Systems and methods for multi-analyte sensing - Patents.com

Abstract

The device includes an analyte sensor, a memory, and a processor. The processor monitors an analyte of the patient over a period of time to obtain measured analyte data for the analyte of the patient using the analyte sensor, and monitors other measured sensor data indicative of a physiological condition of the patient over the period of time. The processor also determines predicted analyte data for the analyte based on the physiological condition of the patient over the period of time, and determines a correction factor based on the predicted analyte data and the measured analyte data. The correction factor is indicative of an error in the calibration of the analyte sensor. The processor also determines whether recalibration of the analyte sensor is possible. If recalibration is possible, the processor recalibrates the analyte sensor based on the correction factor, and if recalibration is not possible, the processor recommends to the patient that the analyte sensor be replaced.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 268,334, filed February 22, 2022, which is assigned to the assignee hereof and is expressly incorporated by reference in its entirety herein as if fully set forth below and for all applicable purposes.

[0002] Systems and methods according to the present principles relate to continuous multi-analyte monitoring and monitoring of multiple analytes using a sensor system. [Background technology]

[0003] Diabetes is a metabolic disease linked to the body's production or use of insulin, a hormone that allows the body to use glucose for energy or store it as fat.

[0004] When a person eats a meal containing carbohydrates, the digestive system absorbs the nutrients, eventually resulting in the accumulation of glucose in the person's blood. Blood glucose can be used as energy or stored as fat. The body normally maintains blood glucose levels in a range that provides enough energy to support bodily functions and avoids problems that can occur from glucose levels that are too high or too low. Regulation of blood glucose levels depends on the production and use of insulin, which regulates the movement of blood glucose into cells.

[0005] When the body does not produce enough insulin or is unable to effectively use the insulin that is present, blood glucose levels can rise above the normal range. A condition in which blood glucose levels are higher than normal is called "hyperglycemia." Chronic hyperglycemia can cause many health problems, such as cardiovascular disease, cataracts and other eye problems, nerve damage (neuropathy), skin ulcers, and kidney damage. Hyperglycemia can also cause acute problems, such as diabetic ketoacidosis, a condition in which the body becomes too acidic due to the production of excess ketones or body acids. A condition in which blood glucose levels are lower than normal is called "hypoglycemia." Severe hypoglycemia can result in acute crises that can result in myocardial damage, neurocognitive dysfunction, and in certain cases, seizures or even death.

[0006] Patients living with diabetes can receive insulin to manage blood glucose levels. Insulin can be received, for example, through a hand injection with a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels.

[0007] Diabetes is sometimes referred to as "Type 1" and "Type 2". Type 1 diabetes patients are typically able to use insulin when it is present, but due to problems with the insulin-producing beta cells of the pancreas, the body is unable to produce sufficient amounts of insulin. Type 2 diabetes patients are able to produce some insulin, but due to reduced sensitivity to insulin, the patient is "insulin resistant". As a result, even though insulin is present in the body, the insulin is not used well by the patient's body and blood glucose levels are not effectively regulated.

[0008] Patients with diabetes can benefit from real-time diabetes management guidance determined based on the patient's physiological state to stay within target glucose ranges and avoid physical complications. In certain cases, the patient's physiological state is determined using a diagnostic system that measures glucose levels and informs the identification and / or prediction of adverse glycemic events, such as hyperglycemia and hypoglycemia, and the type of guidance to be provided to the patient.

[0009] For example, such a diagnostic system may utilize a continuous glucose monitor (CGM) to measure a patient's glucose levels over time. The measured glucose levels may then be processed by the diagnostic system to identify and / or predict adverse glycemic events and / or provide the patient with guidance for treatments and / or actions to mitigate or prevent the occurrence of such adverse glycemic events. For example, trends, statistics, or other metrics may be derived from the glucose levels and used to identify and / or predict adverse glycemic events. Alternatively, in certain cases, the glucose levels themselves may be used to identify and / or predict adverse glycemic events.

[0010] However, managing diabetes presents many challenges to patients, clinicians, and caregivers, as a diverse set of factors can affect a patient's glucose levels and thus affect the accuracy of glycemic event predictions and the guidance provided by diagnostic systems. Thus, multiple different analytes, including glucose, can be monitored to provide a more comprehensive or accurate characterization of a patient's physiological state at any given moment. Monitoring one or more of multiple analytes in addition to or in lieu of glucose can provide a more complete picture of a patient's physiological state, as multiple analytes can affect or be affected by the same physiological event / condition and / or can affect or be affected by related physiological events / conditions. Examples of such analytes include ketones, lactate, insulin, electrolytes, creatinine, and numerous other biomarkers, including proteins, metabolites, and nucleic acids.

[0011] However, by itself, monitoring multiple analytes simultaneously brings many new challenges, involving highly complex mechanisms for sensor calibration, signal, artifact detection, etc. For example, utilizing multiple sensors for multi-analyte sensing can adversely affect sensing accuracy and / or its calibration due to crosstalk between individual sensors. Such crosstalk can be a function of sensor membrane properties, enzyme activity / immobilization, electrode surface defects, etc. Additionally, utilizing a system with multiple sensors (e.g., multiple voltage regulators) can present various hardware and / or power consumption (e.g., battery life) concerns that are typically associated with increased hardware and / or software complexity.

[0012] Furthermore, when a single multi-analyte sensor is used to sense multiple analytes, the sensor may consume more local oxygen compared to a single analyte sensor, making such a multi-analyte sensor more likely to be oxygen-limited rather than diffusion-limited. Also, when using a common resistive layer for multiple analytes in a multi-analyte sensor, it is difficult to maintain selectivity and diffusivity for each analyte. Another unique challenge for multi-analyte sensors arises from pairs of analytes that interact locally with each other. In such instances, the local interactions between the analytes may not accurately reflect systemic changes in either analyte, leading to inaccurate characterization of the sensed analyte levels and / or trends.

[0013] Thus, there is a need in the art for improved systems and methods for multi-analyte monitoring that improve support for disease management. Summary of the Invention [Means for solving the problem]

[0014] In certain embodiments, the apparatus comprises: an analyte sensor; a memory containing executable instructions; a processor communicatively coupled to the memory, the processor causing the apparatus to: monitoring an analyte of the patient over a period of time using the analyte sensor to obtain measured analyte data for the analyte of the patient; monitoring other measured sensor data indicative of a physiological condition of the patient during said period; determining predicted analyte data for said analyte based on said physiological condition of said patient during said period; determining a correction factor based on the predicted analyte data and the measured analyte data, the correction factor being indicative of an error in calibration of the analyte sensor; determining whether the analyte sensor is capable of being recalibrated; if recalibration is possible, recalibrating the analyte sensor based on the correction factor; If recalibration is not possible, the instructions are configured to execute to recommend to the patient that the analyte sensor be replaced.

[0015] In certain embodiments, the apparatus comprises: a first analyte sensor; a second analyte sensor; and Memory and a processor communicatively coupled to the memory, the processor comprising: receiving data indicative of a historical relationship between a first analyte and a second analyte for the patient during a first time period; monitoring the first analyte in the patient during a second time period to obtain first analyte data using the first analyte sensor; monitoring the second analyte in the patient during the second time period to obtain second analyte data using the second analyte sensor; determining a current relationship between the first analyte and the second analyte based on the first analyte data and the second analyte data; determining that a substantial discrepancy exists between the historical relationship and the current relationship, the substantial discrepancy indicating an error in calibration of the first analyte sensor or the second analyte sensor; In response to determining that the substantial mismatch exists, the method is configured to recalibrate the first analyte sensor or the second analyte sensor.

[0016] In certain embodiments, the apparatus comprises: a first analyte sensor; Memory, a processor communicatively coupled to the memory, the processor comprising: monitoring a first analyte in the patient over a period of time to obtain first analyte data using the first analyte sensor; determining a period of stability in the first analyte data; determining a calibration time based on the stability period; and recalibrating the analyte sensor during the determined calibration time. [Brief description of the drawings]

[0017] So that the above-listed features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, can be made by reference to embodiments, some of which are illustrated in the drawings. It should be noted, however, that the attached drawings merely illustrate certain exemplary embodiments of the present disclosure and therefore should not be considered as limiting its scope, since the description may be allowed for other equally valid embodiments.

[0018] [Figure 1] 1 illustrates aspects of an exemplary disease management support system for use in connection with implementing embodiments of the present disclosure. [Diagram 2] FIG. 1 conceptually illustrates an exemplary continuous analyte monitoring system including an exemplary continuous analyte sensor having sensor electronics, in accordance with certain aspects of the present disclosure. [Figure 3A] 2 illustrates generally an exemplary configuration of a continuous analyte sensor with sensor electronics for use with the disease management system of FIG. 1, according to certain embodiments disclosed herein. [Figure 3B] 2 illustrates generally an exemplary configuration of a continuous analyte sensor with sensor electronics for use with the disease management system of FIG. 1, according to certain embodiments disclosed herein. [Figure 3C] 3C illustrates generally an exemplary sensing mechanism of the continuous analyte sensor of FIG. 3B according to certain embodiments disclosed herein. [Figure 3D] 3C and 3D are schematic diagrams illustrating exemplary multiplexed sensing methods for use with the continuous analyte sensor of FIGS. 3A and 3B, according to certain embodiments disclosed herein. [Figure 4A]1 illustrates a flow diagram depicting an exemplary method for automatic correction of a factory-calibrated continuous analyte sensor according to certain embodiments disclosed herein. [Figure 4B] 1 illustrates a flow diagram depicting an exemplary method for automatic correction of a factory-calibrated continuous analyte sensor according to certain embodiments disclosed herein. [Diagram 5] 1 illustrates a flow diagram depicting an example method for determining an optimal calibration time for a continuous analyte sensor according to certain embodiments disclosed herein. [Figure 6A] 1 illustrates a flow diagram depicting an exemplary method for detecting and compensating for compression artifacts using a continuous analyte sensor, according to some embodiments disclosed herein. [Figure 6B] 1 illustrates a flow diagram depicting an exemplary method for detecting and compensating for a compression event, according to certain embodiments disclosed herein. [Figure 6C] 1 illustrates an example waveform indicative of a compression event, according to certain embodiments disclosed herein. [Figure 7A] 1 illustrates a flow diagram depicting an exemplary method for detecting and compensating for dip and recovery events using a continuous analyte sensor according to some embodiments disclosed herein. [Figure 7B] 1A-1C illustrate generally exemplary multi-analyte sensing mechanisms of a continuous analyte sensor according to certain embodiments disclosed herein. [Figure 7C] 1 illustrates a flow diagram depicting an example method for detecting and compensating for dips and recovery events, according to certain embodiments disclosed herein. [Figure 7D] 1 illustrates an example waveform showing a dip and recovery event, according to certain embodiments disclosed herein. [Figure 8] 1 illustrates a flow diagram depicting an exemplary method for verifying a hypoglycemic event, according to certain embodiments disclosed herein. [Figure 9]1 illustrates a flow diagram depicting an exemplary method for detecting and compensating for the wearing position of a continuous analyte sensor according to some embodiments disclosed herein. [Figure 10] FIG. 10 is a flow diagram depicting a method for training a machine learning model for use with the methods of FIGS. 4A-9, according to certain embodiments of the present disclosure.

[0019] For ease of understanding, wherever possible, the same reference numerals have been used to designate identical elements that are common to each of the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized in other embodiments without specific recitation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] Systems and methods according to the present principles relate to continuous multi-analyte monitoring and monitoring of multiple analytes using sensor systems. More particularly, embodiments provided herein include improved systems and methods for multiplexed sensing and data processing of multiple different analytes, calibration of multi-analyte sensors, detection and compensation for artifacts and related events, and detection and compensation for sensor location on a patient. Such systems and methods facilitate more accurate and reliable determination of a patient's physiological state, including, for example, determination and / or prediction of glycemic events, which in turn enables better decision-making for the management of diseases, such as diabetes.

[0021] Accordingly, certain embodiments described herein provide a technical solution to the above technical problem by providing a continuous multi-analyte monitoring system configured to monitor, generate, and analyze data for multiple analytes (e.g., glucose and e.g., lactate) simultaneously and in a multiplexed manner. In certain embodiments, a device for multiplexed continuous analyte sensing is utilized to enable simultaneous monitoring of multiple analytes, the device including one or more voxelated working electrodes, each of the one or more voxelated working electrodes including one or more enzymes deposited thereon. Each of the enzymes may be specific for a different analyte. Further, the device may include a reference electrode electrically coupled to each of the one or more voxelated working electrodes, a counter electrode electrically coupled to each of the one or more voxelated working electrodes, and a potentiostat electrically coupled to each of the one or more voxelated working electrodes, the potentiostat configured to switch a potential bias between the counter electrode and each of the one or more voxelated working electrodes for one or more predetermined time periods to detect binding of one or more different analytes to the multiple enzymes.

[0022] In certain embodiments, a device for multiplexed continuous analyte sensing is utilized to enable simultaneous monitoring of multiple analytes, the device including a working electrode having an enzyme and an aptamer immobilized thereon, the enzyme and aptamer each specific for a different analyte, a reference electrode electrically coupled with high impedance to the working electrode, a counter electrode electrically coupled with low impedance to the working electrode, a potentiostat electrically coupled to the working electrode, the reference electrode, and the counter electrode, the potentiostat configured to apply a potential bias between the working electrode and the counter electrode to determine a response current through the working electrode to detect enzyme catalysis and subsequent redox reaction, and an impedance analyzer electrically coupled to the working electrode, the impedance analyzer configured to determine an impedance change through the working electrode to detect binding of a corresponding analyte to the aptamer.

[0023] Certain embodiments described herein provide a technical solution to the above-mentioned problem by providing a method for automatic correction of one or more factory calibrated continuous multi-analyte sensors. In certain embodiments, such a method includes a method for calibrating a continuous analyte sensor, the method including: continuously monitoring a first analyte of a patient during a time period to obtain measured analyte data for the first analyte using the continuous analyte sensor; continuously monitoring other measured sensor data indicative of a physiological condition of the patient during the time period; processing the measured sensor data from the time period to determine predicted analyte data for the first analyte based on the physiological condition of the patient during the time period; determining a compensation factor between the predicted analyte data and the measured analyte data, the compensation factor being indicative of an error in the calibration of the continuous analyte sensor; and determining whether recalibration of the continuous analyte sensor is possible, and if recalibration is possible, recalibrating the continuous analyte sensor based on the compensation factor, or recommending to the patient to replace the continuous analyte sensor if recalibration is not possible.

[0024] In certain embodiments, such methods for automatic correction of one or more factory-calibrated continuous multi-analyte sensors include a method for calibrating a continuous analyte sensor, the method including receiving data indicative of a historical relationship between a first analyte and a second analyte of a patient during a first time period; continuously monitoring the patient's first analyte during a second time period using at least one continuous analyte sensor to obtain measured first analyte data; continuously monitoring the patient's second analyte during the second time period using the at least one continuous analyte sensor to obtain measured second analyte data; processing the measured first and second analyte data of the second time period to determine a current relationship between the first analyte and the second analyte; and determining whether there is a substantial mismatch between the historical relationship and the current relationship, where a substantial mismatch indicates an error in the calibration of the first and / or second continuous analyte sensors, and if there is a substantial mismatch, recommending to the patient to recalibrate the first and / or second continuous analyte sensors.

[0025] In certain embodiments, the first analyte is glucose and the second analyte is lactate.

[0026] In certain embodiments, the method for calibrating the continuous analyte sensor further includes determining whether recalibration of the first or second continuous analyte sensor is possible, and if recalibration is not possible, recommending to the patient to replace the first and / or second continuous analyte sensor.

[0027] Certain embodiments described herein provide a technical solution to the above-mentioned technical problem by providing a method for determining an optimal calibration time for one or more continuous multi-analyte sensors. In certain embodiments, such a method includes a method for determining when to recalibrate a continuous analyte sensor, the method including continuously monitoring a first analyte of a patient during a period of time to obtain measured first analyte data, determining a period of stability based on the measured first analyte data, and providing a recommendation for an optimal calibration time based on the period of stability.

[0028] In certain embodiments, the method for determining when to recalibrate the continuous analyte sensor further includes continuously monitoring a second analyte in the patient during a period of time to obtain measured second analyte data, wherein the period of stability is further based on the measured second analyte data.

[0029] In certain embodiments, the method for determining when to recalibrate the continuous analyte sensor further includes receiving measured physiological data indicative of a physiological condition of the patient during a period of time, wherein the period of stability is further based on the measured physiological data.

[0030] In certain embodiments, the stable period has a duration of at least 5 minutes.

[0031] In certain embodiments, the optimal calibration time correlates with the time at which the first analyte measurement is taken.

[0032] In certain embodiments, the optimal calibration time correlates with the time at which the measurement value of the first analyte is displayed to the user.

[0033] Certain embodiments described herein provide a technical solution to the above-mentioned technical problem by providing a method for detecting and compensating for a compression event. In certain embodiments, such a method includes a method for continuous analyte sensing, the method including: continuously monitoring a first analyte of a patient during a time period to obtain measured first analyte data; continuously monitoring one or more second analytes of the patient during a time period to obtain measured second analyte data; receiving measured physiological data indicative of a physiological condition of the patient; and determining whether a compression event has occurred based on the measured first and second analyte data and the physiological condition of the patient.

[0034] In certain embodiments, a method for continuous analyte sensing further includes detecting a change in a first analyte level that is greater than a predetermined normal threshold and determining whether the patient was resting or engaged in physical activity during the first analyte level change, determining whether the patient changed position prior to the first analyte level change if the patient was resting, and determining whether there was a detected change in lactate level corresponding to the first analyte level change if the patient was engaged in physical activity.

[0035] In certain embodiments, such methods for detecting and compensating for compression events include a method for calibrating a continuous analyte sensor, the method including: continuously monitoring a patient's glucose during a period of time using the continuous analyte sensor to obtain measured glucose data; continuously monitoring the patient's lactate during a period of time to obtain measured lactate data; determining a decrease in glucose level that is greater than a predetermined normal threshold; determining whether there is a detected increase in lactate level corresponding to the decrease in glucose level; and if there is a corresponding detected increase in lactate level, recalibrating the continuous analyte sensor based on the measured lactate data.

[0036] In certain embodiments, such methods for detecting and compensating for a compression event include a method for calibrating a continuous analyte sensor, the method including: continuously monitoring a patient's glucose over a period of time using the continuous analyte sensor to obtain measured glucose data; continuously monitoring the patient's lactate over a period of time to obtain measured lactate data; determining a decrease in glucose level that is greater than a predetermined normal threshold; determining whether there is a detected increase in lactate level corresponding to the decrease in glucose level; and if there is a corresponding detected increase in lactate level, providing an alert or notification to a user that a compression event has been detected.

[0037] Certain embodiments described herein provide a technical solution to the above-mentioned technical problem by providing a method for determining a wearing position of a continuous analyte sensor. In certain embodiments, the method includes a method for continuous analyte sensing, the method including: using a first continuous analyte sensor; continuously monitoring a first analyte of a patient during a time period to obtain measured first analyte data; receiving measured physiological data indicative of a physiological condition of the patient during the time period; determining a responsiveness of the first analyte to a physiological event of the patient based on the measured first analyte data and the physiological data; determining a wearing position of the first continuous analyte sensor based on the responsiveness of the first analyte; and recalibrating the first continuous analyte sensor based on the detected wearing position.

[0038] In certain embodiments described herein, the continuous multi-analyte monitoring system may be configured to collect a variety of data, including analyte data for multiple analytes, as well as patient information, secondary sensor data (e.g., non-analyte data), patient inputs, etc. For example, the analyte data may include continuously monitored glucose data, as well as continuously monitored data for lactate, ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, and other suitable analytes.

[0039] In certain embodiments described herein, methods for calibration and / or compensation of a continuous multi-analyte monitoring system are based on collected analyte data for multiple analytes in addition to other types of collected data. Such collected data may include accelerometer data, gyrometer data, global positioning system (GPS) data, secondary sensor data such as heart rate, heart rate reserve, heart rate variability (HRV), electrocardiogram (EKG) data, electromyogram (EMG), respiratory rate, temperature, blood pressure, galvanic skin response, oxygen uptake data (e.g., VO2max), sleep, impedance data, strain sensor data, etc. Such collected data may further include patient information such as age, sex, glucose threshold level, previous exercise-related or nutrition-related event data, fitness level, activity frequency, etc.

[0040] In certain embodiments described herein, the continuous multi-analyte monitoring system can use various algorithms or artificial intelligence (AI) models, such as machine learning models trained on patient-specific data and / or population data, to facilitate calibration and / or compensation (e.g., for artifacts or wearing position) based on collected information about the patient. For example, the algorithms and / or machine learning models can take into account parameters such as the patient's analyte levels in addition to secondary sensor data and / or patient information (e.g., personalized patient-specific data) during calibration and / or compensation of the continuous multi-analyte monitoring system. Based on these parameters, the algorithms and / or machine learning models can facilitate improved calibration and / or compensation.

