methods described herein. Embodiments of computer devices and servers are disclosed, which may have one or more memories (e.g., for storing instruction sets), a power supply, a communication circuit, a transmitter, a receiver, a clock, a counter, a time, and a processor (e.g., for executing instruction sets) capable of or enabling the execution of any and all method steps. These reader device embodiments may be used to perform steps performed by any and all methods of the methods described herein.

[0121] The computer program instructions for performing the operations described in this subject may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, JavaScript, Smalltalk, C++, C#, Transact-SQL, XML, and PHP, and traditional procedural programming languages ​​such as the C programming language or similar languages. The program instructions may be executed entirely or partially on the user's computer as a standalone software package, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be to an external computer (for example, via the Internet using an Internet service provider).

[0122] Furthermore, all features, elements, components, functions, and steps described in any embodiment provided herein are intended to be freely combined and substituted with those of any other embodiment. If a feature, element, component, function, or step is described in relation to only one embodiment, it should be understood that, unless otherwise stated, that feature, element, component, function, or step can be used with all other embodiments described herein. Accordingly, this paragraph serves as a precedent and support for introducing claims that combine features, elements, components, functions, and steps of multiple different embodiments, or replace features, elements, components, functions, and steps of one embodiment with those of another embodiment, even if such combinations or substitutions are not explicitly stated in the specific examples herein. Given that a person skilled in the art would readily recognize that all such combinations and substitutions are permissible, it is clearly acknowledged that specifying all possible combinations and substitutions would be an undue burden. Embodiments are specified in independent claims 1, 21, 41, 56, 61, 76, 81, 102, 103, and 124. Preferred features may be specified in dependent claims and implemented in combination with each of the embodiments specified in independent claims. Apparatuses comprising means for carrying out each method are also provided.

[0123] To the extent that the embodiments disclosed herein include or operate with memory, storage devices, and / or computer-readable media, such memory, storage devices, and / or computer-readable media are persistent. Therefore, if memory, storage devices, and / or computer-readable media are included in one or more claims, such memory, storage devices, and / or computer-readable media are simply persistent.

[0124] As used herein and in the appended claims, unless the context clearly indicates otherwise, the English singular forms "a," "an," and "the" refer to a plurality of objects.

[0125] While the embodiments can take on various variations and alternative forms, specific examples of these are illustrated and described in detail herein. However, these embodiments are not limited to the specific forms disclosed; rather, they should be understood to include all variations, equivalents, and alternatives contained within the gist of this disclosure. Furthermore, any feature, function, step, or element of an embodiment may be described or added to the claims, and negative limitations may be described that define the scope of the claims by features, functions, steps, or elements that are not within that scope.

[0126] Preferred embodiments of the present invention are described below in separate sections.

[0127] Embodiment 1 A specimen monitoring system, A sensor control device comprising a sample sensor, a first processing circuit, and a first persistent memory, wherein the sample sensor is configured to be inserted into the user's body in at least a portion thereof, and is configured to collect first data indicating glucose level and second data indicating lactate level. A reading device comprising a second processing circuit and a second persistent memory. Equipped with, At least one of the first or second persistent memory stores a set of instructions, and when the set of instructions is executed, at least one of the first or second processing circuits The first sample metric is calculated based on the first data. The second sample metric is calculated based on the second data. The first sample metric is compared with a first threshold, and the second sample metric is compared with a second threshold. A sample monitoring system that generates an indication of suspected glucose depression in response to a determination that the first sample metric exceeds the first threshold and the second sample metric exceeds the second threshold.

[0128] Embodiment 2 The sample monitoring system according to Embodiment 1, wherein the first sample metric is the glucose derivative.

[0129] Embodiment 3 The sample monitoring system according to Embodiment 1, wherein the second sample metric is the lactate derivative.

[0130] Embodiment 4 The sample monitoring system according to Embodiment 1, wherein the first threshold is a glucose differential threshold.

[0131] Embodiment 5 The sample monitoring system according to Embodiment 1, wherein the second threshold is a lactart derivative threshold.

[0132] Embodiment 6 The sample monitoring system according to Embodiment 1, wherein when the first sample metric is less than or equal to the first threshold, the first sample metric exceeds the first threshold.

[0133] Embodiment 7 The sample monitoring system according to Embodiment 1, wherein when the second sample metric is greater than or equal to the second threshold, the second sample metric exceeds the second threshold.

