Sensing systems and methods for hybrid glucose and ketone monitoring

Abstract

Certain aspects of the present disclosure relate to a monitoring system comprising a continuous analyte sensor configured to generate an analyte measurement associated with an analyte level in a patient, and a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurement.

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 478,005, filed December 30, 2022, which is assigned to the assignee of the present application and is hereby expressly incorporated herein in its entirety for all applicable purposes as if fully set forth below. [Background technology]

[0002] Introduction Diabetes is a metabolic disease related 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.

[0003] When a person eats a meal containing carbohydrates, the food is processed by the digestive system, producing glucose in the blood. Blood glucose can be used for energy or stored as fat. The body normally maintains blood glucose levels within a range that provides enough energy to support bodily functions and avoids problems that can arise from glucose levels that are too high or too low (glycemic variability). Regulation of blood glucose levels depends on the production and use of insulin, which facilitates the movement of blood glucose into cells.

[0004] 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 (e.g., euglycemia). A condition in which blood glucose levels are higher than normal is called "hyperglycemia." Chronic hyperglycemia can lead to many health problems, including cardiovascular disease, cataracts and other eye diseases, nerve damage (neuropathy), liver disease, kidney damage, and amputation. Hyperglycemia can also lead to acute problems, such as diabetic ketoacidosis, a condition in which the blood becomes excessively acidic due to elevated levels of ketone bodies (e.g., acetone, acetoacetate, β-hydroxybutyrate) produced as a result of the metabolic process of gluconeogenesis. A condition in which blood glucose levels are lower than normal is called "hypoglycemia." Severe hypoglycemia can lead to an acute crisis that can result in seizures or death.

[0005] Diabetes is sometimes referred to as "type 1" and "type 2." Type 1 diabetic patients are typically able to use insulin when it is available, but are unable to produce sufficient amounts of insulin in their own bodies due to reduced function of the insulin-producing beta cells in the pancreas. Type 2 diabetic patients may produce sufficient amounts of insulin, but are "insulin resistant" due to reduced sensitivity to insulin at the cellular level. As a result, even when insulin is present in the body, it is insufficiently or ineffectively used by the patient's cells to effectively uptake glucose, resulting in chronically elevated glucose levels in the blood and / or interstitial fluid. Diabetic patients can receive insulin to manage their blood glucose levels. Insulin can be administered, for example, through manual injection with a needle. Wearable insulin pumps are also available. Diet and exercise also affect blood glucose levels. [Brief explanation of the drawings]

[0006] So that the above-listed features of the present disclosure can be understood in detail, a more particular description, briefly summarized above, can be had 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, as the description may be given for other equally valid embodiments. [Figure 1] 1 illustrates aspects of an exemplary health management system that may be used in connection with implementing embodiments of the present disclosure. [Figure 2] FIG. 1 conceptually illustrates an example continuous analyte monitoring system including an example exemplary continuous analyte sensor with sensor electronics, according to certain aspects of the present disclosure. [Figure 3] 2 illustrates example inputs and example metrics calculated based on the inputs for use by the health management system of FIG. 1 according to some embodiments disclosed herein. [Figure 4] 1 illustrates an exemplary workflow for generating one or more decision support recommendations, according to certain embodiments of the present disclosure. [Figure 5] FIG. 1 is a flow diagram illustrating a method for training a machine learning model to provide decision support, according to certain embodiments of the present disclosure. [Figure 6] 6 is a block diagram illustrating a computing device configured to perform the operations of FIG. 4 and / or FIG. 5 in accordance with certain embodiments disclosed herein. [Figure 7A] 1 illustrates an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 7B] 1 illustrates an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 7C] 1 illustrates an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 7D]1 illustrates an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 7E] 1 illustrates an exemplary enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 8A] 1 illustrates an alternative view of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 8B] 1 illustrates an alternative view of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 8C] 1 illustrates an alternative view of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 8D] 1 illustrates an alternative view of an exemplary dual electrode enzyme domain configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein. [Figure 8E] 1 illustrates an exemplary dual electrode configuration for a continuous multi-analyte sensor, according to certain embodiments disclosed herein.

[0007] 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 INVENTION

[0008] To keep blood glucose levels within range, patients are typically instructed to better manage their diet and exercise and / or to administer different types of medications, such as insulin, sulfonylureas, and metformin, which may be administered, for example, via the oral route, via manual injection, or through a body-worn automatic insulin delivery device.

[0009] Another class of medications used in diabetes includes sodium glucose cotransporter 2 (SGLT2) inhibitors, a class of medications approved to lower blood glucose in patients with type 2 diabetes. SGLT2 inhibitors are available as single-component products and in combination with other medications (i.e., prescribed at the same time as insulin). SGLT2 inhibitors lower blood glucose by blocking glucose absorption and allowing the kidneys to remove sugar from the body via urine, thereby helping patients achieve normoglycemia. However, SGLT2 inhibitors can also increase the body's ketone levels beyond safe levels. Therefore, diabetic patients using SGLT2 inhibitors experience an increased risk of euglycemic diabetic ketoacidosis (euDKA). Notably, the euglycemic state induced by SGLT2 inhibitors can mask the underlying euDKA, making it difficult to identify and potentially leading to a dangerous, life-threatening condition. As a result, although sustained high glucose levels over a long period of time may typically indicate a higher risk of euDKA, glucose levels cannot be relied upon to understand the risk profile of developing euDKA in patients using SGLT2 inhibitors.

[0010] Existing body-worn diabetes management systems (e.g., continuous glucose monitors (CGMs)) that assist diabetic patients in managing their diabetes suffer from multiple deficiencies, preventing these systems from helping diabetic patients taking SGLT2 inhibitors manage their increased risk of euDKA. First, existing diabetes management systems fail to provide a continuous ketone sensor in conjunction with a continuous glucose sensor to continuously monitor ketone levels, even when the patient is in a euglycemic state induced in response to the use of SGLT2 inhibitors. Second, existing diabetes management systems do not provide software applications that can receive and analyze both glucose and ketone measurements to identify elevated risk of euDKA. Therefore, existing diabetes management software applications provide a potentially harmful false sense of security to patients, leading them to believe that they are healthy because their glucose levels are within range. Third, due to their inability to receive and analyze ketone and glucose measurements from ketone and glucose sensors, existing software applications do not provide a complete picture of the patient's physiological or metabolic state and, therefore, are unable to provide effective treatment options to reduce the risk of euDKA. For example, existing diabetes systems are unable to analyze and determine how well a particular dose of an SGLT2i prescribed to a patient will be tolerated by that patient and how the dose can be titrated or optimized to not only help the patient achieve normoglycemia but also minimize the risk of euDKA.

[0011] Accordingly, embodiments herein provide a technical solution to the above-mentioned technical problem by providing a health care system including a continuous analyte monitoring system, which may include one or more continuous analyte sensors and / or one or more non-analyte sensors. As used herein, the term "continuous" may refer to fully continuous, semi-continuous, periodic, etc., analyte monitoring. This may provide, for example, a fully continuous, semi-continuous, or periodic stream of analyte data.

[0012] One or more analyte sensors may be used to generate glucose and ketone values ​​indicative of a patient's glucose and ketone levels. The one or more analyte sensors may include a single sensor configured to generate both glucose and ketone values, or may include a sensor array of two or more separate sensors, with one sensor generating glucose values ​​and another generating ketone values. In both single-sensor and two-sensor embodiments, additional analyte sensors may be included to generate analyte values ​​in addition to glucose and ketone values. The analyte sensor system may be configured to communicate the glucose and ketone values ​​to a user display device. Examples of non-analyte sensors may include sensors for measuring blood pressure, oxygen saturation, heart rate, respiratory rate, body temperature, kinesthetic parameters (e.g., pace, steps, gait), renal function, hydration, etc.

[0013] The health management system also includes a user display device. The user display device may include a processor configured to execute a software application for receiving and analyzing the glucose and ketone values, along with other data (e.g., non-analyte sensor data). The software application may then take as input the fusion of at least the user's glucose and ketone values, utilizing a fusion model described below, and provide a decision support output to the user. The decision support output may be in a variety of forms and types.

[0014] As an example, a user prescribed a particular dose and frequency of an SGLT2 inhibitor may be instructed by the user's physician to use the health management system described above to determine whether the prescribed dose / frequency of the SGLT2 inhibitor is safe and well-tolerated by predicting the likelihood of euDKA. In such an example, the user utilizes an analyte sensor system that continuously generates at least glucose and ketone values, and a software application that receives the glucose and ketone measurements, which are used as inputs to a fusion model to predict the likelihood of developing euDKA within a defined period (e.g., one hour, one day, one week, one month, etc.). In particular, in such an example, the prescribed dose and frequency of the SGLT2 inhibitor, as well as the user's own glucose and ketone measurements, and / or other user information, are used as inputs to determine the likelihood of euDKA. In some embodiments, the likelihood of euDKA may include, for example, a risk-adjusted score for euDKA that takes into account the patient's risk profile (e.g., intrinsic factors such as age and BMI, and / or extrinsic factors including other medications such as insulin, measurements from non-analyte sensors, etc.). As an example, after one week of taking an SGLT2 inhibitor, the fusion model may indicate, for example, a 90% chance of developing euDKA within one week of the prediction being made. In such an example, this chance may be displayed to the user and / or transmitted to the physician / prescriber so that the dose and / or frequency of the inhibitor can be adjusted to reduce the likelihood of euDKA. Examples of other user information include other analyte data (analyte data other than glucose and ketone measurements), non-analyte data (e.g., blood pressure, temperature, oxygen saturation, heart rate, accelerometer, etc.), demographic information (e.g., age, sex, ethnicity, etc.), anthropometric information (e.g., height, weight, BMI), clinical chemistry information (e.g., fasting blood glucose level, HbA1c level), compliance with SGLT2 therapy, disease information, diet / meal information, exercise / activity data, renal status information, hydration, body mass index (BMI), etc.

[0015] In another example, a user prescribed a particular dose and frequency of an SGLT2 inhibitor may be instructed by the user's physician to use the health management system described above to predict the optimal inhibitor dosage / frequency. In such an example, the user utilizes an analyte sensor system that continuously generates at least glucose and ketone values, and a software application that receives glucose and ketone measurements that are used as inputs to a fusion model to predict the optimal inhibitor dosage / frequency. In particular, in that example, the user's own glucose and ketone measurements and / or other user information are used as inputs to predict the user's dosage / frequency that minimizes euDKA risk while helping the user achieve euglycemia or otherwise maximize time in range.

[0016] In another example, a model-based unsupervised learning system is invoked to collect glucose analyte data and / or ketone analyte data, optionally in combination with non-analyte data, to build a customized metabolic model for the user. The metabolic model can be built without stimuli (passive intake of data) or can be built using controlled stimuli, such as an oral glucose tolerance test, an oral ketone tolerance test (ketone esters), a specific dose of an SGLT2 inhibitor, or a specific dose of insulin. The metabolic model is then used by the health management system to predict the optimal dosage / frequency of the inhibitor. In particular, in this example, the user's own glucose and ketone measurements and / or other user information are used as inputs to predict the user's dosage / frequency that minimizes the risk of euDKA while helping the user achieve euglycemia or otherwise maximize time in range.

[0017] While embodiments herein may refer to specific therapies such as insulin, inhibitors such as SGLT2, or agonists such as GLP-1, those skilled in the art will understand that the embodiments described herein may provide recommendations for patients on therapy with other drugs. Some embodiments may provide more precise dosing of drugs for improved treatment for patients with limited or changing treatment situations, which may be based on previous treatment response and / or disease / condition type and / or progression.

[0018] In certain embodiments, the fusion model may include one of a variety of algorithms. For example, the fusion model may be a rule-based model, an artificial intelligence (AI) model such as a machine learning model (e.g., a classification model or a regression model), a Kalman filter, a probability model (e.g., a Bayesian network), a probabilistic model (e.g., a Gaussian model), etc. The rule-based model and / or the machine learning model may consider one or more inputs and / or metrics for the user when providing the decision support output described above.

[0019] For example, a rule-based model may predict the risk or likelihood of experiencing euDKA or the optimal dose / frequency of an inhibitor. Rule-based models involve the use of a rule set to map specific inputs to specific outputs. These rules are sometimes referred to as "If statements" because they tend to follow the lines of "if X occurs, then do Y or terminate Y." In particular, a fusion model may apply rule statements (e.g., if-then statements) to determine the risk or likelihood of a patient developing euDKA while on SGLT2 therapy or the optimal dose / frequency of an inhibitor. These rule statements may include, among other things, absolute glucose and / or ketone levels, glucose and / or ketone rate of change (e.g., physiological rate of change vs. pathological rate of change), inflection points in glucose and / or ketone time series data, the ratio of absolute glucose values ​​to absolute lactate values, and cross-correlation between glucose and ketone time series data.

[0020] Such rules may be defined and maintained in a reference library or lookup table. For example, the reference library may maintain ranges of analyte (e.g., glucose, ketones, etc.) levels and ranges of analyte level change rates (and / or other analyte data) and / or other analyte metrics, which may be mapped, for example, to different euDKA risk stratifications or different doses / frequencies of inhibitors. In certain embodiments, such rules may be determined based on empirical studies or analysis of medical history records, such as historical patient records stored in a medical history record database. In some cases, the reference library may be very granular. For example, other factors may be used in the reference library to create such "rules." Other factors may include gender, age, diet, activity, blood pressure, renal status, hydration, oxygen saturation, medical history, family medical history, body mass index (BMI), etc. Increased granularity may provide more accurate output. In certain embodiments, the reference library may comprise embedded memory within a microprocessor located within the wearable CGM / CKM.

[0021] In embodiments using a rule-based model to predict the likelihood of euDKA in a patient, the user's prescribed prescription dosage / frequency, the user's analyte and non-analyte data, and other types of information described above can be used as inputs to determine the likelihood that the user will experience euDKA within a defined time period. In some embodiments, a risk likelihood score for an euDKA event corresponding to a particular SGLT2 dose and frequency given the patient's analyte and non-analyte data can be presented to the prescriber by the health care system. Additionally or alternatively, the prescriber can provide an acceptable level of euDKA risk to the health care system, and the health care system can provide the SGLT2 dose and / or frequency corresponding to this acceptable risk level based on the analyte and non-analyte data fusion model. In embodiments using a rule-based model to predict the optimal inhibitor dose / frequency, the user's analyte and non-analyte data (e.g., user-specific data for days, weeks, and months) and other types of information described above can be used as inputs to determine the optimal inhibitor dose / frequency.

[0022] As an alternative to a rule-based model, the fusion model may be an AI / ML model trained to collect information associated with a user to automatically assess the user's euDKA risk or to recommend changes to the user's current or proposed SGLT2 therapy. In certain embodiments, the training server system may be configured to train the AI / ML model using training data, which may include population data associated with historical users (e.g., users or non-users of continuous analyte monitoring systems and / or applications) who were previously on SGLT2 therapy.

[0023] As another alternative to the rule-based model, a fusion model can be trained to receive glucose and / or ketone measurements and automatically assess euDKA risk. For example, known glucose measurements can enable the determination of estimated ketone measurements, or known ketone measurements can enable the determination of estimated glucose measurements. If a user's known or estimated ketone and / or glucose levels remain elevated (e.g., glucose levels above 180 mg / dL or ketone levels above 3 mM), the user may be determined to be suffering from or at risk of developing euDKA. Furthermore, an increasing duration that glucose and / or ketone measurements remain above a certain threshold (e.g., glucose levels above 180 mg / dL or ketone levels above 3 mM) may indicate that the user is experiencing or developing euDKA, and may provide treatment options and / or home interventions to prevent the onset or worsening of euDKA.

[0024] Furthermore, maximum likelihood estimation can be used to determine the joint probability distribution of the glucose and / or ketone measurements. The glucose signal variance and ketone signal variance can then be used to calculate weights, and thus weighted averages, of the glucose and ketone measurements. The weights may be determined as the inverse of the glucose signal variance and ketone signal variance. For example, in certain embodiments, the weighted average of the glucose and ketone measurements may be determined based on the following formula: F=EGV * Vg -1 +EKV * Vk -1 / (Vg -1 +Vk -1 ) where EGV and EKV are the estimated glucose and ketone values, respectively, obtained from the sensor measurements, and Vg and Vk represent the glucose variance and ketone variance, respectively.

[0025] In some embodiments, training data refers to a characterized and labeled dataset. For example, a dataset may include multiple data records, each containing information corresponding to a different user profile stored in a user database, and each data record is characterized and labeled. In machine learning and pattern recognition, a feature is an individual, measurable characteristic or feature. Generally, features that best characterize patterns in the data are selected to create a predictive machine learning model. Data labeling is the process of adding one or more meaningful and informative labels to data to provide context for learning by a machine learning model.

[0026] As an illustrative example, each relevant feature of a user reflected in a corresponding data record can be a feature used in training a machine learning model. Such features can include demographic-related features (e.g., age, sex, ethnicity), anthropometric-related features (e.g., height, weight, BMI), clinical chemistry-related features (e.g., fasting glucose level, biochemistry test results, HbA1c level), analyte-related features (e.g., time-stamped analyte value, time-stamped analyte rate of change, change in analyte value (e.g., glucose value, ketone value) of a user on SGLT2 therapy from a first timestamp to a second timestamp, change in analyte baseline of a user on SGLT2 therapy from a first timestamp to a subsequent timestamp, derivative of a measured linear system of analyte values ​​at a particular timestamp, and / or difference of derivatives to determine the rate of change of the slope of increase or decrease of analyte value of a user on SGLT2 therapy, etc.). Data records are also labeled with what the AI / ML intends to predict. For example, if the AI / ML model is intended to predict the optimal dosage of an inhibitor, each data record would be labeled with the dosage of the inhibitor used by the corresponding medical history user. In another example, if the AI / ML model is intended to predict the likelihood of developing euDKA within a defined time period (e.g., within one week or one month), each data record would be labeled with the likelihood of developing euDKA within the same time period.

[0027] The model is then trained 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. 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, in certain embodiments, the model may be iteratively refined to reduce the loss and generate accurate predictions related to the likelihood of developing euDKA, optimal dosage / frequency of inhibitors, etc.

[0028] The training server system deploys these trained models to a decision support engine for use during runtime. For example, the decision support may obtain a user profile associated with the user and use information from the user profile as input to the trained model to generate a decision support output. The decision support output may indicate the likelihood of developing euDKA in real time or within a specific time period, the optimal inhibitor dosage / frequency for the user, etc. The decision support output may be provided to the user (e.g., through a user display device such as a smartphone, CGM display, etc.), to the user's caregiver (e.g., parent, relative, guardian, teacher, nurse, etc.), to the user's physician, or any other individual interested in the user's health, possibly with the goal of improving the user's health, such as by achieving a recommended treatment.

[0029] As presented above, embodiments of the fusion model may incorporate data corresponding to a user and generate decision support outputs for the user regarding the optimal SGLT2 dosage / frequency and / or likelihood of developing euDKA. Identifying a patient's risk of developing or existing euDKA and / or the optimal dosage / frequency of an SGLT2 inhibitor to provide to the patient to reduce the risk of euDKA can help avoid life-threatening, potentially dangerous physiological conditions and costly health events, reducing caseloads and costs for healthcare providers.

[0030] 1 illustrates an exemplary health management system 100 for providing treatment recommendations for a user 102 (individually referred to herein as a user and collectively referred to herein as users) using a continuous analyte monitoring system 104 including one or more analyte sensors. The user 102 may be a patient, or in some cases, a caregiver for the patient, in certain embodiments. In certain embodiments, the health management system 100 includes the continuous analyte monitoring system 104, a display device 107 running an application 106, a decision support engine 114, a user database 110, a medical history record database 112, a training server system 140, and the decision support engine 114, each of which is described in more detail below.

[0031] 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, lymph, saliva, mucus, or urine) that can be analyzed. Analytes can include naturally occurring substances, man-made substances, drugs, metabolites, ions, blood gases, hormones, neurotransmitters, vitamins, minerals, peptides, proteins, enzymes, pathogens, toxins, substances of abuse, and / or reaction products. Analytes measured by the device and method include ketones, glucose, potassium, acarboxyprothrombin; acylcarnitines; adenine phosphoribosyltransferase; adenosine deaminase; albumin; alpha-fetoprotein; amino acid profile (arginine (Krebs cycle), histidine / urocanic acid, homocysteine, phenylalanine / tyrosine, tryptophan); androstenedione; antipyrine; arabinitol enantiomers; arginase; benzoylecgonine (cocaine); biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxycholic acid; cortisol; creatine kinase; creatine kinase MM isoenzyme; cyclosporine A; cystatin C; d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphisms, alcohol dehydrogenase, alpha-1-antitrypsin, glucose-6-phosphate dehydrogenase, hemoglobin A, hemoglobin S, hemoglobin C, hemoglobin D, hemoglobin E, hemoglobin F, D-Punjab, hepatitis B virus, HCMV, HIV-1, HTLV-1, MCAD, RNA, PKU, Plasmodium vivax, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D; fatty acids / acylglycines; free β-human chorionic gonadotropin; free erythrocyte porphyrin; free thyroxine (free thyroxine, FT4);Free triiodothyronine tri-iodothyronine, FT3); fumarylacetoacetase; galactose / gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione peroxidase; glycocholate; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-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; quinine; reverse triiodothyronine tri-iodothyronine, rT3); selenium; serum pancreatic lipase; sisomicin; somatomedin C; specific antibodies recognizing any one or more of the following, which may include adenovirus, antinuclear antibody, anti-zeta antibody, arbovirus, pseudorabies virus, dengue virus, guinea worm, Echinococcus granulosus, Entamoeba 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, convoluted thread helminthiasis, parainfluenza virus, malaria parasite, poliovirus, Pseudomonas aeruginosa, respiratory syncytial virus, rickettsia (tsutsugamushi disease), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / rangeli, 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. Salts, sugars, proteins, fats, vitamins, and hormones naturally present in blood or interstitial fluid can also constitute analytes in certain implementations. Ions are charged atoms or compounds that can include the following (e.g., sodium, potassium, calcium, chloride, nitrogen, or bicarbonate). Analytes can be naturally present in biological fluids, for example, metabolites, hormones, antigens, antibodies, ions, etc. Alternatively, the analyte may be introduced into the body or may be exogenous, such as a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, a challenge agent analyte (e.g., introduced for the purpose of measuring the increase and / or decrease in the rate of change of the concentration of a challenge agent analyte or other analyte in response to the introduced challenge agent analyte), or exogenous insulin; glucagon, ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, Ritalin, silyl, propranolol, etc.). Antidepressants (barbiturates, tranquilizers such as methaqualone, Valium, Librium, Miltaunt, Serax, Equuanil, and Tranxine); Hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, and psilocybin); Narcotics (heroin, codeine, morphine, opium, meperidine, Percocet, Percodan, Tasionex, fentanyl, Darvon, Talwin, and Lomotil); Synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and phencyclidine analogs, e.g., ecstasy); Anabolic steroids;The analytes may be drugs or pharmaceutical compositions, including, but not limited to, nicotine and niacin. Metabolites of drugs and pharmaceutical compositions are also contemplated analytes. Analytes such as neurochemicals and other chemicals produced in the body, such as 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.