[0041] According to certain embodiments, prior to deployment, the machine learning model is trained with training data including, for example, population data. As described herein, the population data may be provided in the form of a dataset including data records or samples of past patients (e.g., with diabetes). Each data record is then characterized (e.g., refined into a set of one or more features or predictive variables) and labeled. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model. The features associated with each data record may be used as input to the machine learning model, and the generated output may be compared to the label assigned to each of the data records. The model may calculate a loss based on the difference between the generated output and the provided label. This loss may then be used to modify the internal parameters or weights of the model. By iteratively processing the features associated with each data record corresponding to each past patient, the model may be iteratively refined to facilitate improved calibration and / or compensation.

[0042] In certain embodiments, the combination of a continuous multi-analyte monitoring system with machine learning models and / or algorithms for calibration and / or compensation (e.g., for artifacts or mounting position) can allow the systems and methods described herein to provide the necessary accuracy and reliability that diabetic patients expect from a continuous analyte monitoring system. For example, continuous monitoring of multiple analytes (e.g., glucose and lactate) can provide a more complete picture of a patient's physiological condition, especially compared to utilizing a single analyte. Similarly, utilizing machine learning models and / or algorithms for calibration and / or compensation can provide improved accuracy of continuous multi-analyte sensors compared to traditional calibration methods. Thus, the improved systems and methods herein can facilitate, for example, more reliable determination and / or prediction of glycemic events in diabetic patients, which in turn can enable better decision-making for managing diabetes and / or other diseases.

[0043] As used herein, the term "calibration" is a broad term and is used in its ordinary sense to refer to, but is not limited to, the process of determining the relationship between raw sensor data and clinically meaningful units.

[0044] As used herein, the term "calibration data" is a broad term and is used in its ordinary sense to refer to, but is not limited to, data that has been transformed from its raw state to another state using a function or series of functions, lookup tables, etc., to provide a clinically meaningful value to the user.

[0045] As used herein, the term "continuous" is a broad term and is used in its ordinary sense and can mean continuous, semi-continuous, consecutive, periodic, intermittent, regular, and the like.

[0046] As used herein, the terms "continuous analyte sensor," "continuous multi-analyte sensor," "continuous glucose sensor," and "continuous lactate sensor" are broad terms and are used in their ordinary sense, and refer to, but are not limited to, a device that continuously measures analyte concentrations and / or calibrates the device (e.g., by continuously adjusting or determining the sensitivity and background of the sensor) at time intervals ranging from, for example, a fraction of a second to, for example, 1, 2, or 5 minutes or more.

[0047] The terms "sensitivity" or "sensor sensitivity," as used herein, are broad terms and are used in their ordinary sense to refer to, but are not limited to, the amount of signal generated by a particular concentration of a measured analyte or a measured species (e.g., HO) associated with a measured analyte (e.g., glucose or lactate). For example, a sensor may have a sensitivity of about 1 to about 300 picoamps of current per mg / dL of glucose analyte.

[0048] The term "sensor data" as used herein is a broad term and is used in its ordinary sense to refer to any data associated with a sensor, such as a continuous analyte sensor or a continuous multi-analyte sensor, but is not limited to such. Sensor data includes raw data streams or simply data streams of analog or digital signals directly related to the measured analyte from an analyte sensor (or other signal received from another sensor), as well as calibrated and / or filtered versions of the raw data. The terms "sensor data point" and "data point" generally refer to a digital representation of sensor data at a particular time. The terms broadly encompass multiple time interval data points from a sensor, such as a continuous analyte sensor, including individual measurements taken at time intervals ranging from a fraction of a second to, for example, one, two, or five minutes or more. In another example, sensor data includes an integrated digital value representing one or more data points averaged over a period of time. Sensor data may include calibration data, smoothed data, filtered data, transformed data, and / or any other data associated with a sensor.

[0049] As used herein, the term "sensor electronics" is a broad term and is used in its ordinary sense to refer to, but is not limited to, device components, e.g., hardware and / or software, configured to process sensor data.

[0050] Although certain embodiments herein are described with reference to managing diabetes, diabetes management is merely an example of one application for which the present systems and methods may be utilized. The systems and methods described herein may also be used to manage one or more other diseases or conditions, which may or may not include diabetes. For example, the systems and methods described herein may be utilized to manage kidney disease, liver disease, and other types of diseases or conditions.

[0051] Exemplary Disease Management System Including Exemplary Continuous Multi-Analyte Monitoring System

[0052] FIG. 1 illustrates an exemplary disease management system 100 for assisting a user 102 (individually referred to herein as a user and collectively referred to herein as a user) with decision support for managing a disease, e.g., diabetes, kidney disease, liver disease, and / or other types of disease. The system 100 utilizes a continuous analyte monitoring system 104 configured to continuously measure one or more of a plurality of analytes, such as glucose, lactate, potassium, creatinine, ketones, alcohol, etc. The user may be a patient in certain embodiments, or in some cases, a caregiver for the patient. In certain embodiments, the system 100 includes the continuous analyte monitoring system 104, a display device 107 running an application 106, an analysis engine 114, a user database 110, a medical history record database 112, and a training server system 140, each of which is described in more detail below.

[0053] As used herein, the term "analyte" is a broad term used in its ordinary sense, including, but not limited to, referring to a substance or chemical constituent in a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymphatic fluid, sweat, or urine) that may be analyzed. Analytes may include naturally occurring substances, man-made substances, metabolites, and / or reaction products. Analytes measured by the present devices and methods may include, but may not be limited to, glucose, acarboxyprothrombin; acylcarnitines; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); andrenostenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-beta hydroxy-cholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporine; Porin A; d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphism, alcohol dehydrogenase, alpha 1-antitrypsin, cystic fibrosis, Duchenne / Becker muscular dystrophy, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, beta-thalassemia, hepatitis B virus , HCMV, HIV-1, HTLV-1, Leber's hereditary optic neuropathy, MCAD, RNA, PKU, Plasmodium vivax, sex differentiation, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free beta-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (FT4);Free tri-iodothyronine (FT3); Fumarylacetoacetase; Galactose / gal-1-phosphate; Galactose-1-phosphate uridyltransferase; Gentamicin; Glucose-6-phosphate dehydrogenase; Glutathione; Glutathione peroxidase; Glycocholate; Glycerol; Glycosylated hemoglobin; Halofantrine; Hemoglobin variants; Hexosaminidase A; Human erythrocyte carbonic anhydrase enzyme I;17-α-hydroxyprogesterone;hypoxanthine phosphoribosyltransferase;immunoreactive trypsin;lactate;lead;lipoproteins ((a), B / A-1, β);lysozyme;mefloquine;netilmicin;phenobarbitone;phenytoin;phytanic acid / pristanic acid;progesterone;prolactin;prolidase;purine nucleoside phosphorylase;potassium;quinine;reverse triiodothyronine (reverse tri-iodothyronine, rT3); selenium; serum pancreatic lipase; sisomicin; somatomedin C; specific antibodies (adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea worm, tapeworm, ameba histolytica, enterovirus, giardiasis, Helicobacter pylori, hepatitis B virus, herpes virus, HIV-1, IgE (atopic disease), influenza virus, Leishmania donovani, Leptospirosis, measles / mumps / rubella, Mycobacterium leprae, Mycoplasma pneumoniae, myoglobin, Onchocerca volvulus, parainfluenza virus, malarial Plasmodium, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, Rickettsia (tsutsugamushi disease), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / langeri, Vesicular stomatitis virus, Wuchereria bancrofti, Yellow fever virus); specific antigens (Hepatitis B virus, HIV-1); succinylacetone; sulfadoxine; theophylline; thyrotropin (TSH); thyroxine (T4); thyroxine-binding globulin; trace elements; transferrin; UDP-galactose-4-epimerase; urea; uroporphyrinogen I synthase; vitamin A; leukocytes; and zinc protoporphyrin.

[0054] Salts, sugars, proteins, fats, vitamins, and hormones naturally occurring in blood or interstitial fluids can also constitute analytes in certain implementations. Analytes can be naturally occurring in biological fluids, such as metabolites, hormones, antigens, antibodies, and the like. Alternatively, analytes can be introduced into the body or can be exogenous, such as contrast agents for imaging, radioisotopes, chemical agents, fluorocarbon-based synthetic blood, or drugs or pharmaceutical compositions, including, but not limited to, insulin; glucagon, ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, ritalin, silurt, preludine, didrex, prestate, borane, sandrex, pregin); antidepressants ( Medications that may be used include, but are not limited to, barbiturates, methaqualone, valium, librium, miltuna, serax, equanil, tranxine, and other tranquilizers; hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, percocet, percodan, tasionex, fentanyl, darvon, talwin, lomotil); synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and analogs of phencyclidine, e.g., ecstasy); anabolic steroids; and nicotine. Metabolic products of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals produced in the body, such as, for example, ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), and 5-hydroxyindoleacetic acid (FHIAA), as well as intermediates of the citric acid cycle, can also be analyzed.

[0055] In certain embodiments, the analytes measured and analyzed by the devices and methods described herein include glucose, lactate, ketones, potassium, and in some examples, other analytes listed above, however, other analytes not listed above may also be considered.

[0056] In certain embodiments, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to the display device 107 for use by the application 106. In some embodiments, the continuous analyte monitoring system 104 transmits the analyte measurements to the display device 107 through a wireless connection (e.g., a Bluetooth connection). In certain embodiments, the display device 107 is a smartphone. However, in certain other embodiments, the display device 107 may instead be any other type of computing device, such as a laptop computer, a smart watch, a fitness tracker, a tablet, or any other computing device capable of running the application 106. The continuous analyte monitoring system 104 may be described in more detail with respect to FIG. 2.

[0057] Application 106 is a mobile health application configured to receive and analyze analyte measurements from analyte monitoring system 104. For example, application 106 stores information about the user in the user profile 118, including the user's analyte measurements, for processing and analysis, and for use by decision support engine 152 to provide decision support recommendations or guidance to the user.

[0058] The analysis engine 114 includes a decision support engine 152 and a calibration engine 154. In certain embodiments, the decision support engine 152 and the calibration engine 154 may be implemented as a set of software instructions. As discussed in more detail herein, the decision support engine 152 may provide disease management decision support recommendations to a user, for example, via the application 105, and the calibration engine 154 may drive calibration and / or calibration-related processes of one or more sensors of the continuous analyte monitoring system 104. The decision support engine 152 may provide such recommendations and / or the calibration engine 154 may drive such calibrations based on analyte measurements associated with one or more analytes received from the continuous analyte monitoring system 104, data acquired from one or more non-analyte sensors or devices, and / or information included in the user profile 118. In certain embodiments, the analysis engine 114 executes entirely on one or more computing devices in a private or public cloud. In such embodiments, the application 106 communicates with the analysis engine 114 via a network (e.g., the Internet). In some other embodiments, the analysis engine 114 executes partially on one or more local devices, such as a processor associated with the display device 107 or the continuous analyte monitoring system 104, and partially on one or more computing devices in a private or public cloud. In some other embodiments, the analysis engine 114 executes entirely on one or more local devices, such as a processor associated with the display device 107 or the continuous analyte monitoring system 104.

[0059] The user profile 118 may include information collected about the user from the application 106. For example, the application 106 provides a set of inputs 128 including analyte measurements associated with one or more analytes received from the continuous analyte monitoring system 104, which are stored in the user profile 118. In certain embodiments, the inputs 128 provided by the application 106 include other data in addition to the analyte measurements. For example, the application 106 may obtain the additional inputs 128 through manual user input, one or more other non-analyte sensors or devices (e.g., temperature sensors, etc.), other applications running on the display device 107, etc. The non-analyte sensors and devices include, but are not limited to, one or more of an insulin pump, a respiration sensor, a sensor or device provided by the display device 107 (e.g., an accelerometer, gyrometer, camera, global positioning system (GPS), heart rate monitor, etc.) or other user accessories (e.g., a smart watch), or any other sensor or device that provides relevant information about the user. The user profile 118 inputs 128 provided by the application 106 may include, for example, continuous analyte sensor data, non-analyte sensor data, time, food consumption, physical activity, sleep information, user statistics, medications, and the like.

[0060] 1 , each of decision support engine 152 and calibration engine 154 includes a data analysis module (DAM) 153 or 155, respectively. However, it should be noted that in other embodiments, decision support engine 152 and calibration engine 154 may share a single DAM. In particular embodiments, the DAM is configured to process one or more inputs 128 and determine one or more metrics 130. Metrics 130 may, at least in some cases, generally be indicative of a user's health or condition, e.g., a physiological condition of the user. Examples of metrics 130 include glucose level, glucose threshold, glucose trend, lactate level, lactate baseline, lactate threshold, lactate trend, ketone level, ketone production rate, insulin sensitivity, insulin on-board, dietary state, dietary habits, physical fitness, metabolic rate, temperature, blood pressure, heart rate, heart rate variability, respiration rate, blood alcohol concentration, etc. In certain embodiments, metrics 130 may be used as inputs, in addition to inputs 128, by decision support engine 152 and / or calibration engine 154 to perform decision support or to calibrate one or more continuous multi-analyte sensors, respectively. As shown, metrics 130 may also be stored in user profile 118.

[0061] The user profile 118 further includes demographic information 120, disease information 122, and / or medication information 124. In certain embodiments, such information may be provided through user input or may be obtained from a particular data store (e.g., electronic medical record, etc.). In certain embodiments, the demographic information 120 may include one or more of the user's age, body mass index (BMI), ethnicity, gender, etc. In certain embodiments, the disease information 122 may include information regarding the user's one or more diseases, including relevant information regarding the user's diabetic status and / or other conditions (e.g., liver disease, renal disease, etc.). In certain embodiments, the disease information 122 may also include length of time since diagnosis, level of disease control, level of adherence to disease management therapy, other types of diagnoses (e.g., heart disease, obesity), etc. In certain embodiments, disease information 122 may include other measures of health (e.g., heart rate, stress, sleep, etc.) or health status (e.g., cardiovascular endurance, muscle strength and / or power, muscular endurance, and other measures of health status).

[0062] In certain embodiments, medication information 124 may include information regarding the amount and type of medication taken by the user.In certain embodiments, the dosing information includes insulin (e.g., short-acting insulin, rapid-acting insulin (insulin aspart, insulin glulisine, insulin lispro), intermediate-acting insulin (insulin isophane), long-acting insulin degludec, indulin detemir, insulin glargine, insulin), combination insulin), amylinomimetics, alpha-glucosidase inhibitors (e.g., acarbose, miglitol), biguanides (e.g., metformin-alogliptin, metformin-dopamine), metformin-dopamine, ... metformin-canagliflozin, metformin-dapagliflozin, metformin-empagliflozin, metformin-glipizide, metformin-glyburide, metformin-linagliptin, metformin-pioglitazone, metformin-repaglinide, metformin-rosiglitazone, metformin-saxagliptin, metformin-sitagliptin), dopamine agonists (e.g., bromocriptine), dipeptidyl peptidase-4 (dipeptidyl peptidase-4 (DPP-4) inhibitors (e.g., alogliptin, alogliptin-pioglitazone, linagliptin, linagliptin-empagliflozin, saxagliptin, sitagliptin, simvastatin), glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., albiglutide, dulaglutide, exenatide, repagliptin, semagligliflozin), meglitinides (e.g., nateglinide, repaglinide), sodium-glucose transporters (sodium-glucose The information may include information regarding the consumption of one or more medications for the management of the user's diabetic condition, such as glutamatidine diabetic reductase (SGLT) 2 inhibitors (e.g., dapagliflozin, canagliflozin, exenatide, liraglutide, semagligliflozin), sulfonylureas (e.g., glimepiride, glimepiride-pioglitazone, glimepiride-rosiglitazone, gliclazide, glipizide, glyburide, chlorpropamide, tolazamide, tolbutamide), thiazolidinediones (e.g., rosiglitazone, pioglitazone), and other medications.In certain embodiments, the medication information may include information regarding the consumption of one or more medications for the management or treatment of other diseases or conditions of the user, including medications for cholesterol, high blood pressure, heart disease, etc., in addition to supplements for promoting general health and metabolism, such as vitamins.

[0063] The data stored in the user profile 118 may maintain time series data collected about a user (e.g., a patient) over the period of time that the user utilizes the continuous analyte monitoring system 104 and application 106. For example, analyte data for a user who has used the continuous analyte monitoring system 104 and application 106 for five years to manage a condition may have the analyte data associated with the user maintained in the user profile 118 for the five years.

[0064] Additionally, the data stored in the user profile 118 may provide time series data collected over the lifetime of the user. For example, the data may include information associated with the user throughout the user's lifetime indicative of the user's physiological state, the user's glucose level, the user's lactate level, the user's ketone level, the state / condition of one or more of the user's organs, the user's habits (e.g., alcohol consumption, activity level, food consumption, etc.), prescribed medications, etc.

[0065] In certain embodiments, the user profile 118 is dynamic because at least a portion of the information stored in the user profile 118 may be revised or updated over time and / or new information may be added to the user profile 118 by the analytics engine 114 and / or the applications 106. Thus, the information in the user profile 118 stored in the user database 110 provides an up-to-date repository of information about the user.

[0066] User database 110, in some embodiments, refers to a storage server operating in, for example, a public or private cloud. User database 110 may be implemented as any type of data store, such as a relational database, a non-relational database, a key-value data store, a file system including a hierarchical file system, etc. In some example implementations, user database 110 is distributed. For example, user database 110 may comprise multiple persistent storage devices that are distributed. Additionally, user database 110 may be replicated such that the storage devices are geographically distributed.

[0067] The user database 110 includes user profiles 118 associated with multiple users, including users who similarly interact or have previously interacted with the applications 106 on their devices. The user profiles stored in the user database 110 are accessible to the applications 106 as well as the analysis engine 114. The user profiles in the user database 110 may be accessible to the applications 106 and the analysis engine 114 via one or more networks (not shown). As described above, the analysis engine 114, and more specifically the data analysis modules 153, 155 of the analysis engine 114, can fetch inputs 128 from the user's profile 118 stored in the user database 110 and then calculate one or more metrics 130, which can be stored in the user's profile 118 as application data 126.

[0068] In certain embodiments, the user profiles 118 stored in the user database 110 may also be stored in the medical history record database 112. The user profiles 118 stored in the medical history record database 112 may provide a repository of up-to-date information and medical history information for each user of the application 106. Thus, the medical history record database 112 essentially provides all data related to each user of the application 106, the data being stored using timestamps. The timestamp associated with any information stored in the medical history record database 112 may identify, for example, when the information was obtained and / or updated.

[0069] Additionally, in certain embodiments, historical record database 112 may include data for one or more patients who are not users of continuous analyte monitoring system 104 and / or application 106. For example, historical record database 112 may include information (e.g., user profiles) about one or more patients, diagnosed or not diagnosed with diabetes, analyzed, for example, by a healthcare physician (or other known method). The data stored in historical record database 112 may be referred to herein as population data.

[0070] Although depicted as separate databases for conceptual clarity, in some embodiments, the user database 110 and the medical history record database 112 may operate as a single database, which may be a storage server running on a public or private cloud.

[0071] In addition to decision support, the disease management system 100 is configured to perform various decisions regarding recalibration, as well as detection and compensation for artifacts and other physiological events, using the continuous analyte monitoring system 104 configured to continuously measure one or more analytes. For example, in certain embodiments, the calibration engine 154 is configured to provide real-time or retrospective calibration-related decisions, thus enabling immediate and automatic recalibration or compensation for erroneous measurements, and preventing or reducing the occurrence of erroneous glycemic event predictions or treatment recommendations by the decision support engine 152. In particular, the calibration engine 154 may be used to collect information associated with a user in the user profile 118 via the DAM 155, perform analyses thereon, and use such analyses in making decisions regarding recalibration or sensor measurement compensation. The user profile 118 may be accessible to the calibration engine 154 via one or more networks (not shown) to perform such analyses.

[0072] In certain embodiments, the calibration engine 154 may utilize one or more trained machine learning models that enable the calibration engine 154 to perform analysis on the information collected / received from the user profile 118. In the illustrated embodiment of FIG. 1, the calibration engine 154 may utilize one or more different trained machine learning models provided by the training server system 140 for different types of calibration-related decisions. Although depicted as separate servers for conceptual clarity, in some embodiments the training server system 140 and the analysis engine 114 may operate as a single server or system. That is, one or more models may be trained and used by a single server, or may be trained by one or more servers and deployed for use on one or more other servers or systems.

[0073] The training server system 140 is configured to train one or more machine learning models using training data, which may include data (e.g., from user profiles) associated with one or more patients (e.g., users or non-users of the continuous analyte monitoring system 104 and / or application 106, e.g., diabetes patients). The training data may be stored in the medical history record database 112 and may be accessible to the training server system 140 via one or more networks (not shown) for training the machine learning models.