[0134] Embodiment 8 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A sample monitoring system according to Embodiment 1, which generates an indication of suspected glucose decrease in response to a determination that the first and second thresholds are simultaneously exceeded.

[0135] Embodiment 9 The sample monitoring system according to Embodiment 1, wherein at least a portion of the sample sensor comprises a first detection element configured to detect glucose levels in body fluids and a second detection element configured to detect lactate levels in body fluids.

[0136] Embodiment 10 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A sample monitoring system according to Embodiment 1, which suppresses low glucose alarms.

[0137] Embodiment 11 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A specimen monitoring system according to Embodiment 1, which suppresses commands that alter or trigger drug delivery.

[0138] Embodiment 12 The sample monitoring system according to embodiment 11, wherein the drug is insulin.

[0139] Embodiment 13 The specimen monitoring system according to Embodiment 1, wherein the first data representing the glucose level and the second data representing the lactate level are associated with the insertion site on the user's body.

[0140] Embodiment 14 The sample monitoring system according to Embodiment 1, wherein the set of instructions is stored in the second persistent memory.

[0141] Embodiment 15 The sample monitoring system according to Embodiment 1, wherein the set of instructions is stored in the first persistent memory.

[0142] Embodiment 16 The specimen monitoring system according to Embodiment 1, further comprising a wireless communication circuit configured to transmit the first and second data to the reading device, wherein the sensor control device further comprises a wireless communication circuit.

[0143] Embodiment 17 The specimen monitoring system according to embodiment 16, wherein the wireless communication circuit is configured to transmit the first and second data in accordance with the Bluetooth protocol.

[0144] Embodiment 18 The sample monitoring system according to Embodiment 1, wherein the sample sensor is a first sample sensor, the sensor control device further comprises a second sample sensor, the first sample sensor is configured to detect glucose levels in body fluids, and the second sample sensor is configured to detect lactate levels in body fluids.

[0145] Embodiment 19 A specimen monitoring system according to Embodiment 1, further comprising a drug delivery device.

[0146] Embodiment 20 The sample monitoring system according to embodiment 19, wherein the drug delivery device comprises an insulin pump.

[0147] Embodiment 21 A computer execution method for detecting a suspicious decrease in glucose, A step in which a sensor control device collects first data indicating glucose level and second data indicating lactate level, wherein the sensor control device comprises a sample sensor, at least a portion of which is inserted into the user's body. A step of calculating a first sample metric based on the first data, A step of calculating a second sample metric based on the second data, The steps include comparing the first sample metric with a first threshold and comparing the second sample metric with a second threshold, The steps include generating an indication of suspected glucose depression in response to the determination that the first sample metric exceeds the first threshold and the second sample metric exceeds the second threshold, and A method that includes this.

[0148] Embodiment 22 The method according to Embodiment 21, wherein the first sample metric is the glucose derivative.

[0149] Embodiment 23 The method according to Embodiment 21, wherein the second sample metric is the lactate derivative.

[0150] Embodiment 24 The method according to Embodiment 21, wherein the first threshold is a glucose differential threshold.

[0151] Embodiment 25 The method according to Embodiment 21, wherein the second threshold is a lactart derivative threshold.

[0152] Embodiment 26 The method according to Embodiment 21, wherein when the first sample metric is greater than or equal to the first threshold, the first sample metric exceeds the first threshold.

[0153] Embodiment 27 The method according to Embodiment 21, wherein when the second sample metric is less than or equal to the second threshold, the second sample metric exceeds the second threshold.

[0154] Embodiment 28 The method according to Embodiment 21, further comprising the step of generating the indicator of suspected glucose decrease in response to the first sample metric exceeding the first threshold and the second sample metric simultaneously exceeding the second threshold.

[0155] Embodiment 29 The steps include: detecting the glucose level in body fluid using at least some of the first detection elements of the sample sensor; The steps include: the detection of lactate levels in the body fluid by at least a portion of the second detection elements of the sample sensor; The method according to embodiment 21, further comprising the above.

[0156] Embodiment 30 The method according to embodiment 21, further comprising the step of suppressing a low glucose alarm in response to the indication of the suspected glucose decrease.

[0157] Embodiment 31 The method according to Embodiment 21, further comprising the step of suppressing commands that would alter or cause drug delivery by a drug delivery device in response to the indication of the suspected glucose drop.

[0158] Embodiment 32 The method according to embodiment 31, wherein the drug is insulin.