[0032] Analytes measured and analyzed by the devices and methods described herein include, but are not limited to, ketones and glucose, as well as other analytes listed, which are also contemplated and may be measured, for example, by a continuous analyte monitoring system 104.

[0033] In certain embodiments, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to an electronic medical records (EMR) system (not shown in FIG. 1 ). An EMR system is a software platform that enables the electronic entry, storage, and maintenance of digital medical data. EMR systems are generally used throughout hospitals and / or other care facilities to document clinical information about patients over time. EMR systems organize and present data in a manner that assists clinicians, for example, in interpreting health conditions and providing ongoing care, scheduling, billing, and follow-up. Data contained in the EMR system can also be used to generate reports for patient clinical care and / or disease management. In certain embodiments, the EMR may communicate (e.g., via a network) with a decision support engine 114 to perform the techniques described herein. In other embodiments, the EMR may be utilized for population-level health statistics, health economics, and clinical evidence generation or evaluation of medical outcomes. In particular, as described herein, the decision support engine 114 may obtain data associated with a user, use the obtained data as input to one or more trained models, and output predictions. In some cases, the EMR may provide the decision support engine 114 with data to be used as input to one or more models, e.g., ML models. Furthermore, in some cases, the decision support engine 114 may make a prediction and then provide its output prediction to the EMR.

[0034] In certain embodiments, the continuous analyte monitoring system 104 is configured to continuously measure one or more analytes and transmit the analyte measurements to a display device 107 for use by the application 106. In some embodiments, the continuous analyte monitoring system 104 may function primarily as a monitoring device by pairing with a display device 107 and transmitting analyte measurements to the display device 107 on a continuous or semi-continuous basis. In other embodiments, the continuous analyte monitoring system 104 may function primarily as a diagnostic device configured to store and record analyte measurements. In such embodiments, data logs stored by the continuous analyte monitoring system 104 may be transmitted to a remote service (e.g., a cloud server) without the involvement of the display device 107. In such embodiments, the continuous analyte monitoring system 104 may include a mobile internet of things (IoT) interface (e.g., LTE, Cat-M1, NB-IoT, etc.), cellular radio (e.g., 3G, 4G, LTE, 5G, 6G, etc.), or other means for communicating the analyte measurements in the data log directly to a remote server.

[0035] 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 smartwatch, a tablet, or any other computing device capable of running the application 106. In some embodiments, the continuous analyte monitoring system 104 and / or the analyte sensor application 106 transmits the analyte measurements to one or more other individuals interested in the patient's health (e.g., a family member or physician for real-time treatment and care of the patient). The continuous analyte monitoring system 104 may be described in more detail with respect to FIG. 2.

[0036] Application 106 is a mobile health application configured to receive and analyze analyte measurements from continuous analyte monitoring system 104. In particular, 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 114 to provide decision support recommendations or guidance to the user.

[0037] The decision support engine 114 refers to a set of software instructions having one or more software modules, including a data analysis module (DAM) 116. In particular embodiments, the decision support 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 decision support engine 114 via a network (e.g., the Internet). In some other embodiments, the decision support engine 114 executes partially on one or more local devices, such as the display device 107, and partially on one or more computing devices in a private or public cloud. In some other embodiments, the decision support engine 114 executes entirely on one or more local devices, such as the display device 107. As discussed in more detail herein, the decision support engine 114 may provide decision support recommendations to a user via the application 106. The decision support engine 114 provides decision support recommendations based on information contained in a user profile 118.

[0038] 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 that include analyte measurements 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 received from the continuous analyte monitoring system 104. For example, the application 106 may obtain the additional inputs 128 via manual user input, one or more other non-analyte sensors or devices, other applications running on the display device 107, etc. Non-analyte sensors and devices include, but are not limited to, one or more of an insulin pump, an electrocardiogram (ECG) sensor or heart rate monitor, a blood pressure sensor, a respiration sensor, a thermometer, an oxygenated hemoglobin sensor (spO2), an activity tracker, a peritoneal dialysis machine, a hemodialysis machine, sensors or devices provided by the display device 107 (e.g., an accelerometer, an inclinometer, a gyroscope, a camera, a global positioning system (GPS), a 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. Input 128 of the user profile 118 provided by the application 106 is described in further detail below with respect to FIG. 3.

[0039] The DAM 116 of the decision support engine 114 is configured to process the set of inputs 128 to determine one or more metrics 130. The metrics 130, which will be discussed in more detail below with respect to FIG. 3, may, at least in some cases, generally be indicative of the user's health or condition, such as one or more of the user's physiological state, trends associated with the user's health or condition, etc. In particular embodiments, the metrics 130 may then be used by the decision support engine 114 as inputs to provide guidance to the user. As shown, the metrics 130 are also stored in the user profile 118.

[0040] The user profile 118 also includes demographic information 120, disease information 122, and / or medication information (e.g., medication type, medication brand, dosage, frequency of administration) 124. In certain embodiments, such information may be provided via user input or obtained from a particular data store (e.g., an electronic medical record (EMR), 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 about the user's disease, such as whether the user has previously been diagnosed with or experienced diabetes, the stage (if known) of the disease from prediabetes to a complete lack of insulin production, ketoacidosis, euDKA, a diagnosis of other co-morbidities, etc., or whether the user has had a history of ketoacidosis, euDKA, hyperglycemia, hypoglycemia, co-morbidities, etc. In certain embodiments, information about the user's disease may also include length of time since diagnosis, level of control, level of adherence to disease management therapy, other types of diagnoses (e.g., heart disease, obesity), or health measurements (e.g., heart rate, exercise, stress, sleep, etc.), etc.

[0041] In certain embodiments, the medication information 124 may include information regarding the amount, frequency, and type of medication taken by the user. In certain embodiments, the amount, frequency, and type of medication taken by the user are time-stamped and correlated with the user's analyte levels, thereby indicating the effect the amount, frequency, and type of medication had on the user's analyte levels. In certain embodiments, the medication information 124 may include information regarding the prescribed dosage / frequency and intake of one or more inhibitors (e.g., sodium glucose cotransporter 2 (SGLT2)). Further, the medication information 124 may include the action curve and / or pharmacokinetic and / or pharmacodynamic properties of the SGLT2 inhibitors to determine the efficacy of the medications, etc. Inhibitors may be prescribed to patients for the purpose of treating diabetes. The user 102 may be prescribed an inhibitor to help manage blood glucose levels by blocking the body's absorption of glucose.

[0042] As described in more detail below, the health management system 100 may be configured to use the medication information 124 to determine the effectiveness of inhibitors or the optimal inhibitor dosage and frequency to be prescribed for different users. In particular, the health management system 100 may be configured to identify one or more optimal prescriptions based on the patient's health at the time one or more medications are prescribed, as well as the patient's disease being treated.

[0043] In certain embodiments, the medication information 124 may include information regarding the ingestion of other medications for blood glucose management. For example, the medication information 124 may include metformin, thiazolidinediones, sulfonylureas, GLP-1 receptor agonists, glucagon, and / or insulin action curves, pharmacokinetic and / or pharmacodynamic properties, dosage, and frequency. The medication information 124 may include information provided manually by a user and / or information provided by an automated insulin delivery (AID) device.

[0044] In certain embodiments, the user profile 118 is dynamic because at least some of the information stored in the user profile 118 may be modified over time and / or new information may be added to the user profile 118 by the decision support engine 114 and / or the application 106. Thus, the information in the user profile 118 stored in the user database 110 provides an up-to-date repository of information related to the user.

[0045] User database 110, in some embodiments, refers to a storage server operating in 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, or a file system, including a hierarchical file system. In some exemplary implementations, user database 110 is distributed. For example, user database 110 may comprise multiple distributed persistent storage devices. Furthermore, user database 110 may be replicated such that the storage devices are geographically distributed.

[0046] The user database 110 may include user profiles 118 associated with multiple users who similarly interact with the application 106 running on the display device 107 of other users. The user profiles stored in the user database 110 may be accessible to the application 106 as well as the decision support engine 114. The user profiles in the user database 110 may be accessible to the application 106 and the decision support engine 114 via one or more networks (not shown). As noted above, the decision support engine 114, and more specifically the DAM 116 of the decision support engine 114, may fetch inputs 128 from the user database 110 and calculate multiple metrics 130; these metrics may then be stored as application data 126 in the user profiles 118 and / or used in population data and statistics as may be necessary or desired for use by the application.

[0047] In particular 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, with the data being stored according to an associated timestamp. The timestamp associated with the information stored in the medical history record database 112 may identify, for example, when information related to a user was retrieved and / or updated.

[0048] Additionally, the medical history database 112 may maintain time series data collected about users over a period of time, including time series data about users who use the continuous analyte monitoring system 104 and application 106. For example, analyte data about users who used the continuous analyte monitoring system 104 and application 106 over a five-year period may have the time series analyte data associated with the user maintained over the five-year period.

[0049] Additionally, in certain embodiments, historical record database 112 may also 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) analyzed by, e.g., a healthcare physician, about one or more patients who have not previously been treated for blood glucose management, as well as information (e.g., user profiles) analyzed by, e.g., a healthcare physician, about one or more patients who have previously been treated for blood glucose management. The data stored in historical record database 112 may be referred to herein as population data.

[0050] The data associated with each patient stored in the medical history database 112 may provide time series data collected over the patient's disease lifetime. For example, the data may include information about the patient before diagnosis and related information during the lifespan of treatment, including information about the level of treatment required, as well as information about other diseases or conditions, such as euDKA, adverse events (e.g., hypoglycemia, low blood sugar, dysglycemia), diabetes, heart disease and cardiac disease, or similar diseases or other related co-morbidities. Such information may indicate the patient's symptoms, the patient's physiological state, the patient's ketone levels, the patient's glucose levels, the status / condition of one or more of the patient's organs, the patient's habits (e.g., activity level, food intake, etc.), prescribed medications, etc.

[0051] 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. In other words, historical and current data associated with users of the continuous analyte monitoring system 104 and application 106, as well as historical data associated with patients who were not previous users of the continuous analyte monitoring system 104 and application 106, may be stored in a single database. The single database may be a public or private cloud, or a storage server running on a separate device.

[0052] As previously described, the health management system 100 is configured to provide treatment recommendations to a user using a continuous analyte monitoring system 104 that includes one or more analyte sensors. In certain embodiments, the continuous analyte monitoring system 104 includes at least a continuous glucose monitor (CGM) and a continuous ketone monitor (CKM). In certain embodiments, the decision support engine 114 is configured to provide real-time and / or non-real-time decision support based on glucose and / or ketone levels to the user and / or others, including, but not limited to, healthcare providers, the user's family, the user's caregivers, researchers, artificial intelligence (AI) engines, and / or other individuals, systems, and / or groups that support care or learning from data. In particular, the decision support engine 114 may be used to collect information associated with the user in a user profile 118 and perform analysis of that information to recommend treatment (e.g., recommend an optimal dosage of an inhibitor) and / or predict the onset of euDKA within a specified time period. The decision support engine 114 may also be used to gather information for pharmaceutical research to develop new or more effective treatments. The user profile 118 may be accessible to the decision support engine 114 via one or more networks (not shown) to perform such analyses.

[0053] In certain embodiments, the health management system 100 is designed to predict the risk or likelihood of euDKA, or the presence and / or severity of euDKA, in real time (including near real time) or within a patient-specified time period. In certain embodiments, to enable such prediction, the decision support engine 114 is configured to collect information related to the user in a user profile 118 stored in the user database 110 and analyze the information to (1) automatically detect and classify blood glucose and ketone levels, (2) assess the risk or stage of euDKA, (3) evaluate the effectiveness of the dosage and frequency of current treatment and other potential treatments, and / or (4) provide an optimal treatment regimen (e.g., dosage and frequency) of an SGLT-2 inhibitor.

[0054] In certain embodiments, the decision support engine 114 may utilize one or more trained machine learning models that can determine the presence and / or onset of euDKA and / or treatment recommendations for a user based on information provided by the user profile 118. In the illustrated embodiment of FIG. 1 , the decision support engine 114 may utilize trained machine learning models provided by a training server system 140. While depicted as separate servers for conceptual clarity, in some embodiments, the training server system 140 and the decision support engine 114 may operate as a single server. That is, models may be trained and used by a single server (e.g., a local device, microprocessor, etc.), or may be trained by one or more servers and deployed for use on one or more other servers. In certain embodiments, models may be trained on one or more virtual machines (VMs) running at least in part on one or more physical servers in relational and / or non-relational database formats.

[0055] The training server system 140 is configured to train the 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) who have previously been treated for blood glucose management, as well as patients (e.g., healthy patients) who have not previously been treated for blood glucose management. 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. The training data may also, in some cases, include user-specific data for users over time.

[0056] In some embodiments, training data refers to a characterized and labeled dataset. For example, a dataset may include multiple data records, each containing information corresponding to a different user profile stored in user database 110, with each data record characterized and labeled. In machine learning and pattern recognition, a feature is an individual, measurable characteristic or feature. Generally, features that best characterize patterns in the data are selected to create a predictive machine learning model. Data labeling is the process of adding one or more meaningful, informative labels to data to provide context for learning by a machine learning model.

[0057] As an example, each relevant feature of a user reflected in a corresponding data record may be a feature used in training a machine learning model. Such features may include age, sex, weight, height, body mass index, any current treatments, when the treatment was last applied (e.g., insulin bolus), the amount of treatment applied (e.g., units of insulin), the change (e.g., Δ) in an analyte level (e.g., ketone level, glucose level) from a first timestamp to a second timestamp, the change (e.g., Δ) in blood glucose or ketone from the first timestamp to a second timestamp, the change (e.g., Δ) in an analyte threshold (e.g., ketone threshold, glucose threshold) for a user under blood glucose management treatment from the first timestamp to a subsequent timestamp, the derivative of a measured linear system of analyte measurements (e.g., ketone measurement, glucose measurement) at a particular timestamp, the ratio between the glucose measurement and the ketone measurement at a particular timestamp, and / or a difference in derivatives to determine the rate of change of the slope of an increase or decrease in an analyte value (e.g., ketone value, glucose value), etc. Additionally, the data record may be labeled with an indicator regarding the euDKA diagnosis associated with the patient in the user profile, the assigned severity and / or identified risk of euDKA, and prescribing information (e.g., dosage and frequency) of one or more SGLT-2 inhibitors.

[0058] The model is 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. 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, in certain embodiments, the model is iteratively refined and the loss is minimized to generate treatment recommendations (e.g., optimal dose / frequency of inhibitor administration) and predictions related to the risk, presence, progression, improvement (e.g., regression), and severity of euDKA in a patient within a specified confidence level. Furthermore, in certain other embodiments, by iteratively processing each data record corresponding to each historical patient, in certain embodiments, the model is iteratively refined and can generate accurate treatment recommendations and accurate predictions of the risk and / or presence of euDKA. It should be noted that although certain embodiments herein are described with respect to providing treatment recommendations for reducing the risk of euDKA and prediction of the risk and / or presence of euDKA, the embodiments described herein are equally applicable to providing treatment recommendations for reducing the risk of any type of ketoacidosis and prediction of the risk and / or presence of any type of ketoacidosis.

[0059] As illustrated in FIG. 1 , the training server system 140 deploys these trained models to the decision support engine 114 for use during runtime. For example, the decision support engine 114 may retrieve a user profile 118 associated with the user, use information in the user profile 118 as input to the trained models, and output a treatment recommendation and / or euDKA prediction. The treatment recommendation may indicate the effectiveness of a current treatment based on dosing information 124, analyte data, etc. In some embodiments, the treatment recommendation includes a recommendation for a modification or alternative treatment to an existing treatment based on the effectiveness of the current treatment. For example, the treatment recommendation may include a change in current dosage and / or frequency, a change in medication type, a notification to consult a healthcare provider, etc.

[0060] The decision support engine 114 may provide a prediction that may indicate the presence and / or severity of the user's euDKA in real time or within a specified time period (e.g., shown as output 144 in FIG. 1 ). The output 144 generated by the decision support engine 114 may also provide one or more recommendations for treatment based on the prediction. The output 144 may be provided to the user (e.g., through the application 106), to the user's caregiver (e.g., a parent, relative, guardian, teacher, nurse, etc.), to the user's physician, or any other individual interested in the user's health, possibly with the goal of improving the user's health, such as by achieving the recommended treatment.

[0061] In certain embodiments, the user's own data is used to personalize one or more models that were initially trained based on population data. For example, a model (e.g., trained using population data) can be deployed for use by the decision support engine 114 to provide treatment recommendations and / or predict the presence and / or severity of euDKA for a particular user. In some embodiments, at some point after making a prediction using the model, the decision support engine 114 may be configured to ask the user, or a caregiver, physician, etc., whether the medication information 124 should be updated based on the recommended treatment change. In other embodiments, the decision support engine 114 may provide a query regarding whether the predicted presence and / or severity of euDKA has been confirmed by other diagnostic methods (e.g., blood ketone fingerstick test strips, breath ketone measurement, urine ketone test strips), and / or the decision support engine 114 may confirm the diagnosis using one or more diagnostic tests. In some cases, the user's answers and / or the results of the performed diagnostic tests may negate the presence of euDKA. Thus, the model may be continually retrained and / or personalized using updated medication information 124, the user's responses, the user's test results, and / or the user's physiological parameters. Although specific examples are given, other data may also be used as input to the model to personalize the model for the user.

[0062] In certain embodiments, the output 144 generated by the decision support engine 114 may be stored in the user profile 118. In certain embodiments, the output 144 may be a patient-specific treatment recommendation, treatment effectiveness, identification of one or more indicators of euDKA, etc. For example, in certain embodiments, the output 144 may be a treatment recommendation for updating a medication, medication dosage, frequency of medication use, a prediction regarding the presence and / or severity of euDKA in the user, etc. In certain embodiments, the output 144 may be a prediction regarding the risk of developing euDKA. In certain embodiments, the output 144 may be a prediction regarding the risk of the user having hyperglycemia and / or hypoglycemia. In certain embodiments, the output 144 may be a prediction regarding the patient's risk of mortality. In certain embodiments, the output 144 may be a patient-specific treatment decision or recommendation for the patient's glucose management. In certain embodiments, the output 144 may be a recommendation regarding the use of an inhibitor (e.g., SGLT2), a recommendation regarding the use of insulin, etc.

[0063] In some embodiments, the output 144 stored in the user profile 118 may be continuously updated by the decision support engine 114. Thus, the user's previous diagnostic and / or physiological parameters associated with blood glucose management, initially stored as output 144 in the user profile 118 in the user database 110 and then passed to the historical record database 112, may provide an indication of the effectiveness of a current treatment or may provide the likelihood of the user developing euDKA within a given period of time. Furthermore, the user's previous diagnostic and / or physiological parameters associated with how well a medication was tolerated and / or how effective a particular type / dosage / frequency of medication administration was, initially stored as output 144 in the user profile 118 in the user database 110 and then passed to the historical record database 112, may provide an indication of the effectiveness of a current treatment or may provide the likelihood of the user developing euDKA within a given period of time.

[0064] In certain embodiments, a user's own historical data may be used to provide decision support and insights regarding the user's blood glucose management and / or disease development. For example, the user's historical data may be used as a baseline by an algorithm to indicate improvement or worsening of the user's disease. As an illustrative example, the user's data from two weeks ago may be used as a baseline against which the user's current data may be compared to identify improvement or worsening of the user's glucose and / or ketone levels, thereby identifying whether the risk associated with a future euDKA event has increased or decreased. In certain embodiments, the user's own historical data may be used by training server system 140 to train a personalized model that may be capable of further predicting the presence and / or severity of euDKA, optimal treatment to reduce the predicted presence and / or severity of euDKA, and / or improvement / worsening of the user's ketone and / or glucose data based on recent patterns of the user's data (e.g., exercise data, food intake data, etc.).

[0065] In certain embodiments, the model may be trained to provide lifestyle recommendations, exercise recommendations, dietary recommendations, and other types of decision support recommendations to help the user improve their treatment or prevent the onset and / or progression of euDKA based on the user's historical data, such as how different types of medications, foods, and treatments (e.g., medication type, dosage, frequency) have affected the user's analyte levels in the past. In certain embodiments, the model may be trained to predict the underlying causes of a particular improvement or deterioration in a patient's analyte levels. For example, application 106 may display a user interface with a chart showing the patient's analyte levels or scores thereof along with trend lines, e.g., retrospectively indicating the cause of the change in analyte level at a particular time point (e.g., insulin administration, SGLT2 administration, etc.).

[0066] 2 is a diagram 200 conceptually illustrating an exemplary continuous analyte monitoring system 104 including an exemplary continuous analyte sensor with sensor electronics, according to certain embodiments of the present disclosure. For example, the system 104 may be configured to continuously monitor one or more analytes of a user, according to certain embodiments of the present disclosure.

[0067] Generally, real-time or continuous measurements of analyte levels, rates of change, trends, clearance rates, and / or other analyte data measured in interstitial fluid or blood by a continuous analyte monitoring system can be used to provide treatment recommendations to a user. Such data can indicate changes in analyte levels that indicate less-than-ideal treatment. Thus, continuous analyte monitoring can provide earlier and / or improved treatment recommendations, such as improved dosing of a drug with a narrow therapeutic window or the evolution of a pharmacokinetic profile. Some embodiments may provide screening, diagnosis, prognosis, and / or staging of euDKA compared to traditional diagnosis.