[0074] Training data refers to a characterized and labeled data set. For example, the data set may include multiple data records, each containing information corresponding to a different user profile stored in the user database 110, and each data record is characterized and labeled. In machine learning and pattern recognition, a feature is an individual measurable characteristic or feature. Generally, the feature that best characterizes the patterns in the data is selected to create a predictive machine learning model. Data labeling is the process of adding one or more meaningful and informative labels to provide context to the data for learning by the machine learning model. As an illustrative example, each relevant feature or data point of a user reflected in the corresponding data record may be a feature used in training the machine learning model. Such features may include age, gender, normal glucose range, normal lactate range (e.g., lactate baseline and / or peak), normal ranges for other analytes and / or other secondary sensor data, time of glycemic event and / or duration associated with the glycemic event, glucose information associated with the glycemic event (e.g., glucose level associated with a hypoglycemic or hyperglycemic event, etc.), treatment administered, glucose information after treatment, data related to fitness level (e.g., resting heart rate (RHR), minutes of activity per day, etc.), data related to meals, data related to sensor placement or positioning, etc. Such features may be utilized to classify whether a measured analyte range is an outlier or within an expected range for a given user profile 118 and may then be utilized for calibration related recommendations or other decisions.

[0075] One or more models are then trained by the training server system 140 using the characterized and labeled training data. In particular, the features of each data record may be used as input to a machine learning model, and the generated output may be compared to the label associated with the corresponding data record. In certain embodiments, the model may calculate a loss based on the difference between the generated output and the provided label. This loss is then used to modify the internal parameters or weights of the model. By iteratively processing each data record corresponding to each historical patient, the model may be iteratively refined to generate accurate calibration related decisions.

[0076] As illustrated in FIG. 1, the training server system 140 deploys these trained models to the analysis engine 114 for use during runtime. For example, the calibration engine 154 may retrieve a user profile 118 associated with a user and stored in the user database 110, use information in the user profile 118 as input to the trained model, and output a decision (e.g., shown as output 144 in FIG. 1) that is utilized to calibrate or recalibrate a sensor. In certain embodiments, the output 144 generated by the calibration engine 154 may be utilized to automatically recalibrate a continuous analyte sensor or adjust a sensor measurement upon detection of an artifact or other physiological event. In certain embodiments, the output 144 may be utilized to provide a recommendation regarding recalibration to the user. In further embodiments, the output 144 may be utilized in further decisions by the calibration engine 154 and may further be utilized to train or retrain a trained model stored in the user database 110. In certain embodiments, the output 144 generated by the calibration engine 154 may be stored in the user profile 118.

[0077] 2 is a diagram 200 that conceptually illustrates an example continuous analyte monitoring system 104 including an example continuous analyte sensor with sensor electronics, according to certain aspects of the present disclosure. For example, the system 104 can be configured to continuously monitor one or more analytes of a user, according to certain aspects of the present disclosure.

[0078] The continuous analyte monitoring system 104 in the illustrated embodiment includes a sensor electronics module 204 and one or more continuous analyte sensors 202 (individually referred to herein as a continuous analyte sensor 202 and collectively referred to herein as continuous analyte sensors 202) associated with the sensor electronics module 204. The sensor electronics module 204 can be in wired or wireless communication (e.g., directly or indirectly) with one or more of the display devices 210, 220, 230, and 240. In certain embodiments, the sensor electronics module 204 may also communicate wired or wirelessly (e.g., directly or indirectly) with one or more medical devices, such as a medical device 208 (individually referred to herein as medical device 208 (singular) and collectively referred to herein as medical devices 208 (plural)), and / or one or more other non-analyte sensors 206 (individually referred to herein as non-analyte sensors 206 and collectively referred to herein as non-analyte sensors 206).

[0079] In certain embodiments, the continuous analyte sensor 202 may comprise one or more sensors for detecting and / or measuring an analyte. The continuous analyte sensor 202 may be a multi-analyte sensor configured to continuously measure two or more analytes (e.g., glucose, lactate, potassium, ketones, etc.) or a single-analyte sensor configured to continuously measure a single analyte (e.g., where one continuous analyte sensor 202 is used to measure glucose and then a second continuous analyte sensor 202 is used to measure lactate). In certain embodiments, the continuous analyte sensor 202 may be a non-invasive device, a subcutaneous device, a transdermal device, and / or an intravascular device. In certain embodiments, the continuous analyte sensor 202 may be configured to continuously measure an analyte level of a user using one or more techniques, such as enzymatic techniques, chemical techniques, physical techniques, electrochemical techniques, spectrophotometric techniques, polarimetric techniques, colorimetric techniques, iontophoretic techniques, radiometric techniques, immunochemical techniques, etc. The term "continuous" as used herein can mean continuous, semi-continuous, intermittent, periodic, intermittent, regular, etc. Exemplary continuous sensing techniques are described with reference to Figures 3A-3B. In certain embodiments, the continuous analyte sensor 202 provides a data stream indicative of the concentration of one or more analytes in a user. The data stream may include a raw data signal, which is then converted into a calibrated and / or filtered data stream, such as by the calibration engine 154 described above, and used to provide an estimated analyte value to a user.

[0080] In certain embodiments, the continuous analyte sensor 202 may be a multi-analyte sensor configured to continuously measure multiple analytes in a user's body. For example, in certain embodiments, the continuous multi-analyte sensor 202 may be a single sensor configured to measure glucose, lactate, ketones, glycerol, potassium (e.g., hyperkalemia), sodium, CO2, or anion gap, or similar analytes in a user's body.

[0081] In certain embodiments, the sensor electronics module 204 includes electronic circuitry for measuring and processing the continuous analyte sensor data, including predictive algorithms associated with processing and calibrating the sensor data. The sensor electronics module 204 can be physically connected to the continuous analyte sensor 202 and can be integral (non-releasably attached) or releasably attached to the continuous analyte sensor 202. The sensor electronics module 204 can include hardware, firmware, and / or software that enables measurement of the level of the analyte via the continuous analyte sensor 202. For example, the sensor electronics module 204 can include a potentiostat, a power supply for providing power to the sensor, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module, for example, to one or more display devices. The electronics can be affixed to a printed circuit board (PCB) or the like and can take a variety of forms. For example, the electronics can take the form of an integrated circuit (IC), such as an application-specific integrated circuit (ASIC), a microcontroller, and / or a processor. As another example, the sensor electronics module 204 may include a memory and a processor. The memory may store software instructions that the processor executes to perform any of the functions or operations of the continuous analyte monitoring system 104 described herein.

[0082] The display devices 210, 220, 230, and / or 240 are configured to display displayable sensor data, including analyte data, that may be transmitted by the sensor electronics module 204. In one embodiment, the sensor electronics module 204 may transmit raw sensor data that is converted to displayable sensor data via one or more of the display devices 210, 220, 230, and / or 240. In another embodiment, the sensor electronics module 204 may convert the raw sensor data to displayable sensor data and transmit the displayable sensor data to one or more of the display devices 210, 220, 230, and / or 240. Each of the display devices 210, 220, 230, or 240 may include a display, such as a touch screen display 212, 222, 232, and / or 242, for displaying the sensor data to a user and / or accepting input from the user. For example, a graphical user interface (GUI) may be presented to the user for such purposes. In some embodiments, the display device may include other types of user interfaces, such as a voice user interface, instead of or in addition to a touch screen display, for communicating sensor data to a user of the display device and / or accepting user input. Display devices 210, 220, 230, and 240 may be examples of display device 107 illustrated in FIG. 1 used to display sensor data to and / or receive input from a user of the system of FIG.

[0083] In some embodiments, one, some, or all of the display devices are configured to display or otherwise communicate the sensor data as it is communicated from the sensor electronics module (e.g., in a data package sent to the display device based on their respective preferences) without any additional possible processing required for calibration and / or real-time display of the sensor data.

[0084] The display devices may include custom display devices designed specifically to display a particular type of displayable sensor data associated with the analyte data received from the sensor electronics module. Display device 210 is an example of such a custom device. In some embodiments, one of the display devices is a smart phone, such as display device 220 representing a mobile phone, configured to display a graphical representation of continuous sensor data (e.g., including current and historical data) using a commercially available operating system (OS). Other display devices may include display device 230 representing a tablet, display device 240 representing a smartwatch, medical device 208 (e.g., an insulin delivery device or a blood glucose meter), and / or a desktop or laptop computer (not shown).

[0085] Because different display devices have different user interfaces, the contents of the data package (e.g., amount, format, and / or type of data displayed, alarms, etc.) can be customized (e.g., programmed differently by the manufacturer and / or end user) for each particular display device. Thus, in certain implementations, multiple different display devices can wirelessly communicate directly with the sensor electronics module (e.g., the on-skin sensor electronics module 204 physically connected to the continuous analyte sensor 202) during a sensor session to enable multiple different types and / or levels of display and / or functionality associated with the displayable sensor information.

[0086] As mentioned, the sensor electronics module 204 may be in communication with a medical device 208. The medical device 208 may be a passive device in some exemplary embodiments of the present disclosure. For example, the medical device 208 may be an insulin pump for administering insulin to a user. For various reasons, it may be desirable for such an insulin pump to receive and track analyte values, e.g., glucose values, transmitted from the continuous analyte monitoring system 104, and the continuous analyte sensor 202 comprises at least a glucose sensor.

[0087] Additionally, as mentioned, the sensor electronics module 204 may also communicate with other non-analyte sensors 206. The non-analyte sensors 206 may include, but are not limited to, altimeter sensors, accelerometer sensors, gyrometer sensors, global positioning system (GPS) sensors, temperature sensors, respiration sensors, electromyogram (EMG) sensors, galvanic skin response (GSR) sensors, impedance sensors, electrocardiogram sensors, sweat sensors, and the like. The non-analyte sensors 206 may also include monitors such as heart rate monitors, blood pressure monitors, pulse oximeters, caloric intake monitors, and medication delivery devices. One or more of these non-analyte sensors 206 may provide data to the calibration engine 154, which is described further below. In some aspects, a user may manually provide a portion of the data for processing by the training server system 140 and / or the calibration engine 154 of FIG. 1.

[0088] In certain embodiments, the non-analyte sensor 206 may be combined in any other configuration, such as, for example, combined with one or more continuous analyte sensors 202. As an illustrative example, a non-analyte sensor, such as a temperature sensor, may be combined with a continuous glucose sensor 202 to form a glucose / temperature sensor that is used to transmit sensor data to the sensor electronics module 204 using a common communication circuit. As another illustrative example, a non-analyte sensor, such as a temperature sensor, may be combined with a multi-analyte sensor 202 configured to measure glucose and lactate to form a glucose / lactate / temperature sensor that is used to transmit sensor data to the sensor electronics module 204 using a common communication circuit.

[0089] One or more of the continuous analyte monitoring system 104, the multiple display devices, the medical device 208, and / or the non-analyte sensors 206 may be configured to communicate together wirelessly using one of a variety of wireless communication technologies (e.g., Wi-Fi, Bluetooth, Near Field Communication (NFC), cellular, etc.). In certain embodiments, a wireless access point (WAP) may be used to couple one or more of the continuous analyte monitoring system 104, the multiple display devices, the medical device 208, and / or the non-analyte sensors 206 to one another. For example, the WAP may provide Wi-Fi, Bluetooth, and / or cellular connectivity between these devices. NFC may also be used between the devices depicted in the diagram 200 of FIG. 2.

[0090] Exemplary Continuous Multi-Analyte Sensors

[0091] 3A and 3B illustrate the sensor electronics of exemplary multi-analyte sensors 300 and 301, respectively, for use with the disease management system of FIG 1, according to some embodiments disclosed herein. In particular, multi-analyte sensors 300 and 301 are examples of continuous analyte sensor 202 in FIG 2.

[0092] As shown in Figure 3A, the multi-analyte sensor 300 is an enzyme-based "three-electrode" electrochemical sensor having a sensing region 302 that includes multiple working electrodes 304 (two working electrodes 304A and 304B are illustrated in Figure 3A), a reference electrode 306, and a counter electrode 308, where in certain embodiments the counter electrode 308 has a surface area that is greater (e.g., at least 10 times) than the surface area of ​​the multiple working electrodes 304 combined. Although only two working electrodes 304 are depicted, it is contemplated that the multi-analyte sensor 300 may include any suitable number of working electrodes for multiplexed sensing of multiple analytes.

[0093] The electrodes 304, 306, and 308 may be formed from any suitable material and by any suitable method. For example, in certain embodiments, the reference electrode 306 comprises silver (Ag) or silver / silver chloride (Ag / AgCl) and is kept currentless. In certain embodiments, the counter electrode 308 is formed from, for example, platinum (Pt), carbon (C), or other suitable inert materials. In certain embodiments, the working electrode 304 is formed from one or more precious metals, such as Pt or platinum / iridium (Pt / Ir). In other embodiments, the working electrode 304 is carbon-based and comprises carbon (C) or carbon / ruthenium (C / Ru). In further embodiments, the working electrode 304 is formed from graphite, gold, a conductive polymer, or the like. In general, suitable methods for forming the electrodes 304, 306, and 308 include microfabrication techniques, such as physical vapor deposition, chemical vapor deposition, electrodeposition, lithography, and / or etching techniques. However, other methods are contemplated, including spray deposition or dip coating.

[0094] Each of the multiple working electrodes 304 includes an active surface 312 to facilitate electrochemical sensing of a desired analyte. In certain embodiments, the active surface 312 of each working electrode 304 is voxelized or divided into discrete sections (e.g., cubic sections). In certain embodiments, a plurality of different enzymes (not shown), each specific to a different analyte, are deposited and immobilized on the respective active surface 312 of each working electrode 304 (e.g., on each active surface 312 of each working electrode 304A and 304B in FIG. 3A ). For example, in certain embodiments, both glucose oxidase and lactate or urate oxidase (and / or other analyte-specific enzymes) may be deposited on the active surface 312 of one working electrode 304A. In further embodiments, a plurality of different enzymes are immobilized on each voxel of each working electrode 304, while in other embodiments, only one type of enzyme is deposited on each voxel of each working electrode 304. In certain embodiments, each of the working electrodes 304 includes an enzyme for one particular analyte, although different working electrodes 304 may include enzymes for different analytes. In certain embodiments, one or more of each of the working electrodes 304 includes two or more enzymes (e.g., three or more enzymes) that together allow for the sensing of a single analyte. For example, in an embodiment in which the working electrodes 304 are configured to sense creatinine, the working electrodes 304 may include three or more enzymes specific for creatinine. An example of a one-enzyme sensor working electrode 304 may include a lactate-specific electrode, and an example of a two-enzyme sensor working electrode 304 may include a ketone-specific electrode. The enzymes may be immobilized via adsorption, entrapment, cross-linking, covalent binding, or any other suitable immobilization method.

[0095] During sensing, the deposited enzymes are utilized to convert the respective analytes to intermediate products (e.g., hydrogen peroxide), which are then oxidized at the surface of the working electrode 304. The resulting current is measured by a potentiostat 310 or an ammeter (described below) in communication with the potentiostat 310 and is proportional to the analyte concentration. Examples of suitable enzymes include glucose oxidase for sensing glucose species, lactate oxidase for sensing lactate species, lactose oxidase for sensing lactose species, glutamate oxidase for sensing glutamate species, and the like. In certain embodiments, in addition to the enzymes, the active surface 312 further includes immobilized redox mediators (e.g., relays) (not shown), which are small electroactive molecules for shuttling electrons between the enzymes and the working electrode 304. In certain embodiments, the enzymes are immobilized exclusively on the skived regions of the working electrode 304 to minimize or avoid crosstalk between different analytes. In other words, the area of ​​the active surface 312 on each of the working electrodes 304 is less than the geometric surface area of ​​the respective working electrode. In such embodiments, maintaining a potential bias at the working electrode 304 promotes near-zero peroxide flux from the skive region, accompanied by active consumption of hydrogen peroxide intermediates.

[0096] As further shown, the multi-analyte sensor 300 includes a potentiostat 310 that applies a potential to the working electrodes 304 and the reference electrode 306 and, in certain embodiments, measures a current value at each of the working electrodes. In certain embodiments, the potentiostat 310 is communicatively coupled to an ammeter (not shown) for measuring a current value at each of the working electrodes. The potentiostat 310 includes an integrated multiplexer interface for isolating signals from each of the working electrodes 304, thus facilitating measurements from individual working electrodes without the need for multiple dedicated "single channel" potentiostats. For example, in certain embodiments, the potentiostat 310 is configured to switch the potential bias between the different working electrodes 304 for a predetermined period or sampling frequency. For example, the potentiostat 310 may provide a potential to working electrode 304A for a predetermined period of 1 second, 5 seconds, 10 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 10 minutes, or more, and then switch to provide a potential to working electrode 304B, etc., to collect a response current for each working electrode 304. The response current measurements may be made via a variety of suitable methods, including differential pulse voltammetry (DPV), linear voltage sweep, or other voltammetric sweep methods, where different voltages are applied to each working electrode 304 corresponding to different redox couples of different analytes.

[0097] In certain embodiments, to sense multiple analytes via a single working electrode 304, the potentiostat 310 is configured to enable / disable and / or switch the potential waveform (e.g., shape, frequency, etc.) applied to the working electrode 304 using a mixture of impedance and amperometric modes. For example, in certain embodiments, the potentiostat 310 can apply a first signal having a first waveform to the working electrode 304 during a first time period and the impedance of the circuit can be measured to determine the analyte level of the first analyte. Then, a second signal having a second waveform can be applied to the same working electrode 304 during a second time period and the current through the circuit can be measured to determine the analyte level of the second analyte.

[0098] FIG. 3B illustrates the sensor electronics of another exemplary multi-analyte sensor 301, which comprises an enzyme and aptamer-based "three-electrode" electrochemical sensor for detecting the presence of multiple desired analytes. Similar to the multi-analyte sensor 300, the multi-analyte sensor 301 includes a reference electrode 306 and a counter electrode 308, which may be formed from the same materials and by the same techniques as described above. In addition, the multi-analyte sensor 301 further includes one or more working electrodes 314 and a potentiostat 316, which may be integrated with or in communication with an impedance analyzer (e.g., a wideband impedance analyzer). It should be noted that the electrode arrangements in the examples of FIG. 3A and FIG. 3B may be utilized in combination in certain embodiments. For example, in certain embodiments, the multi-analyte sensor may include the working electrode 304 of FIG. 3A as well as the working electrode 314 of FIG. 3B to detect multiple different types of analytes.

[0099] Each of the one or more working electrodes 314 includes an active surface 322 to facilitate electrochemical sensing of a desired analyte. In certain embodiments, an analyte-specific enzyme (not shown) is deposited and immobilized on the active surface 322. The enzyme may be immobilized via adsorption, entrapment, cross-linking, covalent bonding, or any other suitable immobilization method. In certain embodiments, in addition to the enzyme, the active surface 322 further includes an immobilized redox mediator (e.g., a relay) (not shown) that facilitates electron transfer between the enzyme and the working electrode 314 to generate a measurable current.

[0100] In certain embodiments, the active surface 322 further comprises one or more aptamers immobilized thereon. An exemplary aptamer 324 and corresponding analyte 326 are depicted in FIG. 3C for reference. The aptamer 324 is a short, single-stranded DNA or RNA molecule (e.g., ssDNA or ssRNA) that selectively binds to a particular analyte 326 (e.g., glucose, lactate, glutamate, etc.). In examples where the aptamer 324 is thiolated, the aptamer 324 may be immobilized on the gold active surface 322 via direct immobilization. However, other examples of suitable aptamer immobilization techniques that may be utilized with the systems and compositions herein include, but are not limited to, the use of short linkers, streptavidin / biotin interactions, the use of DNA nanostructures and reduced graphene oxide as immobilization platforms, and / or physical adsorption.

[0101] Upon binding to the analyte 326, the aptamer 324 changes its secondary structure conformation, as depicted in FIG. 3C, thereby causing a measurable change in impedance through the working electrode 314 that can be measured by the potentiostat 316, or an impedance analyzer in communication therewith (e.g., a wideband impedance analyzer, not shown), using a frequency sweep method. The signal accurately represents the concentration of bound analyte (e.g., sufficient available binding sites for accurate detection of the analyte over the duration of sensor use) as long as sufficient aptamer is present on the active surface 322 at a concentration that persists over the duration of sensor use or wear. In certain embodiments, frequency sweeps can also be performed to monitor the sensor-tissue interface for noise sources, inaccuracies, etc. In embodiments where a particular perturbation frequency strongly correlates with analyte binding, an impedance analyzer may not be necessary. For example, in certain embodiments, impedance changes can be determined by recording current in response to a time-varying voltage perturbation, but generally low in amplitude compared to most chronoamperometric applications.

[0102] Thus, the presence of both an enzyme, which may be specific for a first analyte, and an aptamer, which may be specific for a second analyte, on the active surface 322 allows the multi-analyte sensor 301 to detect multiple different analytes by switching waveforms between a chronoamperometric method that measures the current through the working electrode 314 and a frequency sweep detection method that measures the impedance through the working electrode 314. In other words, binding of the enzyme to the first analyte can be detected by measuring the magnitude of the current through the working electrode 314, while binding of the aptamer to the second analyte can be detected by measuring the change in impedance through the working electrode 314. In certain embodiments, the multi-analyte sensor 301 comprises multiple working electrodes 314, each working electrode having a different enzyme and a different aptamer immobilized thereon, and the potentiostat 316 further comprises a multiplexer interface for separating signals from each of the working electrodes 314, thus facilitating separation of measurements from individual working electrodes for multiplexed multi-analyte sensing.