[0159] Embodiment 33 The method according to embodiment 31, wherein the drug delivery device comprises an insulin pump.

[0160] Embodiment 34 The method according to embodiment 31, further comprising the step of the drug delivery device receiving the indication of the suspected glucose drop.

[0161] Embodiment 35 The method according to Embodiment 21, wherein the first data representing the glucose level and the second data representing the lactate level are associated with the insertion site on the user's body.

[0162] Embodiment 36 The method according to Embodiment 21, wherein the step of generating the suspicious glucose drop indicator in response to the determination that the first sample metric exceeds the first threshold and the second sample metric exceeds the second threshold is performed by a reader that communicates wirelessly with the sensor control device.

[0163] Embodiment 37 The method according to Embodiment 21, wherein the step of generating the suspicious glucose drop indicator in response to the determination that the first sample metric exceeds the first threshold and the second sample metric exceeds the second threshold is performed by the sensor control device.

[0164] Embodiment 38 The method according to embodiment 21, further comprising the step of the wireless communication circuit of the sensor control device transmitting the first and second data to the reading device.

[0165] Embodiment 39 The method according to embodiment 38, wherein the wireless communication circuit is configured to transmit the first and second data in accordance with the Bluetooth protocol.

[0166] Embodiment 40 The aforementioned sample sensor is the first sample sensor of the sensor control device, The steps include: the first sample sensor of the sensor control device detects the glucose level in the body fluid; The second sample sensor of the sensor control device detects the lactate level in the body fluid. The method according to embodiment 21, further comprising the above.

[0167] Embodiment 41 A specimen monitoring system, A sensor control device comprising a sample sensor, a first processing circuit, and a first persistent memory, wherein the sample sensor is configured to be inserted into the user's body in at least a portion thereof, and is configured to collect first data indicating glucose level and second data indicating lactate level. A reading device comprising a second processing circuit and a second persistent memory. Equipped with, At least one of the first or second persistent memory stores a set of instructions, and when the set of instructions is executed, at least one of the first or second processing circuits A sample monitoring system that calculates a corrected glucose level based on a function of the first and second data.

[0168] Embodiment 42 The sample monitoring system according to Embodiment 41, wherein the function of the first data and the second data includes a measured glucose level, a measured lactate level, and a baseline lactate value.

[0169] Embodiment 43 The sample monitoring system according to Embodiment 42, wherein the function of the first data and the second data further includes a sensor batch constant related to a group (batch) of sensors including the sample sensor.

[0170] Embodiment 44 The sample monitoring system according to Embodiment 42, wherein the measured glucose level indicates the glucose level detected in the first period.

[0171] Embodiment 45 The sample monitoring system according to Embodiment 42, wherein the measured lactate level indicates the lactate level detected in the second period.

[0172] Embodiment 46 The sample monitoring system according to Embodiment 42, wherein the measured glucose level indicates the glucose level detected in the first period, the measured lactate level indicates the lactate level detected in the second period, and the first period partially overlaps with or falls within the second period.

[0173] Embodiment 47 The sample monitoring system according to Embodiment 45, wherein the lactate level detected in the second period is a lactate value smoothed over one hour.

[0174] Embodiment 48 The sample monitoring system according to Embodiment 42, wherein the baseline lactate value is the average lactate value over a period of one day or more.

[0175] Embodiment 49 The sample monitoring system according to Embodiment 48, wherein the aforementioned one day or more falls within the median period of the sensor life of the sample sensor.

[0176] Embodiment 50 The sample monitoring system according to Embodiment 41, wherein at least a portion of the sample sensor comprises a first detection element configured to detect glucose levels in body fluids and a second detection element configured to detect lactate levels in body fluids.

[0177] Embodiment 51 The sample monitoring system according to embodiment 41, wherein the set of instructions is stored in the first persistent memory.

[0178] Embodiment 52 The sample monitoring system according to embodiment 41, wherein the set of instructions is stored in the second persistent memory.

[0179] Embodiment 53 The sample monitoring system according to embodiment 41, further comprising a wireless communication circuit configured to transmit at least one of the first data, the second data, and the corrected glucose level to the reading device.

[0180] Embodiment 54 The sample monitoring system according to embodiment 53, wherein the wireless communication circuit is configured to transmit at least one of the first data, the second data, or the corrected glucose level in accordance with the Bluetooth protocol.