[0068] In certain embodiments, clinical indicators can be used to determine whether a continuous analyte monitoring system, e.g., continuous analyte monitoring system 104, may be needed to assess treatment effectiveness or to assess a patient's risk, propensity, presence, and / or stage of euDKA. In one example, such clinical indicators include glucose measurements, ketone measurements, lactate measurements, dissolved oxygen measurements, ion measurements, blood pressure measurements, renal metrics, hydration measurements, physical activity metrics, sleep metrics, heart rate, respiratory rate, core body temperature, nutritional status information, etc. Analyte and other information generally may indicate needed optimization of medication dosage and / or frequency, or may indicate the presence, risk, or likelihood of euDKA.

[0069] In yet another example, the clinical indicator may include prescribed or taken medications. For patients taking certain medications related to blood glucose control or medications associated with an increased likelihood of euDKA, it may be desirable to monitor euDKA. For example, a patient taking a medication known to be a contributing factor to euDKA can use continuous analyte monitoring to optimize the medication or to predict euDKA that may be at least partially caused by the medication.

[0070] In yet another example, a clinical indicator may include an assessment of a patient's adherence to a treatment. Comparing the prescribed treatment to the actual treatment may allow for a more accurate assessment of the effectiveness of the prescribed treatment by quantifying how well the patient adheres to the prescribed treatment. This comparison may be useful for healthcare providers to further adjust the treatment or provide additional treatment instructions / education to the patient. This comparison may also be of interest to healthcare insurers and / or healthcare payers for compensation or other considerations (e.g., gratuities, discounts, etc.).

[0071] In yet another example, the clinical indicators may include comorbidities that are often associated with and / or increase the risk of euDKA, such as cardiovascular disease, chronic kidney disease, liver disease (e.g., NAFLD, NASH), obesity, activity level, diet, hypertension, dyslipidemia, etc.

[0072] In certain embodiments, the continuous analyte monitoring system 104 may be utilized as a short-term diagnostic tool to monitor the type, dosage, and / or frequency of new or updated medications for a patient's response, assess how well the medication is tolerated, and evaluate any negative outcomes (e.g., euDKA). For example, a triggering action (e.g., a trigger while wearing the analyte sensor) may indicate the usefulness of the patient wearing an analyte sensor (continuous or discontinuous) for a short period of time to monitor the patient's response to an inhibitory medication and / or to monitor euDKA in the patient. In one example, the patient may wear a short-term continuous or discontinuous analyte sensor for a predetermined period of time after initiation of a new medication regimen. In another example, the patient may wear a short-term continuous or discontinuous analyte sensor periodically (e.g., every four weeks) to monitor the effectiveness of a new medication and provide recommendations for further changes or optimizations to treatment. In yet another example, the patient may wear a short-term continuous or discontinuous analyte sensor to predict the likelihood of euDKA. In certain scenarios, data from the analyte sensor may be presented directly to the user. In other diagnostic scenarios, the analyte sensor can be operated in a data logging mode, thereby hiding the analyte data from the user while allowing a physician to review the data at a later time.

[0073] In some embodiments, the continuous analyte monitoring system 104 can be utilized as a long-term diagnostic tool (i.e., greater than 14 days) to monitor patient response and further update treatment recommendations. Patient monitoring can also be useful in the ongoing prediction and alerting of the onset of euDKA. For example, patients at high risk for euDKA and / or adverse events can utilize a continuous analyte sensor to provide ongoing insight into the effectiveness of currently prescribed treatment, recommend further updates to treatment, or predict euDKA over the course of days, weeks, months, etc.

[0074] 2, 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 continuous analyte sensors 202 and collectively referred to herein as continuous analyte sensors 202) associated with the sensor electronics module 204. The sensor electronics module 204 may communicate wirelessly (e.g., directly or indirectly) with one or more display devices 210, 220, 230, and 240. In certain embodiments, the sensor electronics module 204 may also wirelessly communicate (e.g., directly or indirectly) with one or more medical devices 208 (individually referred to herein as medical devices 208 and collectively referred to herein as medical devices 208) 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). In other embodiments, including but not limited to diagnostic implementations, the sensor electronics module operates independently (e.g., not paired with a display device) and may be queried at the end of a wearing session to wirelessly transfer data recorded during the session to a local device or cloud database for future review, retrieval, or further analysis.

[0075] In certain embodiments, the continuous analyte sensor 202 may include a sensor 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., ketone, glucose) or a single-analyte sensor configured to continuously measure a single analyte as a noninvasive, subcutaneous, transdermal, transdermal, skin-based, intradermal, subcutaneous, implanted, and / or intravascular device. In certain embodiments, the continuous analyte sensor 202 may be configured to continuously measure analyte levels in a user using one or more measurement techniques, such as enzymatic, immunoassay, aptameric, amperometric, voltammetric, potentiometric, impedancemetric, conductimetric, conductometric, capacitive, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, iontophoretic, radiometric, immunochemical, optical, ion-selective, etc. 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 that is converted into a calibrated and / or filtered data stream that is used to provide an estimated analyte value to the user.

[0076] 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, ketones, and / or other analytes circulating in a user's body.

[0077] In certain embodiments, one or more multi-analyte sensors may be used in combination with one or more single-analyte sensors. As an illustrative example, a multi-analyte sensor may be configured to continuously measure ketones and glucose, and may optionally be used in combination with one or more analyte sensors configured to measure only lactate levels, oxygen levels, hydration levels, or hormone levels, for example. In various embodiments, the multi-analyte sensor may comprise a single body-worn wearable or two separate body-worn wearables. Information from each of the multi-analyte and single-analyte sensors may be combined to provide therapeutic decision support using the methods described herein.

[0078] In certain embodiments, the sensor electronics module 204 includes electronic circuitry associated with measuring and processing 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 with (non-releasably attached to) 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 analyte levels via the continuous analyte sensor 202. For example, the sensor electronics module 204 can include an electrochemical analog front end (e.g., potentiostat, galvanostat, impedance measuring device), a power supply for providing power to the sensor, a microprocessor for executing embedded data processing or algorithmic routines, other components useful for signal processing and data storage, and a telemetry module for transmitting data from the sensor electronics module to one or more display devices or centralized data repositories. The electronics can be affixed to a printed circuit board (PCB), a flexible PCB (flexPCB), or the like, and can be in a variety of forms. For example, the electronics may be in the form of an integrated circuit (IC), such as an Application-Specific Integrated Circuit (ASIC), a field-programmable gate array (FPGA), a system-on-a-chip (SoC), a microcontroller, and / or a processor.

[0079] In some embodiments, display devices 210, 220, 230, and / or 240 are configured to display displayable sensor data, including analyte data, that may be transmitted by sensor electronics module 204. Each of display devices 210, 220, 230, or 240 may include a display, such as a touchscreen display 212, 222, 232, or 242, for displaying sensor data to a user and / or receiving input from a user. For example, a graphical user interface (GUI) may be presented to the user for such purposes. In some embodiments, display devices 210, 220, 230, and 240 may include other types of user interfaces, such as a voice user interface, instead of or in addition to a touchscreen 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 FIG.

[0080] 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 respective display device) without any additional anticipated processing required for calibration and real-time display of the sensor data.

[0081] The display devices may include custom display devices specifically designed to display a particular type of displayable sensor data associated with the analyte data received from the sensor electronics module. In certain embodiments, the display devices may be configured to provide alerts / alarms / notifications based on the displayable sensor data. Display device 210 is an example of such a custom device. In some embodiments, one of the display devices is a smartphone, 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).

[0082] Because different display devices have different user interfaces, the content of the data package (e.g., the 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. Accordingly, in certain embodiments, 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 data. In certain embodiments, the types of alarms customized for each particular display device, the number of alarms customized for each particular display device, the timing of the alarms customized for each particular display device, and / or the threshold levels configured (e.g., for activation) for each alarm are based on the outputs 144 stored in the user profile 118 for each user (e.g., as mentioned, the outputs 144 may indicate the user's current health, the user's ketone and / or glucose level status, the user's current recommended treatment, and / or the user's physiological parameters).

[0083] 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 ketone and / or glucose values ​​transmitted from the continuous analyte monitoring system 104, and the continuous analyte sensor 202 is configured to measure ketones and / or glucose.

[0084] Additionally, as noted, 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, temperature sensors, respiration rate sensors, sweat sensors, and the like. The non-analyte sensors 206 may also include monitors such as heart rate monitors, ECG monitors, blood pressure monitors, pulse oximeters, calorie intake, and drug delivery devices. The non-analyte sensors 206 may also include data systems for measuring non-patient-specific phenomena such as time, ambient pressure, or ambient temperature, which may include barometric pressure sensors, outside air temperature sensors, or clocks, timers, or other time measurements when the sensor is initially inserted, or measurements of remaining sensor life compared to insertion time can be used for calibration of algorithmic models or other data inputs. One or more of these non-analyte sensors 206 may provide data to the decision support engine 114, described further below. In some aspects, a user may manually provide portions of the data for processing by the training server system 140 and / or decision support engine 114 of FIG.

[0085] 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 glucose sensor, may be combined with a continuous analyte sensor 202 configured to measure ketones to form a ketone / glucose sensor that is used to transmit sensor data to the sensor electronics module 204 using a common communications circuit.

[0086] 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 devices 208, and / or the non-analyte sensors 206 to one another. For example, the WAP 138 may provide Wi-Fi, cellular, and / or IoT (e.g., NB-IoT, LTE Cat-M1) connectivity between these devices. Near Field Communication (NFC) and / or Bluetooth may also be used between the devices depicted in the diagram 200 of FIG. 2.

[0087] Figure 3 shows a diagram 300 of example inputs and example metrics calculated based on the inputs for use by the health management system of Figure 1, according to certain embodiments disclosed herein. In particular, Figure 3 provides a more detailed illustration of the example inputs and example metrics introduced in Figure 1.

[0088] FIG. 3 illustrates exemplary inputs 128 on the left, the decision support engine 114, including the application 106 and the DAM 116, in the center, and metrics 130 on the right. In particular embodiments, each of the metrics 130 may correspond to one or more values, such as discrete numeric values, ranges, or qualitative values ​​(e.g., high / medium / low, stable / unstable, rate of change, inflection point, etc.). The application 106 obtains the inputs 128 through one or more channels (e.g., manual user input, sensors / monitors, other applications running on the display device 107, EMR, etc.). As previously mentioned, in particular embodiments, the inputs 128 may be processed by the DAM 116 and / or the decision support engine 114 to output the metrics 130. The inputs and metrics 130 may be used by the decision support engine 114 to provide decision support to a user. For example, the inputs 128 and metrics 130 may be used by the training server system 140 to train and deploy one or more machine learning models for use by the decision support engine 114 to provide decision support regarding patient treatment.

[0089] In certain embodiments, starting with input 128, food intake information may include information about one or more of meals, snacks, and / or beverages, such as amount, content (milligrams (mg) of sodium, potassium, carbohydrates, fat, protein, etc.), order of intake, and time of intake. In certain embodiments, food intake may be provided by a user via manual input, by providing a photograph via an application configured to recognize food types and amounts, by scanning a barcode or menu, and / or by interrogating an NFC / RFID tag embedded in food packaging. In various examples, meal amounts may be manually entered as one or more of calories, amount (e.g., “3 cookies”), menu items (e.g., “Royale with Cheese”), and / or food exchanges (e.g., 1 fruit, 1 dairy product). In some examples, meal information may be received via a convenient user interface provided by application 106. In some examples, meal information may be provided via one or more other applications synchronized with application 106, such as one or more other mobile health applications executed by display device 107. In such an example, the synchronized application may include, for example, an electronic food diary application or a photo application.

[0090] In certain embodiments, the dietary intake information entered by the user may relate to nutrients ingested by the user. Intake may include any natural or engineered food or beverage. The dietary intake information entered by the user may also relate to other analytes, including any of the other analytes described herein.

[0091] In certain embodiments, exercise information is also provided as an input. The exercise information can be any information surrounding the activity, such as the activity requiring physical exercise by the user. For example, the exercise information can range from information related to low-intensity (e.g., walking a few steps) and high-intensity (e.g., running five miles) physical activity, or can be in the form of wattage (e.g., stationary cycle), speed (e.g., a GPS-enabled smartwatch), and / or resistance (e.g., an elliptical machine) over a specified time interval. In certain embodiments, the exercise information can be provided by, for example, an accelerometer sensor or heart rate monitor on a wearable device such as a watch, fitness tracker, and / or patch. In certain embodiments, the exercise information can also be provided through manual user input, through a training machine, and / or through a surrogate sensor and predictive algorithm that measures changes in heart rate (or other cardiac metrics). When predicting that the user is exercising based on their sensor data, the user can be asked to confirm whether exercise is occurring, what type of exercise, and / or the level of intensity used during the exercise over a specified period of time. This data can be used to train the system to learn about the user's movement patterns to reduce the need for confirmation questions over time. Other analyte and sensor data can also be included in this training set, including the analytes and other measured factors described herein, including time factors such as hour and day.

[0092] In certain embodiments, user statistics such as one or more of age, height, weight, BMI, body composition (e.g., body fat percentage), height, build, or other information may also be provided as input. In certain embodiments, user statistics may be provided through a user interface, by interfacing with an electronic source such as an electronic medical record, and / or from a measurement device. In certain embodiments, the measurement device may include, for example, one or more of a wireless, e.g., Bluetooth-enabled, weight scale, and / or camera that may communicate with the display device 107 to provide user data.

[0093] In certain embodiments, treatment information is also provided as an input. The treatment information may include information regarding the type, dosage, and / or timing of one or more medications (e.g., SGLT2, insulin, glucagon, sulfonylurea, metformin, GLP-1) to be taken by the user. As mentioned herein, the treatment information may include information about one or more inhibitors, one or more drugs known to lower blood glucose levels, one or more drugs known to affect ketones, and / or one or more medications for treating acute or chronic diseases and one or more symptoms of diseases the user may have. The treatment information may include information regarding different lifestyle habits, surgical procedures, and / or other non-invasive procedures recommended by the user's physician. For example, the user's physician may recommend to the user increasing or decreasing carbohydrate intake, exercising for at least 30 minutes per day, or increasing insulin dosage or other medications, etc., to maintain, improve, and / or reduce hyperglycemic and / or hypoglycemic episodes. As another example, a healthcare professional may recommend that the user engage in home and / or clinic-based treatment. The treatment information may also indicate the patient's adherence to the prescribed type, dosage, and / or timing of medication. For example, the treatment / medication information may indicate whether a medication was taken, the exact time the medication was taken, and the dosage / type of medication taken.

[0094] In certain embodiments, analyte sensor data may also be provided as an input, for example, through continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include ketone data (e.g., a user's ketone levels) measured by at least a ketone sensor (or a multi-analyte sensor configured to measure at least ketones) that is part of the continuous analyte monitoring system 104. In certain embodiments, the analyte sensor data may include glucose data measured by at least a glucose sensor (or a multi-analyte sensor configured to measure at least glucose) that is part of the continuous analyte monitoring system 104.

[0095] In certain embodiments, input may also be received from one or more non-analyte sensors, such as the non-analyte sensor 206 described with respect to Figure 2. Input from the non-analyte sensor 206 may include information related to the user's heart rate, heart rate variability (e.g., the variance in time between heart beats), ECG data, respiratory rate, oxygen saturation, blood pressure, or temperature (e.g., to detect illness, physical activity, etc.). In certain embodiments, the electromagnetic sensor may also detect low-power radio frequency (RF) fields emitted from an object or a tool in contact with or proximity to an object, which may provide information about user activity or location.

[0096] In some embodiments, the non-analyte sensor 206 may include an embedded scanner / reader for detecting medication-related information (e.g., type, brand, dosage, frequency). An example of a scanner may include a reader configured to detect near field communication (NFC) and / or radio frequency identification (RFID) information provided by a corresponding active or passive tag provided within or otherwise associated with the medication. Another example of a scanner may be a barcode, QR, or other optical scanner capable of accessing information associated with a visual pattern provided on the packaging or otherwise associated with the medication.

[0097] In certain embodiments, the input received from the non-analyte sensor may include input regarding the user's insulin delivery. In particular, the input regarding the user's insulin delivery may be received via a wireless connection on the smart pen, via user input, and / or from an insulin pump. The insulin delivery information may include one or more of the following: insulin manufacturer, insulin dose, insulin formulation, insulin volume, basal dose vs. bolus dose, intended pharmacokinetic profile (e.g., short-acting, long-acting), number of units of insulin delivered, delivery time, etc. Other parameters, such as insulin action time or duration of insulin action, may also be received as input.

[0098] In certain embodiments, time may also be provided as an input, such as time of day, UTC time, or time from a real-time clock. The real-time clock may be external (synchronized to a server via a WiFi, cellular, or Bluetooth wireless connection) or embedded as an integrated real-time clock (RTC) circuit within the wearable / sensor electronics. For example, in certain embodiments, input analyte data may be time-stamped to indicate the date and time when the analyte measurement was taken for the user.

[0099] Any user input of the above-mentioned inputs 128 may be input via a user interface, such as the user interface of the display device 107 of FIG.

[0100] As mentioned above, in certain embodiments, the DAM 116 and / or decision support engine (e.g., using one or more trained models) determines or calculates metrics 130 for a user based on the inputs 128. An exemplary list of metrics 130 is shown in FIG.

[0101] In certain embodiments, ketone levels and / or glucose levels may be determined from sensor data, for example, ketone levels may refer to time-stamped ketone measurements or values ​​that are continuously generated and stored over time.

[0102] In certain other embodiments, the DAM 116 may use ketone and / or glucose levels measured over a period of time, during which the user (i.e., GPS coordinated) engaged in exercise and / or ingested nutrients and / or external conditions affecting ketone / glucose levels were present for at least a subset of the period. In such embodiments, the DAM 116 may, in some examples, first identify measured analyte values ​​that should not be used in calculating the baseline by identifying which analyte values ​​were affected by external events, such as food intake, exercise, medication, or other perturbations that prevented the capture of a baseline measurement for the analyte. The DAM 116 may then exclude such measurements when calculating the user's analyte baseline. In some other examples, the DAM 116 may calculate the analyte baseline by first determining the percentage of the number of analyte values ​​measured during this period that represents the lowest analyte value measured. The DAM 116 may then average such analyte values ​​to determine the analyte baseline level.

[0103] In certain embodiments, an absolute maximum analyte level (e.g., ketone, glucose) may be determined from sensor data, health / illness metrics (e.g., described in more detail below), and / or other disease metrics. The absolute maximum analyte level represents a user's maximum analyte level determined to be safe over a period of time (e.g., hourly, weekly, daily, etc.). In certain embodiments, the absolute maximum analyte level may be consistent across all users. In certain other embodiments, each patient may have a different absolute maximum analyte level. In certain embodiments, the absolute maximum analyte level per patient may change over time. For example, a user may initially be assigned an absolute maximum analyte level based on clinical input. This assigned absolute maximum analyte level may be adjusted over time based on other sensor data, comorbidities, etc. for the patient. Minimum analyte values ​​may be determined in a similar manner.

[0104] In certain embodiments, analyte thresholds other than a user's absolute maximum and / or minimum analyte levels may be determined from sensor data (e.g., ketone / glucose measurements obtained from continuous sensors in continuous analyte monitoring system 104), health / illness metrics (e.g., as described in more detail below), and / or other disease metrics. Such analyte thresholds may represent, for example, maximum or minimum analyte levels determined to be safe during a particular activity, which may vary across different activities. For example, because exercise is known to affect ketone / glucose levels, a user's maximum and / or minimum ketone / glucose thresholds during exercise may differ from the user's maximum and / or minimum ketone / glucose thresholds during other activities.

[0105] In certain embodiments, the rate of change of analyte levels may be determined from sensor data (e.g., ketone / glucose measurements obtained over time from the continuous analyte monitoring system 104). For example, the rate of change of ketone levels refers to the rate at which one or more time-stamped ketone measurements or values ​​change relative to one or more other time-stamped ketone measurements or values. The rate of change of ketone levels may be determined over one second or more, one minute or more, one hour or more, one day or more, etc.

[0106] In certain embodiments, a determined analyte level rate of change may be marked as "rapidly increasing" or "rapidly decreasing." As used herein, "rapidly" may refer to an analyte level rate of change that is clinically significant and indicates a trend in the analyte level that is likely to exceed an absolute maximum analyte level or an absolute minimum analyte level within a defined time period. In other words, a predictive trend (e.g., generated by the decision support engine 114 using one or more trained models) may, in some cases, indicate that a patient is likely to reach an absolute maximum analyte level within a specified time period (e.g., one or two hours) based on the determined analyte level rate of change. Thus, such an analyte level rate of change may be marked as "rapidly increasing." Similarly, a predictive trend (e.g., generated by the decision support engine 114 using one or more trained models) may, in some cases, indicate that a patient is likely to reach an absolute minimum analyte level within a specified time period (e.g., one or two hours) based on the determined analyte level rate of change. Thus, such an analyte level rate of change may be marked as "rapidly decreasing."

[0107] In certain embodiments, an analyte (e.g., ketone, glucose) baseline rate of change can be determined from analyte baselines determined for a user over time. For example, ketone baseline rate of change refers to the rate at which one or more time-stamped ketone baselines for a user change relative to one or more other time-stamped ketone baselines for the same user. Ketone baseline rate of change can be determined over one second or more, one minute or more, one hour or more, one day or more, etc.

[0108] In certain embodiments, the analyte clearance rate may be determined from sensor data (e.g., ketone / glucose measurements obtained from a CKM / CGM of the continuous analyte monitoring system 104) following ingestion of a known or estimated amount of analyte. The analyte clearance rate analyzed over time may indicate the effectiveness of a drug or the development of a disease. In particular, the slope of the analyte clearance curve during a first period (e.g., after administration of an inhibitor) compared to the slope of the analyte clearance curve during a second period (e.g., after ingestion of the same inhibitor) may indicate the effectiveness of a treatment.

[0109] In certain embodiments, the analyte clearance rate may be determined by calculating the slope between a first value at t0 (e.g., during a period of rising levels) and the user's analyte baseline reached at t1. In certain embodiments, the analyte clearance rate may be calculated over time until the user's increased analyte level reaches some value relative to the user's analyte baseline (e.g., a % of the user's analyte baseline). The analyte clearance rate calculated over time may be time-stamped and stored in the user's profile 118.

[0110] In certain embodiments, a standard deviation (not shown) of the analyte levels can be determined from the sensor data. In some examples, the standard deviation of one or more analyte levels can be determined based on the variability of one or more analyte levels compared to the average analyte level over one or more time periods. In some embodiments, a time-in-range metric (not shown) can be determined from the analyte data. For example, established upper and lower limits can be used to determine the period of time that the analyte data was between the upper and lower limits. The time-in-range can be determined for each individual instance of analyte data falling within the range, or can be determined over a predetermined period (e.g., one day) where the individual in-range periods are summed.