[0103] FIG. 3D diagrammatically illustrates an exemplary multiplexed sensing method for use with the continuous analyte sensor of FIG. 3A and FIG. 3B according to some embodiments disclosed herein. In certain embodiments, signal processing methods, such as filtering, can be utilized to deconvolute different analyte signals collected by the same ammeter (e.g., an ammeter integrated or communicatively coupled with a potentiostat) but different working electrodes. In certain embodiments, machine learning models can be utilized for feature extraction to distinguish analyte signals collected by the same ammeter but different working electrodes. For example, such models can be utilized when multiplexing is not available, e.g., when a sensor comprises a potentiostat without a multiplexer interface or when a sensor comprises a single working electrode.

[0104] In certain embodiments, the machine learning model may be particularly beneficial when the presence of one desired analyte (e.g., a detectable concentration of the analyte) or analyte-related event (e.g., an unusually high or low concentration of the analyte) is separated in time from the presence of a second analyte, e.g., when one analyte is particularly concentrated (e.g., has high levels) in the morning or afternoon and a second analyte is particularly concentrated in the evening. In such an example, an enzyme specific for a first analyte and an enzyme specific for a second analyte may be uniformly deposited on the same working electrode surface (e.g., the same active surface). During sensing, current readings from the working electrode may be collected in the time domain, e.g., as time domain sensor readings, as shown in the top left of FIG. 3D. Such readings may then be processed, e.g., via a Fourier transform by the calibration engine 154 of FIG. 1, into the frequency domain, as shown in the bottom left of FIG. 3D. In the frequency domain, the signals of each of the different analytes (e.g., the first and second analytes) appear at different frequencies as a result of the temporal separation of the analytes. Thus, the different frequency responses of the different analytes can be separated, either digitally or by filtering, and then deconvolved separately by a machine learning model to recover time-domain signals for each of the analytes that are mutually exclusive with respect to the analyte concentration (shown in FIG. 3D, right).

[0105] Exemplary methods and systems for calibrating continuous multi-analyte sensors using continuously monitored analyte data - Patents.com

[0106] Automatic correction of factory calibration

[0107] Systems and methods for in vitro calibration (e.g., factory calibration) of continuous analyte sensors by manufacturers may be inadequate for the high levels of sensor accuracy required for diabetes management without reliance on recalibration. In some cases, this may be due in part to the initial factory calibration of the continuous analyte sensor being incorrect, for example, due to the manufacturer's mischaracterization of the sensitivity of the sensor's resistive layer. In some cases, this may also be due in part to changes in sensor characteristics (e.g., sensor sensitivity) prior to use by a user. Thus, continuous analyte sensors are typically recalibrated at the beginning of wear by a user to ensure sensor accuracy.

[0108] Conventional in vivo continuous analyte sensing techniques have relied on reference measurements performed during a sensor session to recalibrate the continuous analyte sensor. The reference measurements are matched to substantially time-corresponding sensor data to create matched pairs. Regression is then performed on the matched data pairs (e.g., by using least squares regression) to generate a transfer function that defines the relationship between the sensor signal and the estimated glucose concentration.

[0109] In critical care settings, recalibration of a continuous analyte sensor may be performed by using a calibration solution with a known concentration of the analyte as a reference. This recalibration procedure can be cumbersome because a calibration bag separate from the intravenous (IV) bag is typically used. In ambulatory environments, recalibration of a continuous analyte sensor is sometimes performed by capillary blood measurements (e.g., finger stick glucose tests) through which reference data is obtained and entered into the continuous analyte sensor system. This recalibration procedure typically involves frequent finger stick measurements, which can be inconvenient and painful. Thus, conventional methods of recalibration after an initial factory calibration involve periodic input of reference data, whether associated with a calibration solution or finger stick measurements, which can be very cumbersome for patients in an outpatient environment or for hospital staff in a critical care environment.

[0110] Described herein are systems and methods for automatically recalibrating a continuous analyte sensor, such as a continuous multi-analyte sensor, using data of at least one analyte in combination with other sensor data to correct for erroneous factory calibration or changes in sensor characteristics. Such systems and methods are particularly beneficial when events (or error-causing components) affecting calibration sensitivity are shared among multiple analytes on a continuous multi-analyte sensor, multiple analyte readings are abnormal or atypical, and at least one other analyte exhibits an expected concentration range during the wear period. The systems and methods can achieve a high level of accuracy without (or with reduced reliance on) reference data from a reference analyte monitor (e.g., from a blood glucose meter). Thus, certain systems and methods described herein provide technical solutions to the above-mentioned technical problems in the field of sensor calibration.

[0111] 4A illustrates a flow diagram of an exemplary method 400A for recalibrating a continuous analyte sensor for a first analyte using data for the first analyte in combination with other sensor data to correct for an erroneous factory calibration or changes in sensor characteristics according to certain embodiments of the present disclosure. For example, method 400A may be performed to recalibrate one or more continuous analyte sensors 202 of a continuous analyte monitoring system 104 as illustrated in FIGS. 1 and 2. Method 400A is described below with reference to FIGS. 1 and 2 and their components.

[0112] At block 402, the method 400A begins by continuously or non-continuously monitoring a first analyte of a patient, such as a user 102 illustrated in FIG. 1, using a first, factory calibrated sensor for a period of time to obtain measured analyte data for the first analyte. Block 402 may be performed in certain embodiments by a continuous analyte monitoring system 104 illustrated in FIGS. 1 and 2, and more specifically, a continuous analyte sensor 202 illustrated in FIG. 2. For example, the continuous analyte monitoring system 104 may include a factory calibrated continuous glucose sensor 202 to measure the glucose level of the user. Alternatively, the continuous analyte monitoring system 104 may include a factory calibrated continuous multi-analyte sensor configured to measure at least the glucose level of the user. In general, the first analyte may include glucose, although other analytes are contemplated.

[0113] At block 404, the method 400A continues by continuously or non-continuously monitoring other sensor data of the user during the time period to obtain other measured sensor data indicative of the user's physiological condition. The patient's physiological condition may refer to the user's general or physical condition, including any physical stresses thereof. For example, in certain embodiments, the user's physiological condition may include the user's engagement in physical activity or rest during the time period, the user's consumption of food during the time period, the administration of medication to the user during the time period, the occurrence of a glycemic event (e.g., hypoglycemia or hyperglycemia), and the like. As another example, the physiological condition may include the user's oxygen level or the user's temperature. The continuous analyte monitoring system 104 may include an oxygen sensor or a temperature sensor that measures the user's oxygen level or body temperature. In some cases, the oxygen sensor or temperature sensor may be combined with or embodied in the continuous analyte sensor 202. As another example, the physiological condition may include motion data indicative of whether the user is moving or stationary. The continuous analyte monitoring system 104 may include an accelerometer that detects when a user is moving.

[0114] The patient's physiological condition may be indicated by a variety of different types of sensor data, alone or in combination. In certain embodiments, the other sensor data monitored at block 404 includes measured analyte data for one or more additional analytes, including at least a second analyte, of the patient during the time period. For example, in certain embodiments, the other sensor data includes measured analyte data (e.g., concentration values, trends, and / or patterns) for electrolytes such as lactate, ketones, glycerol, sodium, and potassium, calculated measurements such as anion gap, and / or any other suitable analytes described herein, or combinations thereof. In certain other embodiments, the other sensor data monitored in block 404 includes secondary sensor data such as one or more of accelerometer data, gyrometer data, acoustic data, global positioning system (GPS) data, heart rate, cardiac reserve, heart rate variability (HRV), electrocardiogram (EKG) data, sweat, electromyogram (EMG), respiratory rate, temperature, blood pressure, galvanic skin response, oxygen intake data (e.g., VO2max), sleep, impedance data, etc. In certain embodiments, the other sensor data can be utilized to calculate a metric, such as metric 130 of FIG. 1 to further inform an analysis to recalibrate the first sensor.

[0115] In certain embodiments, block 404 is performed by the same sensor as block 402, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in Figures 1 and 2 (e.g., the continuous analyte sensor 202 can include a continuous multi-analyte sensor configured to measure both a second analyte and a first analyte). In other embodiments, block 404 is performed by a second sensor that is different from the first sensor used in block 402.

[0116] At block 406, the calibration engine 154 processes the measured other sensor data and determines predicted analyte data for the first analyte based at least in part on the measured other sensor data and / or the indicated physiological state of the user during the time period. The predicted analyte data for the first analyte refers to predicted analyte data for the patient's first analyte during the time period, where the prediction is informed by the indicated physiological state and / or the measured other sensor data, but not by the measured analyte data for the first analyte. In certain embodiments, the predicted analyte data for the first analyte is determined utilizing a predefined mapping (i.e., correlation) of the historical (i.e., previously measured) analyte data of the first analyte to the other sensor data and / or the physiological state of the user. For example, if the first analyte is glucose and the other sensor data includes lactate data and heart rate data, the calibration engine 154 can utilize a historical relationship of glucose, lactate, and heart rate data to determine predicted glucose data based on the measured (e.g., current) lactate data and heart rate data. In certain embodiments, the expected analyte data determined in block 406 is a range (e.g., a normal range) for a first analyte based at least in part on the other measured sensor data and / or the patient's indicated physiological state during the time period. For example, if the first analyte is glucose and the other sensor data includes lactate data, accelerometer data, and heart rate data, the calibration engine 154 may determine a normal range for glucose based on the lactate data, accelerometer data, and heart rate data.

[0117] In certain embodiments, the predicted analyte data for the first analyte is determined utilizing one or more trained machine learning models, such as those provided by the training server system 140 in FIG. 1, or other AI models. In certain embodiments, such models may be configured to use measured data from one or more sensors measuring multiple analytes as inputs and provide as outputs a determination utilized in recalibrating a continuous analyte sensor. Exemplary inputs for such models may include, for example, continuous analyte sensor data (e.g., for the first analyte and any other analytes), non-analyte sensor data, time, etc. Thus, given the interaction of various physiological conditions, parameters and / or thresholds of such algorithms or models may be altered based at least in part on the number of analytes or characteristics being measured for the inputs to reflect knowledge gained from each of the other analytes or characteristics being measured, or physical conditions associated with the additional analytes or characteristics being measured.

[0118] At block 408, the calibration engine 154 compares the expected analyte data for the first analyte (obtained from block 406) and the measured analyte data for the first analyte (obtained from block 402) to determine whether there is a substantial discrepancy therebetween. A substantial discrepancy may indicate an error in the calibration of one or more of the analyte sensors. A substantial discrepancy may, in certain embodiments, refer to a numerical delta (i.e., deviation or dissimilarity) in the analyte data that is greater than a predetermined acceptable range or threshold. For example, a substantial discrepancy between the expected analyte data for the first analyte and the measured analyte data for the first analyte may occur if the data values ​​diverge (i.e., differ) by more than a predetermined threshold of 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, or more. In certain embodiments, a substantial discrepancy between the expected analyte data for the first analyte and the measured analyte data for the first analyte includes different concentration values, trends, patterns, and / or other analyte data dynamics. If the expected analyte data determined in block 406 is within the range of the first analyte, in certain embodiments, a "substantial discrepancy" in block 408 can refer to the measured analyte data for the first analyte being outside of a determined range for the first analyte. For example, with respect to glucose, a "substantial discrepancy" in block 408 can refer to the measured glucose data being above or below the user's normal threshold for glucose.

[0119] In certain embodiments, to determine whether there is a substantial discrepancy between the predicted analyte data for a first analyte and the measured analyte data for the first analyte, one or more data points of the predicted analyte data can be matched to substantially time corresponding data points of the measured analyte data for the first analyte to provide one or more matched data pairs. In certain embodiments, one predicted analyte data point is matched to one time corresponding measured analyte data point to form a matched data pair. In other embodiments, multiple predicted analyte data points are combined (e.g., equally or unequal weighted average, mean, median, or equivalent) and matched to one time corresponding measured analyte data point to form a matched data pair. In certain embodiments, one or more matched data pairs may be used in recalibration.

[0120] If the calibration engine 154 determines that there is no substantial discrepancy between the expected analyte data for the first analyte and the measured analyte data for the first analyte (i.e., the measured analyte data is "normal"), then recalibration of the first sensor for the first analyte is not necessary. In other words, the first sensor provides measured analyte data for the first analyte that is acceptably accurate or within a predetermined acceptable accuracy range. Thus, method 400A proceeds to block 414 and no recalibration is performed.

[0121] If the calibration engine 154 determines that a substantial discrepancy exists between the measured analyte data for the first analyte and the expected analyte data for the first analyte (i.e., the measured analyte data is “anomalous”), then in block 410, the calibration engine 154 determines one or more correction factors for the measurement of the first analyte obtained with the first sensor. The one or more correction factors may be indicative of an error in the calibration of the first sensor. In certain embodiments, the one or more correction factors are based at least in part on the degree of discrepancy between the measured analyte data for the first analyte and the expected analyte data for the first analyte. In certain embodiments, the one or more correction factors are based at least in part on a predetermined mapping of the historical analyte data for the first analyte to other sensor data and / or a physiological condition of the patient. In certain embodiments, the one or more correction factors may generally include adjustments to sensitivity (m), rate of change of sensitivity (ddm / ddt), baseline / intercept (b), rate of change of baseline (ddb / ddt), sensitivity profile, relationship between a particular stimulation signal output (e.g., impedance) and sensor sensitivity, relationship between a particular stimulation signal output and sensor temperature or temperature, and / or a calibration code associated with the sensor being recalibrated for the first sensor. In certain embodiments, multiple possible values ​​for each of the one or more correction factors may be determined, and a weighting or averaging function may be applied thereto. In certain embodiments, if there is a determination that a substantial discrepancy exists between the measured analyte data for the first analyte and the expected analyte data for the first analyte, new values ​​may be determined for one or more sensor calibration parameters (e.g., sensitivity (m), baseline / intercept (b), etc.) without adjusting the previously known calibration parameters via one or more correction factors.

[0122] In block 412, the first sensor may be automatically recalibrated for the first analyte based at least in part on the determined one or more correction factors. In other words, the calibration engine 154 may be configured to convert at least one sensor data point received from the first sensor for the first analyte into recalibration data in real time based at least in part on the correction factor and / or sensor calibration parameters (e.g., sensitivity and / or baseline) determined in block 410. In certain embodiments, recalibration of the first sensor is performed following a determination by the calibration engine 154 that recalibration of the first sensor is possible. However, if the calibration engine 154 determines that recalibration of the first sensor is not possible, in certain embodiments, the data stream from the first sensor may be terminated and a notification or alert may be generated and provided to the patient, for example, via the display 107 in FIG. 1, recommending replacement of the first sensor.

[0123] FIG. 4B illustrates a flow diagram of another exemplary method 400B for recalibrating one or more continuous analyte sensors using first and second analyte data to correct for erroneous factory calibration or changes in sensor characteristics, according to certain embodiments of the present disclosure. In the example of FIG. 4B, one or more sensors for the first analyte and / or the second analyte may be recalibrated. Similar to method 400A, method 400B may be performed to recalibrate one or more continuous analyte sensors 202 of a continuous analyte monitoring system 104, as illustrated in FIGS. 1 and 2. Method 400B is described below with reference to FIGS. 1 and 2 and their components.

[0124] At block 401, method 400B begins with a calibration engine receiving (or accessing) data indicative of a historical relationship between a first analyte and a second analyte of a patient, such as user 102 illustrated in FIG. 1, during one or more first time periods. In certain embodiments, the data, which may be raw data or processed data, may be received, for example, from a user database 110 and accessed, for example, by the DAM 155. As used herein, a "historical relationship" may refer to a historical mapping (i.e., correlation) between historical (i.e., previously measured) analyte data for a first analyte and historical analyte data for a second analyte. Such a historical mapping may be based at least in part on one or more matched data pairs including one or more data points of historical analyte data for the first analyte matched with substantially time-corresponding data points of the historical analyte data for the second analyte during the first time period. In certain embodiments, one historical analyte data point for the first analyte is matched with one time-corresponding historical analyte data point for the second analyte to form a matched data pair. In other embodiments, multiple historical analyte data points for a first analyte are combined (e.g., equally or unequal weighted average, mean, median, etc.) and matched with one time-corresponding historical analyte data point for a second analyte, or vice versa, to form a matched data pair. In certain embodiments, one or more matched data pairs during a first time period facilitate the determination of a historical relationship.

[0125] In certain embodiments, the first analyte and / or the second analyte include glucose, lactate, ketones, glycerol, electrolytes such as sodium and potassium, a calculated measurement such as anion gap, or any other suitable analytes described herein, or a combination thereof. In certain embodiments, the first analyte includes glucose and the second analyte includes one or more of lactate, ketones, glycerol, electrolytes such as sodium and potassium, a calculated measurement such as anion gap, or any other suitable analytes described herein. In certain embodiments, the first analyte includes glucose and the second analyte includes lactate.

[0126] In block 403, the patient's first analyte is monitored continuously or non-continuously during a second time period using a factory calibrated first sensor to obtain measured analyte data for the first analyte. Block 403 may be performed in certain embodiments by the continuous analyte monitoring system 104 illustrated in FIGS. 1 and 2, and more specifically, the continuous analyte sensor 202 illustrated in FIG. 2. For example, the continuous analyte monitoring system 104 may include a factory calibrated continuous glucose sensor 202 to measure a user's glucose level. Alternatively, the continuous analyte monitoring system 104 may include a factory calibrated continuous multi-analyte sensor configured to measure at least a user's glucose level. As discussed above, the first analyte may include glucose, although other analytes are contemplated. In still other embodiments, a finger prick sample may be taken and utilized to obtain measured analyte data for the first analyte.

[0127] At block 405, method 400B continues by continuously or non-continuously monitoring a second analyte of the patient during a second time period to obtain measured analyte data for the second analyte of the patient. In certain embodiments, block 405 is performed with the same sensor as block 403, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in FIGS. 1 and 2 (e.g., the continuous analyte sensor 202 may include a continuous multi-analyte sensor configured to measure both the second analyte and the first analyte). In other embodiments, block 405 may be performed with a second factory calibrated sensor that is different from the first sensor used in block 403. In still other embodiments, a finger stick sample may be taken and utilized to obtain measured analyte data for the second analyte.

[0128] At block 407, the calibration engine 154 processes the measured first analyte data and the measured second analyte data to determine a current relationship between the first analyte and the second analyte during the second time period (i.e., determine the current relationship based on the measured first analyte data and the measured second analyte data). For example, the analyte measurements obtained during the second time period are processed to determine a directional trend, which may include data such as a minimum measurement of the analyte during the second time period, a maximum measurement of the analyte during the second time period, a direction of change of the analyte level during the second time period (e.g., rising upward or falling downward), a rate of change of the analyte level during the second time period, etc. For example, with respect to lactate, the lactate trend may include a lactate production rate, a lactate clearance rate, a lactate threshold, etc. during the second time period.

[0129] The current relationship may include a mapping (i.e., correlation) between the measured analyte data for the first analyte and the measured analyte data for the second analyte during the second time period. Such mapping may be based at least in part on one or more matched data pairs including one or more data points of measured analyte data for the first analyte matched with a substantially time-corresponding data point of measured analyte data for the second analyte during the second time period. In certain embodiments, one measured analyte data point for the first analyte is matched with one time-corresponding measured analyte data point for the second analyte to form a matched data pair. In other embodiments, multiple measured analyte data points for the first analyte are combined (e.g., equally or unequal weighted averages, mean values, medians, etc.) and matched with one time-corresponding measured analyte data point for the second analyte, or vice versa, to form a matched data pair. In certain embodiments, one or more matched data pairs during the first time period facilitate the determination of the current relationship.

[0130] In certain embodiments, block 407 further includes processing the received data (e.g., received raw data) indicative of the historical relationship to determine the historical relationship in a manner substantially similar to determining the current relationship, as described above. However, it should be noted that in some embodiments, the data indicative of the historical relationship has already been processed prior to or upon receipt in block 401, and thus the historical relationship has already been determined prior to the determination of the current relationship in block 405. In certain embodiments, the current relationship and / or the historical relationship is determined utilizing one or more trained machine learning models, such as trained machine learning models provided by the training server system 140 in FIG. 1, or other AI models. In certain embodiments, such models may be configured to use measured data from one or more sensors measuring multiple analytes as inputs and provide as outputs a determination utilized in recalibrating a continuous analyte sensor. Exemplary inputs for such models may include, for example, continuous analyte sensor data (e.g., for the first analyte and any other analytes), non-analyte sensor data, time, etc. Thus, given the interaction of various physiological conditions, the parameters and / or thresholds of such algorithms or models may be modified based at least in part on the number of analytes or properties being measured for an input to reflect knowledge gained from each of the other analytes or properties being measured, or physical conditions associated with the additional analytes or properties being measured.

[0131] In block 409, the calibration engine 154 compares the current relationship between the first and second analytes to the historical relationship between the first and second analytes to determine whether there is a substantial discrepancy between them. A substantial discrepancy, in certain embodiments, may refer to a numerical delta in the data that is greater than a predetermined acceptable range or threshold. For example, a substantial discrepancy between the current and historical relationships may occur when the data values ​​diverge (i.e., differ) by more than a predetermined threshold of 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, or more. In certain embodiments, a substantial discrepancy between the current and historical relationships includes different concentration values, trends, patterns, and / or other analyte data dynamics.