[0181] Embodiment 55 The sample monitoring system according to Embodiment 41, wherein the sample sensor is a first sample sensor, the sensor control device further comprises a second sample sensor, the first sample sensor is configured to detect glucose levels in body fluids, and the second sample sensor is configured to detect lactate levels in body fluids.

[0182] Embodiment 56 A specimen monitoring system, A sensor control device comprising a sample sensor, a first processing circuit, and a first persistent memory, wherein the sample sensor is configured to be inserted into the user's body in at least a portion thereof, and is configured to collect first data indicating glucose level and second data indicating lactate level. A reading device comprising a second processing circuit and a second persistent memory. Equipped with, At least one of the first or second persistent memory stores a set of instructions, and when the set of instructions is executed, at least one of the first or second processing circuits A specimen monitoring system that generates an indication of a suspected sensor malfunction based on the first and second data.

[0183] Embodiment 57 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A sample monitoring system according to embodiment 56, which compares a baseline lactate value with a predetermined baseline lactate value threshold.

[0184] Embodiment 58 The sample monitoring system according to Embodiment 57, wherein the baseline lactate value is the average lactate value over a period of one day or more.

[0185] Embodiment 59 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A sample monitoring system according to Embodiment 57, which generates an indication of a suspected sensor malfunction in response to a determination that the baseline lactate value exceeds a predetermined baseline lactate value threshold.

[0186] Embodiment 60 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A sample monitoring system according to embodiment 56, which generates a command to terminate the sample sensor.

[0187] Embodiment 61 A computer execution method for calculating corrected glucose levels, A step in which a sensor control device collects first data indicating glucose level and second data indicating lactate level, wherein the sensor control device comprises a sample sensor, at least a portion of which is inserted into the user's body. The steps include: calculating the corrected glucose level based on a function of the first and second data; A method that includes this.

[0188] Embodiment 62 The method according to Embodiment 61, wherein the function of the first data and the second data includes a measured glucose level, a measured lactate level, and a baseline lactate level.

[0189] Embodiment 63 The method according to Embodiment 62, wherein the function of the first data and the second data further includes a sensor batch constant related to a group (batch) of sensors including the sample sensor.

[0190] Embodiment 64 The method according to Embodiment 62, wherein the measured glucose level indicates the glucose level detected in the first period.

[0191] Embodiment 65 The method according to embodiment 62, wherein the measured lactate level indicates the lactate level detected in the second period.

[0192] Embodiment 66 The method according to Embodiment 62, wherein the measured glucose level indicates the glucose level detected in the first period, the measured lactate level indicates the lactate level detected in the second period, and the first period partially overlaps with or falls within the second period.

[0193] Embodiment 67 The method according to embodiment 65, wherein the lactate level detected in the second period is a lactate value smoothed over one hour.

[0194] Embodiment 68 The method according to Embodiment 62, wherein the baseline lactate value is the average lactate value over a period of one day or more.

[0195] Embodiment 69 The method according to embodiment 68, wherein the aforementioned one day or more falls within the median period of the sensor life of the sample sensor.

[0196] Embodiment 70 At least a portion of the sample sensor comprises a first detection element and a second detection element, The first detection element detects the glucose level in the body fluid, The second detection element detects the lactate level in the body fluid. The method according to embodiment 61, further comprising the following:

[0197] Embodiment 71 The method according to embodiment 61, wherein the step of calculating the corrected glucose level is performed by a processing circuit of the sensor control device.

[0198] Embodiment 72 The method according to embodiment 61, wherein the step of calculating the corrected glucose level is performed by a processing circuit of a reading device.

[0199] Embodiment 73 The method according to embodiment 61, further comprising the step of the wireless communication circuit of the sensor control device transmitting at least one of the first data, the second data, or the corrected glucose level to a reader.

[0200] Embodiment 74 The method according to Embodiment 73, further comprising the step of transmitting at least one of the first data, the second data, or the corrected glucose level to the reader, in accordance with the Bluetooth protocol.

[0201] Embodiment 75 The aforementioned sample sensor is a first sample sensor, and the sensor control device further comprises a second sample sensor. The first sample sensor detects the glucose level in the body fluid, The second sample sensor detects the lactate level in the body fluid. The method according to embodiment 61, further comprising the following:

[0202] Embodiment 76 A computer execution method for detecting a suspected sensor malfunction, A step in which a sensor control device collects first data indicating glucose level and second data indicating lactate level, wherein the sensor control device comprises a sample sensor, at least a portion of which is inserted into the user's body. The steps of generating an indication of the suspected sensor malfunction based on the first data and the second data, and A method that includes this.