[0111] In certain embodiments, an analyte trend may be determined based on analyte levels over a specific period of time. In certain embodiments, a ketone trend / glucose trend may be determined based on a ketone baseline / glucose baseline over a specific period of time. In certain embodiments, an analyte trend may be determined based on an absolute analyte level minimum over a specific period of time. In certain embodiments, an analyte trend may be determined based on an absolute maximum analyte level over a specific period of time. In certain embodiments, an analyte trend may be determined based on an analyte level rate of change over a specific period of time. In certain embodiments, an analyte trend may be determined based on an analyte baseline rate of change over a specific period of time. In certain embodiments, an analyte trend may be determined based on a calculated analyte clearance rate over a specific period of time.

[0112] In certain embodiments, the glucose level may be determined from sensor data (eg, glucose measurements obtained from a continuous glucose sensor of the continuous analyte monitoring system 104).

[0113] In certain embodiments, the glucose level change rate may be determined from sensor data (e.g., glucose measurements obtained over time from a continuous glucose monitor (CGM) of the continuous analyte monitoring system 104). For example, the glucose level change rate refers to the rate at which one or more time-stamped glucose measurements or values ​​change relative to one or more other time-stamped glucose measurements or values. The glucose level change rate may be determined over one second or more, one minute or more, one hour or more, one day or more, etc.

[0114] In certain embodiments, glucose trends may be determined based on glucose levels over a particular period of time. In certain embodiments, glucose trends may be determined based on the rate of change of glucose levels over a particular period of time.

[0115] In certain embodiments, glycemic variability may be determined from sensor data (e.g., glucose measurements obtained over time from the continuous analyte monitoring system 104). For example, glycemic variability refers to the standard deviation of glucose levels over a period of time. Glycemic variability may be determined over one minute or more, one hour or more, one day or more, etc.

[0116] In certain embodiments, the glucose clearance rate can be determined from sensor data (e.g., glucose levels obtained from a continuous glucose sensor of the continuous analyte monitoring system 104) after ingestion of a known or estimated amount of glucose, or from known nutrients that result in the production of glucose. The glucose clearance rate analyzed over time can indicate glucose homeostasis. Glucose trends can indicate the effectiveness of medication type, dosage, and / or frequency.

[0117] In certain embodiments, the glucose clearance rate can be determined by calculating the slope between an initial high glucose value at t0 (e.g., the highest glucose level during a 20-30 minute period after glucose consumption) and a subsequent low glucose value at t1. L ) is the user's initial high glucose value (G H ) and baseline glucose values ​​before glucose consumption (G B ) can be determined based on. In certain embodiments, G L is G H and G B Glucose levels between, e.g., G L =G B +K * (G H -G B ) / 2, where K can be a percentage representing how much the user's glucose level has returned to the user's baseline value. When K is equal to 0, the low glucose value is equal to the baseline glucose value. When K is equal to 0.5, the low glucose value is equal to the average glucose value between the initial glucose value and the baseline glucose value.

[0118] In certain embodiments, glucose clearance rates may be determined over one or more time periods after glucose ingestion, such as after an oral glucose tolerance test (OGTT). Glucose clearance rates may be calculated for each time period to represent the kinetics of glucose clearance rate after glucose consumption. Glucose clearance rates calculated over time may be time-stamped and stored in the user profile 118. Certain metrics may be derived from the time-stamped glucose clearance rates, such as mean, median, standard deviation, percentiles, etc.

[0119] In certain embodiments, insulin sensitivity may be determined using historical data, real-time data, or a combination thereof, and may be based on one or more inputs 128, such as, for example, one or more of food intake information, continuous analyte sensor data, non-analyte sensor data (e.g., insulin delivery information from an insulin device), etc. Insulin sensitivity refers to how a user's cells respond to insulin. Improving insulin sensitivity for a user may help reduce insulin resistance in the user.

[0120] In certain embodiments, remaining insulin (insulin on board) may be determined using non-analyte sensor data inputs (e.g., insulin delivery information) and / or known or learned (e.g., from user data) insulin time-action profiles that may take into account both basal metabolic rate (e.g., insulin updates to keep the body moving) and insulin use driven by activity or food intake.

[0121] In certain embodiments, the insulin clearance rate may be determined using historical data, real-time data, or a combination thereof, for example, by calculating the slope between an initial insulin value at t0 (e.g., during a period of rising insulin levels) and the user's final insulin value at t1. Ketone metrics may also be monitored and determined similarly.

[0122] In certain embodiments, the health and illness metrics may be determined, for example, from physiological sensors (e.g., temperature), activity sensors, or a combination thereof, based on one or more of user inputs (e.g., pregnancy information or known disease or illness information). In certain embodiments, based on the values ​​of the health and illness metrics, for example, the user's state may be defined as one or more of healthy, sick, rest, or fatigued.

[0123] In certain embodiments, the meal state metric may indicate a user's state with respect to food intake. For example, the meal state may indicate whether the user is in one of a fasting state, a pre-meal state, a fed state, a post-meal reaction state, or a stable state. In certain embodiments, the meal state may also indicate remaining nutrients, e.g., ingested meals, snacks, or beverages, which may be determined, for example, from food intake information, mealtime information, and / or digestibility information, which may correlate to food type, amount, and / or order (e.g., which food / drink was eaten first).

[0124] In certain embodiments, the eating habits metric is based on the content and timing of a user's meals. For example, if the eating habits metric is on a scale of 0 to 1, in an embodiment, the better / healthier the user eats, the higher the user's eating habits metric will be relative to 1. Also, in an embodiment, the more a user's food intake adheres to a particular time schedule or recommended diet, the closer their eating habits metric will be relative to 1.

[0125] In certain embodiments, medication adherence (not shown) is measured by one or more metrics that indicate how committed a user is to their medication regimen. In certain embodiments, medication adherence metrics are calculated based on one or more of the timing of a user's medication (e.g., whether the user is on time or on schedule), the type of medication (e.g., whether the user is taking the correct type of medication), and the dosage of the medication (e.g., whether the user is taking the correct dosage). In certain embodiments, a user's medication adherence may be determined through user input and / or based on analyte data received from the continuous analyte monitoring system 104 in a clinical trial in which medication intake and the timing of such medication intake are monitored.

[0126] In certain embodiments, the activity level metric may indicate a user's activity level. In certain embodiments, the activity level metric may be determined based on input from an activity sensor or other physiological sensor, such as, for example, a non-analyte sensor 206. In certain embodiments, the activity level metric may be calculated by the DAM 116 based on one or more of inputs 128, such as one or more of exercise information, non-analyte sensor data (e.g., accelerometer data), time, user input, etc. In certain embodiments, the activity level may be expressed as a user's step rate. The activity level metric may be time-stamped so that it can be contemporaneously correlated with the user's lactate level.

[0127] In certain embodiments, the exercise regimen metric (not shown) may indicate one or more of the type of activity in which the user engages, the corresponding intensity of such activity, the frequency with which the user engages in such activity, etc. In certain embodiments, the exercise regimen metric may be calculated based on one or more of analyte sensor data inputs (e.g., from a lactate monitor, a glucose monitor, etc.), non-analyte sensor data inputs (e.g., from an accelerometer, a heart rate monitor, a respiration rate sensor, etc.), calendar inputs, user inputs, etc.

[0128] In certain embodiments, body temperature metrics may be calculated by the DAM 116 based on input 128, more specifically, non-analyte sensor data from a temperature sensor. In certain embodiments, heart rate metrics (including, for example, heart rate and heart rate variability) may be calculated by the DAM 116 based on input 128, more specifically, non-analyte sensor data from a heart rate sensor. In certain embodiments, respiration metrics (not shown) may be calculated by the DAM 113 based on input 128, more specifically, non-analyte sensor data from a respiration rate sensor. In certain embodiments, blood pressure measurement metrics (such as, for example, blood pressure level and blood pressure trend) may be calculated by the DAM 113 based on input 128, more specifically, non-analyte sensor data from a blood pressure sensor.

[0129] In certain embodiments, as described in more detail below, physiological parameters associated with a user (e.g., ketone levels, rate of change of ketone levels, glucose levels, heart rate, blood pressure, etc.) may be stored as metrics 130 when a user's condition or disease is confirmed. In certain embodiments, such physiological parameters may be analyzed over time to provide an indication of changes in the user's condition or disease. In certain embodiments, the user-specific values ​​of the physiological parameters experienced by the user may be useful input for training one or more models designed to assess the user's current treatment and propensity for euDKA in the user. In certain embodiments, the user-specific values ​​of the physiological parameters experienced by the user may be used to create one or more personalized models specific to the user for greater accuracy.

[0130] Exemplary methods and systems for providing decision support to patients FIG. 4 is a flow diagram illustrating an exemplary method 400 for providing decision support based on patient treatment data. Method 400 may be executed by health management system 100 to collect / generate data such as inputs 128 and metrics 130, including, for example, the analyte data, patient information, and non-analyte sensor data described above. For example, method 400 may be executed by decision support engine 114 to provide decision support to a user using a continuous analyte monitoring system 104 including at least a continuous analyte sensor 202, as illustrated in FIGS. 1 and 2 . Method 400 is described below with reference to FIGS. 1 and 2 and their components. Method 400 may provide decision support in real time or within a specified time period. Generally, real-time or continuous measurements of analyte levels, rates of change, trends, clearance rates, and / or other analyte data measured in interstitial fluid or blood may be used to determine a user's condition (e.g., drug effectiveness, onset of euDKA). Therefore, continuous analyte monitoring of analytes such as ketones and glucose can provide improved treatment recommendations and / or improved determination of euDKA compared to traditional diagnostics.

[0131] In certain embodiments, the decision support engine 114 of the health management system 100 may use various algorithms or artificial intelligence (AI) models, such as machine learning models trained based on patient-specific and / or population data, to provide treatment recommendations and / or euDKA predictions. The algorithms and / or machine learning models may consider one or more inputs 128 and / or metrics 130 described with respect to FIG. 3 for the patient.

[0132] One or more machine learning models described herein for making such predictions may be trained, at least initially, using the population data. Methods for training one or more machine learning models may be described in more detail below with respect to FIG. 5.

[0133] In certain embodiments, as an alternative to using machine learning models, the decision support engine 114 may use rule-based models to provide treatment recommendations and / or predict a patient's risk or likelihood of experiencing euDKA. Rule-based models involve using a set of rules to analyze data. These rules are sometimes referred to as "If statements" because they tend to follow the lines of "if X occurs, then do Y or end Y." In particular, the decision support engine 114 may apply rule statements (e.g., if, then statements, do-while statements, catch statements, switch statements, finite state machine frameworks) to generate treatment recommendations and / or determine a patient's risk or likelihood of developing euDKA.

[0134] Such rules may be maintained in a reference library by the decision support engine 114. For example, the reference library may maintain ranges of analyte (e.g., potassium) levels and ranges of rates of change of analyte levels (and / or other analyte data), and / or other analyte metrics. In certain embodiments, such rules may be determined based on empirical studies or analysis of historical patient records, such as records stored in the historical medical records database 112. In some cases, the reference library may be very granular. For example, other factors may be used in the reference library to create such "rules." Other factors may include gender, age, diet, medical history, family medical history, body mass index (BMI), etc. Increased granularity may provide more accurate output.

[0135] At block 402, the method 400 can begin by receiving patient treatment data. This data can be used as a starting point for generating a euDKA prediction and generating treatment recommendations at block 410. For example, the treatment data can be retrieved from the user profile 118. As described above, the treatment information can include information regarding the type, dosage, and / or timing of one or more medications (e.g., SGLT2, insulin) that should be taken by the user. As mentioned herein, the treatment information can include information about one or more inhibitors, one or more drugs known to lower blood glucose levels, one or more drugs known to affect ketones, and / or one or more medications for treating one or more symptoms of acute or chronic conditions and diseases (e.g., diabetes, cardiovascular disease, renal disease) that the user may have. The treatment information can also indicate the patient's adherence to the prescribed type, dosage, and / or timing of the medication. For example, the treatment / medication information can indicate whether a medication was taken, the exact time the medication was taken, and the dosage / type of medication taken.

[0136] At block 404, method 400 continues by monitoring one or more analytes of a patient, such as user 102 shown in FIG. 1, during one or more time periods (e.g., multiple time periods) to obtain analyte data. The one or more analytes monitored may, in certain embodiments, include at least ketone and glucose. Thus, the analyte data may include at least ketone data and glucose data. Block 402 may, in certain embodiments, be performed 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, in certain embodiments, may include a continuous analyte monitor 202 configured to measure analyte levels (e.g., ketone, glucose) of the patient.

[0137] Thus, in certain embodiments, the continuous analyte monitoring sensor 202 can collect analyte measurements that can be utilized to generate analyte data including analyte baseline, analyte rate of change, analyte baseline rate of change, personalized analyte level, mean analyte level, maximum and / or minimum analyte level, absolute maximum and / or minimum analyte level, standard deviation of analyte level, analyte clearance rate, analyte trend, etc.

[0138] In certain embodiments, the combined use of multiple analyte data, including ketone and glucose data, compared to single analyte data, can help further inform treatment recommendations and euDKA predictions. For example, monitoring additional types of analytes in addition to ketones and glucose, as measured by continuous analyte monitoring system 104, can provide additional insight compared to insight derived from ketones alone. Some examples of additional types of analytes include, but are not limited to, lactate, uric acid, ethanol, ascorbic acid, creatinine, glutamate, glutathione, sodium, potassium, chloride, cortisol, and insulin. Such additional insight may include indicators of other health conditions that may affect the effectiveness of medications, such as inhibitors.

[0139] The additional insight gained from using a combination of analytes may increase the accuracy of treatment recommendations and / or euDKA predictions. For example, the probability of correctly recommending a treatment or treatment change or the accuracy of predicting euDKA may be a function of the number of analytes measured for a patient. For example, in some examples, the probability of correctly predicting that a patient is experiencing or is likely to experience euDKA during a particular treatment regimen using only ketone data (in addition to other non-analyte data) may be less than the probability of correctly predicting using ketone data and glucose data (in addition to other non-analyte data), which may also be less than the probability of correctly performing an analysis using ketone data, glucose data, and lactate data (in addition to other non-analyte data) for the analysis.

[0140] Additionally, the use of analyte combinations allows for the determination of various analyte-related ratios (e.g., ketone to glucose ratios) to further inform the analysis. Such ratios can be determined based on measured analyte values, analyte thresholds, analyte rate of change, analyte variance, analyte clearance rate, and / or any other analyte data associated with the analyte combination.

[0141] Thus, in certain embodiments described herein, for example, the analyte combination measured and collected by one (e.g., multiple analytes) or multiple sensors for treatment recommendation and / or euDKA prediction includes at least ketones and glucose, although other analyte combinations are also contemplated.

[0142] In certain embodiments, in block 404, the continuous analyte monitoring system 104 may continuously monitor the patient's ketone and glucose levels over multiple time periods. In certain embodiments, the measured ketone concentrations may be used in conjunction with glucose levels to determine treatment recommendations or predict the likelihood of euDKA. In particular, glucose is a simple sugar (e.g., monosaccharide). Glucose can be ingested as well as produced in the body from proteins, fats, and carbohydrates. Increased glucose stimulates insulin release. Insulin causes cells to take up glucose as fuel. Thus, insulin stimulates glucose uptake by cells, thereby lowering glucose levels. In some cases, if a patient's glucose levels increase and the patient's body experiences a high rate of change in glucose levels, excessive insulin may be produced. On the other hand, if a patient's glucose levels decrease and the patient's body experiences a low rate of change in glucose levels, less insulin may be secreted. Low insulin may lead to limited access to glucose by cells, thus increasing extracellular glucose levels.

[0143] Insulin is partially removed from the circulation by the kidneys. Inhibitors (e.g., SGLT2) can block glucose absorption in the kidneys. In certain embodiments, glucose data can be collected by a CGM and monitored by the health management system 100 for glycemic variability. Glycemic variability can generally include time-in-range (TIR) ​​data as well as the standard deviation of glucose levels over a period of time. TIR refers to one or more periods during which a patient's glucose levels are within a particular desired range (e.g., a healthy range).

[0144] In certain embodiments, one or more algorithms and / or models described herein, for example, for generating treatment recommendations or for predicting the onset of euDKA, may be configured to use input from one or more sensors measuring one or more of the analytes described above. Parameters and / or thresholds of such algorithms and / or models may be altered based at least in part on the number of analytes being measured for an input to reflect knowledge gained from each of the other analytes being measured.

[0145] At block 404, method 400 may include, optionally, monitoring non-analyte sensor data during one or more time periods using one or more non-analyte sensors or devices (e.g., non-analyte sensor 206 and / or medical device 208 of FIG. 2).

[0146] As previously mentioned, the non-analyte sensors 206 and devices may include, but are not limited to, one or more of an insulin pump, a tactile sensor, an electrocardiogram (ECG) sensor or heart rate monitor, a blood pressure sensor, a sweat sensor, a respiration sensor, a thermometer, a pulse oximeter, an impedance sensor, a sensor or device provided by the display device 107 (e.g., an accelerometer, a camera, a global positioning system (GPS), a heart rate monitor, etc.) or other user accessory (e.g., a smart watch), or any other sensor or device that provides relevant information about the user. One or more of these non-analyte sensors 206 and devices can provide data to the decision support engine 114 described herein. In some aspects, a user, e.g., a patient, can manually input data for processing by the decision support engine 114.

[0147] For example, an accelerometer can be used to determine physical activity status. The accelerometer data can be analyzed and correlated with changes in analyte data (e.g., a sudden downward trend in glucose levels) that can be separated from the calculated effect of a medication or other therapy. In another example, a heart rate monitor can be used to provide heart rate data indicative of physical activity status to further separate the effect of physical activity from the effect of a medication or other treatment. In another example, a sweat sensor can provide data indicative of physical activity status, allowing for separation of physical activity factors from the effectiveness of a treatment.

[0148] Certain metrics, such as one or more of the metrics 130 illustrated in Figure 3, can be calculated using measurement data from each of these additional sensors. Additionally, as illustrated in Figure 3, one or more of the metrics 130 calculated from non-analyte sensor or device data may include body temperature, heart rate (including heart rate variability), respiratory rate, etc. In certain embodiments described in more detail below, one or more of the metrics 130 calculated from non-analyte sensor or device data may be used to further inform treatment recommendations and / or euDKA predictions.

[0149] In certain embodiments, one or more non-analyte sensors and / or devices that may be worn by a patient may include a blood pressure sensor. Blood pressure measurements collected from the blood pressure sensor may be used to provide additional insight into the patient's health.

[0150] In certain embodiments, the one or more non-analyte sensors and / or devices that may be worn by the patient may include an ECG sensor and / or a heart rate monitor. As known in the art, an ECG device is a device that measures the electrical activity of the heart. In certain embodiments, heart rate measurements and heart rate variability information collected from the ECG sensor and / or heart rate monitor may be used in combination with ketone and glucose data to better inform treatment recommendations and / or euDKA predictions.

[0151] At block 406, the method 400 continues by processing the analyte data, and in certain embodiments, other non-analyte sensor data, from one or more time periods to determine analyte metrics, such as the glucose and / or ketone-related metrics 130 described in connection with Figure 3. Block 406 may be performed by the decision support engine 114 in certain embodiments.

[0152] In certain embodiments, the machine learning models described herein used to provide treatment recommendations and / or euDKA-related predictions may include one or more features related not only to a patient's analyte levels, but also to trends in analyte levels, rates of change in analyte levels, and similar metrics 130 for the patient. For example, an exemplary machine learning model may include weights applied to features related to one or more trends, rates of change, and / or similar metrics 130 related to, e.g., ketone levels, glucose levels. Thus, in certain embodiments, prior to using a machine learning model, at least a portion of the metrics 130 related to ketone levels and / or glucose levels may be calculated for input into the model.

[0153] In some examples, the machine learning model may perform a fusion of analyte levels (e.g., ketones alone, glucose and ketones, glucose and ketones and lactate). Non-analyte data (e.g., heart rate, blood pressure, temperature) may also be used in conjunction with the analyte levels. In some embodiments, weighting may be applied to features associated with one or more trends, rates of change, and / or similar metrics of the data obtained from the analyte sensors (i.e., ketones alone, ketones and glucose) and, in some examples, the non-analyte sensors. In some embodiments, the use of such features may enable a machine learning model to be trained to distinguish between diabetic ketoacidosis and nutritional ketoacidosis.

[0154] Furthermore, in certain embodiments, the rule-based models described herein used to provide treatment recommendations and / or euDKA predictions may include one or more rules related not only to a patient's analyte levels but also to the patient's analyte metrics (e.g., analyte level trends, analyte level rate of change, and other analyte metrics 130). For example, a reference library used to define one or more rules of a rule-based model may maintain, for example, various ketone levels, glucose levels, rates of change of ketone levels, rates of change of glucose levels, and / or other ketone and glucose metrics, which may be mapped to different treatment recommendations and / or different euDKA incident predictions. Thus, prior to using a rule-based model, at least some of the metrics 130 related to ketone levels and / or glucose levels may be calculated for input into the model. Additional features, such as rates of change of ketone levels and / or glucose levels, added to the model may, in some cases, enable more accurate treatment recommendations and / or euDKA incident predictions for the patient.

[0155] In some examples, a rule-based model can be implemented to distinguish between diabetic ketoacidosis (DKA) and nutritional ketosis (NK). For example, the rule-based model can consider the rate of change of an analyte. For example, in euDKA, the measured value of ketone bodies can increase rapidly (i.e., within a few hours), but it takes a longer time (several days) to reach nutritional ketosis. Therefore, by considering the rate of change of ketone bodies, the rule-based model can distinguish between diabetic ketoacidosis (DKA) and nutritional ketosis (NK). The rule-based model can also consider the absolute level of ketone bodies. For example, in euDKA, the ketone level can be high (i.e., increased risk at >1 mM - 3 mM, very high risk at >3 mM), while nutritional ketosis can be limited within about 0.5 < NK < 3.0 mM. Therefore, by considering the absolute level of ketone bodies, the rule-based model can distinguish between diabetic ketoacidosis (DKA) and nutritional ketosis (NK).