[0132] If the calibration engine 154 determines that there is no substantial discrepancy between the current and historical relationships (i.e., the current relationship is "normal"), then recalibration of the first sensor for either the first analyte or the second analyte is not necessary. In other words, the first sensor provides measured analyte data for the first analyte and the second analyte that is acceptable accuracy or is within a predetermined acceptable accuracy range. Thus, method 400B proceeds to block 413 and no recalibration is performed.

[0133] If the calibration engine 154 determines that a substantial discrepancy exists between the current relationship and the historical relationship (i.e., the current relationship is "anomalous"), then in block 411 the calibration engine 154 generates and provides a notification or alert to the patient, e.g., via the display 107 of FIG. 1, recommending recalibration of the one or more sensors for the measurement of the first analyte and / or the second analyte, or the calibration engine 154 automatically recalibrates the one or more sensors for the first analyte and / or the second analyte based at least in part on the historical relationship. In other words, the calibration engine 154 may be configured to convert in real time at least one sensor data point received from the one or more sensors for the first and / or second analyte into recalibration data based at least in part on the historical relationship.

[0134] In certain embodiments, determining which sensors for the first analyte and / or the second analyte require recalibration is further based on a mapping or correlation between the measured first analyte data, the measured second analyte data, and / or secondary sensor data. Examples of secondary sensor data include accelerometer data, gyrometer data, global positioning system (GPS) data, heart rate, heart rate reserve, heart rate variability (HRV), electrocardiogram (EKG) data, electromyogram (EMG), respiratory rate, body temperature, blood pressure, galvanic skin response, oxygen uptake data (e.g., VO2max), sleep, impedance data, strain sensor data, etc. In certain embodiments, determining which sensors for the first analyte and / or the second analyte require recalibration is further based on a mapping or correlation between the measured first analyte data, the measured second analyte data, and / or the measured analyte data for one or more additional analytes. For example, if the first analyte is glucose and the second analyte is lactate, the one or more additional analytes can include ketones, glycerol, potassium (e.g., hyperkalemia), sodium, CO2, or anion gap, or a similar analyte.

[0135] In certain embodiments, determining which sensors require recalibration for the first analyte and / or the second analyte is further based on predicted analyte data for the first analyte and / or predicted analyte data for the second analyte. Such predicted analyte data may be based at least in part on other sensor data, e.g., measured secondary sensor data and / or measured analyte data for one or more additional analytes. The predicted analyte data for the first and / or second analyte refers to predicted analyte data for the patient's first and / or second analyte during the second time period, where the prediction is informed, e.g., by the measured secondary sensor data and / or measured analyte data for one or more additional analytes, but not by the measured analyte data for the first or second analyte. In certain embodiments, the predicted analyte data for the first and / or second analyte is determined utilizing a predetermined mapping (e.g., correlation) of historical (e.g., previously measured) analyte data for the first and / or second analyte to other sensor data. In certain embodiments, the predicted analyte data for the first and / or second analytes is a range (e.g., a normal range) for the first and / or second analyte that is based at least in part on other sensor data for the patient during the second time period. For example, if the first analyte is glucose and the second analyte is lactate, the other sensor data may include, for example, ketone levels, accelerometer data, and heart rate data.

[0136] In certain embodiments, before providing a recalibration recommendation or automatically recalibrating one or more sensors, the calibration engine 154 first determines whether recalibration of the one or more sensors for measurement of the first analyte and / or the second analyte is possible. If the calibration engine 154 determines that recalibration is not possible, instead of providing a recommendation for recalibration or recalibration of the sensors, in certain embodiments, the data stream from the one or more sensors for the first analyte and / or the second analyte may be terminated and a notification or alert may be generated and provided to the patient recommending replacement of the one or more sensors.

[0137] Determining the Optimal Calibration Time

[0138] As discussed above, conventional in vivo continuous analyte sensing techniques have typically relied on reference measurements made during a sensor session for recalibration of the continuous analyte sensor to account for changes in sensor sensitivity and / or stability over time and to ensure accurate measurements by the sensor. In critical care settings, recalibration of the continuous analyte sensor may be performed by using a calibration solution with a known concentration of the analyte as a reference. In ambulatory environments, recalibration of the continuous analyte sensor is sometimes performed by a finger prick sample measurement through which reference data is obtained and input into the continuous analyte sensor system. Although not required by some recent continuous analyte sensing systems, obtaining such reference measurements for recalibration is useful in minimizing any bias introduced by imperfections in the factory calibration algorithm and / or sensor manufacturing process, as well as for measuring surrogate analytes and for specialized applications of the continuous analyte sensing system, such as, for example, in-hospital and / or post-discharge monitoring, monitoring patients with diminished cerebral glucose metabolism (dCGM), etc.

[0139] Typically, a baseline measurement is collected and the continuous analyte sensor is recalibrated at the beginning or middle of a user wear period. It is preferable to collect such a baseline sample for recalibration when the patient's analyte level is stable and therefore within the normal range for the patient, or during another period of interest when the analyte level is expected or exhibiting normal behavior. For example, with glucose, the patient's levels typically change most significantly immediately after a meal. Because it takes time to reach equilibrium between blood glucose levels and interstitial fluid glucose levels, there is a delay, for example, between the fingerstick sample value (e.g., blood) and the measured sensor value (e.g., interstitial fluid). Therefore, it is preferable not to recalibrate the continuous analyte sensor immediately after a meal. Similar preferences apply to recalibration after exercise.

[0140] However, patients do not always follow recommendations or guidelines regarding when to recalibrate their continuous analyte sensors. For example, many patients obtain their continuous analyte sensors from an in-store pharmacy or similar channel, which may not always provide the patient with complete recalibration instructions (e.g., when is the best time to recalibrate). Thus, some patients may not be informed of the optimal time to recalibrate their continuous analyte sensor. Furthermore, patients may believe they are recalibrating during the optimal period, when in fact their levels may be unstable during the particular period.

[0141] Described herein are systems and methods for automatically determining an optimal calibration time for a continuous analyte sensor, such as a continuous multi-analyte sensor, using measured analyte data for at least one analyte, optionally in combination with other sensor data. Such systems and methods may inform the patient of the determined optimal calibration time, thus facilitating user-directed recalibration during periods when the patient's analyte levels are stable, without relying on any timing guesswork by the patient. Such systems and methods may also automatically calibrate the analyte sensor during the optimal calibration time, once the optimal calibration time is automatically determined using the techniques described herein.

[0142] 5 illustrates a flow diagram of an exemplary method 500 for determining an optimal calibration time for a continuous analyte sensor measuring a first analyte using measured analyte data for at least a first analyte, optionally in combination with other sensor data, according to certain embodiments of the present disclosure. Method 500 may be performed to determine an optimal calibration time for one or more continuous analyte sensors 202 of a continuous analyte monitoring system 104, as illustrated in FIGS. 1 and 2. Method 500 is described below with reference to FIGS. 1 and 2 and their components.

[0143] At block 502, the method 500 begins by continuously or non-continuously monitoring a first analyte of a patient, such as the user 102 illustrated in FIG. 1, using a first, factory calibrated sensor for a period of time to obtain measured analyte data for the first analyte. Block 502 may be performed in certain embodiments by the continuous analyte monitoring system 104 illustrated in FIGS. 1 and 2, and more specifically, the continuous analyte sensor 202 illustrated in FIG. 2. For example, the continuous analyte monitoring system 104 may include a continuous glucose sensor 202 that is factory calibrated to measure the glucose level of the user. Alternatively, the continuous analyte monitoring system 104 may include a continuous, factory calibrated, multi-analyte sensor configured to measure at least the glucose level of the user. In general, the first analyte may include glucose, although other analytes such as lactate, ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, or any other suitable analyte described herein are also contemplated.

[0144] At block 504, method 500 optionally continues by continuously or non-continuously monitoring one or more additional analytes including at least a second analyte of the patient over a period of time to obtain measured analyte data for the one or more additional analytes. In certain embodiments, block 504 may be performed by the same sensor as block 502, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in Figures 1 and 2 (e.g., the continuous analyte sensor 202 may include a continuous multi-analyte sensor configured to measure both the second analyte and the first analyte). In other embodiments, block 504 may be performed by a second factory-calibrated sensor that is different from the first sensor used in block 502.

[0145] In certain embodiments, the one or more additional analytes include lactate, ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, or any other suitable analytes described herein, or combinations thereof. In certain embodiments, the first analyte includes glucose and the second analyte includes one or more of lactate, ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, or any other suitable analytes described herein. In certain embodiments, the first analyte includes glucose and the second analyte includes lactate or ketones. In certain embodiments, the first analyte includes glucose, the second analyte includes lactate, and the third analyte includes ketones.

[0146] At block 506, method 500 continues by optionally continuously or non-continuously monitoring other sensor data of the patient during the time period to obtain other measured sensor data indicative of the patient's physiological condition. As mentioned above, the patient's physiological condition may refer to the user's general or physical condition, including any physical stress in the patient. For example, in certain embodiments, the user's physiological condition includes the user's engagement in physical activity or rest during the time period, the consumption of food by the user during the time period, the administration of medication to the user during the time period, the occurrence of a glycemic event (e.g., hypoglycemia or hyperglycemia), etc.

[0147] The patient's physiological condition may be indicated by a variety of different types of sensor data, alone or in combination. In certain embodiments, other sensor data optionally monitored in block 506 includes secondary sensor data, such as one or more of accelerometer data, gyrometer data, global positioning system (GPS) data, heart rate, cardiac reserve, heart rate variability (HRV), electrocardiogram (EKG) data, electromyogram (EMG), respiratory rate, temperature, blood pressure, galvanic skin response, sweat, oxygen uptake data (e.g., VO2max), sleep, impedance data, and the like. In certain embodiments, the other sensor data may be utilized to calculate metrics, such as metric 130 of FIG. 1, to further inform the analysis to determine the optimal calibration time.

[0148] In certain embodiments, block 506 is performed by the same sensor as blocks 502 and / or 504, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in Figures 1 and 2 (e.g., the continuous analyte sensor 202 can include a continuous multi-analyte sensor configured to measure a first analyte, a second analyte, and / or other sensor data). In other embodiments, block 506 is performed by a third sensor that is different from the sensor used in blocks 502 and / or 504.

[0149] At block 508, the calibration engine 154 determines a time period of interest for the first analyte based at least on the measured first analyte data. The time period of interest for the first analyte may be determined by monitoring the first analyte during a period of time and identifying a period of expected or normal behavior, which may be based at least in part on one or more concentration thresholds and / or time thresholds for the first analyte. Identifying the period of expected or normal behavior may be based on the concentration of the first analyte, the change in the concentration of the first analyte, the rate of change of the concentration of the first analyte, etc. In certain embodiments, the time period of interest is a stable period of the first analyte, which may be determined by identifying the most stable period during the period. For example, in certain embodiments, the stable period of the first analyte is determined by identifying a period that meets a minimum time threshold (e.g., as long as or longer than a minimum amount of time), and the measured analyte is within minimum and maximum concentration levels corresponding to the user's normal first analyte levels. In certain other embodiments, the time period of interest is a time period of expected or normal patterns or trends for the first analyte, such as a time period of expected change for the first analyte. For example, in certain embodiments, the time period of interest is a time period during which the measured analyte level or rate of change thereof for the first analyte is within a predetermined tolerance range or threshold.

[0150] In certain embodiments, the minimum time threshold for a first analyte can be a duration of about 1 minute, about 2 minutes, about 3 minutes, about 4 minutes, about 5 minutes, about 6 minutes, about 7 minutes, about 8 minutes, about 9 minutes, about 10 minutes, about 11 minutes, about 12 minutes, about 13 minutes, about 14 minutes, about 15 minutes, about 16 minutes, about 17 minutes, about 18 minutes, about 19 minutes, about 20 minutes, about 21 minutes, about 22 minutes, about 23 minutes, about 24 minutes, about 25 minutes, about 26 minutes, about 27 minutes, about 28 minutes, about 29 minutes, about 30 minutes, or more.

[0151] In certain embodiments, the time period of interest for the first analyte is further determined by cross-checking or cross-tabling the measured analyte level for the first analyte with the measured analyte levels for one or more additional analytes including at least the second analyte, and / or with the measured other sensor data. For example, in certain embodiments, at block 508, the calibration engine 154 may determine a time period of interest for the first analyte that corresponds to a time period of interest for the second analyte based at least on the measured first analyte data and the measured second analyte data. In further embodiments, at block 508, the calibration engine 154 may determine a time period of interest for the first analyte that corresponds to a time period of interest for the second analyte and a time period of interest for the other sensor data based on the measured first analyte data, the measured second analyte data, and the measured other sensor data. In such examples, the determination of the time period of interest for the second analyte and / or the other sensor data may be based on satisfaction of one or more thresholds, such as minimum and maximum thresholds and a minimum time threshold. In certain examples, the minimum time threshold for the second analyte and / or other sensor data is the same as the minimum time threshold for the first analyte, while in certain other examples, the minimum time threshold for the second analyte and / or other sensor data is different from the minimum time threshold for the first analyte.

[0152] In one particular example, the period of interest for glucose is a stable period determined when the glucose signal (e.g., measured glucose data) from a continuous analyte sensor or continuous multi-analyte sensor is stable or within the patient's normal range for at least 5, 10, 15, 20, 25, or 30 minutes or more, there is no elevated lactate level (e.g., therefore no eating or exercise) for at least 60, 70, 80, or 90 minutes or more, and there is no high step count, high accelerometer data, or high heart rate data measured (e.g., therefore no exercise or stress) for 30, 45, or 60 minutes or more.

[0153] Additional parameters that may be utilized to determine the time period of interest for the first analyte in block 508 include, but are not limited to, the insertion time of the continuous analyte or continuous multi-analyte sensor, time of day, sensor signal quality, etc. Sensor calibration may be more beneficial at the beginning of a wear period by a patient, as opposed to later in the wear period, for example, to help ensure the accuracy of the sensor over its use period. Furthermore, as the chemical stability of a given sensor decreases over its lifespan, sensor readings may be more prone to errors or noise toward the end of the sensor's lifespan. Thus, more calibrations may be recommended as the sensor approaches the end of its lifespan. Furthermore, due to the patient's circadian rhythm, recalibration may be more beneficial at certain times of a given day (e.g., during times of the day when sensor readings may be more stable).

[0154] In certain embodiments, the calibration engine utilizes one or more trained machine learning models, such as those provided by the training server system 140 in FIG. 1, or other AI models, to predict or determine data associated with a patient's typical daily schedule (e.g., periods of rest versus periods of exercise, periods of food consumption versus periods of fasting, etc.) Such data associated with the patient's daily schedule may also be utilized as a parameter in determining the period of interest for the first analyte, in addition to, or in lieu of, one or more additional analytes or other sensor data.

[0155] In certain embodiments, the period of interest corresponds to the actual time (e.g., during a data collection interval) that a continuous analyte sensor or continuous multi-analyte sensor makes measurements of a first analyte, one or more additional analytes, and / or other sensor data. In further embodiments, in addition to or alternatively to a data collection interval, the period of interest corresponds to the time that measurements are displayed to the patient (e.g., during a time display interval). Such data collection and / or time display intervals may occur every 30 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9 minutes, 10 minutes, 15 minutes, 20 minutes, 25 minutes, 30 minutes, etc.

[0156] After determining the time period of interest for at least the first analyte, in block 510, the calibration engine 154 determines an optimal calibration time based on the time period of interest and, in certain embodiments, may generate and provide a notification or alert to the patient, for example, via the display 107 of FIG. 1, recommending recalibration of the continuous analyte sensor or continuous multi-analyte sensor (for measuring the first analyte) during the optimal calibration time. In certain embodiments, the notification or alert is provided to the patient via a mobile phone or receiver notification, email, text message, etc. In certain embodiments, the notification or alert is provided in a passive form, in which case the patient may need to check their phone (e.g., an application on their phone) or receiver for a notification. In certain embodiments, in addition to or as an alternative to providing a recommendation to the patient, in block 510, the calibration engine 154 automatically calibrates the analyte sensor for the first analyte during the determined optical calibration time based at least on the time period of interest. In certain embodiments, after determining the period of interest for at least the first analyte, the system may be configured to automatically recalibrate by requesting data from a connected reference source (e.g., a system and / or device that acquires and / or analyzes blood glucose measurements without user intervention). The system may communicate a message to the user or patient indicating that recalibration is necessary or requesting that the user or patient perform a recalibration.

[0157] In certain embodiments, before providing a recalibration recommendation, the calibration engine 154 first determines whether recalibration of one or more sensors for measurement of the first analyte and / or the second analyte is possible. If the calibration engine 154 determines that recalibration is not possible, instead of providing a recalibration recommendation, in certain embodiments, the data stream from the one or more sensors for the first analyte and / or the second analyte may be terminated and a notification or alert may be generated and provided to the patient recommending replacement of the one or more sensors.

[0158] Artifact Detection and Compensation

[0159] Compression artifact

[0160] Conventional in vivo continuous analyte sensors, particularly continuous glucose and continuous lactate sensors, are periodically affected by compression artifacts, or changes in the measured analyte concentration levels caused by compression of the appendage or body part on which the sensor is attached. For example, compression artifacts are most commonly observed at night while the patient is sleeping, as the patient may lie on their abdomen, arms, or legs (where continuous analyte sensors are typically worn), thereby creating external pressure on or around the worn sensor. Compression artifacts can manifest in many different ways in the measured analyte data. Often, such artifacts appear as sudden deviations and recoveries in the raw sensor signal.

[0161] It is speculated that at least the glucose and lactate compression artifacts are caused by a reduction in local blood supply when the tissue adjacent to the sensor is compressed. As a result of this compression, cells near the sensor-tissue interface do not receive sufficient oxygen for aerobic respiration and therefore switch to anaerobic respiration to support cell metabolism, converting glucose to lactate instead of CO2, with lactate being a good indicator of a compression event. And, again due to the reduction in local blood supply, glucose is not resupplied to the compressed tissue quickly enough, causing the detected drop in glucose and the simultaneous spike in lactate.

[0162] Stress artifacts can cause many problems for continuous analyte monitoring systems. For example, stress artifacts can result in nuisance false alerts, gaps in measured analyte data, unnecessary or erroneous recommendations of therapy, erroneous decision making by automated insulin delivery (AID) systems, misadjustments of manual insulin delivery, inaccurate changes in retrospective blood glucose metrics, and / or inaccurate analyte trend or pattern analysis. Thus, there is a need for systems and methods to automatically detect a patient's stress event, thus allowing appropriate action to be taken thereafter to address the stress event and avoid any errors in measured analyte data analysis and / or patient therapy. Systems and methods for automatically detecting stress events are described herein.

[0163] 6A illustrates a flow diagram of an exemplary method 600 for automatically detecting the occurrence of a compression artifact, according to certain aspects of the present disclosure. Method 600 may be performed using one or more continuous analyte sensors 202 of continuous analyte monitoring system 104, as illustrated in Figures 1 and 2. Method 600 is described below with reference to Figures 1 and 2 and their components.

[0164] At block 602, the method 600 begins by continuously or non-continuously monitoring a first analyte of a patient, such as a user 102 illustrated in FIG. 1, during a period of time to obtain measured analyte data for the first analyte. Block 602 may be performed in certain embodiments by a continuous analyte monitoring system 104 illustrated in FIGS. 1 and 2, and more specifically, a continuous analyte sensor 202 illustrated in FIG. 2. For example, the continuous analyte monitoring system 104 may include a continuous glucose sensor 202 to measure the glucose level of the user. Alternatively, the continuous analyte monitoring system 104 may include a continuous multi-analyte sensor configured to measure at least the glucose level of the user. In general, the first analyte may include glucose, although other analytes, such as ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, or any other suitable analyte described herein are also contemplated.

[0165] At block 604, method 600 continues by continuously or non-continuously monitoring one or more additional analytes, including at least lactate, during the period of time to obtain measured analyte data for the one or more additional analytes. In certain embodiments, block 604 may be performed with the same sensor as block 602, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in Figures 1 and 2 (e.g., the continuous analyte sensor 202 may include a continuous multi-analyte sensor configured to measure both glucose and lactate). In other embodiments, block 604 may be performed with a second sensor that is different from the first sensor used in block 602.

[0166] As mentioned above, the one or more additional analytes include at least lactate, which may be a good indicator of a compression event, especially when utilized in combination with measured glucose data. In certain embodiments, the one or more additional analytes further include one or more of ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, or any other suitable analytes or combinations thereof described herein that may provide an indication of the occurrence of a compression event.

[0167] At block 606, the method 600 continues by receiving measured physiological data indicative of the patient's physiological state during the time period. As discussed above, the patient's physiological state may refer to the user's general or physical state, including its physical stress. For example, in certain embodiments, the user's physiological state may include the user's engagement in physical activity or rest during the time period, the consumption of food by the user during the time period, the administration of medication to the user during the time period, the occurrence of a glycemic event (e.g., hypoglycemia or hyperglycemia), etc.

[0168] The measured physiological data at block 606 may include other sensor data measured by one or more other sensors during the time period. In certain embodiments, the measured physiological data received at block 606 includes measured analyte data for one or more additional analytes during the time period. For example, in certain embodiments, the measured physiological data includes measured analyte data (e.g., concentration values, trends, and / or patterns) for electrolytes such as ketones, glycerol, sodium and potassium, calculated measurements such as anion gap, and / or any other suitable analytes described herein, or combinations thereof.