[0203] Embodiment 77 The method according to embodiment 76, further comprising the step of comparing a baseline lactate value with a predetermined baseline lactate value threshold.

[0204] Embodiment 78 The method according to Embodiment 77, wherein the baseline lactate value is the average lactate value over a period of one day or more.

[0205] Embodiment 79 The method according to Embodiment 77, wherein the step of generating the suspicious sensor malfunction indicator further includes generating the suspicious sensor malfunction indicator in response to a determination that the baseline lactate value exceeds a predetermined baseline lactate value threshold.

[0206] Embodiment 80 The method according to embodiment 76, further comprising the step of generating a command to terminate the sample sensor.

[0207] Embodiment 81 A specimen monitoring system, A sensor control device comprising a sample sensor, a first processing circuit, and a first persistent memory, wherein at least a portion of the sample sensor is inserted into the user's body to collect first data indicating glucose levels, A secondary detection element configured to collect second data showing secondary physiological measurements, A reading device comprising a second processing circuit and a second persistent memory. Equipped with, At least one of the first or second persistent memory stores a set of instructions, and when the set of instructions is executed, at least one of the first or second processing circuits Based on the aforementioned first data, determine whether there are any suspicious or false glucose states. Based on the second data, determine whether a correlated physiological state exists. A sample monitoring system that, if the aforementioned suspicious false glucose state is absent and the aforementioned correlated physiological state is present, causes the system to perform a first corrective action.

[0208] Embodiment 82 The specimen monitoring system according to Embodiment 81, wherein the secondary detection element comprises one or more of the following: a heart rate monitor, an implantable cardiac monitor, an implantable electrocardiogram (ECG) device, or an implantable electroencephalogram (EEG) device.

[0209] Embodiment 83 The specimen monitoring system according to Embodiment 82, wherein the secondary physiological measurement consists of one or more of heart rate, QT interval, ECG, or EEG.

[0210] Embodiment 84 The sample monitoring system according to embodiment 81, wherein the secondary detection element consists of one or more of a ketone sensor, a continuous ketone monitor, or a ketone fragment reader.

[0211] Embodiment 85 The sample monitoring system according to embodiment 84, wherein the secondary physiological measurement consists of ketone levels.

[0212] Embodiment 86 The sample monitoring system according to Embodiment 81, wherein the suspicious false glucose state is a suspicious false high glucose state or a suspicious false low glucose state.

[0213] Embodiment 87 The suspicious false glucose state is a suspicious false low glucose state, and the set of instructions for detecting the suspicious false glucose state includes a set of instructions for determining whether one or more glucose sensor data quality inspections indicate the suspicious false low glucose state, a set of instructions for determining whether the glucose level based on the first data is less than a predetermined first low glucose threshold, a set of instructions for determining whether the area under the curve (AUC) calculation based on the first data and a predetermined second low glucose threshold exceeds a predetermined low glucose AUC threshold, a set of instructions for determining whether a glucose percentile metric exceeds a predetermined low glucose percentile threshold, or a set of instructions for determining whether the average glucose level within a predetermined recent time window exceeds a predetermined third low glucose threshold The sample monitoring system according to Embodiment 81, including one or more of the above.

[0214] Embodiment 88 The suspicious false glucose state is a suspicious false high glucose state, and the set of instructions for detecting the suspicious false glucose state includes a set of instructions for determining whether one or more glucose sensor data quality inspections indicate the suspicious false high glucose state, a set of instructions for determining whether the glucose level based on the first data exceeds a predetermined first high glucose threshold, a set of instructions for determining whether the area under the curve (AUC) calculation based on the first data and a predetermined second high glucose threshold exceeds a predetermined high glucose AUC threshold, a set of instructions for determining whether a glucose percentile metric exceeds a predetermined high glucose percentile threshold, or Instructions for determining whether the average glucose level within a predetermined recent time window exceeds a predetermined third high glucose threshold The sample monitoring system according to Embodiment 81, including

[0215] Embodiment 89 The sample monitoring system according to Embodiment 81, wherein the correlated physiological state is one of the presumed absence of high glucose, the presumed presence of high glucose, the presumed absence of low glucose, or the presumed presence of low glucose.