[0156] In another example, both DKA and nutritional ketosis can be weighted by risk factors to determine the likelihood of the presence of DKA or nutritional ketosis based on the administered agent. In other words, "classical" DKA can occur in patients with insulin-dependent diabetes, euDKA can occur in patients with insulin-dependent diabetes (or non-insulin-dependent diabetes) receiving SGLT-2 therapy, and nutritional ketosis is more likely to occur in patients on a low-carbohydrate diet or non-diabetic patients. In short, the rules for distinguishing DKA, euDKA, and nutritional ketosis can be implemented based on the rate of change of the analyte, the absolute value of the analyte, and / or weighted risk factors.

[0157] At optional block 408, method 400 continues by generating a euDKA prediction, which may include (1) the likelihood or risk that the patient is experiencing (or will experience) euDKA and / or (2) the presence and / or severity of euDKA experienced by the patient, using at least (a) the patient's analyte metrics (e.g., determined in block 406), such as ketone levels, ketone rate of change, and other relevant ketone metrics (as described in connection with FIG. 3), and glucose levels, glucose rate of change, and other relevant glucose metrics (as described in connection with FIG. 3), (b) the patient's treatment data, and (c) the trained AI / ML model or rule-based model.

[0158] As an example, a user prescribed a particular dosage and frequency of an SGLT2 inhibitor may be instructed by the user's physician to use the above-described health management system 100 to determine whether the prescribed dosage / frequency of the SGLT2 inhibitor is safe and well-tolerated by predicting the likelihood of euDKA. In such an example, the user utilizes an analyte sensor system 104 that continuously generates at least glucose and ketone values, and a software application 106 that receives the glucose and ketone measurements to be used as inputs to a fusion model to predict the likelihood of euDKA occurring within a defined period (e.g., hourly, daily, weekly, monthly, etc.).

[0159] In particular, in this example, the prescribed dosage and frequency of the SGLT2i, as well as the user's own glucose and ketone measurements, and / or other user information, are used as inputs to determine the likelihood of euDKA. As an example, after one week of taking the SGLT2i, the fusion model may indicate, for example, a 90% chance of developing euDKA within the week in which the prediction is made. In such an example, this chance may be displayed to the user and / or transmitted to the physician / prescriber so that the dosage and / or frequency of the inhibitor can be adjusted to reduce the likelihood of euDKA. Examples of other user information include other analyte data (analyte data other than glucose and ketone measurements), non-analyte data (e.g., blood pressure, temperature, oxygen saturation, heart rate, accelerometer, etc.), demographic information (e.g., age, sex, ethnicity, etc.), anthropometric information (e.g., height, weight, BMI), clinical chemistry information (e.g., fasting blood glucose level, HbA1c level), compliance with SGLT2 therapy, disease information, diet / meal information, exercise / activity data, renal status information, hydration, body mass index (BMI), etc.

[0160] The fusion model can be a fusion rule-based model configured to provide real-time decision support for euDKA development. A fusion rule-based model, as used herein, refers to a rule-based model that considers both a patient's ketone metrics and glucose metrics to make a determination (e.g., (1) the likelihood that the patient is experiencing (or will experience) euDKA, (2) the risk of euDKA, or (3) the presence and / or severity of euDKA).

[0161] As previously mentioned, a rule-based model involves using a rule set to map inputs to outputs. In particular, the decision support engine 114 may apply rule statements (e.g., if, then statements, do-while statements, catch statements, switch statements) to patient metrics (e.g., analyte metrics or other metrics described in connection with FIG. 3 ) and treatment data as inputs to determine the likelihood that the patient will experience euDKA within a defined time period, perform euDKA risk stratification for the patient, and / or identify the patient's risks associated with euDKA.

[0162] The rules implemented by the fusion rule-based model may be defined by the decision support engine 114 and maintained in a reference library. For example, the reference library may maintain various metrics, such as analyte metrics (e.g., glucose and / or ketone levels and / or rates of change) and various dosages / frequencies of medications (e.g., inhibitors), which together may be mapped to different probabilities of developing euDKA. In certain embodiments, such rules may be determined based on empirical studies and analyzing historical patient records from the medical record database 112. For example, an example rule may include: if a patient's glucose level is A, ketone level is B, prescribed SGLT2 dose is C, and prescribed SGLT2 intake frequency is D, then there is a 90% chance that the patient will experience euDKA within a certain defined time period (e.g., 2 minutes, 2 hours, 2 days, 2 weeks, 2 months, etc.). The rules may be more granular and complex such that additional analyte and / or non-analyte metrics, as well as other patient information, such as any of the information stored in the patient profile 118, may be used as inputs to make the above-mentioned decisions.

[0163] In certain embodiments, the fusion model may be an AI model, such as a machine learning model (e.g., supervised) used to provide a likelihood of euDKA. In certain embodiments, the decision support engine 114 may deploy one or more fusion machine learning models to predict euDKA in a patient. Examples of fusion models may include Bayesian models, regression models, classification models, support vector machines, decision trees, Monte Carlo models, neural networks, artificial neural networks, convolutional neural networks, recurrent neural networks, clustering, principal component analysis, discriminant analysis, maximum likelihood estimators, long short-term memory, etc.

[0164] In particular, the decision support engine 114 may retrieve information from a user profile 118 associated with the patient stored in the user database 110, characterize the information about the patient stored in the user profile 118 into one or more features, and use these features as inputs to such a model. Alternatively, the information provided by the user profile 118 may be characterized by another entity, and the features may then be provided to the decision support engine 114 to be used as inputs to the ML model. In certain embodiments, features associated with the patient may be used as inputs to one or more of the models. Details associated with how one or more machine learning models may be trained are further discussed in connection with FIG. 5.

[0165] For example, the fusion AI model may use patient metrics (e.g., analyte metrics or other metrics described in connection with FIG. 3) and treatment data (e.g., dosage / frequency information) as inputs and output the likelihood that the patient will experience euDKA within a particular defined time period, the patient's euDKA risk stratification, or other determination.

[0166] The output of the fusion model (e.g., a rule-based model or an AI model) can then be used to adjust the patient's treatment. For example, if, based on the patient's metrics and treatment data, the fusion model predicts a 95% likelihood that the patient will experience euDKA, the patient's physician can adjust the SGLT2 dosage / frequency. In certain embodiments, the output of the fusion model can be used to provide a health care provider (HCP) intervention recommendation to the patient, which may include a recommendation that the patient seek treatment. For example, in certain embodiments, if, based on the patient's metrics and treatment data, the fusion model predicts a 95% likelihood that the patient will experience euDKA within a defined time period (e.g., 2 hours), the HCP intervention recommendation may indicate to the patient, or another individual interested in the patient's health, that the patient should immediately go to the emergency room and / or contact the patient's health care provider.

[0167] In certain other embodiments, the HCP intervention recommendation may automatically alert the patient's healthcare provider regarding the patient's condition for physician intervention. In certain other embodiments, the HCP intervention recommendation may alert healthcare personnel to assist the patient, such as activating an ambulance service or emergency medical service to provide urgent pre-hospital care and stabilization to the patient and / or transporting the patient to definitive care. In certain embodiments, the decision support engine 114 may make the HCP intervention recommendation based on the patient's ability to seek medical assistance and / or the patient's accessibility to medical assistance.

[0168] In some cases, method 400 continues at block 410 by the decision support engine 114 generating one or more treatment recommendations based at least in part on the patient treatment data collected at block 402 and the analyte metrics generated at blocks 404 / 406. The treatment recommendations may include recommendations for optimizing medication type, dosage, and / or frequency to reduce the likelihood of euDKA. In certain embodiments, the treatment recommendations may include a recommendation for the patient to stop taking a previously prescribed medication and, in some cases, may recommend an alternative medication for the patient to take. In certain embodiments, the medication recommendations may include recommending that the patient take a lower or higher dosage of a previously prescribed medication. In certain embodiments, the treatment recommendations may include recommendations for dosage titration or dosage timing of a medication previously prescribed to the patient to determine the patient's ideal dosage (e.g., while monitoring the user's health). In certain embodiments, medication recommendations may be generated to reduce the risk of euDKA.

[0169] To illustrate this with an example, a user prescribed a particular dosage and frequency of an SGLT2 inhibitor may be instructed by the user's physician to use the health management system 100 to predict the optimal dosage / frequency of the inhibitor. In such an example, the user utilizes an analyte sensor system 104 that continuously generates at least glucose and ketone values, and a software application 106 that receives glucose and ketone measurements that are used as inputs to a fusion model for predicting the optimal dosage / frequency of the inhibitor. In particular, in that example, the user's own glucose and ketone measurements and / or other user information are used as inputs to predict the user's dosage / frequency that will minimize euDKA risk while helping the user achieve euglycemia or otherwise maximize time in range.

[0170] The treatment recommendation may be provided by a rule-based model or a fusion model, such as an AI / ML model. Using the fusion rule-based model, the decision support engine 114 may apply rule statements (e.g., if, then statements, do-while statements, catch statements, switch statements) to determine an optimized dosage / frequency of SGLT2 intake to minimize the risk of euDKA using patient metrics (e.g., analyte metrics or other metrics described in connection with FIG. 3) as inputs. For example, an example rule may include: if a patient's glucose level is A and their ketone level is B, then the optimized dosage and / or frequency of SGLT2 intake is C. The rules may be more granular and complex, such that additional analyte metrics and / or non-analyte metrics, as well as other patient information, such as any of the information stored in the patient profile 118, may be used as inputs to make the above-mentioned decisions.

[0171] In certain embodiments, the treatment recommendations may be provided by an AI model, such as a machine learning model, hi certain embodiments, the decision support engine 114 may deploy one or more fused machine learning models to predict the optimal dosage and / or frequency of SGLT2 intake.

[0172] In particular, the decision support engine 114 may retrieve information from a user profile 118 associated with the patient stored in the user database 110, characterize the information about the patient stored in the user profile 118 into one or more features, and use these features as inputs to such a model. Alternatively, the information provided by the user profile 118 may be characterized by another entity, and the features may then be provided to the decision support engine 114 to be used as inputs to the ML model. In certain embodiments, features associated with the patient may be used as inputs to one or more of the models. Details associated with how one or more machine learning models may be trained are further discussed in connection with FIG. 5.

[0173] For example, the fusion AI model may use patient metrics (e.g., analyte metrics or other metrics described in connection with FIG. 3) as inputs and output the optimal dosage and / or frequency of SGLT2 intake, euDKA risk stratification of the patient, or other decisions. Note that SGLT2 is used as an example, and the dosage / frequency of any other inhibitor may be optimized similarly.

[0174] After generating one or more recommendations, at block 412, method 400 continues by sending an indication (e.g., an alert, alarm, or other type of notification) to the user regarding the prediction and / or generated treatment recommendation related to euDKA, examples of which are described above. In certain embodiments, the indication is sent to the patient via application 106, and the indication is displayed to the user on a display device 107, such as a smartphone or other computing device. In certain embodiments, the indication is sent to a healthcare provider in addition to, or as an alternative to, the patient.

[0175] In certain embodiments, any one or more components or devices of the health management system 100 may include a “share / follow” feature for alerting, warning, providing recommendations, and sharing historical and / or predictive data with a patient's healthcare professional, clinician, and / or other caregiver. For example, such a “share / follow” feature may be included in one or more continuous analyte sensors 202 of the continuous analyte monitoring system 104 and / or application 106 running on the display device 107. In certain embodiments, such decision support alarms, alerts, and / or recommendations may be tailored to the patient's healthcare professional, clinician, and / or caregiver rather than the patient. In certain embodiments, such support alarms, alerts, and / or recommendations may be automatically provided to the patient's healthcare professional, clinician, and / or other caregiver. In certain embodiments, the patient may request the health management system 100 to provide such support alarms, alerts, and / or recommendations to the patient's healthcare professional, clinician, and / or other caregiver, for example, via the interface of the display device 107 associated with the patient or the patient's interaction with the continuous analyte sensor 202. Assistive alarms, alerts, and / or recommendations may generally be provided to the patient's healthcare professional, clinician, and / or other caregiver via wired / wireless communication and / or other means of communicating data.

[0176] In certain embodiments, the machine learning models deployed by the decision support engine 114 include one or more models trained by the training server system 140, as illustrated in Figure 1. Figure 5 describes in further detail a technique for training the machine learning models deployed by the decision support engine 114 to generate treatment recommendations and / or euDKA predictions, according to certain embodiments of the present disclosure.

[0177] In certain embodiments, method 500 is used to train a model to generate as output a treatment recommendation and / or a prediction related to euDKA for a patient. Method 500 begins at block 502 with retrieving data from a medical history record database, such as medical history record database 112 illustrated in FIG. 1, or an electronic medical record / electronic health record by a training server system, such as training server system 140 illustrated in FIG. 1. As referred to herein, medical history record database 112 may provide a repository of current and historical medical information for users of continuous analyte monitoring systems and connected mobile health applications, such as users of continuous analyte monitoring system 104 and application 106 illustrated in FIG. 1, as well as data for one or more patients who are not or were not previously users of continuous analyte monitoring system 104 and / or application 106. In certain embodiments, medical history record database 112 may include one or more datasets of historical patients with no cases of inhibitor use and / or no cases of euDKA.

[0178] Retrieval of data from historical record database 112 by training server system 140 in block 502 may include retrieval of all or any subset of the information maintained by historical record database 112. For example, if historical record database 112 stores information about 100,000 patients (e.g., non-users and users of continuous analyte monitoring system 104 and application 106), the data retrieved by 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.

[0179] As an illustrative example, integration with homegrown or cloud-based medical record databases through Fast Healthcare Interoperability Resources (FHIR), web application programming interfaces (APIs), Health Level 7 (HL7), and / or other computer interface languages ​​may enable aggregation of historical medical records for baseline assessment in addition to aggregation of de-identified patient data from cloud-based repositories.

[0180] As an illustrative example, in block 502, the training server system 140 may retrieve information about 100,000 patients under treatment with an inhibitor (e.g., SGLT2) stored in the medical history record database 112 to train a model that determines treatment recommendations to optimize inhibitor use in users. Each of the 100,000 patients may have a corresponding data record (e.g., based on their corresponding user profile) stored in the medical history record database 112. Each user profile 118 may include information such as the information discussed with respect to FIG. 3.

[0181] The training server system 140 then uses the information in each of the records to train an artificial intelligence or ML model (for simplicity, referred to herein as an "ML model"). Examples of the types of information included in a patient's user profile are provided above. The information in each of these records may be characterized (e.g., manually or by the training server system 140) to yield features that can be used as input features for training an ML model. For example, a patient record may include or be used to generate features related to the patient's age, the patient's gender, the patient's BMI, the patient's occupation, analyte metrics (e.g., the patient's analyte levels over time, the patient's analyte level change rate and / or trend over time), the patient's physiological parameters over time, treatment data, disease information, etc. The features used to train the machine learning model may vary in different embodiments.

[0182] In certain embodiments, each historical patient record retrieved from the historical records database 112 is further associated with a label indicating the dosage and / or frequency of inhibitor intake, information regarding euDKA (e.g., whether the user experienced euDKA), and / or similar metrics. What the record is labeled depends on what the model is trained to predict.

[0183] At block 504, the method 500 continues by the training server system 140 training one or more machine learning models based on the features and labels associated with the historical patient record. In some embodiments, the training server does so by providing the features as input to the model. This 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. In certain embodiments, the output may recommend a treatment or treatment change for the user or provide a prediction of euDKA.

[0184] In certain embodiments, the training server system 140 compares this generated output to the actual labels associated with the corresponding historical patient record to calculate 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 more accurately predict optimized treatment recommendations and / or the onset of euDKA.

[0185] One of a variety of machine learning algorithms may be used to train the models described above, such as a supervised learning algorithm, a neural network algorithm, a deep neural network algorithm, a deep learning algorithm, etc.

[0186] At block 506, the training server system 140 deploys the trained model to make treatment recommendations and / or predictions associated with euDKA. In some embodiments, this includes transmitting some instructions 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 may transmit the weights of the trained model to the decision support engine 114. The model can then be used to evaluate the user's treatment in real time, predict the onset of euDKA, provide treatment recommendations, etc. In certain embodiments, the training server system 140 may continue to train the model in an “online” manner by using input features and labels associated with new patient records.

[0187] Additionally, similar methods for training illustrated in FIG. 5 using historical patient records can also be used to train a model using patient-specific records to create a more personalized model for making treatment recommendations and / or predictions related to euDKA. For example, a model trained using historical patient records deployed for a particular user can be further retrained after deployment. For example, a model can be retrained after it is deployed for a particular patient to create a more personalized model for the patient. A more personalized model can more accurately make treatment recommendations and / or predict euDKA for a patient based on the patient's own data (rather than just historical patient record data), including the patient's own analyte (e.g., ketone, glucose) metrics. In some embodiments, training a model can include individualizing the model based on population health metrics. For example, tuning the model using segments of the training dataset associated with patients who share one or more aspects with the patient can enable more refined, personalized treatment recommendations.

[0188] 6 is a block diagram illustrating a computing device 600 configured to provide treatment recommendations and / or predict the onset of euDKA, according to certain embodiments disclosed herein. While depicted as a single physical device, in some embodiments, computing device 600 may be implemented using virtual devices and / or across several devices, such as in a cloud environment. As illustrated, computing device 600 includes a processor 605, memory 610, storage 615, a network interface 625, and one or more I / O interfaces 620. In the illustrated embodiment, processor 605 retrieves and executes programming instructions stored in memory 610 and stores and retrieves application data present in storage 615. Processor 605 generally represents a single CPU and / or GPU, multiple CPUs and / or GPUs, a single CPU and / or GPU with multiple processing cores, etc.

[0189] Memory 610 is included to generally represent random access memory (RAM). Storage 615 may be any combination of disk drives, flash-based storage devices, etc., and may include fixed and / or removable storage devices such as fixed disk drives, memory modules, removable memory cards, internal memory, on-die memory, cache, optical storage, network attached storage (NAS), or storage area network (SAN).

[0190] In some embodiments, I / O devices 635 (e.g., keyboard, monitor, etc.) can be connected via I / O interface 620. Additionally, via network interface 625, computing device 600 can be communicatively coupled to one or more other devices and components, such as user database 110 and / or medical history record database 112. In particular embodiments, computing device 600 is communicatively coupled to other devices via a network, which may include the Internet, a local network, etc. The network may include wired connections, wireless connections, or a combination of wired and wireless connections. As illustrated, processor 605, memory 610, storage 615, network interface 625, and I / O interface 620 are communicatively coupled by one or more interconnects 630. In particular embodiments, computing device 600 represents a display device 107 associated with a user. In particular embodiments, as discussed above, display device 107 may include the user's laptop, computer, smartphone, etc. In another embodiment, computing device 600 is a server running in a cloud environment.

[0191] In the illustrated embodiment, storage 615 includes user profiles 118. Memory 610 includes a decision support engine 114, which itself includes a DAM 116. The decision support engine 114 is executed by computing device 600 to perform the operations of method 400 of FIG. 4 and method 500 of FIG. 5 to provide decision support in the form of treatment recommendations and / or euDKA onset predictions.

[0192] Embodiments described herein may enable increased efficacy and safety of medications (e.g., inhibitors, agonists, hormones such as insulin, etc.) by providing recommendations for medication dosage and frequency to improve therapeutic efficacy and reduce adverse effects (e.g., euDKA). Recommendations may be provided automatically or through healthcare provider review and input.

[0193] As described above, the continuous analyte monitoring system 104 described in connection with Figure 1 may be a multi-analyte sensor system that includes a multi-analyte sensor. Figures 7-8 describe an exemplary multi-analyte sensor used to measure multiple analytes.

[0194] The following description and drawings illustrate in detail the embodiments of the present disclosure. Those skilled in the art will recognize that there are many variations and modifications of the present invention that are encompassed by the scope of the present disclosure. Therefore, the embodiments herein should not be considered as limiting the scope of the present disclosure. To facilitate understanding of the embodiments disclosed herein, several terms are defined below.

[0195] As used herein, the term "about" is a broad term and is to be given its ordinary and accustomed meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to allowing for a degree of variability in a value or range, for example, within 10%, within 5%, or within 1% of the stated limits of the stated value or range, including, but not limited to, the exactly stated value or range.

[0196] As used herein, the terms "stick" and "adhere" are broad terms and are to be given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, holding, joining, or fastening, for example, by adhering, bonding, grasping, interpenetrating, or fusing.