[0169] In certain embodiments, the measured physiological data received at block 606 includes secondary sensor data measured during the time period, such as one or more of accelerometer data, gyrometer data, global positioning system (GPS) data, heart rate, cardiac reserve, heart rate variability (HRV), electrocardiogram (EKG) data, electromyogram (EMG), respiratory rate, temperature, blood pressure, galvanic skin response, oxygen uptake data (e.g., VO2max), sleep, impedance data, etc. In certain embodiments, the measured physiological data can be utilized to calculate a metric, such as metric 130 of FIG. 1, to further inform the determination of a compression event.

[0170] In certain embodiments, at block 606, the measured physiological data is received from the same sensor utilized in blocks 602 and / or 604, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in Figures 1 and 2 (e.g., the continuous analyte sensor 202 may include a continuous multi-analyte sensor configured to measure multiple analytes). In other embodiments, the measured physiological data is received at block 606 from a different sensor than the sensor used in blocks 602 and / or 604.

[0171] At block 608, the method 600 continues with the calibration engine 154 processing the measured first analyte data to detect the occurrence of a change, such as a sudden drop (e.g., a decrease or a drop downward) in the first analyte level that meets one or more predetermined thresholds. For example, the first analyte measurements obtained during a period of time are processed to determine a directional trend, which may include data such as the minimum measurement of the first analyte during the period, the maximum measurement of the first analyte during the period, the direction of change of the first analyte level during the period (e.g., rising upward or dropping downward), the rate of change of the first analyte level during the period, etc.

[0172] In certain embodiments, the one or more predetermined thresholds include a concentration threshold. For example, in certain embodiments, the one or more predetermined thresholds include a minimum first analyte concentration threshold, which may be met when the patient's measured first analyte value substantially equals, remains near, or falls below the minimum first analyte concentration threshold. In certain embodiments, the one or more predetermined thresholds include a rate of change threshold. For example, in certain embodiments, the one or more predetermined thresholds include a maximum first analyte rate of change threshold, which may be met when the patient's measured first analyte value declines at a rate substantially equal to or greater than the maximum first analyte rate of change threshold. In certain embodiments, the one or more predetermined thresholds include a duration threshold, which may be utilized in combination with one or more concentration thresholds and / or rate of change thresholds. For example, in certain embodiments, the one or more predetermined thresholds include a maximum duration threshold that may be met when a patient's measured first analyte value substantially equals, remains near, or falls below a minimum first analyte concentration threshold and / or when a patient's measured first analyte value declines at a rate substantially equal to or greater than a maximum first analyte rate of change threshold for a period of time substantially equal to or greater than the maximum duration threshold.

[0173] If the detected drop in the first analyte level does not meet one or more predetermined thresholds, continuous monitoring of the first analyte continues at block 602. However, if the detected drop in the first analyte level does meet one or more predetermined thresholds, method 600 proceeds to block 610.

[0174] In certain embodiments, at block 610, the calibration engine 154 determines whether the patient (e.g., the user 102) is sleeping or resting. Because a compression event is most commonly observed when the patient is sleeping and lying on the body part on which the patient's continuous analyte sensor is attached, sleeping or resting may be a good indicator that the first analyte level drop is caused by a compression event. In such embodiments, to determine whether the patient is sleeping, the calibration engine 154 may process and correlate the measured analyte data for the first analyte and / or one or more additional analytes from blocks 602 and 604 in addition to one or more types of received physiological data from block 606. In certain examples, the calibration engine 154 may determine whether the patient is sleeping by processing and mapping (e.g., correlating) the measured analyte levels for, for example, glucose and lactate to one another during the period in addition to heart rate data, accelerometer data, respiration data, blood pressure data, and / or other suitable types of secondary sensor data, or other data (e.g., time of day).

[0175] In particular embodiments, at block 610, the calibration engine 154 determines whether the patient (e.g., the user 102) is in a horizontal (i.e., lying) position. Since many compression events are caused by the patient lying on the body part on which the continuous analyte sensor is attached, determining whether the patient is in a horizontal position may be a good indicator that the first analyte level drop is caused by a compression event. In such embodiments, to determine whether the patient is in a horizontal position, the calibration engine 154 may process and correlate one or more types of physiological data received from block 606. In particular examples, the calibration engine 154 may determine whether the patient is lying by processing gyroscope data, accelerometer data, and / or other suitable types of secondary sensor data and mapping (e.g., correlating) them to each other during the period.

[0176] If the calibration engine 154 determines in block 610 that the patient is sleeping or lying down, the method 600 proceeds to block 612. However, if it is determined that the patient is not sleeping or lying down, the method 600 proceeds to block 620.

[0177] At block 612, the method 600 continues by the calibration engine 154 determining whether a patient posture change occurred prior to the first analyte level sudden drop. A determination that the patient changed posture prior to the first analyte level sudden drop provides a good indication that the posture change caused the patient to lie down on the body part on which the patient's continuous analyte sensor was attached, thus applying external pressure on or around the sensor and causing a compression event. For example, the patient may be sleeping or resting, and in the process, the patient is lying down on the body part on which the patient's continuous analyte sensor was attached. Alternatively, the patient was already lying down and then repositioned so that he or she was lying down on the body part on which the patient's continuous analyte sensor was attached.

[0178] To determine whether the patient has changed posture, the calibration engine 154 may process and correlate one or more types of physiological data received from block 606. In particular examples, the calibration engine 154 may process gyroscope data, accelerometer data, and / or other suitable types of secondary sensor data and map (e.g., correlate) them to each other over time to determine whether a posture change has occurred.

[0179] If the calibration engine 154 determines in block 612 that the patient has changed position, the method 600 continues to block 614 where a suspected compression event is determined. In certain embodiments, upon determination of a compression event, any inaccurate data resulting from the compression event may be invalidated (e.g., removed), or the measured first analyte data may be corrected or compensated with measured analyte data for other analytes. In certain embodiments, delivery of a supplemental insulin dose may be inhibited upon determination of a compression event. For example, the calibration engine 154 may send a signal to an insulin pump (an example of a medical device 208) to cause the pump to refrain from delivering a supplemental insulin dose or to override the decision to deliver a supplemental insulin. In certain embodiments, a notification or alert may be generated and provided to the patient regarding the compression event, for example, via the display 107 of FIG. 1, and may further include a recommendation to the patient to change position to relieve external pressure on or around the sensor. In further embodiments, the notification or alert may require confirmation input from the patient or caregiver that a compression event occurred, or an indication that the notification or alert was in error.

[0180] If, at block 612, it is determined that no posture change is detected prior to the first analyte level drop, the method 600 continues to block 616 where the calibration engine 154 processes the measured lactate data from block 604 to determine whether there is an increase in lactate level corresponding to the first analyte level drop. Processing of lactate measurements obtained during the time period may include determining a directional trend, which may include data such as the minimum measured lactate during the time period, the maximum measured lactate during the time period, the direction of change in lactate level during the time period (e.g., rising upward or falling downward), the rate of change of lactate level during the time period, etc.

[0181] As described above, during a compression event, cells near the sensor-tissue interface do not receive enough oxygen for aerobic respiration and therefore switch to anaerobic respiration to support cell metabolism, converting glucose to lactate instead of CO2. Thus, a compression event typically results in a detected spike in lactate, and the occurrence of a compression event can be confirmed by determining that a corresponding increase in lactate level occurred during the period. In certain embodiments, similar to block 608, determining the corresponding increase in lactate level includes determining whether the lactate level meets one or more predefined thresholds. For example, in certain embodiments, the one or more predefined thresholds include a measured lactate value threshold, a lactate rate of change threshold, and / or a period threshold, which may be utilized in combination.

[0182] If the calibration engine 154 detects a corresponding rise in lactate level in block 616, the method 600 proceeds to block 614 where a compression event is determined. However, if a corresponding rise in lactate level is not detected in block 616, further processing and analysis of the measured first analyte data, the measured additional analyte data, and / or the received physiological data is performed by the calibration engine 154 in block 618 to determine the cause of the sudden drop in the first analyte level. In certain embodiments, the measured first analyte data and the measured lactate data are cross-checked with one or more additional types of received physiological data, such as accelerometer data and / or heart rate data, to ascertain whether a compression event occurred. Thus, the cross-check with the received physiological data can rule out a compression event, or even other types of physiological events that may cause a sudden drop in the first analyte level.

[0183] Returning to block 610, if the calibration engine 154 determines that the patient is not sleeping or lying down, the method 600 proceeds to block 620. In block 620, the calibration engine 154 determines whether the patient is moving during the period or whether the patient is in a vertical (i.e., sitting or standing) position. Determining whether the patient is moving or in a vertical position may be a good indicator that the drop in the first analyte level was not caused by a compression event, but may instead be caused by the patient engaging in physical activity or some other physiological event.

[0184] In particular embodiments, calibration engine 154 determines whether a patient (e.g., user 102) is exercising by processing and correlating the measured analyte data for the first analyte and / or one or more additional analytes from blocks 602 and 604 in addition to one or more types of received physiological data from block 606. In particular examples, calibration engine 154 can determine whether a patient is exercising by processing and mapping (e.g., correlating) measured analyte levels for, for example, glucose and lactate to one another over the period of time in addition to heart rate data, accelerometer data, respiration data, blood pressure data, and / or other suitable types of secondary sensor data.

[0185] In particular embodiments, calibration engine 154 determines whether the patient is in a vertical position by processing and correlating one or more types of physiological data received from block 606. In particular examples, calibration engine 154 can determine whether the patient is in a vertical position by processing gyroscope data, accelerometer data, and / or other suitable types of secondary sensor data and mapping (e.g., correlating) them to each other over time.

[0186] If the calibration engine 154 determines in block 620 that the patient is exercising or in a vertical position, then further processing and analysis of the measured first analyte data, the measured additional analyte data, and / or the received physiological data is performed by the calibration engine 154 in block 618 to determine the cause of the sudden drop in the first analyte level. In certain examples, the measured first analyte data and the measured lactate data are cross-checked with one or more additional types of received physiological data, such as accelerometer data and / or heart rate data, to ascertain whether the sudden drop in the first analyte level was caused by, for example, exercise or other physiological event, or whether the sudden drop was caused by a compression event (although this is unlikely).

[0187] However, if it is determined that the patient is not moving or is in a vertical position, the method 600 proceeds to block 622 where the calibration engine 154 processes the measured lactate data from block 604 to determine whether there is an increase in lactate level corresponding to the sudden drop in the first analyte level. Processing of the lactate measurements taken during the time period may include determining a directional trend, which may include data such as the minimum measured lactate during the time period, the maximum measured lactate during the time period, the direction of change in lactate level during the time period (e.g., rising upward or falling downward), the rate of change of lactate level during the time period, etc.

[0188] As discussed above, a compression event typically results in a detected spike in lactate, and determining that a corresponding rise in lactate level occurred during a period of time without the patient engaging in exercise or being in a vertical position may confirm the occurrence of a compression event. In certain embodiments, similar to blocks 608 or 616, determining the corresponding rise in lactate level includes determining whether the lactate level meets one or more predefined thresholds. For example, in certain embodiments, the one or more predefined thresholds include a measured lactate value threshold, a lactate rate of change threshold, and / or a time period threshold, which may be utilized in combination.

[0189] If the calibration engine 154 detects a corresponding rise in lactate level at block 622, the method 600 proceeds to block 614 where a compression event is determined. However, if a corresponding rise in lactate level is not detected at block 622, further processing and analysis of the measured first analyte data, the measured additional analyte data, and / or the received physiological data is performed by the calibration engine 154 at block 618 to determine the cause of the sudden drop in the first analyte level. In certain embodiments, the measured first analyte data and the measured lactate data are cross-checked with one or more additional types of received physiological data to ascertain whether a compression event occurred. For example, the further analysis at block 618 may rule out the occurrence of a compression event or any other type of physiological event that may cause a sudden drop in the first analyte level.

[0190] In certain embodiments, one or more blocks of method 600 are performed utilizing one or more trained machine learning models, such as those provided by training server system 140 in FIG. 1, or other AI models. For example, one or more decisions of method 600 may utilize regression models, decision tree models, neural networks such as deep neural networks, and / or fuzzy logic, as well as other types of computational models. In certain embodiments, such models may be configured to use measured data from one or more sensors measuring multiple analytes as inputs and provide as outputs decisions utilized for recalibration of a continuous analyte sensor. Exemplary inputs for such models may include, for example, continuous analyte sensor data (e.g., for glucose, lactate, and / or any other analyte), non-analyte sensor data (e.g., gyroscope data, heart rate data, accelerometer data), time, etc. Thus, given the interaction of various physiological conditions, the parameters and / or thresholds of such algorithms or models may be modified based at least in part on the number of analytes or properties being measured for an input to reflect knowledge gained from each of the other analytes or properties being measured, or physical conditions associated with the additional analytes or properties being measured.

[0191] 6B illustrates a flow diagram depicting an exemplary method 630 for detecting and compensating for a squeeze event. In certain embodiments, the continuous analyte monitoring system 104 performs the method 630. By performing the method 630, the continuous analyte monitoring system 104 detects a squeeze event using two different analyte signal streams.

[0192] At block 632, the continuous analyte monitoring system 104 begins by generating a first analyte signal stream indicative of a level of a first analyte (e.g., glucose). The continuous analyte monitoring system 104 may use a first sensor to continuously or non-continuously monitor a first analyte of a patient, such as the user 102 illustrated in FIG. 1. In certain embodiments, the first sensor may be a factory calibrated continuous glucose sensor 202. In certain other embodiments, the first sensor may be a factory calibrated continuous multi-analyte sensor that measures at least the glucose level of the user. The first analyte signal stream may be a time-varying analyte signal indicative of a change in the level of the first analyte.

[0193] At block 634, the continuous analyte monitoring system 104 generates a second analyte signal stream indicative of the level of a second analyte (e.g., lactate). The continuous analyte monitoring system 104 may use the second sensor or the first sensor to continuously or non-continuously monitor data (e.g., concentration values, trends, or patterns) from a user to obtain the second analyte signal stream. The second analyte signal stream may be a time-varying analyte signal indicative of a change in the level of the second analyte.

[0194] In certain embodiments, the continuous analyte monitoring system 104 uses the same sensor in block 634 as was used in block 632, such as the continuous analyte sensor 202. In other embodiments, the continuous analyte monitoring system 104 uses a different sensor in block 634 than in block 632.

[0195] At block 636, the continuous analyte monitoring system 104 compares the first analyte signal stream to the second analyte signal stream and monitors the data trends of each signal stream. Specifically, the continuous analyte monitoring system 104 monitors the rises and falls of each of the first analyte signal stream and the second analyte signal stream. If there is a fall in the first analyte signal stream and a rise in the second analyte signal stream during the same time period, the continuous analyte monitoring system 104 proceeds to block 638. If there is not a fall in the first analyte signal stream and a rise in the second analyte signal stream during the same time period, the continuous analyte monitoring system 104 returns to block 632 and continues monitoring the sensor data.

[0196] At block 638, the continuous analyte monitoring system 104 detects a squeeze event by determining that a pattern of a sudden drop in the first analyte signal stream and an increase in the second analyte signal stream during the same time period is indicative of a squeeze event (e.g., a first sensor measuring a first analyte is being squeezed). Squeezing of the sensor can result in small spikes in the analyte data that do not truly represent the concentration of the first analyte in the user's bloodstream. These spikes can lead to inaccurate data being presented to a medical professional or user.

[0197] 6C, a first plot 650 shows a first analyte signal stream 652 and a second plot 660 shows a second analyte signal stream 662. The first analyte signal stream 652 may undergo a dip 654 during a period 656. A compression event is detected when the second analyte signal stream 662 shows an increase 664 during the same period 656. For example, the first analyte signal stream 652 may indicate a measured glucose level and the second analyte signal stream 662 may indicate a measured lactate level. The continuous analyte monitoring system 104 may determine that a compression event has occurred when the continuous analyte monitoring system 104 determines that a dip 654 in the glucose level has occurred during the same period 656 as the increase 664 in the lactate level has occurred.

[0198] Returning to FIG. 6B, in block 640, if a squeeze event is detected in block 408, the continuous analyte monitoring system 104 may then adjust at least one of the first sensor or the first analyte signal stream. In adjusting the first sensor, the continuous analyte monitoring system 104 may temporarily power down the first sensor so that it does not collect data for a predetermined period of time. The predetermined period of time may be a set time or may be based on data from the second analyte signal stream (e.g., when the second analyte signal stream plummets back down from the rise caused by the squeeze). Adjusting the first analyte signal stream may be accomplished by applying a correction to the first analyte signal stream for a predetermined period of time. The amount of the correction may depend on the second analyte signal stream (e.g., when the second analyte signal stream plummets back down from the rise caused by the squeeze). Alternatively, adjusting the first analyte signal stream may be accomplished by ignoring, purging, or not recording the first analyte signal stream for a predetermined period of time. The predetermined period of time may be a set time or may be based on the second analyte signal stream (e.g., when the second analyte signal stream plummets back down from the compression-induced rise).

[0199] Dip and Recovery (DnR)

[0200] Often, upon insertion of an in vivo continuous analyte sensor, the sensor may exhibit a prolonged dip in measured glucose values ​​that eventually recovers to expected levels. This phenomenon is generally referred to as "dip and recovery" (DnR) and may be caused by increased consumption of glucose for immediate energy to drive an immune response at the insertion site. Such DnR events may persist anywhere from hours to days from the time of insertion, which may lead to excessive low glucose alerts and erroneous glucose readings.

[0201] Currently, some continuous analyte monitoring systems compensate for DnR events by applying an average compensation to all patients in a population using empirically derived models. However, because such methods are population-based, accuracy is sacrificed at the individual level. Thus, there is a need for systems, devices, and methods for automatically detecting DnR events in patients and / or automatically adjusting measurements at the individual level to compensate for DnR events. Systems, devices, and methods for accomplishing the foregoing are described herein.

[0202] 7A illustrates a flow diagram of an exemplary method 700 for determining the occurrence of a DnR event upon insertion of a continuous analyte sensor and calibrating the continuous analyte sensor based on the determined DnR event, according to certain embodiments of the present disclosure. Method 700 may be performed to determine the wearing position of one or more continuous analyte sensors 202 of a continuous analyte monitoring system 104, as illustrated in FIGS. 1 and 2. Method 700 is described below with reference to FIGS. 1 and 2 and their components.

[0203] At block 702, the method 700 begins by continuously or non-continuously monitoring a glucose level of a patient, such as user 102 illustrated in Figure 1, using a first factory-calibrated sensor recently inserted into the patient for a period of time during which measured glucose data is obtained. Block 702 may be performed in certain embodiments by the continuous analyte monitoring system 104 illustrated in Figures 1 and 2, and more specifically, the continuous analyte sensor 202 illustrated in Figure 2, which may be a glucose sensor.

[0204] At block 704, the method 700 continues by continuously or non-continuously monitoring the patient's lactate level over a period of time to obtain measured lactate data. Lactate levels typically exhibit the opposite phenomenon to glucose DnR upon insertion of a continuous analyte sensor. For example, lactate signals typically rise over a prolonged period of time after sensor insertion and then eventually recover to expected levels in a manner that reflects the DnR of glucose signals. This effect may be due to increased lactate production by aerobic glycolysis via β2 receptor stimulation, as well as reduced clearance during immunological activation and inflammation. Thus, due to the nearly opposite response of lactate to sensor insertion, lactate levels may be utilized to recalibrate or equilibrate measured glucose values, as further described below.

[0205] In certain embodiments, block 704 is performed with the same sensor as block 702, such as the continuous analyte sensor 202 (e.g., the continuous analyte sensor 202 can include a continuous multi-analyte sensor configured to measure both glucose and lactate). In other embodiments, block 704 is performed with a second factory-calibrated sensor that is different from the first sensor used in block 702.

[0206] To rule out elevated or irregular lactate levels and / or trends caused by other events (e.g., eating, exercise, emotional stress, or sepsis), the measured lactate data may be mapped against other types of data, including levels and / or trends of other analytes as well as other non-analyte data. For example, to rule out emotional stress or other confounding causes caused by lactate changes, the measured lactate data may be compared by the calibration engine 154 to non-analyte sensor data, for example, from a physical activity sensor such as a heart rate monitor or an accelerometer, which may confirm that the user is engaged in physical activity. In certain embodiments, to rule out lactate changes caused by consumption of food, which may cause only a small increase in lactate and a small increase in glucose, the calibration engine 154 may further compare the measured lactate data and / or the measured glucose data to data and / or metrics associated with food consumption by the user (e.g., metrics 130), which may confirm that the user has or has not recently consumed a meal.

[0207] At block 706, method 700 continues with calibration engine 154 processing the measured glucose data to detect occurrences of glucose level dips (e.g., decreases or dips downward) that meet one or more predetermined thresholds. For example, glucose measurements taken during a period of time are processed to determine a directional trend, which may include data such as the minimum glucose measurement during the period of time, the maximum glucose measurement during the period of time, the direction of change in glucose level during the period of time (e.g., increases upward or dips downward), the rate of change of glucose level during the period of time, etc.