[0216] Embodiment 90 The sample monitoring system according to Embodiment 81, wherein the instructions for determining the correlated physiological state include instructions for comparing the ketone level with a predetermined ketone threshold.

[0217] Embodiment 91 The sample monitoring system according to Embodiment 81, wherein the instructions for determining the correlated physiological state include instructions for comparing the heart rate measurement with a predetermined heart rate threshold.

[0218] Embodiment 92 The sample monitoring system according to Embodiment 81, wherein the suspected false glucose state is a suspected false low glucose state, the correlated physiological state is the presumed absence of low glucose, and the first correction measure is a drastic delay correction.

[0219] Embodiment 93 The sample monitoring system according to Embodiment 81, wherein the suspected false glucose state is a suspected false high glucose state, the correlated physiological state is the presumed absence of high glucose, and the first correction measure is a drastic delay correction.

[0220] Embodiment 94 The sample monitoring system according to Embodiment 81, wherein the first correction measure is a drastic delay correction.

[0221] Embodiment 95 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... The sample monitoring system according to Embodiment 81, which, if the aforementioned suspicious false glucose state is absent and the aforementioned correlated physiological state is absent, causes a second correction measure consisting of one or more of the following: mild delay correction or increased glucose sensor signal smoothing.

[0222] Embodiment 96 The sample monitoring system according to Embodiment 81, wherein the set of commands for determining whether a correlated physiological state exists further includes a set of commands for calculating the degree of correlation between the correlated physiological state and the suspected false glucose state.

[0223] Embodiment 97 When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... If the aforementioned suspicious false glucose state is absent and the aforementioned correlated physiological state is absent, a second correction procedure consisting of variable delay correction or variable glucose sensor signal smoothing is performed. The variable delay correction is a function of the degree of correlation between the correlated physiological state and the questionable false glucose state, The sample monitoring system according to Embodiment 96, wherein the variable glucose sensor signal smoothing is an inverse function of the degree of correlation between the correlated physiological state and the suspected false glucose state.

[0224] Embodiment 98 The set of instructions is stored in the second persistent memory of the reader device. The set of instructions further includes a first mobile phone application configured to receive the first data and a second mobile phone application configured to receive the second data. The specimen monitoring system according to Embodiment 81, wherein the first or second mobile phone application is configured to determine the absence or presence of the suspected false glucose state and the correlated physiological state and to perform the first corrective action.

[0225] Embodiment 99 The specimen monitoring system according to Embodiment 98, wherein the reading device is a smartphone.

[0226] Embodiment 100 The specimen monitoring system according to Embodiment 87, wherein the AUC calculation is based on the first data within a first recent predetermined time window, and the glucose percentile metric is based on the first data within a second recent predetermined time window.

[0227] Embodiment 101 The specimen monitoring system according to Embodiment 88, wherein the AUC calculation is based on the first data within a first recent predetermined time window, and the glucose percentile metric is based on the first data within a second recent predetermined time window.

[0228] Embodiment 102 A specimen monitoring system, comprising: A sensor control device including a specimen sensor, a first processing circuit, and a first persistent memory, wherein at least a part of the specimen sensor is inserted into the user's body and configured to collect first data indicating a glucose level; A secondary sensing element configured to collect second data indicating a secondary physiological measurement value; A reading device including a second processing circuit and a second persistent memory; and at least one of the first or second persistent memories stores a set of instructions, which when executed, cause at least one of the first or second processing circuits to determine whether the first data conflicts with the second data; In response to a determination that the first data conflicts with the second data, end the specimen monitoring system or temporarily mask the first data.

[0229] Embodiment 103 A computer-executable method, comprising: A step in which a sample sensor configured to be inserted into the user's body at least partially collects first data indicating glucose levels, The steps include: collecting second data in which a secondary detection element indicates a secondary physiological measurement; A step of determining whether there are any suspicious false glucose states based on the first data mentioned above, A step of determining whether a correlated physiological state exists based on the second data, If there is no suspicious false glucose state and the corresponding physiological state exists, the first step is to perform a corrective action. A computer execution method that includes this.

[0230] Embodiment 104 The computer execution method according to Embodiment 103, wherein the secondary detection element consists of one or more of a heart rate monitor, an implantable cardiac monitor, an implantable electrocardiogram (ECG) device, or an implantable electroencephalogram (EEG) device.