[0197] As used herein, the term "analyte" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, a substance or chemical constituent in a biological fluid (e.g., blood, interstitial fluid, cerebrospinal fluid, lymphatic fluid, urine, sweat, saliva, etc.) that can be analyzed. Analytes can include naturally occurring substances, man-made substances, metabolites, and / or reaction products. In some examples, the analyte measured by the sensing region, devices, and methods is glucose. However, other analytes are contemplated as well, including 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); bilirubin; biotinidase; biopterin; c-reactive protein; carnitine; carnosinase; CD4; ceruloplasmin; chenodeoxycholic acid; chloroquine; cholesterol; cholinesterase; conjugated 1-β hydroxycholic acid; cortisol; creatine; creatine kinase; creatine ATP kinase MM isoenzyme; creatinine; cyclosporine A; d-penicillamine; deethylchloroquine; dehydroepiandrosterone sulfate; DNA (acetylation polymorphisms, 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, 21-deoxycortisol); desbutylhalofantrine; dihydropteridine reductase; diphtheria / tetanus antitoxin; erythrocyte arginase; erythrocyte protoporphyrin; esterase D;Fatty acids / acylglycines; free beta-human chorionic gonadotropin; free erythrocyte porphyrins; free thyroxine (FT4); free triiodothyronine (FT3); fumarylacetoacetase; galactose / gal-1-phosphate; galactose-1-phosphate uridyltransferase; gentamicin; glucose-6-phosphate dehydrogenase; glutathione; glutathione peroxidase; glycerol; glycocholate; glycosylated hemoglobin; halofantrine; hemoglobin variants; hexosaminidase A; human erythrocyte carbonic anhydrase I; 17-alpha-hydroxyprogesterone ron; hypoxanthine phosphoribosyltransferase; immunoreactive trypsin; beta-hydroxybutyrate; ketones; lactate; lead; lipoproteins ((a), B / A-1, β); lysozyme; mefloquine; netilmicin; oxygen; phenobarbitone; phenytoin; phytanic acid / pristanic acid; potassium, sodium, and / or other blood electrolytes; progesterone; prolactin; prolidase; purine nucleoside phosphorylase; quinine; reverse triiodothyronine 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, Echinococcus granulosus, Entamoeba 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, Plasmodium, Poliovirus, Pseudomonas aeruginosa, Respiratory syncytial virus, Rickettsia (tsutsugamushi disease), Schistosoma mansoni, Toxoplasma gondii, Treponema pallidum, Trypanosoma cruzi / rangeli, Vesicular stomatitis virus, Wuchereria bancrofti, Yellow fever virus); Specific antigens (Hepatitis B virus, HIV-1); Succinylacetone; Sulfadoxine; Theophylline; Thyrotropin (TSH); Thyroxine (T4);Analytes include, but are not limited to, thyroxine-binding globulin, trace elements, transferrin, UDP-galactose-4-epimerase, urea, uric acid, uroporphyrinogen I synthase, vitamin A, leukocytes, and zinc protoporphyrin. Salts, sugars, proteins, fats, vitamins, and hormones naturally present in blood or interstitial fluid can also constitute analytes in certain embodiments. Analytes can be naturally present in biological fluids or can be endogenous, e.g., metabolites, hormones, antigens, antibodies, etc. Alternatively, the analyte can be introduced into the body or can be exogenous, such as a contrast agent for imaging, a radioisotope, a chemical agent, a fluorocarbon-based synthetic blood, or a drug or pharmaceutical composition, including, but not limited to, insulin; ethanol; cannabis (marijuana, tetrahydrocannabinol, hashish); inhalants (nitrous oxide, amyl nitrite, butyl nitrite, chlorohydrocarbons, hydrocarbons); cocaine (crack cocaine); stimulants (amphetamine, methamphetamine, RITALIN®, CYLERT®, PRELUDIN®, DIDREX®, PRESTATE®, VORANIL®, SANDREX®, PLEGINE®); antidepressants ( barbiturates, methaqualone, valium®, librium®, miltown®, serax®, equinanil®, tranxene®, and other tranquilizers; hallucinogens (phencyclidine, lysergic acid, mescaline, peyote, psilocybin); narcotics (heroin, codeine, morphine, opium, meperidine, percocet®, percodan®, tussionex®, fentanyl, darvon®, talwin®, lomotil®); synthetic narcotics (fentanyl, meperidine, amphetamine, methamphetamine, and phencyclidine analogues, e.g., ecstasy); anabolic steroids;Analytes of interest include, but are not limited to, nicotine and nicotine. Metabolites of drugs and pharmaceutical compositions are also contemplated. Analytes such as neurochemicals and other chemicals produced in the body, such as ascorbic acid, uric acid, dopamine, noradrenaline, 3-methoxytyramine (3MT), 3,4-dihydroxyphenylacetic acid (DOPAC®), homovanillic acid (HVA), 5-hydroxytryptamine (5HT), 5-hydroxyindoleacetic acid (FHIAA), and histamine, can also be analyzed.

[0198] As used herein, the phrases “analyte measuring device,” “analyte monitoring device,” “analyte sensing device,” and / or “multi-analyte sensor device” are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, apparatus and / or systems responsible for detecting a specific analyte or combination of analytes or transducing a signal associated therewith. For example, but not limited to, these terms may refer to an instrument responsible for detecting a specific analyte or combination of analytes. In one example, the instrument includes a sensor coupled to a circuit arranged within a housing and configured to process a signal associated with the analyte concentration into information. In one example, such a device and / or system is capable of providing specific quantitative, semi-quantitative, qualitative, and / or semi-qualitative analytical information using a biological recognition element combined with a transduction (detection) element.

[0199] As used herein, the term "amphiphilic" is a broad term and is to be given its ordinary and customary meaning to those of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, chemical compounds or polymers that have both hydrophilic and hydrophobic segments or properties.

[0200] As used herein, the terms "biosensor" and / or "sensor" are broad terms and are to be given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, an analyte measuring device, an analyte monitoring device, an analyte sensing device, and / or a portion of a multi-analyte sensing device responsible for detecting a specific analyte or combination of analytes or transducing a signal associated therewith. In one example, a biosensor or sensor generally comprises a body and working, reference, and / or counter electrodes coupled to the body and forming a surface configured to provide a signal during an electrochemical reaction. One or more membranes may be affixed to the body and cover the electrochemically reactive surface. In one example, such biosensors and / or sensors are capable of providing a specific quantitative, semi-quantitative, qualitative, or semi-qualitative analytical signal using a biological recognition element combined with a transduction (detection) element.

[0201] As used herein, the phrases "sensing moiety," "sensing membrane," and "sensing mechanism" are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, the portion of a biosensor and / or sensor responsible for detecting a particular analyte or combination of analytes or transducing a signal associated therewith. In one example, the sensing moiety, sensing membrane, and / or sensing mechanism generally comprises electrodes configured to provide a signal during an electrochemical reaction with one or more membranes covering an electrochemically reactive surface. In one example, such a sensing moiety, sensing membrane, and / or sensing mechanism can provide specific quantitative, semi-quantitative, qualitative, or semi-qualitative analytical information using a biological recognition element combined with a transduction (detection) element.

[0202] As used herein, the term "substantially" refers to a majority or majority, such as at least about 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%, 99.5%, 99.9%, 99.99%, or at least about 99.999% or more, or 100%.

[0203] As used herein, the phrase "substantially free" can mean having no or an insignificant amount of the material, such that the amount of material present does not affect the material properties of the composition including the material, such as about 0% to about 5% by weight of the composition being the material, or about 0% to about 1%, or about 5% by weight or less, or about 4.5% by weight or less, 4, 3.5, 3, 2.5, 2, 1.5, 1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0.01, or about 0.001% by weight or less, or about 0% by weight.

[0204] As used herein, the terms "stick" and "adhere" are broad terms and are to be given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, holding, joining, or fastening, for example, by adhering, bonding, grasping, interpenetrating, or fusing.

[0205] As used herein, the phrase "barrier cell layer" is a broad phrase that is to be given its ordinary and customary meaning to those skilled in the art (and is not to be limited to any special or customized meaning), and refers, without limitation, to the portion of the foreign body response that forms a cohesive monolayer of cells (e.g., macrophages and foreign body giant cells) that substantially blocks the transport of molecules and other substances into the implantable device.

[0206] As used herein, the term "bioactive agent" is a broad term and is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, any substance that has an effect on or elicits a response from living tissue.

[0207] As used interchangeably herein, the phrases "biointerface membrane" and "biointerface layer" are broad terms that are to be given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, a permeable membrane (which may include multiple domains) or layer that acts as a bioprotective interface between the host tissue and the implantable device. The terms "biointerface" and "bioprotective" are used interchangeably herein.

[0208] As used herein, the term "biostable" is a broad term that is to be given its ordinary and customary meaning to those of skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, materials that are relatively resistant to degradation by processes encountered in vivo.

[0209] As used herein, the phrase "cell protrusion" is a broad phrase and is to be given its ordinary and customary meaning to those skilled in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, the pseudopodia of cells.

[0210] As used herein, the phrase "cell attachment" is a broad phrase and is to be given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, the adhesion of cells and / or cell processes to a material at a molecular level and / or the attachment of cells and / or cell processes to a microporous or macroporous material surface. One example of a material used in the prior art that promotes cell attachment to a porous surface is the BIOPORE™ cell culture support, as marketed by Millipore (Bedford, Mass.) and described in U.S. Patent No. 5,741,330 to Brauker et al.

[0211] As used herein, the term "cofactor" is a broad term that is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning) and refers, without limitation, to one or more substances whose presence contributes to or is required for the analyte-related activity of an enzyme. The analyte-related activity can include, but is not limited to, any one or combination of binding, electron transfer, and chemical transformation. Cofactors include coenzymes, non-protein chemical compounds, metal ions, and / or metal-organic complexes. Coenzymes include prosthetic groups and cosubstrates.

[0212] As used herein, the term "continuous" is a broad term and is to be given its ordinary and customary meaning to those of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, an uninterrupted or unbroken portion, domain, coating, or layer.

[0213] As used herein, the phrases "continuous analyte sensing" and "continuous multi-analyte sensing" are broad phrases that are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning) and refer to, but are not limited to, continuous, intermittent, and / or intermittent (but periodic) monitoring of analyte concentrations, for example, for periods of time that range from about 1 second or less to about 1 week or more. In further embodiments, monitoring of the analyte concentration is performed between about every 2, 3, 5, 7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 seconds to about every 1.25, 1.50, 1.75, 2.00, 2.25, 2.50, 2.75, 3.00, 3.25, 3.50, 3.75, 4.00, 4.25, 4.50, 4.75, 5.00, 5.25, 5.50, 5.75, 6.00, 6.25, 6.50, 6.75, 7.00, 7.25, 7.50, 7.75, 8.00, 8.25, 8.50, 8.75, 9.00, 9.25, 9.50, or 9.75 minutes. In further embodiments, the analyte concentration monitoring is performed between about every 10, 20, 30, 40, or 50 minutes and about every 1, 2, 3, 4, 5, 6, 7, or 8 hours. In further embodiments, the analyte concentration monitoring is performed between about every 8 hours and about every 12, 16, 20, or 24 hours. In further embodiments, the analyte concentration monitoring is performed between about every day and about every 1.5, 2, 3, 4, 5, 6, or 7 days. In further embodiments, the analyte concentration monitoring is performed between about every week and about every 1.5, 2, 3, or more weeks.

[0214] As used herein, the term "coaxial" should be interpreted broadly to include sensor architectures having elements aligned along a shared axis around a core that may be configured to have a circular, elliptical, triangular, polygonal, or other cross-section, and such elements may include electrodes, insulating layers, or other elements that may be positioned circumferentially around a core layer, such as a core electrode or core polymer wire.

[0215] As used herein, the term "coupled" is a broad term and is to be given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, two or more system elements or components that are configured to be electrically, mechanically, thermally, operatively, chemically, or otherwise attached to at least one other element. For example, an element is "coupled" if it is covalently, communicatively, electrostatically, thermally connected, mechanically connected, magnetically connected, or ionically associated with, or physically captured, adsorbed, or absorbed by, another element. Similarly, as used herein, the phrases "operably connected," "operably linked," and "operably coupled" can refer to one or more components coupled to another component in a manner that facilitates transmission of at least one signal between the components. In some examples, components are part of the same structure and / or integrated with one another, such as when they are covalently, electrostatically, mechanically, thermally, magnetically, ionically associated, or physically trapped or absorbed (i.e., "directly coupled," as in the absence of intervening elements). In other examples, components are connected via remote means. For example, one or more electrodes can be used to detect analytes in a sample and convert that information into a signal, which can then be transmitted to an electronic circuit. In this example, the electrodes are "operably coupled" to the electronic circuit. As used herein, the phrase "removably coupled" can refer to two or more system elements or components that are configured or configured to be attached and detached electrically, mechanically, thermally, operably, chemically, or otherwise, without damaging any of the coupled elements or components.As used herein, the phrase "permanently coupled" may refer to two or more system elements or components that are configured to be or are attached electrically, mechanically, thermally, operatively, chemically, or otherwise, but cannot be separated without damaging at least one of the coupled elements or components, being covalently, electrostatically, ionically associated, or being physically trapped or absorbed.

[0216] As used herein, the phrase "defined edge" is a broad phrase and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, an abrupt, distinct edge or boundary between layers, domains, coatings, or portions. A "defined edge" is in contrast to a gradual transition between layers, domains, coatings, or portions.

[0217] As used herein, the term "discontinuous" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, cut, interrupted, or separated portions, layers, coatings, or domains.

[0218] As used herein, the term "distal" is a broad term and is to be given its ordinary and customary meaning to those skilled in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, an area that is relatively far away from a reference point such as an origin or attachment point.

[0219] As used herein, the term "domain" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, a region of a membrane system that may be a layer, a uniform or non-uniform gradient (e.g., an anisotropic region of a membrane), or a portion of a membrane that is capable of sensing one, two, or more analytes. The domains discussed herein can be formed as a single layer, as two or more layers, as a pair of bilayers, or as combinations thereof.

[0220] As used herein, the term "drift" is a broad term and is to be given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), referring to, but not limited to, a gradual increase or decrease in signal over time that is unrelated to changes in host systemic analyte concentration (e.g., the host's postprandial glucose concentration). Without being bound by theory, it is believed that drift may be the result of a local decrease in glucose transport to the sensor, for example, due to the formation of a foreign body capsule (FBC) or due to an insufficient amount of interstitial fluid surrounding the sensor, resulting in reduced oxygen and / or glucose transport to the sensor. In one example, an increase in local interstitial fluid may slow or reduce drift, thus improving sensor performance. Drift may also be the result of sensor electronics or algorithmic models used to compensate for noise or other anomalies that may occur with electrical signals, for example, in the picoampere range, femtoampere range, nanoampere range, microampere range, milliampere range, ampere range, etc.

[0221] As used interchangeably herein, the phrases "drug-release membrane" and "drug-release layer" are each broad phrases that are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, a permeable or semi-permeable membrane that is permeable to one or more bioactive agents. In one example, the "drug-release membrane" and "drug-release layer" are typically several microns or more thick and can be composed of two or more domains. In one example, the drug-release layer and / or drug-release membrane are substantially the same as the biointerface layer and / or biointerface membrane. In another example, the drug-release layer and / or drug-release membrane are separate from the biointerface layer and / or biointerface membrane.

[0222] As used herein, the term "electrochemically reactive surface" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, the surface of an electrode on which an electrochemical reaction occurs. In one example, the reaction is faradaic, resulting in a charge transfer between the surface and its environment. In one example, hydrogen peroxide produced by an enzyme-catalyzed reaction of an analyte being oxidized on the surface results in a measurable electronic current. For example, in the detection of glucose, glucose oxidase produces hydrogen peroxide (HO) as a by-product. HO reacts with the surface of the working electrode to release two protons (2H + ), two electrons (2e - ), and one oxygen molecule (O), which produces an electronic current that is detected. At the counter electrode, a reducible species, e.g., O, is reduced at the electrode surface to balance the current produced by the working electrode.

[0223] As used herein, the term "electrolysis" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers, without limitation, to the electro-oxidation or electro-reduction (collectively "redox") of a compound, either directly or indirectly, by one or more enzymes, cofactors, or mediators.

[0224] As used herein, the phrase "hard segment" is a broad phrase and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, an element of a copolymer, such as a polyurethane, polycarbonate polyurethane, or polyurethane urea copolymer, that imparts resistance properties, such as resistance to bending or twisting. The term "hard segment" can be further characterized as a crystalline, semi-crystalline, or glassy material that has a glass transition temperature, typically determined by dynamic scanning calorimetry ("Tg"), above ambient temperature. Exemplary hard segment elements used to prepare polycarbonate polyurethane or polyurethane urea hard segments include norbornane diisocyanate (NBDI), isophorone diisocyanate (IPDI), tolylene diisocyanate (TDI), 1,3-phenylene diisocyanate (MPDI), trans-1,3-bis(isocyanatomethyl)cyclohexane (1,3-H6XDI), bicyclohexylmethane-4,4'-diisocyanate, and cyclohexane-4,4'-diisocyanate. isocyanate (HMDI), 4,4'-diphenylmethane diisocyanate (MDI), trans-1,4-bis(isocyanatomethyl)cyclohexane (1,4-H6XDI), 1,4-cyclohexyl diisocyanate (CHDI), 1,4-phenylene diisocyanate (PPDI), 3,3'-dimethyl-4,4'-biphenyl diisocyanate (TODI), 1,6-hexamethylene diisocyanate (HDI), or combinations thereof.

[0225] As used herein, the term "host" is a broad term and is given its ordinary and customary meaning to those of skill in the art (and is not limited to any special or customized meaning), and refers to mammals, such as, but not limited to, humans.

[0226] As used herein, the terms "indwelling," "indwelling," "implanted," or "implantable" are broad terms that are to be given their ordinary and customary meaning to those of ordinary skill in the art (and are not to be limited to any special or customized meaning) and refer to an object (e.g., a sensor) that is inserted or configured to be inserted subcutaneously (i.e., within the fatty layer between the skin and muscle), intradermally (i.e., penetrating the stratum corneum and located within the epidermal or dermal layer of the skin), or transcutaneously (i.e., penetrating, entering, or passing through intact skin), which may result in a sensor having an in vivo portion and an ex vivo portion. The term "indwelling" also encompasses an object configured to be inserted subcutaneously, intradermally, or percutaneously, whether or not it is itself inserted.

[0227] As used herein, the phrase "insertable surface area" is a broad phrase that is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning) and refers to the geometric surface area utilized in the analyte sensors described herein, e.g., the surface area of ​​the insertable portion of the analyte sensor, including, but not limited to, the surface area of ​​a planar, flat, substantially planar, and / or coaxial substrate.

[0228] As used herein, the phrase "insertable volume" is a broad phrase and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, the volume anterior and lateral to the insertion path of the insertable portion of the analyte sensor, as described herein, and the incision made in the skin for inserting the insertable portion of the analyte sensor. The insertable volume also includes up to 5 mm radially or perpendicularly to the volume anterior and lateral to the insertion path.

[0229] As used herein, the terms "interferent" and "interfering species" are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, effects and / or species that interfere with the measurement of an analyte of interest in a sensor, producing a signal that does not accurately represent the analyte measurement. In one example of an electrochemical sensor, an interfering species is a compound that produces a signal that is not analyte-specific due to a reaction on an electrochemically active surface.

[0230] As used herein, the term "in vivo" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), encompassing, but not limited to, portions of a device (e.g., a sensor) adapted for insertion into and / or presence within the body of a host.

[0231] As used herein, the term "ex vivo" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), encompassing, but not limited to, a portion of a device (e.g., a sensor) that is adapted to reside and / or exist outside the host organism.

[0232] As used herein, the terms “mechanical storage medium,” “device storage medium,” and “computer storage medium” (collectively referred to as “mechanical storage medium”) mean the same thing and may be used interchangeably in this disclosure. The term refers to single or multiple storage devices and / or media (e.g., centralized or distributed databases and / or associated caches and servers) that store executable instructions and / or data, as well as cloud-based storage systems or storage networks that include multiple storage devices or devices. Accordingly, the term is intended to include, but is not limited to, solid-state memory, optical media, and magnetic media (including memory that is internal or external to a processor). Specific examples of mechanical storage media, computer storage media, and / or device storage media include non-volatile memory, examples of which include semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), electrically erasable programmable devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The terms mechanical storage media, computer storage media, and device storage media specifically exclude carrier waves, modulated data signals, and other such media, at least some of which are covered by the term "signal media" discussed below.

[0233] As used herein, the terms "mediator" and "redox mediator" are broad terms and phrases that are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning) and refer to, but are not limited to, any chemical compound or collection of compounds that is capable of direct or indirect electron transfer between an analyte, analyte precursor, analyte surrogate, analyte reductase or analyte oxidase, or cofactor and an electrode surface held at an electric potential. In one example, the mediator accepts electrons from or transfers electrons to one or more enzymes or cofactors and / or exchanges electrons with a sensor system electrode. In one embodiment, the mediator comprises an electroreducible ion, an electrooxidizable ion, a complex, or has a redox potential above or below that of a standard calomel electrode (SCE), e.g., about 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, or 750 millivolts above or below that of the SCE, and is a transition metal-coordinated organic molecule capable of reversible oxidation and reduction. In other embodiments, the mediator can be an organic molecule or a metal capable of reversible oxidation and reduction.

[0234] As used herein, the term "membrane" is a broad term and is to be given its ordinary and customary meaning to those skilled in the art (and is not limited to any special or customized meaning), and refers to a structure configured to perform functions including, but not limited to, protecting exposed electrode surfaces from the biological environment, resisting (limiting) the diffusion of analytes, serving as a matrix for catalysts (e.g., one or more enzymes) to enable enzymatic reactions, limiting or screening interfering species, providing hydrophilicity at electrochemically reactive surfaces of a sensor interface, serving as an interface between host tissue and an implantable device, modulating host tissue response via drug (or other substance) release, and combinations thereof. As used herein, the terms "membrane" and "matrix" are meant to be interchangeable.

[0235] As used herein, the phrase "membrane system" is a broad phrase and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers, without limitation, to a permeable or semi-permeable membrane that may be composed of two or more domains, layers, or layers within domains, is typically composed of materials several microns or more in thickness, and is permeable to oxygen and, optionally, for example, glucose or another analyte. In one example, the membrane system includes an enzyme that allows an analyte reaction to occur, thereby allowing the concentration of the analyte to be measured.

[0236] The phrases "machine-readable medium," "computer-readable medium," and "device-readable medium" mean the same thing and can be used interchangeably in this disclosure. These phrases include both mechanical storage media and signal media operably coupled to a sensor, biosensor, analyte sensing device, or analyte monitoring device. Thus, these phrases include both storage devices / media and carrier / modulated data signals operably coupled to a sensor, biosensor, analyte sensing device, or analyte monitoring device.

[0237] As used herein, the term "micro" is a broad term and is to be given its ordinary and customary meaning to those skilled in the art (and is not to be limited to any special or customized meaning), and refers to a size of approximately 10 microns that is not visible without magnification. -6 "Micro" refers to objects or scales that are small, but not limited to, those on the order of 10 microns. The term "micro" is in contrast to the term "macro," which refers to objects that are large enough to be seen without magnification. Similarly, the term "nano" refers to objects that are approximately 10 -9 Refers to a small object or scale of m.

[0238] As used herein, the term "noise" is a broad term and is used in its ordinary sense to include, but is not limited to, signals detected by a sensor or sensor electronics that are unrelated to analyte concentration and may result in degraded sensor performance. Some types of noise are observed for several hours (e.g., about 2 to about 24 hours) after sensor insertion. After the first 24 hours, noise may disappear or diminish, but in some hosts, noise may persist for about 3 to 4 days. In some cases, noise can be reduced using predictive modeling, artificial intelligence, and / or algorithmic means. In other cases, noise can be reduced by addressing immune response factors associated with the presence of an implanted sensor, such as by using a drug-releasing layer with at least one bioactive agent. For example, the noise of one or more exemplary biosensors, such as those disclosed herein, can be determined and then compared qualitatively or quantitatively. For example, by acquiring raw signal time series at a fixed sampling interval (in pA), a smoothed version of the raw signal time series can be obtained, for example, by applying a third-order low-pass digital Chebyshev Type II filter. Other smoothing algorithms can also be used. At each sampling interval, the absolute difference in pA can be calculated to provide a smoothed time series. This smoothed time series can be converted to mg / dL (units of "noise") using the glucose sensitivity time series in pA / mg / dL, where the glucose sensitivity time series is derived by using a mathematical model between the raw signal and reference blood glucose measurements (e.g., obtained from a blood glucose meter). Optionally, the time series can be aggregated, for example, by hour or day, as desired. Comparison of corresponding time series between different exemplary biosensors having a drug-releasing layer and one or more bioactive agents of the present disclosure provides a qualitative or quantitative determination of noise improvement.