[0208] In certain embodiments, the one or more predetermined thresholds include a concentration threshold. For example, in certain embodiments, the one or more predetermined thresholds include a minimum glucose concentration threshold, which may be met when the patient's measured glucose value substantially equals, remains near, or falls below the minimum glucose concentration threshold. In certain embodiments, the one or more predetermined thresholds include a rate of change threshold. For example, in certain embodiments, the one or more predetermined thresholds include a maximum glucose rate of change threshold, which may be met when the patient's measured glucose value falls at a rate substantially equal to or greater than the maximum glucose rate of change threshold.

[0209] In certain embodiments, the one or more predetermined thresholds include a duration threshold, which may be utilized in combination with one or more concentration thresholds and / or rate of change thresholds. For example, in certain embodiments, the one or more predetermined thresholds include a maximum duration threshold, which may be met when the patient's measured glucose value substantially equals, remains near, or falls below a minimum glucose concentration threshold, and / or when the patient's measured glucose value declines at a rate substantially equal to or greater than a maximum glucose rate of change threshold for a period of time substantially equal to or greater than the maximum duration threshold.

[0210] If the detected glucose level dip does not meet one or more predetermined thresholds, continuous glucose monitoring continues at block 702. However, if the detected glucose level dip does meet one or more predetermined thresholds, at block 708, the calibration engine 154 processes the measured lactate data to determine whether there is an increase in lactate level corresponding to the detected glucose level dip. Processing of lactate measurements taken during the time period may include determining a directional trend, which may include data such as the minimum lactate measurement during the time period, the maximum lactate measurement during the time period, the direction of change in lactate level during the time period (e.g., rising upward or falling downward), the rate of change of lactate level during the time period, etc.

[0211] As discussed above, lactate levels typically exhibit the opposite phenomenon to glucose DnR upon insertion of a continuous analyte sensor. Thus, determining that a corresponding increase in lactate level occurred during a period of time may confirm the presence of a concurrent DnR event. In certain embodiments, similar to block 706, determining a corresponding increase in lactate level includes determining whether the lactate level met one or more predefined thresholds. For example, in certain embodiments, the one or more predefined thresholds include a measured lactate value threshold, a lactate rate of change threshold, and / or a period of time threshold, which may be utilized in combination.

[0212] If the calibration engine 154 detects a corresponding increase in lactate level at block 708, then at block 710 the calibration engine 154 may recalibrate the first sensor based at least in part on the measured lactate data. In other words, the calibration engine 114 may be configured to convert at least one sensor data point received from the first sensor related to glucose into recalibrated data in real time based at least in part on the measured lactate data. In certain embodiments, the calibration engine 154 determines one or more correction factors for the glucose measurements obtained by the first sensor. In certain embodiments, the one or more correction factors are based at least in part on a predetermined historical mapping of glucose and lactate data. In certain embodiments, the one or more correction factors may generally include adjustments to sensitivity (m), rate of change of sensitivity (ddm / ddt), baseline / intercept (b), rate of change of baseline (ddb / ddt), sensitivity profile, relationship between a particular stimulation signal output (e.g., impedance) and sensor sensitivity, relationship between a particular stimulation signal output and sensor temperature or temperature, and / or a calibration code associated with the first sensor. In certain embodiments, multiple possible values ​​for each of the one or more correction factors may be determined, and a weighting or averaging function may be applied thereto. In certain embodiments, new values ​​may be determined for one or more sensor calibration parameters (e.g., sensitivity (m), baseline / intercept (b), etc.) without adjusting the previously known calibration parameters via one or more correction factors during recalibration.

[0213] However, if no corresponding rise in lactate levels is detected at block 708, further processing and analysis of the measured glucose data is performed by calibration engine 154 to determine the cause of the temporary decrease in glucose levels at block 712. For example, such analysis may result in a prediction that the patient is experiencing or will experience, for example, a glycemic or other physiological event.

[0214] Although the method 700 is described above with respect to utilizing lactate for balancing glucose DnR events, other sensor data, including other measured analyte data and / or secondary sensor data, may be used in combination with or instead of measured lactate data for DnR compensation. For example, in certain embodiments, glutamate may be monitored to detect DnR events, as well as end-of-life events for continuous analyte sensors. Typically, upon insertion of an in vivo continuous analyte sensor into tissue, the surrounding tissue environment initiates an injury response that forms a physical diffusion barrier around the insertion site to prevent diffusion of foreign material into the patient's body. In certain cases, the physical diffusion barrier prevents glutamate, and therefore glucose, from freely accessing the recently inserted sensor. Thus, as interstitial glutamate levels are maintained within a known physiological range for a given patient, sensor glutamate readings (e.g., electrochemical sensors utilizing glutamate oxidase and / or glutamate dehydrogenase enzymes to detect interstitial glutamate) may be monitored to identify whether an injury response has created a diffusion barrier, thereby preventing glutamate and glucose from accessing the sensor and causing a DnR type event. Thus, the measured glutamate data may be utilized to identify or confirm the occurrence of a DnR event, and may also be utilized to equilibrate glucose sensor readings.

[0215] In yet further embodiments, measured analyte data for one or more additional analytes in addition to lactate may be utilized in detecting and / or balancing a glucose DnR event. For example, in certain embodiments, at blocks 706 and / or 708, the calibration engine 154 may further determine the presence of a glucose DnR event based on a mapping or correlation between the measured glucose data, the measured lactate data, and / or secondary sensor data. Examples of secondary sensor data include accelerometer data, gyrometer data, global positioning system (GPS) data, heart rate, heart rate reserve, heart rate variability (HRV), electrocardiogram (EKG) data, electromyogram (EMG), respiratory rate, temperature, blood pressure, galvanic skin response, oxygen uptake data (e.g., VO2max), sleep, impedance data, strain sensor data, and the like. In certain embodiments, at blocks 706 and / or 708, the calibration engine 154 can further determine the presence of a glucose DnR event based on a mapping or correlation between the measured glucose data, the measured lactate data, and / or the measured analyte data for one or more additional analytes, such as ketones, glycerol, potassium (e.g., hyperkalemia), sodium, CO2 or anion gap, or similar analytes.

[0216] 7B generally illustrates a portion of a sensing region 701 of a continuous glucose sensor according to certain aspects of the present disclosure. The sensing region 701 is configured to facilitate compensation of DnR events in glucose readings measured upon initial insertion of the continuous multi-analyte sensor into a patient's tissue. In general, the sensing region 701 may be used with any suitable continuous glucose sensor, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104, as illustrated in FIGS. 1 and 2. In certain embodiments, the sensing region 701 may be utilized with a continuous multi-analyte sensor.

[0217] As illustrated, the sensing region 701 includes an enzyme membrane 703 having an enzyme matrix 705 comprised of a predetermined amount of immobilized lactate or lactose oxidase (LO) enzyme 709 in addition to glucose oxidase (GO) enzyme 707. The addition of the LO enzyme 709 to the enzyme matrix 705 facilitates measurable oxidation of lactate or lactose in the sensing region 701, which compensates for the reduction in glucose oxidation by the GO enzyme 707 during the DnR event, thus producing a more leveled glucose signal during the DnR event. The amount of LO enzyme 709 loaded onto the enzyme matrix 705 is sufficient to affect the glucose sensor reading for a limited amount of time, after which the LO enzyme 709 has significantly reduced or no enzymatic activity. For example, in certain embodiments, the amount of LO enzyme 709 loaded onto the enzyme matrix 705 is sufficient to affect glucose sensor readings for about 5 days or less, or about 4 days or less, or about 3 days or less, or about 2 days or less, or about 1 day or less.

[0218] Thus, in addition to or in the alternative to the method 700 described above, utilization of a continuous glucose sensor having a working electrode with a sensing region 701 can facilitate improving glucose sensor readings during DnR events, as the addition of LO enzyme 709 to the sensing region provides a countervailing effect to the reduced glucose signal.

[0219] 7C illustrates a flow diagram of an exemplary method 730 for identifying sensor dip and recovery events in accordance with certain aspects of the present disclosure. In certain embodiments, the continuous analyte monitoring system 104 can perform the method 730 to detect and compensate for dip and recovery events.

[0220] At block 732, the continuous analyte monitoring system 104 begins by generating a first analyte signal stream indicative of a level of a first analyte (e.g., glucose). The continuous analyte monitoring system 104 may use a first analyte sensor to continuously or non-continuously monitor a first analyte of a patient, such as the user 102 illustrated in FIG. 1. The continuous analyte monitoring system 104 generates a first analyte signal stream indicative of a concentration or level of the first analyte. In certain embodiments, the first sensor may be a factory calibrated continuous glucose sensor 202. In certain other embodiments, the first sensor may be a factory calibrated continuous multi-analyte sensor that measures at least the glucose level of the user. The first analyte signal stream may be a signal that changes over time to indicate a change in the concentration or level of the first analyte.

[0221] At block 734, the continuous analyte monitoring system 104 continues by generating a second analyte signal stream indicative of the level of a second analyte (e.g., lactate). The continuous analyte monitoring system 104 may use the second analyte sensor or the first analyte sensor to continuously or non-continuously monitor data (e.g., concentration values, trends, or patterns) from a user and generate a second analyte signal stream indicative of the concentration or level of the second analyte. The second analyte signal stream may be a signal that changes over time to indicate changes in the concentration or level of the second analyte.

[0222] In certain embodiments, the continuous analyte monitoring system 104 uses the same sensor in block 734 as in block 732. In other embodiments, the continuous analyte monitoring system 104 uses a different sensor in block 734 than in block 732.

[0223] In block 736, the continuous analyte monitoring system 104 compares the first analyte signal stream to the second analyte signal stream and monitors the data trends of each signal stream. Specifically, the continuous analyte monitoring system 104 monitors rises and falls in each of the first analyte signal stream and the second analyte signal stream. If there is a fall in the first analyte signal stream and a fall in the second analyte signal stream followed by a rise in both the first analyte signal stream and the second analyte signal stream during the same time period, the continuous analyte monitoring system 104 proceeds to block 738. If not, the continuous analyte monitoring system 104 returns to block 732 and continues monitoring the sensor data. In some embodiments, the rise in the second analyte signal stream occurs faster than the rise in the first analyte signal stream.

[0224] At block 738, the continuous analyte monitoring system 104 detects the dip and recovery event by determining that a pattern of a drop in the first analyte signal stream and the second analyte signal stream followed by an increase in both signal streams is indicative of a dip and recovery event, particularly when the first and second analyte sensors are first being used (e.g., when the first analyte sensor and the second analyte sensor are first inserted into the user's body). Dip and recovery events often occur during initial use of the continuous analyte monitoring system 104 and can produce inaccurately measured analyte data.

[0225] 7D, a first analyte signal stream 750 and a second analyte signal stream 752 are plotted. The first analyte signal stream 750 may exhibit a sharp drop 754 and a gradual recovery 756 during a period 758. A dip and recovery event is detected when the second analyte signal stream 752 exhibits a sharp drop 760 followed by a recovery 762 during the same period 758. Notably, the recovery 762 of the second analyte signal stream 752 may occur faster and to a greater extent than the recovery 756 of the first analyte signal stream 750.

[0226] Returning to FIG. 7C, in block 740, after the dip and recovery event is detected in block 738, the continuous analyte monitoring system 104 may adjust at least one of the first analyte sensor or the first analyte signal stream. In adjusting the first analyte sensor, the continuous analyte monitoring system 104 may temporarily power down the first analyte sensor such that the first analyte sensor does not collect data for a predetermined period of time. The predetermined period of time may be a set time or may be based on data from the second analyte signal stream (e.g., when the second analyte signal stream ends the rise and recovers from the dip and recovery event). Adjusting the first analyte signal stream may be accomplished by applying a correction to the first analyte signal stream for the predetermined period of time. The amount of the correction may depend on the second analyte signal stream (e.g., when the second analyte signal stream ends the rise and recovers from the dip and recovery event). Alternatively, adjusting the first analyte signal stream may be accomplished by ignoring or not recording the first analyte signal stream for a predetermined period of time, which may be a set time or may be based on data from the second analyte signal stream (e.g., when the second analyte signal stream ends the rise and recovers from a dip and recovery event).

[0227] Hypoglycemic events

[0228] A user may experience a hypoglycemic event when the user's glucose level is too low. However, detecting a hypoglycemic event based solely on measured glucose concentrations or levels may not be sufficient or accurate, especially at night when the user is sleeping. For example, some measured glucose levels may be inaccurate due to stress events that occur during sleep.

[0229] The continuous analyte monitoring system 104 may perform several additional checks if the measured glucose concentration or level is too low to verify whether a hypoglycemic event has actually occurred. For example, the continuous analyte monitoring system 104 may check whether the user is sleeping. The continuous analyte monitoring system 104 may determine the time of day to see if the glucose concentration or level was measured overnight or at a time when the user would normally be sleeping. Additionally, the continuous analyte monitoring system 104 may use accelerometer readings (e.g., using an accelerometer in the continuous analyte monitoring system 104) to determine whether the user's movements are consistent with sleep, sleep apnea, or compression (e.g., using some of the processes illustrated using FIG. 6A). As another example, the continuous analyte monitoring system 104 may check the measured local tissue oxygen saturation (e.g., using an oxygen sensor in the continuous analyte monitoring system 104) to determine whether a compression event is occurring. If the oxygen level is too low in that area, the continuous analyte monitoring system 104 may determine that compression is occurring, which may adversely affect the accuracy of the glucose measurement. As another example, the continuous analyte monitoring system 104 can check the measured lactate concentration or lactate level within a similar time frame as the glucose measurement. If the lactate concentration does not increase around the time the glucose concentration or level was measured, the continuous analyte monitoring system 104 can determine that the glucose measurement was an erroneous measurement. After performing the check, if the continuous analyte monitoring system 104 determines that the measured glucose concentration or level was accurate, the continuous analyte monitoring system 104 can issue an alert indicating a detected hypoglycemic event.

[0230] 8 illustrates a flow diagram of an exemplary method 800 for verifying a hypoglycemic event. In certain embodiments, the continuous analyte monitoring system 104 performs the method 800. By performing the method 800, the continuous analyte monitoring system 104 performs additional checks using other sensors before concluding that a hypoglycemic event is occurring.

[0231] At block 802, the continuous analyte monitoring system 104 measures the user's glucose level. The continuous analyte monitoring system 104 may use a glucose sensor of the continuous analyte monitoring system 104 to measure the user's glucose level after the user inserts the glucose sensor into the user's body. The continuous analyte monitoring system 104 may determine that the measured glucose level is too low, indicating a hypoglycemic event. The continuous analyte monitoring system 104 may analyze information from other sensors before concluding that a hypoglycemic event is occurring.

[0232] At block 804, the continuous analyte monitoring system 104 can determine a user state. For example, the continuous analyte monitoring system 104 can use the time of day and an accelerometer reading (e.g., from an accelerometer of the continuous analyte monitoring system 104) to determine whether the user is sleeping. As another example, the continuous analyte monitoring system 104 can determine a local tissue oxygen saturation level (e.g., from an oxygen sensor of the continuous analyte monitoring system 104). As yet another example, the continuous analyte monitoring system 104 can determine the user's lactate concentration or level (e.g., from a lactate sensor of the continuous analyte monitoring system 104).

[0233] At block 806, the continuous analyte monitoring system 104 can verify the measured glucose level. For example, the continuous analyte monitoring system 104 can determine from the time and accelerometer readings that the user is not sleeping, indicating that the measured glucose level is accurate. As another example, the continuous analyte monitoring system 104 can determine from the local tissue oxygen saturation level that no compression event has occurred that would affect the accuracy of the glucose measurement. As yet another example, the continuous analyte monitoring system 104 can determine from the lactate measurement that the glucose measurement was not an erroneous reading (e.g., the lactate measurement increased within a similar time frame of the glucose measurement). In response to one or more of these checks, the continuous analyte monitoring system 104 can verify that the glucose measurement was accurate.

[0234] At 808, the continuous analyte monitoring system 104 can trigger an alert indicating the hypoglycemic event. The alert may include a message, an audible sound, or a vibration indicating to a user wearing the continuous analyte monitoring system 104 that a hypoglycemic event has been detected.

[0235] Sensor placement and compensation determination

[0236] Wearable in-vivo continuous analyte sensors are typically recommended to be worn on a part of the body (e.g., the abdomen), although many patients prefer to wear the device on other parts of the body, such as the upper arm, forearm, thigh, calf, back, or buttocks. However, the accuracy and / or quality of the sensor signal (e.g., presence of signal noise) may vary slightly between these different wearing locations, especially between the abdomen and the arm. For example, a continuous glucose sensor may be more accurate when worn on the arm compared to the abdomen, but may be more prone to signal noise and earlier operational failure due to frequent movement of the arm causing mechanical interference. Such differences in signal accuracy or quality between different wearing locations may also be explained by temperature compensation (e.g., temperature differences) between body parts.

[0237] In view of the above, there is a need for improved systems and methods for automatically detecting the placement of a wearable in-vivo continuous analyte sensor on a patient and automatically recalibrating or adjusting measurements according to the determined placement. Systems and methods for accomplishing such are described herein.

[0238] 9 illustrates a flow diagram of an exemplary method 900 for determining a wearing position of a continuous analyte sensor and calibrating the continuous analyte sensor based on the determined wearing position according to certain embodiments of the present disclosure. Method 900 may be performed to determine a wearing position of one or more continuous analyte sensors 202 of a continuous analyte monitoring system 104 as illustrated in FIGS. 1 and 2. Method 900 is described below with reference to FIGS. 1 and 2 and their components.

[0239] At block 902, the method 900 begins by using a first, factory calibrated sensor to continuously or non-continuously monitor a first analyte of a patient, such as user 102 illustrated in Figure 1, for a period of time to obtain measured analyte data for the first analyte. The first sensor may be inserted at a desired location by the patient, such as the general area, arm, abdomen, back, thigh, etc., with the location being determined and compensated for by the current method.

[0240] Block 902 may be performed in certain embodiments by the continuous analyte monitoring system 104 illustrated in Figures 1 and 2, and more specifically, the continuous analyte sensor 202 illustrated in Figure 2. For example, the continuous analyte monitoring system 104 may include a continuous glucose sensor 202 that is factory calibrated to measure a user's glucose level. Alternatively, the continuous analyte monitoring system 104 may include a factory calibrated continuous multi-analyte sensor configured to measure at least a user's glucose level. Generally, the first analyte may include glucose, although other analytes such as lactate, ketones, glycerol, electrolytes such as sodium and potassium, calculated measurements such as anion gap, or any other suitable analyte described herein are also contemplated.

[0241] At block 904, the method 900 optionally continues by receiving measured physiological data associated with the patient during the time period. The measured physiological data may include other sensor data measured continuously or non-continuously by one or more other sensors, which in certain embodiments may be indicative of a patient's physiological event during the time period. For example, the measured physiological data may be indicative of the onset, presence, and / or conclusion of a patient's physiological event. In certain embodiments, the physiological data may be indicative of one or more patient's physiological events during the time period. In general, a patient's physiological event may refer to any event or activity experienced or engaged in by a patient that causes physical stress to the patient. Examples of physiological events may include engaging in physical activity, sleeping or resting, eating a meal, administering a medication, the occurrence of a glycemic event (e.g., hypoglycemia or hyperglycemia), etc.

[0242] The measured physiological data may be provided by a variety of different types of sensors, either alone or in combination. In certain embodiments, the measured physiological data received at block 904 includes measured analyte data for one or more additional analytes, including at least a second analyte, for the patient during the time period. For example, in certain embodiments, the measured physiological data includes measured analyte data (e.g., concentration values, trends, and / or patterns) for electrolytes such as lactate, ketones, glycerol, sodium, and potassium, calculated measurements such as anion gap, and / or any other suitable analytes described herein, or combinations thereof.

[0243] In certain embodiments, the measured physiological data received at block 904 includes secondary sensor data measured during the time period, such as one or more of accelerometer data, gyrometer data, global positioning system (GPS) data, heart rate, cardiac reserve, heart rate variability (HRV), electrocardiogram (EKG) data, electromyogram (EMG), respiration rate, temperature, sweat, blood pressure, galvanic skin response, oxygen intake data (e.g., VO2max), sleep, impedance data, etc. In certain embodiments, the measured physiological data can be utilized to calculate metrics, such as metric 130 of FIG. 1, to further inform the determination of and compensation for wearing position.

[0244] In certain embodiments, the physiological data measured at block 904 is received from the same sensor utilized at block 902, such as the continuous analyte sensor 202 of the continuous analyte monitoring system 104 illustrated in Figures 1 and 2 (e.g., the continuous analyte sensor 202 may include a continuous multi-analyte sensor configured to measure both a second analyte and a first analyte). In other embodiments, the physiological data measured at block 904 is received from a second sensor that is different from the first sensor used at block 902.