[0231] Embodiment 105 The computer execution method according to Embodiment 103, wherein the secondary physiological measurement consists of one or more of heart rate, QT interval, ECG, or EEG.

[0232] Embodiment 106 The computer execution method according to Embodiment 103, wherein the secondary detection element consists of one or more of a ketone sensor, a continuous ketone monitor, or a ketone fragment reader.

[0233] Embodiment 107 The computer execution method according to Embodiment 103, wherein the secondary physiological measurement consists of ketone levels.

[0234] Embodiment 108 The computer execution method according to Embodiment 103, wherein the aforementioned suspicious false glucose state is a suspicious false high glucose state or a suspicious false low glucose state.

[0235] Embodiment 109 The aforementioned questionable false glucose state is a questionable false low glucose state, A step of determining whether one or more glucose sensor data quality tests indicate the aforementioned suspicious false low glucose state, A step of determining whether the glucose level based on the first data is less than a predetermined first low glucose threshold, A step of determining whether the area under the curve (AUC) calculated based on the first data exceeds a predetermined low glucose AUC threshold. A step of determining whether the glucose percentile metric exceeds a predetermined low glucose percentile threshold, or A step to determine whether the average glucose level within a given recent time window exceeds a given second low glucose threshold. A computer execution method according to embodiment 103, further comprising one or more of the above.

[0236] Embodiment 110 The aforementioned questionable false glucose state is a questionable false high glucose state, A step of determining whether one or more glucose sensor data quality tests indicate the aforementioned suspicious false high glucose state, A step of determining whether the glucose level based on the first data exceeds a predetermined first high glucose threshold, A step of determining whether the area under the curve (AUC) calculated based on the first data exceeds a predetermined high glucose AUC threshold. A step of determining whether the glucose percentile metric exceeds a predetermined high glucose percentile threshold, or A step to determine whether the average glucose level within a given recent time window exceeds a given second high glucose threshold. A computer execution method according to embodiment 103, further comprising one or more of the above.

[0237] Embodiment 111 The computer execution method according to Embodiment 103, wherein the correlated glucose state is one of the following: estimated absence of high glucose, estimated presence of high glucose, estimated absence of low glucose, or estimated presence of low glucose.

[0238] Embodiment 112 A computer execution method according to embodiment 103, further comprising the step of comparing a ketone level with a predetermined ketone threshold.

[0239] Embodiment 113 A computer execution method according to embodiment 103, further comprising the step of comparing a heart rate measurement with a predetermined heart rate threshold.

[0240] Embodiment 114 The computer execution method according to Embodiment 103, wherein the suspected false glucose state is a suspected false low glucose state, the correlated glucose state is an estimated absence of low glucose, and the first corrective measure is a bold delay correction.

[0241] Embodiment 115 The computer execution method according to Embodiment 103, wherein the suspect false glucose state is a suspect false high glucose state, the correlated glucose state is an estimated absence of high glucose, and the first correction measure is a bold delay correction.

[0242] Embodiment 116 The computer execution method according to Embodiment 103, wherein the first correction measure is a drastic delay correction.

[0243] Embodiment 117 The computer execution method according to Embodiment 103 further includes the step of performing a second correction measure consisting of one or more of mild delay correction or increased glucose sensor signal smoothing if the aforementioned suspicious false glucose state is absent and the aforementioned correlated physiological state is absent.

[0244] Embodiment 118 A computer execution method according to Embodiment 103, further comprising the step of calculating the degree of correlation between the correlated physiological state and the suspected false glucose state.

[0245] Embodiment 119 If the aforementioned suspicious false glucose state is absent and the aforementioned correlated physiological state is absent, the step further includes performing a second correction measure consisting of variable delay correction or variable glucose sensor signal smoothing. The variable delay correction is a function of the degree of correlation between the correlated physiological state and the questionable false glucose state, The computer execution method according to Embodiment 118, wherein the variable glucose sensor signal smoothing is an inverse function of the degree of correlation between the correlated physiological state and the questionable false glucose state.

[0246] Embodiment 120 The steps include: the reading device receiving the first mobile phone application; The second mobile phone application of the reader receives the second data. It further includes, The computer execution method according to Embodiment 103, wherein the steps of determining and executing are performed by the first mobile phone application or the second mobile phone application of the reading device.

[0247] Embodiment 121 The computer execution method according to embodiment 120, wherein the reading device is a smartphone.