[0239] As used herein, the terms "optional" or "optionally" are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning), meaning, without limitation, that the subsequently described event or circumstance may or may not occur, and that the description includes instances when the event or circumstance occurs and instances when it does not occur.

[0240] As used herein, the term "planar surface" should be broadly interpreted to describe a sensor architecture having a substrate including at least a first surface and an opposing second surface, and including, for example, a plurality of elements disposed on one or more surfaces or edges of the substrate. The plurality of elements may include conductive or insulating layers or elements configured to operate as a circuit. The plurality of elements may or may not be electrically or otherwise coupled. In one example, the planar surface includes one or more edges separating the opposing surfaces.

[0241] As used herein, the term "proximal" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, the spatial relationship between various elements compared to a particular reference point. For example, some examples of devices include a membrane system having a biological interface layer and an enzyme domain or enzyme layer. If the sensor is considered to be the reference point, and the enzyme domain is located closer to the sensor than the biological interface layer, then the enzyme domain is more proximal to the sensor than the biological interface layer.

[0242] As used herein, the phrases and terms "processor module" and "microprocessor" are each broad phrases and terms that are to be given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning) and refer to, but are not limited to, a computer system, state machine, processor, or the like, designed to perform arithmetic or logical operations using logic circuitry that responds to and processes the basic instructions that drive a computer.

[0243] As used herein, the term "semi-continuous" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, a portion, coating, domain, or layer that includes one or more continuous and discontinuous portions, coatings, domains, or layers. For example, a coating that is disposed around but not over a sensing area is "semi-continuous."

[0244] As used herein, the phrases "sensing moiety," "sensing membrane," and "sensing mechanism" are broad terms and are to be given their ordinary and customary meaning to one of ordinary skill in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, the portion of a biosensor and / or sensor responsible for detecting a particular analyte or combination of analytes or transducing a signal associated therewith. In one example, a sensing moiety, sensing membrane, and / or sensing mechanism generally comprises electrodes configured to provide a signal during an electrochemical reaction with one or more membranes covering an electrochemically reactive surface. In one example, such a sensing moiety, sensing membrane, and / or sensing mechanism is capable of providing specific quantitative, semi-quantitative, qualitative, or semi-qualitative analytical information using a biological recognition element combined with a transduction (detection) element.

[0245] During typical operation of an analyte measuring device, biosensor, sensor, sensing region, sensing moiety, or sensing mechanism, a biological sample, e.g., blood or interstitial fluid, or components thereof, contacts, either directly or after passing through one or more membranes, an enzyme, e.g., glucose oxidase, DNA, RNA, or a protein or aptamer, e.g., one or more periplasmic binding proteins (PBPs) or variants or fusion proteins thereof, having one or more analyte-binding regions, each region capable of specifically or reversibly binding and / or reacting with at least one analyte. The interaction of the biological sample or its components with the analyte measuring device, biosensor, sensor, sensing region, sensing moiety, or sensing mechanism results in the transduction of a signal that allows for the qualitative, semi-qualitative, quantitative, or semi-qualitative determination of analyte levels, e.g., glucose, ketone, lactate, potassium, etc., in the biological sample.

[0246] In one example, the sensing region or sensing portion can comprise at least a portion of a conductive substrate or at least a portion of a conductive surface, e.g., a substantially planar substrate including wires (coaxial) or conductive traces, or substantially planar traces, and a membrane. In one example, the sensing region or sensing portion can comprise a non-conductive body, a working electrode, a reference electrode, and a counter electrode (optional) forming an electrochemically reactive surface at one location on the body and forming electronic connections at another location on the body, and a sensing membrane affixed to the body and covering the electrochemically reactive surface. In some examples, the sensing membrane further comprises an enzyme domain, e.g., an enzyme domain, and an electrolyte phase, e.g., a free-flowing liquid phase comprising an electrolyte-containing fluid, as further described below. These terms are broad enough to include the entire device, or just the sensing portion thereof (or anything in between).

[0247] In another example, the sensing region can comprise one or more periplasmic binding proteins (PBPs), including mutants or fusion proteins thereof, or an aptamer having one or more analyte-binding regions, each capable of specifically and reversibly binding at least one analyte. Alterations in the aptamer or mutations in the PBP can contribute to or alter one or more of the binding constants, long-term stability of the protein, including thermal stability, to bind the protein to a specific encapsulation matrix, membrane, or polymer, or to attach a detectable reporter group or "label" to indicate changes in the binding region, or to transduce a signal corresponding to one or more analytes present in the biological fluid. Specific examples of alterations in the binding region include, but are not limited to, changes in the hydrophobic / hydrophilic environment, three-dimensional conformational changes, changes in the orientation of amino / nucleic acid side chains in the protein's binding region, and the redox state of the binding region. Such changes to the binding region provide for the transduction of a detectable signal corresponding to one or more analytes present in the biological fluid.

[0248] In one example, the sensing region determines selectivity between one or more analytes such that only the analyte that must be measured results in (transduces) a detectable signal. This selection can be based on any chemical or physical recognition of the analyte by the sensing region, where the chemical composition of the analyte does not change, or where the sensing region causes or catalyzes a reaction of the analyte that changes the chemical composition of the analyte.

[0249] As used herein, the term "sensitivity" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers, without limitation, to the amount of signal (e.g., in the form of current and / or voltage) produced by a given amount (unit) of analyte measured. For example, in one embodiment, the sensor has a sensitivity (or slope) of about 1 to about 100 picoamps of current per 1 mg / dL of analyte.

[0250] The phrases "signal media" or "transmission media" should be interpreted to include all forms of modulated data signals, carrier waves, etc. The phrase "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal.

[0251] As used herein, the phrases and terms "small diameter sensor," "miniature structured sensor," and "microsensor" are broad phrases and terms that are to be given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, a sensing mechanism having at least one dimension that is less than about 2 mm. In further embodiments, the sensing mechanism has at least one dimension that is less than about 1 mm. In some embodiments, the sensing mechanism (sensor) is less than about 0.95, 0.9, 0.85, 0.8, 0.75, 0.7, 0.65, 0.6, 0.5, 0.4, 0.3, 0.2, or 0.1 mm. In some embodiments, the largest independently measured dimension of length, width, diameter, thickness, or circumference of the sensing mechanism does not exceed about 2 mm. In some embodiments, the sensing mechanism is a coaxial sensor having a diameter of less than about 1 mm (see, e.g., U.S. Patent No. 6,613,379 to Ward et al. and U.S. Patent No. 7,497,827 to Brister et al., both of which are incorporated by reference in their entireties. In some alternative embodiments, the sensing mechanism includes electrodes deposited on a planar or substantially planar substrate, and the thickness of the implantable portion is less than about 1 mm (see, e.g., U.S. Patent No. 6,175,752 to Say et al. and U.S. Patent No. 5,779,665 to Mastrototaro et al., both of which are incorporated by reference in their entireties.

[0252] As used herein, the phrase "soft segment" is a broad phrase and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, an element of a copolymer, such as a polyurethane, polycarbonate-polyurethane, or polyurethane-urea copolymer, that imparts flexibility to the chain. The phrase "soft segment" can be further characterized as an amorphous material having a low Tg, e.g., a Tg that is typically no higher than ambient temperature or normal mammalian body temperature.

[0253] As used herein, the phrase "solid portion" is a broad phrase that is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not to be limited to any special or customized meaning), and refers to, but is not limited to, the portion of the material of the membrane that has a mechanical structure that defines a cavity, void, or other non-solid portion.

[0254] As used herein, the terms "transduce" or "transduction" and their grammatical equivalents are broad terms and are given their ordinary and customary meaning to those skilled in the art (and are not limited to any special or customized meaning), and refer to, but are not limited to, optical, electrical, electrochemical, acoustic / mechanical, or colorimetric techniques and methods. Electrochemical properties include current and / or voltage, inductance, capacitance, impedance, and electric potential. Optical properties include absorbance, fluorescence / phosphorescence, fluorescence / phosphorescence decay rate, wavelength shift, dual-wave phase modulation, bio / chemiluminescence, reflectance, light scattering, and refractive index. For example, a sensing region converts the recognition of an analyte into a semi-quantitative or quantitative signal.

[0255] As used herein, the phrase "transducing element" is a broad term and is to be given its ordinary and customary meaning to one of ordinary skill in the art (and is not limited to any special or customized meaning), and refers to, but is not limited to, an analyte recognition moiety capable of directly or indirectly facilitating detectable signal transmission corresponding to the presence and / or concentration of a recognized analyte. In one example, the transducing element is one or more enzymes, one or more aptamers, one or more ionophores, one or more capture antibodies, one or more proteins, one or more biological cells, one or more oligonucleotides, and / or one or more DNA or RNA moieties. Transdermal continuous multi-analyte sensors can be used in vivo for various lengths of time. The continuous multi-analyte sensor systems discussed herein can be transdermal devices in that a portion of the device can be inserted through the host's skin into the underlying soft tissue, while a portion of the device remains on the surface of the host's skin. In one aspect, to overcome problems associated with short-term noise or other sensor function, an embodiment uses a material that promotes the formation of a fluid pocket around the sensor, e.g., a structure such as a porous biointerface membrane or matrix that creates a space between the sensor and the surrounding tissue. In some embodiments, the sensor includes a spacer adapted to provide a fluid pocket between the sensor and the host tissue. This spacer, e.g., a biointerface material, matrix, structure, etc., as described in more detail elsewhere herein, is believed to provide oxygen and / or glucose transport to the sensor.

[0256] Membrane System The membrane systems disclosed herein are suitable for use in analyte measurement devices that contact biological fluids. For example, the membrane systems can be utilized with analyte measurement devices such as devices for monitoring and determining analyte levels in biological fluids, e.g., devices for monitoring glucose levels in individuals with diabetes. In some embodiments, the analyte measurement device is a continuous device. The analyte measurement device can employ any suitable sensing element to provide a raw signal, including, but not limited to, those involving enzymatic, chemical, physical, electrochemical, spectrophotometric, amperometric, potentiometric, polarimetric, calorimetric, radiometric, immunochemical, etc. elements.

[0257] Suitable membrane systems for the aforementioned multi-analyte systems and devices can include, for example, the membrane systems disclosed in U.S. Pat. No. 6,015,572, U.S. Pat. No. 5,964,745, and U.S. Pat. No. 6,083,523, which are incorporated herein by reference in their entireties for their teachings of membrane systems.

[0258] Generally, membrane systems include multiple domains, such as an electrode domain, an interference domain, an enzyme domain, a resistance domain, and a biointerface domain. The membrane system can be deposited on the exposed electroactive surface using known thin-film techniques (e.g., vapor deposition, spraying, electrodeposition, dipping, brush coating, film coating, droplet coating, etc.). Additional steps, such as drying, annealing, and curing (e.g., UV curing, thermal curing, moisture curing, radiation curing, etc.), can be applied following deposition of the membrane material to enhance specific properties, such as mechanical properties, signal stability, and selectivity. In a typical process, upon deposition of the resistance domain membrane, a biointerface / drug-release layer is formed having a "dry film" thickness of about 0.05 microns (μm) or less to about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 16 μm. The "dry film" thickness refers to the thickness of a cured film cast from a coating formulation using standard coating techniques.

[0259] In certain examples, the biointerface / drug-release layer is formed from a biointerface polymer, which comprises one or more membrane domains containing polyurethane and / or polyurea segments and one or more zwitterionic repeat units. In some examples, the biointerface / drug-release layer coating is formed from a polyurethaneurea having carboxyl betaine groups and nonionic hydrophilic polyethylene oxide segments incorporated into the polymer. The polyurethaneurea polymer is dissolved in an organic or non-organic solvent system according to a predetermined coating formulation, crosslinked with an isocyanate crosslinker, and cured at a moderate temperature of about 50°C. The solvent system can be a single solvent or a mixture of solvents to aid in dissolving or dispersing the polymer. The solvent can be the one selected as the polymerization medium or can be added after polymerization is complete. The solvent is selected from those with lower boiling points to facilitate drying and to be less toxic for implant applications. Examples of these solvents include aliphatic ketones, esters, ethers, alcohols, hydrocarbons, etc.

[0260] Depending on the final thickness of the biointerface / drug-release layer and the solution viscosity (related to the percent polymer solids), the coating can be applied in a single step or multiple repeated steps of a selected process, such as dipping, to build up the desired thickness. In yet another example, the bioprotective polymer is formed from a polyurethaneurea having carboxylic acid and carboxyl betaine groups incorporated into the polymer and non-ionic hydrophilic polyethylene oxide segments, where the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system in the coating formulation, crosslinked with a carbodiimide (e.g., 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) or a polycarbodiimide crosslinker), and cured at a moderate temperature of about 50°C. In one example, a polycarbodiimide crosslinker is used.

[0261] In another embodiment, the biointerface / drug-release layer coating is formed from a polyurethaneurea having sulfobetaine groups and nonionic hydrophilic polyethylene oxide segments incorporated into the polymer. The polyurethaneurea polymer is dissolved in an organic or non-organic solvent system according to a predetermined coating formulation, crosslinked with an isocyanate crosslinker, and cured at a moderate temperature of approximately 50°C. The solvent system can be a single solvent or a mixture of solvents to aid in dissolving or dispersing the polymer. The solvent can be the one selected as the polymerization medium or can be added after polymerization is complete. The solvent is selected to have a lower boiling point to facilitate drying and to be less toxic for implant applications. Examples of these solvents include aliphatic ketones, esters, ethers, alcohols, hydrocarbons, etc.

[0262] Depending on the final thickness and solution viscosity (related to the percent polymer solids) of the biointerface / drug-release layer, the coating can be applied in a single step or multiple repeated steps of a selected process, such as dipping, to build up the desired thickness. In yet another example, the biointerface polymer is formed from a polyurethaneurea having unsaturated hydrocarbon groups and sulfobetaine groups incorporated into the polymer, and nonionic hydrophilic polyethylene oxide segments, where the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system in the coating formulation and crosslinked in the presence of an initiator by heat or irradiation, including UV, LED light, electron beam, etc., and cured at a moderate temperature of about 50°C. Examples of unsaturated hydrocarbons include allyl groups, vinyl groups, acrylates, methacrylates, alkenes, alkynes, etc.

[0263] In some embodiments, tethers are used. Tethers are polymers or chemical moieties that do not participate in the (electro)chemical reactions involved in sensing but form chemical bonds with the (electro)chemically active components of the membrane. In some embodiments, these bonds are covalent bonds. In one embodiment, the tethers can be formed in solution before one or more intermediate layers of the membrane are formed, and the tethers directly connect two (electro)chemically active components to each other, or alternatively, the tethers connect the (electro)chemically active components to a polymer backbone structure. In another embodiment, the (electro)chemically active components are mixed with a crosslinker (and optionally a polymer) having a tunable length, and the tethering reaction occurs as an in situ crosslink. Tethering can be used to maintain a predetermined number of degrees of freedom of NAD(P)H for efficient enzyme catalysis, and "efficient" enzyme catalysis allows the analyte sensor to continuously monitor one or more analytes over a period of about 5 to about 15 days or longer.

[0264] membrane manufacturing Polymers can be processed by solution-based techniques, such as spraying, dipping, casting, electrospinning, vapor deposition, spin-coating, and coating. Water-based polymer emulsions can be made to form films by methods similar to those used for solvent-based materials. In both cases, evaporation of the volatile liquid (e.g., organic solvent or water) leaves a film of polymer. Crosslinking of the deposited film or layer can be carried out through the use of multifunctional reactive components by a number of methods. Liquid systems can be cured by heat, moisture, high-energy radiation, ultraviolet light, or by driving the reaction to completion, which produces the final polymer in the mold or on the substrate to be coated.

[0265] In some examples, the wetting properties of the membrane (and thus the degree of sensor drift exhibited by the sensor) can be tuned and / or controlled by creating covalent crosslinks between surface-active group-containing polymers, functional group-containing polymers, polymers with zwitterionic groups (or precursors or derivatives thereof), and combinations thereof. Crosslinking can have a substantial effect on the film structure, which in turn can affect the surface wetting properties of the film. Crosslinking can also affect the tensile strength, mechanical strength, water absorption rate, and other properties of the film.

[0266] The crosslinked polymers can have different crosslink densities. In certain embodiments, a crosslinking agent is used to promote crosslinking between layers. In other embodiments, instead of (or in addition to) the crosslinking techniques described above, heat is used to form crosslinks. For example, in some embodiments, imide and amide bonds can form between two polymers as a result of elevated temperatures. In some embodiments, photocrosslinking is performed to form covalent bonds between the polycation and polyanion layers. One major advantage of photocrosslinking is that it offers the possibility of patterning. In certain embodiments, patterning using photocrosslinking is performed to modify the film structure and therefore tailor the wetting properties of membranes and membrane systems, as discussed herein.

[0267] Polymers having domains or segments functionalized to enable crosslinking can be prepared by at least the methods discussed herein. For example, polyurethaneurea polymers having aromatic or aliphatic segments with electrophilic functional groups (e.g., carbonyl, aldehyde, anhydride, ester, amide, isocyano, epoxy, allyl, or halo groups) can be crosslinked with a crosslinker having multiple nucleophilic groups (e.g., hydroxyl, amine, urea, urethane, or thiol groups). In a further example, polyurethaneurea polymers having aromatic or aliphatic segments with nucleophilic functional groups can be crosslinked with a crosslinker having multiple electrophilic groups. In one example, a polycarbodiimide crosslinker is used. Furthermore, polyurethaneurea polymers having hydrophilic segments with nucleophilic or electrophilic functional groups can be crosslinked with a crosslinker having multiple electrophilic or nucleophilic groups. The unsaturated functional groups on the polyurethaneurea can also be used for crosslinking by reacting with a multivalent free radical agent. Non-limiting examples of suitable crosslinking agents include isocyanates, carbodiimides, glutaraldehyde, aziridines, silanes or other aldehydes, epoxies, acrylates, free radical-based agents, ethylene glycol diglycidyl ether (EGDE), poly(ethylene glycol) diglycidyl ether (PEG-DE), or dicumyl peroxide (DCP). In one embodiment, about 0.1% to about 15% w / w of crosslinking agent is added based on the total dry weight of crosslinking agent and polymer added when blending the components. In another embodiment, about 1% to about 10% w / w of crosslinking agent is added based on the total dry weight of crosslinking agent and polymer added when blending the components. In yet another embodiment, about 5% to about 15% w / w of crosslinking agent is added based on the total dry weight of crosslinking agent and polymer added when blending the components.During the curing process, it is believed that substantially all of the crosslinker reacts, leaving substantially no detectable unreacted crosslinker in the final film.

[0268] The polymers disclosed herein can be formulated into a mixture that can be drawn into a film or applied to a surface using methods such as spraying, self-assembling monolayer (SAM), painting, dip coating, vapor deposition, molding, 3D printing, slot die coating, picojet printing, piezo inkjet printing, lithography techniques (e.g., photolithography), micro- and nanopipetting printing techniques, silkscreen printing, etc. The mixture can then be cured under elevated temperatures (e.g., about 30°C to about 150°C). Other suitable curing methods can include, for example, ultraviolet light, electron beam, or gamma radiation.

[0269] In some situations, using a continuous multi-analyte monitoring system including a sensor configured with a bioprotective and / or drug-releasing membrane, it is believed that the foreign body response is a primary event surrounding the extended implantation of the implanted device and can be managed or engineered to support analyte transport rather than impede or block it. In another aspect, to extend the sensor's lifespan, one embodiment employs a material that promotes vascularized tissue ingrowth, for example, within a porous biointerface membrane. For example, tissue ingrowth into the porous biointerface material surrounding the sensor can promote sensor function over extended periods of time (e.g., weeks, months, or years). It has been observed that tissue ingrowth and formation of a tissue bed can take up to three weeks. Tissue ingrowth and tissue bed formation are believed to be part of the foreign body response. As discussed herein, the foreign body response can be engineered through the use of a porous bioprotective material that surrounds the sensor and promotes tissue and microvasculature ingrowth over time.

[0270] Thus, sensors such as those discussed in the Examples herein may include a biointerface layer. The biointerface layer may include, for example, but is not limited to, a porous biointerface material including a solid portion and interconnected cavities, as well as a drug-release layer, all of which are described in more detail elsewhere herein. The biointerface layer may be used to improve sensor function over the long term (e.g., after tissue ingrowth).

[0271] Thus, sensors such as those discussed in the examples herein can include a drug-releasing membrane that functions at least partially as or in combination with a biointerface membrane. The drug-releasing membrane can include, for example, a material including a hard-soft segment polymer having hydrophilic and optionally hydrophobic domains, all of which are described in more detail elsewhere herein and can be used to improve sensor function over time (e.g., after tissue ingrowth). In one example, a material including a hard-soft segment polymer having hydrophilic and optionally hydrophobic domains is configured to release dexamethasone or a combination of a derivative form of dexamethasone acetate and dexamethasone, such that one or more different release rates of the anti-inflammatory drug are achieved, extending the useful life of the sensor. Other suitable drug release membranes of the present disclosure include silicone polymers, polytetrafluoroethylene, expanded polytetrafluoroethylene, ethylene tetrafluoroethylene copolymers, polyolefins, polyesters, polycarbonates, biostable polytetrafluoroethylene, polyurethane homopolymers, copolymers, and terpolymers, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polyvinyl alcohol (PVA), polyvinyl acetate, ethylene vinyl acetate (EVA), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone ether (polyether ether ketone ether), and the like.ketone, PEEK), polyamides, polyurethanes and copolymers and blends thereof, polyurethaneurea polymers and copolymers and blends thereof, cellulose polymers and copolymers and blends thereof, poly(ethylene oxide) and copolymers and blends thereof, poly(propylene oxide) and copolymers and blends thereof, polysulfones and block copolymers (e.g., including diblock, triblock, alternating, random, and graft cellulose copolymers), hydrogel polymers, poly(2-hydroxyethyl methacrylate, pHEMA) and copolymers and blends thereof, hydroxyethyl methacrylate (HEMA) and copolymers and blends thereof, polyacrylonitrile-polyvinyl chloride (PCC) chloride, PAN-PVC) and copolymers and blends thereof, acrylic copolymers and copolymers and blends thereof, nylon and copolymers and blends thereof, polyvinyl difluoride, polyanhydrides, poly(l-lysine), poly(L-lactic acid), pan and copolymers and blends thereof, hydroxyapeptite and copolymers and blends thereof.