[0245] At block 906, the method 900 continues by the calibration engine 154 optionally processing the measured first analyte data and the received physiological data to determine a responsiveness of the first analyte to the indicated physiological event based on the measured first analyte data and the measured physiological data during the period. The responsiveness of the first analyte may refer to the degree of change, the delay of change, and / or the rate of change of the measured first analyte value in relation to and as affected by the indicated physiological event. For example, a patient's engagement in physical activity may cause a decrease in the patient's measured glucose level, and the amount of the decrease, the delay between the onset of the physical activity and the measured decrease, and / or the rate of the decrease may be referred to as the "responsiveness" of the patient's glucose level. In general, the responsiveness of an analyte to a physiological event will vary with different wearing positions (e.g., a particular analyte may be more responsive on the arm due to the arm's proximity to the heart). Thus, after determining the responsiveness of the first analyte to the indicated physiological event, the responsiveness may be utilized to determine the placement location of the first sensor.

[0246] At block 908, the method 900 continues by the calibration engine 154 determining a wearing position of the first sensor based at least in part on the measured first analyte data during the time period, and in certain embodiments, the received physiological data during the time period. In certain embodiments, the wearing position is determined utilizing a pre-defined mapping (i.e., correlation) of past (i.e., previously measured) analyte data for the first analyte and past physiological data of the patient and / or the general population.

[0247] In certain embodiments, the wearing position of the first sensor is determined utilizing one or more trained machine learning models, such as trained machine learning models provided by the training server system 140 in FIG. 1, or other AI models to which the historical analyte data and historical physiological data of the first analyte are input as training data. In certain embodiments, the predicted analyte data for the first analyte is determined utilizing one or more trained machine learning models, such as trained machine learning models provided by the training server system 140 in FIG. 1, or other AI models. In certain embodiments, such models may be configured to use measured data from one or more sensors measuring multiple analytes as input and provide as output a decision that is utilized for recalibration of the continuous analyte sensor. Exemplary inputs for such models may include, for example, continuous analyte sensor data (e.g., glucose, lactate, and / or any other analyte), non-analyte sensor data (heart rate data, accelerometer data, gyrometer data, etc.), etc. Thus, given the interaction of various physiological conditions, parameters and / or thresholds of such algorithms or models may be modified based at least in part on the number of analytes or properties being measured for an input to reflect knowledge gained from each of the other analytes or properties being measured or physical conditions associated with the additional analytes or properties being measured.

[0248] In certain embodiments, the wearing position of the first sensor is determined based on the measured first analyte data during the period in combination with multiple secondary sensor data types. For example, in certain embodiments, in addition to the measured first analyte data, two or more of temperature data, gait data, and impedance data from a temperature sensor, an accelerometer, and an impedance sensor, respectively, can be utilized to determine the wearing position of the first sensor, since each of these parameters may vary between wearing positions. However, other secondary sensor data types and / or combinations thereof are also contemplated.

[0249] In certain embodiments, the wearing position of the first sensor is determined based on the responsiveness of the measured first analyte data to the indicated physiological event determined in block 906. For example, it has been shown that for lactate and other analytes, sensors placed in upper body wearing positions (e.g., arms) may have faster response rates compared to sensors placed in lower body wearing positions (e.g., abdomen, legs). Thus, in such embodiments, the wearing position may be determined by comparing the determined responsiveness of the measured first analyte to historical responsiveness data for the first analyte at one or more wearing positions (e.g., historical mapping or correlation between wearing positions and the responsiveness of the first analyte thereat). Such historical responsiveness data may include the patient's own historical data and / or population-based historical data. In certain embodiments, the historical responsiveness data may be input into one or more trained machine learning models, such as trained machine learning models provided by the training server system 140 in FIG. 1, or other AI models, to determine the current wearing position of the first sensor, and the determination of the wearing position may be probabilistic or categorical.

[0250] In certain embodiments, the wearing location is determined based on the circadian rhythm of the measured first analyte data and the received physiological data, the time period including at least 24 hours. For example, in embodiments where the received physiological data includes measured second analyte data measured by the same sensor as the first analyte, the measured analyte data for both analytes can be analyzed to determine the day-night pattern for each analyte. The wearing location can then be determined by comparing the determined day-night patterns for the first and second analytes to past day-night patterns for the first and second analytes at one or more wearing locations. In certain embodiments, the past day-night patterns can be utilized to train one or more trained machine learning or other AI models, which can be deployed to determine the current wearing location of the first sensor.

[0251] In certain embodiments, in addition to determining the wearing location of the first sensor in block 908, the calibration engine 154 may determine tissue composition at the wearing location. For example, the calibration engine 154 may determine muscle and fat composition at the wearing location, which may be determined at least in part based on the determination of the wearing location and / or secondary sensor data, such as impedance data. The determined tissue composition may then be utilized for measurement adjustment and / or recalibration of the first sensor in block 910, as tissue composition may affect the accuracy and quality of the sensor signal. Thus, determining the tissue composition at the wearing location may facilitate improved calibration of the first sensor and / or improved accuracy of the sensor readings.

[0252] In certain embodiments, the determination of the wearing position is based on secondary sensor data in addition to or in place of the measured first analyte data and / or physiological data. More specifically, in certain embodiments, the wearing position may be determined based on accelerometer data, alone or in combination with the measured first analyte data and / or physiological data. For example, the wearing position may be depicted based on the movement of the wearing position as determined by the accelerometer (e.g., a sensor on the abdomen may detect up and down movement indicating that the wearing position is the abdomen, and a sensor on the arm may detect rocking movement indicating that the wearing position is the arm).

[0253] After determining the wearing position of the first sensor, in block 910, the calibration engine 154 may switch the first sensor and / or any corresponding system to an appropriate analyte measurement mode or recalibration mode specific to the determined wearing position and / or recalibrate the first sensor. In other words, the calibration engine 154 may switch to a location-specific algorithm and / or use location-specific calibration parameters for measuring analyte levels that compensate for the wearing position and / or a location-specific algorithm for recalibrating the first sensor with a unique location-specific compartment / sensitivity model. In certain embodiments, the calibration engine 154 optimizes or reoptimizes the model parameters to best fit the data received from the determined wearing position.

[0254] Training Machine Learning Models

[0255] 10 is a flow diagram depicting a method 1000 for training a machine learning model and providing decisions for the sensor recalibration and measurement compensation methods described above, in accordance with certain embodiments of the present disclosure. In certain embodiments, the method 1000 is used to train any of the machine learning models discussed herein for use in recalibrating or sensor measurement compensation of a continuous analyte sensor 202 of a patient, such as a user illustrated in FIGS. 1 and 2.

[0256] The method 1000 begins at block 1002 with retrieving data from a medical history record database, such as the medical history record database 112 illustrated in Figure 1, by a training server system, such as the training server system 140 illustrated in Figure 1. As mentioned herein, in certain embodiments, the record database 112 can provide a repository of up-to-date information and medical history information for users of the continuous analyte monitoring system and connected mobile health application, such as users of the continuous analyte monitoring system 104 and application 106 illustrated in Figure 1, as well as data for one or more patients who are not or were not previously users of the continuous analyte monitoring system 104 and / or application 106. In certain embodiments, the medical history record database 112 can include historical data or patient-specific information associated with the patient for whom the model is being used in real time.

[0257] Retrieval of data from the historical record database 112 by the training server system 140, in block 1002, may include retrieval of all or any subset of the information maintained by the historical record database 112. For example, if the historical record database 112 stores information about 100,000 patients (e.g., non-users and users of the continuous analyte monitoring system 104 and application 106), the data retrieved by the training server system 140 to train one or more machine learning models may include information about all 100,000 patients, or only a subset of the data for those patients, e.g., data associated with only 50,000 patients, or only data from the past 10 years.

[0258] As an illustrative example, in block 1002, the training server system 140 can retrieve information for 100,000 patients stored in the medical history record database 112 to train a model to provide decisions utilized in continuous analyte sensor recalibration or sensor measurement compensation. Each of the 100,000 patients can have a corresponding data record stored in the medical history record database 112, such as the user profile 118 illustrated in FIG. 1. Each data record can include information such as the information discussed with respect to FIG.

[0259] At block 1004, the method 1000 continues by the training server system 140 selecting one of the historical patient records retrieved by the training server system 140 at block 1002. The record contains information associated with the patient, such as information stored in the patient's user profile. Examples of the types of information contained in the patient's user profile are provided above. The training server system 140 may use any suitable criteria (e.g., start with the oldest record, start with the newest record, etc.) for the selection of the historical patient record, as the training server system 140 will iterate through each historical access record in the training set until all records have been used to train the machine learning model or the machine learning model has been accurately determined for recalibration and / or sensor measurement compensation for each historical patient record input to the model.

[0260] At block 1006, method 1000 continues by the training server system 140 extracting one or more features of the selected historical patient record. These features are extracted to be used as input features for the machine learning model. For example, the user profile associated with the patient selected at block 1004 may include information regarding at least the patient's age, the patient's gender, the patient's normal glucose range, the patient's normal lactate range (e.g., lactate baseline and / or peak), normal ranges for the patient's other analytes and / or other secondary sensor data, etc. The features used to train the machine learning model may vary in different embodiments.

[0261] At block 1008, the method 1000 continues by the training server system 140 training one or more machine learning models based on the selected historical patient records. In some embodiments, the training server does so by providing the features (e.g., extracted at block 1006) as input to the model. The model may be a new model initialized with random weights and parameters, or may be partially or fully pre-trained (e.g., based on a previous training round). Based on the input features, the model in training generates some output. The output may include decisions for recalibration and / or sensor measurement compensation, or similar metrics.

[0262] In certain embodiments, the training server system 140 compares this generated output to the actual labels associated with the historical patient record and calculates a loss based on the difference between the actual and generated results. This loss is then used to refine one or more internal weights and parameters of the model (e.g., via backpropagation) so that the model learns to make calibration-related decisions more accurately.

[0263] At block 1010, the method 1000 continues by the training server system 140 determining whether additional training is required. This may include evaluating whether any additional historical patient records remain in the training dataset. If at block 1010, the training server system 140 determines that all training data has been input to the machine learning model, at block 1012, the training server system 140 deploys the trained model during runtime. In some embodiments, this includes transmitting some indication of the trained model (e.g., a weight vector) that can be used to instantiate the model on another device. For example, the training server system 140 can deploy the trained model to the calibration engine 154. The model can then be utilized for recalibration of the sensor of a user using the application 106.

[0264] If, at block 1010, the training server system 140 determines that not all historical patient records of the training data have been input into the model for training, at block 1014, the training server system 140 determines whether the model has reached a predefined minimum accuracy (e.g., 90% accuracy, 95% accuracy, etc.). If the predefined minimum accuracy has not been met, the training server system 140 determines that additional training remains and the method 1000 returns to block 1004. Alternatively, if the machine learning model meets the predefined minimum accuracy (e.g., makes the determination correctly 90% or 95% of the time), at block 1012, the training server system 140 deploys the trained model during runtime.

[0265] By iteratively processing each data set corresponding to each historical patient, the model can be iteratively refined to generate accurate calibration association decisions for the patient.

[0266] Additional Considerations

[0267] The methods disclosed herein include one or more steps or actions for achieving the method. These method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specifically specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims.

[0268] As used herein, a phrase referring to "at least one of" a list of items refers to any combination of those items, including single members. By way of example, "at least one of a, b, or c" is intended to cover a, b, c, ab, ac, bc, and abc, as well as any combination of multiples of the same element (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other permutation of a, b, and c).

[0269] The foregoing description is provided to enable those skilled in the art to practice the various embodiments described herein. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments. Thus, the claims are not intended to be limited to the embodiments shown herein, but are to be given the full scope consistent with the claim language, in which reference to an element in the singular is not intended to mean "one and only one" unless specifically stated otherwise, but is intended to mean "one or more". The term "some" refers to one or more, unless specifically stated otherwise. All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure that are now known or that will become known in the future to those skilled in the art are expressly incorporated herein by reference and are intended to be encompassed within the scope of the claims. Furthermore, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is expressly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. 112(f) unless the element is expressly recited using the phrase "means for," or, in the case of a method claim, the element is recited using the phrase "step for."

[0270] While various examples of the present invention have been described above, it should be understood that the examples are presented merely as examples and not as limitations. Similarly, various figures may depict exemplary architectural or other configurations for the present disclosure, which configurations are made to aid in understanding features and functionality that may be included in the present disclosure. The present disclosure is not limited to the illustrated exemplary architecture or configurations, but may be implemented using various alternative architectures and configurations. Additionally, while the present disclosure has been described above with respect to various exemplary embodiments and aspects, it should be understood that various features and functions described in one or more of the individual embodiments are not limited in their applicability to the particular embodiment in which they are described. On the contrary, those features and functions may be applied, alone or in any combination, to one or more of the other embodiments of the present disclosure, regardless of whether such an embodiment is described and regardless of whether such features are presented as part of a described embodiment. Thus, the breadth and scope of the present disclosure should not be limited by any of the exemplary embodiments described above.

[0271] All references cited herein are incorporated herein by reference in their entirety. To the extent that publications and patents or patent applications incorporated by reference conflict with the present disclosure contained herein, the present specification is intended to supersede and / or take precedence over any such conflicting material.

[0272] Unless otherwise defined, all terms (including technical and scientific terms) are to be given their ordinary and customary meanings to those of ordinary skill in the art and are not to be limited to any special or customized meaning unless expressly defined as such herein.

[0273] Terms and phrases used in this application, and variations thereof, unless expressly stated otherwise, particularly in the appended claims, are to be construed as open ended, as opposed to limiting. As an example of the foregoing, the term "including" should be read to mean "including without limitation," "including, but not limited to," and the like. The term "comprising" as used herein is synonymous with "including," "containing," or "characterized by," and is inclusive or open ended and does not exclude additional, unrecited elements or method steps. The term "having" should be interpreted as "having at least." The term "including" should be interpreted as "including, but not limited to." The term "examples" is used to provide illustrative examples of the items in the discussion, and is not an exhaustive or limiting list of such examples. Adjectives such as "known," "conventional," "standard," and terms of similar import should not be construed as limiting the items being described to a given period of time or to items available at a given time, but rather should be read to embrace known, conventional, or standard technology that may be available or known at any time now or in the future, and words such as "preferably," "preferred," "desired," or "desirable," and similar imports should not be understood to imply that a particular feature is critical, essential, or even essential to the structure or function of the invention, but rather are merely intended to highlight alternative or additional features that may or may not be utilized in a particular example of the invention. Similarly, a group of items connected with the conjunction "and" should not be read as requiring that any and all of those items be present in the group, but rather should be read as "and / or" unless expressly stated otherwise.Similarly, a group of items connected with the conjunction "or" should not be read as requiring mutual exclusivity among the group, but rather as "and / or" unless expressly stated otherwise.

[0274] As used herein, "comprising" is synonymous with "including," "containing," or "characterized by" and is inclusive or open-ended and does not exclude additional or unrecited elements or method steps.

[0275] All numerical values ​​expressing amounts of raw materials, reaction conditions, and the like used herein should be understood to be modified in all instances by the term "about." Accordingly, unless otherwise indicated, the numerical parameters set forth herein are approximations that may vary depending upon the desired properties sought to be obtained. At the very least, and not as an attempt to limit the application of the doctrine of equivalents to the scope of any claims in any application claiming priority to this application, each numerical parameter should be construed in light of the number of significant digits and ordinary rounding approaches.

[0276] Moreover, although the foregoing has been described in some detail by way of illustration and example for purposes of clarity and understanding, it will be apparent to those skilled in the art that certain changes and modifications may be practiced. Thus, the description and examples should not be construed as limiting the scope of the invention to the specific examples and embodiments described herein, but rather as covering all modifications and alternatives that come within the true scope and spirit of the invention. [Explanation of symbols]

[0277] 100 Disease Management System 102 users 104 Analyte Monitoring Systems 104 System 105 Applications 106 Applications 107 Display Devices 110 User Database 112 Medical History Record Database 114 Analysis Engine 118 User Profile 120 Demographic Information 122 Disease Information 124 Medication Information 126 Application Data 128 inputs 130 Metric 140 Training Server System 144 Output 152 Decision Support Engine 153 Data Analysis Module (DAM) 154 Calibration Engine 155 Data Analysis Module (DAM) 202 Continuous Analyte Sensors 204 Sensor Electronics Module 206 Non-analyte sensors 208 Medical Devices 210 Display Device 212 Touch Screen Display 220 Display Device 222 Touch Screen Display 230 Display Device 232 Touch Screen Display 240 Display Devices 300 Multi-analyte sensor 301 Multi-analyte sensor 302 Detection area 304 Working electrode 306 Reference electrode 308 Counter Electrode 310 Potentiostat 312 Active Surface 314 Working electrode 316 Potentiostat 322 Active Surface 324 Aptamers 326 Analyte 650 First Plot 652 first analyte signal stream 654 Descent 656 period 660 Second Plot 662 Second Analyte Signal Stream 664 Increased 700 methods 701 Detection Area 703 Enzyme membrane 705 Enzyme Matrix 707 Glucose oxidase (GO) enzyme 709 Lactose oxidase (LO) Enzyme 750 first analyte signal stream 752 Second Analyte Signal Stream 754 Swoop 756 Recovery 758 period 760 steep drop 762 Recovery

Claims

1. An apparatus comprising: an analyte sensor; a memory containing executable instructions; a processor communicatively coupled to the memory; and wherein the processor causes the device to: monitoring an analyte of the patient over a period of time using the analyte sensor to obtain measured analyte data for the analyte of the patient; monitoring other measured sensor data indicative of a physiological condition of the patient during said period; determining predicted analyte data for said analyte based on said physiological condition of said patient during said period; determining a correction factor based on the predicted analyte data and the measured analyte data, the correction factor being indicative of an error in calibration of the analyte sensor; determining whether the analyte sensor is capable of being recalibrated; if recalibration is possible, recalibrating the analyte sensor based on the correction factor; If recalibration is not possible, recommending to the patient that the analyte sensor be replaced. An apparatus configured to execute said instructions.

2. The device of claim 1 , wherein the analyte is glucose.

3. The apparatus of claim 1 , wherein the physiological condition comprises an oxygen level of the patient or a temperature of the patient.

4. The apparatus of claim 1 , wherein the predicted analyte data is determined based on a mapping between historical analyte data for the analyte and the physiological condition.

5. The apparatus of claim 1 , wherein the processor is further configured to determine that the measured analyte data deviates from the expected analyte data by an amount that exceeds a threshold value.

6. The apparatus of claim 5 , wherein determining the correction factor is in response to determining that the measured analyte data deviates from the expected analyte data by the amount that exceeds the threshold.

7. 6. The apparatus of claim 5, wherein determining that the measured analyte data deviates from the predicted analyte data by the amount that exceeds the threshold comprises matching the data points from the analyte data with data points from the predicted analyte data based on a time at which the data points from the analyte data were measured.

8. The apparatus of claim 1 , wherein the correction factor is based on the amount that the measured analyte data deviates from the expected analyte data.

9. The apparatus of claim 1 , wherein the correction factor comprises an adjustment to a sensitivity of the analyte sensor or a rate of change of the sensitivity of the analyte sensor.

10. An apparatus comprising: a first analyte sensor; a second analyte sensor; and Memory and a processor communicatively coupled to the memory; wherein the processor: receiving data indicative of a historical relationship between a first analyte and a second analyte for the patient during a first time period; monitoring the first analyte in the patient during a second time period to obtain first analyte data using the first analyte sensor; monitoring the second analyte in the patient during the second time period to obtain second analyte data using the second analyte sensor; determining a current relationship between the first analyte and the second analyte based on the first analyte data and the second analyte data; determining that a substantial discrepancy exists between the historical relationship and the current relationship, the substantial discrepancy indicating an error in calibration of the first analyte sensor or the second analyte sensor; in response to determining that a substantial mismatch exists, recalibrating the first analyte sensor or the second analyte sensor. The apparatus is configured to:

11. 11. The apparatus of claim 10, wherein the processor is further configured to determine that recalibration of the first analyte sensor or the second analyte sensor is possible, and recalibrating the first analyte sensor or the second analyte sensor is responsive to determining that recalibration of the first analyte sensor or the second analyte sensor is possible.

12. 11. The device of claim 10, wherein the first analyte is glucose and the second analyte is lactate.

13. An apparatus comprising: a first analyte sensor; Memory, a processor communicatively coupled to the memory; wherein the processor: monitoring a first analyte in a patient over a period of time to obtain first analyte data using the first analyte sensor; determining a period of stability in the first analyte data; determining a calibration time based on the stability period; The apparatus is configured to recalibrate the analyte sensor during the determined calibration time.

14. The apparatus of claim 13 , wherein the processor is further configured to provide a recommendation to recalibrate during the calibration period.

15. The apparatus of claim 13 , wherein during the stable period, the first analyte data remains within a predetermined range.

16. 14. The device of claim 13, further comprising a second analyte sensor, and wherein the processor is further configured to monitor a second analyte in the patient during the period using the second analyte sensor to obtain second analyte data, and wherein the period of stability is further based on the second analyte data.

17. 14. The apparatus of claim 13, wherein the processor is further configured to receive measured physiological data indicative of a physiological condition of the patient during the period, and wherein the stable period is further based on the measured physiological data.

18. The apparatus of claim 13 , wherein the stable period has a duration of at least 5 minutes.

19. The apparatus of claim 13 , wherein the calibration time occurs during the stability period.

20. The apparatus of claim 13 , wherein determining the calibration time is further based on a time of insertion of the first analyte sensor into the patient.

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