[0248] Embodiment 122 The computer execution method according to Embodiment 109, wherein the AUC calculation is based on the first data within a first recent predetermined time window, and the glucose percentile metric is based on the first data within a second recent predetermined time window.

[0249] Embodiment 123 The computer execution method according to Embodiment 110, wherein the AUC calculation is based on the first data within a first recent predetermined time window, and the glucose percentile metric is based on the first data within a second recent predetermined time window.

[0250] Embodiment 124 A computer execution method, A step in which a sample sensor configured to be inserted into the user's body at least partially collects first data indicating glucose levels, The steps include: collecting second data in which a secondary detection element indicates a secondary physiological measurement; A step of determining whether the first data contradicts the second data, In response to a determination that the first data is inconsistent with the second data, the sample monitoring system is terminated or the first data is temporarily masked. A computer execution method that includes this. [Explanation of symbols]

[0251] 102 Sensor control device 104 Sample Sensor 105 Adhesive Patches 120 Reader 121 Input Components 122 Display 123 Data communication port Channels 140, 141, 142, and 143 170 Local Computer System 180 Reliable Computer Systems 222 Communication Processors 223, 225, 230 memory 224 Application Processors 226 Power supply 228 RF Transceiver 232 Multifunctional circuit 238 Power management circuit 250 Sensor Electronic Circuits 252 Analog Front End 253 memory 256 processors 258 Communication Circuit

Claims

1. A specimen monitoring system, A sensor control device comprising a sample sensor, a first processing circuit, and a first persistent memory, wherein the sample sensor is configured to be inserted into the user's body in at least a portion thereof, and is configured to collect first data indicating glucose level and second data indicating lactate level. A reading device comprising a second processing circuit and a second persistent memory. Equipped with, At least one of the first or second persistent memory stores a set of instructions, and when the set of instructions is executed, at least one of the first or second processing circuits The first sample metric is calculated based on the first data. The second sample metric is calculated based on the second data. The first sample metric is compared with a first threshold, and the second sample metric is compared with a second threshold. A sample monitoring system that generates an indication of suspected glucose depression in response to a determination that the first sample metric exceeds the first threshold and the second sample metric exceeds the second threshold.

2. The sample monitoring system according to claim 1, wherein the first sample metric is the glucose derivative.

3. The sample monitoring system according to claim 1, wherein the second sample metric is the lactate derivative.

4. The sample monitoring system according to claim 1, wherein the first threshold is a glucose differential threshold.

5. The sample monitoring system according to claim 1, wherein the second threshold is a lactart derivative threshold.

6. The sample monitoring system according to claim 1, wherein when the first sample metric is less than or equal to the first threshold, the first sample metric exceeds the first threshold.

7. The sample monitoring system according to claim 1, wherein when the second sample metric is greater than or equal to the second threshold, the second sample metric exceeds the second threshold.

8. When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... The sample monitoring system according to claim 1, which generates an indication of suspected glucose decrease in response to a determination that the first and second thresholds are simultaneously exceeded.

9. The sample monitoring system according to claim 1, wherein at least a portion of the sample sensor comprises a first detection element configured to detect glucose levels in a body fluid and a second detection element configured to detect lactate levels in the body fluid.

10. When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A sample monitoring system according to claim 1, which suppresses a low glucose alarm.

11. When the aforementioned set of instructions is executed, at least one of the first or second processing circuits further... A specimen monitoring system according to claim 1, which suppresses commands that alter or trigger drug delivery.

12. The specimen monitoring system according to claim 11, wherein the drug is insulin.

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

14. The sample monitoring system according to claim 1, wherein the set of instructions is stored in the second persistent memory.

15. The sample monitoring system according to claim 1, wherein the set of instructions is stored in the first persistent memory.

16. The sample monitoring system according to claim 1, wherein the sensor control device further comprises a wireless communication circuit configured to transmit the first and second data to the reading device.

17. The sample monitoring system according to claim 16, wherein the wireless communication circuit is configured to transmit the first and second data in accordance with the Bluetooth protocol.

18. The sample monitoring system according to claim 1, wherein the sample sensor is a first sample sensor, the sensor control device further comprises a second sample sensor, the first sample sensor is configured to detect glucose levels in body fluids, and the second sample sensor is configured to detect lactate levels in body fluids.

19. The specimen monitoring system according to claim 1, further comprising a drug delivery device.

20. The sample monitoring system according to claim 19, wherein the drug delivery device comprises an insulin pump.

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