[0272] Sensing mechanism Generally, analyte sensors of the present disclosure include a sensing mechanism having, at least in part, a miniature structure (e.g., a miniature structured sensor, a microsensor, or a small diameter sensor), e.g., a coaxial or planar sensor. As used herein, "miniature structure" refers to an architecture having at least one dimension less than about 1 mm. The miniature structured sensing mechanism may be coaxial-based, or substrate-based (a flat or substantially planar substrate that may be single-sided or double-sided and may include one or more sensor elements on either the side or surface), or any other architecture. In some alternative embodiments, the term "miniature structure" can also refer to slightly larger structures, such as those having a smallest dimension greater than about 1 mm, but where the architecture (e.g., mass or size) is designed to minimize foreign body response due to size and / or mass.

[0273] The present disclosure includes sensor systems including two or more sensors, each configured to sense a different analyte. The two or more sensors may be configured to function independently or simultaneously to sense the two or more analytes simultaneously, sequentially, and / or randomly (including events that may occur independently in picoseconds, nanoseconds, milliseconds, seconds, or minutes), or in an alternating or overlapping manner. The two or more sensors of a sensor system may be communicatively coupled to electronics, such as a single transmitter or receiver. The two or more sensors of a sensor system may be communicatively coupled to separate, independent electronics.

[0274] In one embodiment of a continuous analyte monitoring system, a first single sensor is configured to continuously monitor at least a first analyte (e.g., glucose, glycerol, lactate, bilirubin, oxygen concentration, etc.) and a second, different analyte. In this embodiment, the single sensor may include a single coaxial or planar sensor configured to monitor at least a first analyte and a second analyte. In another embodiment, the first sensor is configured to monitor a first analyte and the second sensor is configured to continuously monitor a second analyte (e.g., ketones). Each of the first and second sensors may be planar, substantially planar, or coaxial, or a combination of two or more top, side, or cross-sectional shapes. In one embodiment, each of the first and second sensors is communicatively coupled to the same sensor electronics and network elements to continuously monitor and provide feedback to a device, such as a mobile device, tablet, laptop, wearable technology (clothing, jewelry, other accessories), or other Internet of Things (IoT) device, or combination of devices. In another embodiment, the first sensor and the second sensor are communicatively coupled to independent sensor electronics and network elements. Each of the first sensor and the second sensor is positioned within the subject through a skin layer in a subcutaneous layer. In another embodiment, the sensor system is configured as a monolithic sensor body including both the first sensor and the second sensor with electrodes configured to detect two or more analytes. At least one of the plurality of electrodes of the sensor system is configured to detect a first analyte, and the second plurality of electrodes is configured to detect a second analyte. The sensor system is positioned within the subject through a skin layer in a subcutaneous layer. In yet another embodiment, the sensor system includes a first sensor and a second sensor, each sensor of the sensor system including one or more fiber elements. For example, two or more sensors, such as the first sensor and the second sensor, may be electrically, mechanically, or otherwise coupled together ex vivo, in vivo, or both. Each of the first sensor and the second sensor of the sensor system is positioned within the subject through a skin layer in a subcutaneous layer.

[0275] The multi-analyte sensor devices and systems discussed herein may include elements such as on-body wearable devices, wireless communication capabilities, electronics, software, GUIs, or other elements configured to allow a continuous analyte monitoring system to continuously monitor analyte levels in a host. In response to this monitoring, various alerts and actions may be taken. As discussed herein, an "on-body" or wearable device includes a device configured to couple to a host for at least a predetermined period of time via one or more coupling elements, including sensors and / or in vivo components such as adhesives, mechanical elements, electrical elements, magnetic elements, or other combinations of elements.

[0276] sensing membrane In some embodiments, as shown in FIG. 7 , a sensing membrane is disposed on the electroactive surface of a continuous multi-analyte sensor 700 and includes one or more domains or layers. Generally, the sensing membrane functions, for example, to control the flow of biological fluid therethrough and / or to protect the sensitive area of ​​the sensor from contamination by the biological fluid. Some electrochemical enzyme-based analyte sensors generally include a sensing membrane that, for example, controls the flow of the analyte to be measured, protects the electrodes from contamination by the biological fluid, and / or provides an enzyme that catalyzes the reaction of the analyte with a cofactor. See, for example, U.S. Patent Application Publication No. 2005 / 0245799 to Brauker et al. and U.S. Patent No. 7,497,827 to Brister et al., which are incorporated by reference in their entireties.

[0277] The sensing membranes of the present disclosure can include any membrane configuration suitable for use with any analyte sensor (as described in more detail above). Generally, the sensing membranes of the present disclosure include one or more domains, all or some of which can be adhered or deposited on the analyte sensor, as will be understood by those skilled in the art. In one example, the sensing membrane generally provides one or more of the following functions, as described in the above-referenced U.S. Patent No. 7,497,827 to Brister et al.: 1) protection of exposed electrode surfaces from the biological environment; 2) analyte diffusion resistance (limitation); 3) catalysis to enable enzymatic reactions; 4) limiting or blocking interfering species; and 5) hydrophilicity at the electrochemically reactive surface of the sensor interface. The sensing membranes discussed herein may also include one or more adhesive layers positioned between two adjacent membrane layers. In one example, the one or more adhesive layers can increase the robustness and adhesion, thus improving the integrity of the sensing membrane. In various embodiments, the adhesive layer may include silane groups, polyvinyl alcohol (PVA), glutaraldehyde, or silicone-based or silicone-containing materials, or other adhesives or combinations of adhesives.

[0278] Electrode Domain In some embodiments, the membrane system includes an optional electrode domain. The electrode domain is provided to ensure that the electrochemical reaction between the electroactive surface of the working electrode and the electroactive surface of the reference electrode is promoted and / or enhanced, and therefore the electrode domain is positioned closer to the electroactive surface than the enzyme domain. In some embodiments, the electrode domain includes a semi-permeable coating that maintains a layer of water on the electrochemically reactive surface of the sensor; for example, a wetting agent in a binder material can be used as the electrode domain, which allows for complete transport of ions in an aqueous environment. The electrode domain can also help stabilize sensor operation by overcoming electrode activation and drift problems caused by insufficient electrolyte. The material forming the electrode domain can also protect against pH-mediated damage that can result from the formation of large pH gradients due to the electrochemical activity of the electrodes.

[0279] In one embodiment, the electrode domain comprises a flexible, water-swellable hydrogel film having a "dry film" thickness of about 0.05 microns or less to about 20 microns or more. In some examples, the "dry film" thickness is about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 microns to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns. In further embodiments, the "dry film" thickness is about 2, 2.5, or 3 microns to about 3.5, 4, 4.5, or 5 microns. "Dry film" thickness refers to the thickness of a cured film cast from a coating formulation by standard coating techniques.

[0280] In certain embodiments, the electrode domains are formed from a curable mixture of a urethane polymer and a hydrophilic polymer. A particularly preferred coating is formed from a polyurethane polymer having carboxylate functional groups and nonionic hydrophilic polyether segments, where the polyurethane polymer is crosslinked with a water-soluble carbodiimide (e.g., 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) or a polycarbodiimide crosslinker) in the presence of polyvinylpyrrolidone and cured at a moderate temperature of about 50°C.

[0281] In some embodiments, the electrode domains are deposited by spray coating or dip coating the electroactive surface of the sensor. In further embodiments, the electrode domains are formed by dip coating the electroactive surface in an electrode solution and curing the domain for about 15 to about 30 minutes at a temperature of about 40 to about 55°C (and can be accomplished under vacuum, e.g., 20 to 30 mmHg). In embodiments where dip coating is used to deposit the electrode domains, an insertion speed of about 1 to about 3 inches / minute, a residence time of about 0.5 to about 2 minutes, and a withdrawal speed of about 0.25 to about 2 inches / minute provide a functional coating. However, values ​​other than those listed above may be acceptable or even desirable in certain embodiments, depending, for example, on viscosity and surface tension, as will be understood by those skilled in the art. In one embodiment, the electroactive surface of the electrode system is dip coated in one coat and cured under vacuum at 50°C for 20 minutes.

[0282] As discussed herein, the insertable portion coating composition applied to the insertable portion may have a viscosity of about 10 centipoise (cP) to about 350 cP. In another example, the insertable portion coating composition as applied to the insertable portion has a viscosity of about 20 cP to about 200 cP. In yet another example, the insertable portion coating composition as applied to the insertable portion has a viscosity of about 30 cP to about 300 cP.

[0283] Although separate electrode domains are described herein, in some examples, sufficient hydrophilicity may be provided in the interference domain and / or the enzyme domain (depending on which domain is adjacent to the electroactive surface) to provide complete transport of ions in an aqueous environment (e.g., without a separate electrode domain).

[0284] Unmediated System Interference Domain In some embodiments, an optional interference domain is provided for the non-mediated systems disclosed herein, and generally comprises a polymer domain that restricts the flow of one or more interferents to the working electrode. In some embodiments, the interference domain functions as a molecular sieve that allows the analyte and other substances measured by the electrode to pass through, but prevents the passage of other substances, including interferents such as ascorbate and urea (see U.S. Patent No. 6,001,067 to Shults). Some known interferents for glucose oxidase-based electrochemical sensors include acetaminophen, ascorbic acid, bilirubin, cholesterol, creatinine, dopamine, ephedrine, ibuprofen, L-dopa, methyldopa, salicylic acid, tetracycline, tolazamide, tolbutamide, triglycerides, and uric acid.

[0285] Some polymer types that can be utilized as base materials for the interference domain include, for example, polyurethanes, polymers with pendant ionic groups (e.g., polyurethane-zwitterionic), NAFION™, chitosan, cellulose, or alternating layers of polyallylamine and polyacrylate acid, as well as polymers with controlled pore sizes. In one example, the interference domain comprises a thin, hydrophobic membrane that is non-swelling and limits the diffusion of low molecular weight species. The interference domain is permeable to relatively low molecular weight substances such as hydrogen peroxide, but restricts the passage of higher molecular weight substances, including glucose and ascorbic acid. In one example, the interference domain comprises a charged species (e.g., a polymer with pendant charged groups as disclosed herein) that functions to interact with one or more species of the sensing system, such as a cofactor, to reduce or eliminate migration from the domain.

[0286] Other systems and methods for reducing or eliminating interfering species that can be applied to the membrane systems of the present disclosure are described in U.S. Patent No. 7,816,004 to Muradov et al., U.S. Patent Application Publication No. 2005 / 0176136 to Burd et al., U.S. Patent No. 7,081,195 to Simpson et al., and U.S. Patent No. 7,715,893 to Kamath et al. In alternative embodiments, no separate interference domains are included.

[0287] In one embodiment, the interference domain is deposited on the electrode domain (or directly on the electroactive surface if no separate electrode domain is included) with a dry film domain thickness of about 0.05 microns or less to about 20 microns or more. In other embodiments, the dry film domain thickness is about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 microns to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns. In further embodiments, the dry film domain thickness is about 2, 2.5, or 3 microns to about 3.5, 4, 4.5, or 5 microns. Thicker membranes may also be useful, but in some embodiments thinner membranes have less effect on the rate of diffusion of hydrogen peroxide from the enzyme domain to the electroactive species.

[0288] As discussed herein, when two or more sensors are employed in a sensor system, each sensor optionally includes an interference domain configured to prevent the same interferent(s) from permeating the membrane. In another example, when two or more sensors are employed in a sensor system, each sensor optionally includes an interference domain configured to prevent different or overlapping but distinct interferent(s) from permeating the membrane.

[0289] Mediated System Interference Domain Some second-generation electrochemical analyte sensor technologies (second generation) utilize immobilized redox mediators to reduce the overpotential required to detect an analyte. This reduction can be significant in contrast to the typical operating potential of first-generation electrochemical analyte sensors (e.g., sensors that operate based on the principle of hydrogen peroxide detection on a catalytic metal surface). As an example, second-generation analyte sensors can be biased between +0.0 V and +0.3 V, whereas first-generation sensors can be biased between +0.5 V and +0.8 V. However, despite this reduction in operating potential and reduced susceptibility to electroactive interference from endogenous and pharmaceutical agents, these second-generation sensors can still succumb to the excessive influence of residual interferences.

[0290] For example, exemplary second-generation analyte sensors utilize polymer-bound covalent redox mediators (e.g., polyvinylimidazole (PVI)-Os(4,4'-dimethyl-2,2'-bipyridine)2Cl]+ / 2+) that reduce the overpotential required for enzymatic detection of target analytes. Examples of such mediator-based sensors include systems in which excessive signal contributions resulting from the presence of cocirculating endogenous electroactive species can occur, as evidenced by product-labeled alerts for large amounts of ascorbic acid / ascorbate ion (i.e., vitamin C), resulting in false hyperglycemia alerts and the like. Charge-selective membranes or further reduction of overpotentials can mitigate such interference effects, but can result in significant impacts on sensitivity and signal-to-noise figures of merit. Thus, currently, mediated electrochemical analyte sensing systems continue to exhibit excessive signal contributions from endogenous metabolites such as ascorbic acid.

[0291] Thus, the present disclosure includes mediator system interference domains developed for second-generation sensor systems, whether they are continuous glucose monitoring systems or multi-analyte monitoring systems, e.g., ketone-glucose monitoring, in which the mediator system interference domain includes one or more oxidase enzymes that induce enzymatic decomposition of an interfering metabolite or ensemble of metabolites into a peroxide product, e.g., hydrogen peroxide. The present disclosure provides domains that include oxidase enzymes alone or in combination with any of the conventional membranes (electrodes, enzymes, resistive domains / layers) used with indwelling second-generation (e.g., mediator) analyte sensors. Exemplary oxidase enzymes include, for example, ascorbic acid oxidase or uric acid oxidase configured to catalytically convert undesired interfering species (e.g., ascorbic acid, uric acid) to a hydrogen peroxide product, which exhibits significantly less sensitivity to bias voltages / overpotentials conventionally applied in second-generation sensing systems. This conversion provides a reduction in the overall concentration of the interfering species at the electrode surface (e.g., exchanging the flux of the interfering species for the flux of hydrogen peroxide) and provides a less detrimental effect on the sensed signal than would otherwise be possible in the presence of the interfering species. The interfering domain may be used alone or in combination with other interfering domains, membranes, or domains, which may comprise, for example, the same polymer(s) matrix without one or more oxidase enzymes, peroxidase, or catalase.

[0292] In some embodiments of the mediator system interference domain, the oxidase enzyme can be combined with one or more peroxidase or peroxidase-like enzymes (e.g., horseradish peroxidase, catalase) to further cleave the generated hydrogen peroxide product from the oxidase enzyme(s), thereby inactivating the peroxide electroactive material and rendering it unable to undergo redox reactions at the electrode surface. The present disclosure includes the placement of the mediator system interference domain in one or more of an electrode domain, an enzyme domain, a resistor domain, and an interference membrane. The present disclosure includes the placement of the mediator system interference domain in one or more of an electrode domain, an enzyme domain, a resistor domain, and an interference domain.

[0293] Thus, in one example, an exemplary ketone / glucose multi-analyte sensor system can be used to include a mediator system interference domain comprising at least one of ascorbate oxidase, urate oxidase, horseradish peroxidase, or catalase present in an enzyme domain comprising a dehydrogenase enzyme (e.g., beta-hydroxybutyrate dehydrogenase), an NADH-acting enzyme (e.g., diaphorase, NAD(P)H dehydrogenase), a redox polymer (e.g., PVI-Os(bpy)Cl), and optionally a cofactor (e.g., NAD, NADP, if required), optionally crosslinked using, for example, PEG-DGE, CDI, or a polycarbodiimide crosslinker. A resistance domain of a biocompatible material or a hydrophobic / hydrophilic polymer blend, for example, PVP / PEG-DGE, can be applied over the enzyme domain / mediator system interference domain.

[0294] In another example, using an exemplary ketone / glucose multi-analyte sensor system, a mediator system interference domain comprising at least one of ascorbate oxidase, urate oxidase, horseradish peroxidase, or catalase resides in a resistance domain comprising a biocompatible material or a hydrophobic / hydrophilic polymer blend (e.g., PVP / PEG-DGE). A separate enzyme domain may be positioned adjacent to the mediator system interference domain present in the resistance domain, proximal to the electrode, and the enzyme domain may comprise a dehydrogenase enzyme (e.g., beta-hydroxybutyrate dehydrogenase, an NADH-acting enzyme (e.g., diaphorase, NAD(P)H dehydrogenase), a redox polymer (e.g., PVI-Os(bpy)Cl), optionally a cofactor (e.g., NAD+, NADP+, if required), and may optionally be crosslinked, for example, using PEG-DGE or a polycarbodiimide crosslinker.

[0295] In another example, using an exemplary ketone / glucose multi-analyte sensor system, a mediator interference domain comprising at least one of ascorbate oxidase, urate oxidase, horseradish peroxidase, or catalase is present between the enzyme domain and at least one electrode surface. A resistive domain of a biocompatible material or a hydrophobic / hydrophilic polymer blend, for example, PVP / PEG-DGE, can be applied over the enzyme domain.

[0296] In other examples, as discussed further herein, an exemplary mediator-free ketone or ketone / glucose multi-analyte sensor system is provided. In one example, an exemplary ketone or ketone / glucose multi-analyte sensor system is provided that does not include a metal-based mediator, e.g., an osmium complex of a biimidazole and / or imidazole ligand. In one example, an exemplary ketone or ketone / glucose multi-analyte sensor system is provided that does not include a metal-based mediator, e.g., an osmium complex of a biimidazole and / or imidazole ligand, configured to provide an amperometric signal at applied voltages greater than +0.2 V, greater than or equal to +0.3 V, greater than or equal to +0.4 V, greater than or equal to +0.5 V, or greater than or equal to +0.6 V. In one example, an exemplary ketone or ketone / glucose multi-analyte sensor system is provided that does not include a metal-based mediator and includes an interference layer. In one embodiment, an exemplary ketone or ketone / glucose multi-analyte sensor system is provided that does not include a metal-based mediator, e.g., an osmium complex of a biimidazole and / or imidazole ligand, and includes an interference layer, configured to provide an amperometric signal at applied voltages greater than +0.2 V, greater than or equal to +0.3 V, greater than or equal to +0.4 V, greater than or equal to +0.5 V, or greater than or equal to +0.6 V.

[0297] Transducer domain In one embodiment, the membrane system further comprises a transducer domain, e.g., an enzyme, RNA, DNA, aptamer, binding protein, etc., located more distally from the electroactive surface than the interference domain (or the electrode domain when no separate interference is included). In some embodiments, the transducer domain is deposited directly on the electroactive surface (when neither the electrode nor the interference domain is included). In one embodiment, the transducer domain provides an enzyme that catalyzes the reaction of the analyte and its co-reactant, as described in more detail below. In some embodiments, the transducer domain comprises glucose oxidase. However, other oxidases, e.g., galactose oxidase or uricase oxidase, can also be used.

[0298] For an enzyme-based electrochemical glucose sensor to function effectively and accurately, the sensor response must be limited by neither enzyme activity nor co-reactant concentration. Because enzymes, including glucose oxidase, can undergo inactivation as a function of time even under ambient conditions, this behavior is compensated for by forming an enzyme domain. In some embodiments, the enzyme domain is preferably comprised of an aqueous dispersion of a colloidal polyurethane polymer containing the enzyme. However, in alternative embodiments, the enzyme domain is comprised of an oxygen-enhancing material, such as at least one of silicone or fluorocarbon, to provide an excess supply of oxygen to ensure that oxygen does not limit the sensing reaction. In some embodiments, the enzyme is immobilized within the enzyme domain. See U.S. Patent No. 7,379,765 to Petisce et al.

[0299] In one embodiment, the transduction element domain is deposited on the interference domain at a "dry film" domain thickness of about 0.05 microns or less to about 20 microns or more. In other embodiments, the dry film domain thickness is about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 microns to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns. In further embodiments, the dry film domain thickness is about 2, 2.5, or 3 microns to about 3.5, 4, 4.5, or 5 microns. "Dry film" thickness refers to the thickness of a cured film cast from a coating formulation by standard coating techniques, including post-cure of the film.

[0300] However, in some embodiments, the transducer domain is deposited directly onto the electrode domain or onto the electroactive surface. In some embodiments, the transducer domain is deposited by spray or dip coating, slot die coating, 3D printing, picojet printing, piezo inkjet printing, or the like. In further embodiments, the transducer domain is formed by dip coating the electrode domain into a transducer domain solution and curing the transducer domain at a temperature of about 40 to about 55°C for about 15 to about 30 minutes (and can be achieved under vacuum, e.g., 20 to 30 mmHg). In embodiments where dip coating is used to deposit the transducer domain at room temperature, an insertion speed of about 1 inch / min to about 3 inches / min, a dwell time of about 0.5 minutes to about 2 minutes, and a withdrawal speed of about 0.25 inches / min to about 2 inches / min provides a functional coating. However, values ​​other than those listed above may be acceptable or even desirable in certain embodiments, depending, for example, on viscosity and surface tension, as will be understood by those skilled in the art. In one example, the transducer domain is formed by dip-coating twice in a coating solution (i.e., forming two layers) and curing under vacuum at 50°C for 20 minutes. However, in some examples, the transducer domain can be formed by dip-coating and / or spray-coating one or more layers at a predetermined concentration, insertion rate, dwell time, withdrawal rate, and / or desired thickness of the coating solution. In yet another example, the transducer layer is formed from multiple intermediate layers deposited by self-assembled monolayers (SAMs), typically formed by immersion in a solution that promotes surface chemistry. The substrate may be placed in the solution for a period of about 30 minutes to about 24 hours to form the desired transducer layer to a predetermined thickness. In another example, the substrate may be placed in the solution for a period of about 1 hour to about 18 hours. In another example, the substrate may be placed in the solution for a period of about 3 hours to about 12 hours.

[0301] In yet other embodiments, the conversion element layer is formed from multiple intermediate layers, one or more of which may be varied in various aspects, such as chemistry (composition), thickness, or other mechanical, electrical, biological, or other material properties, either alone or in combination, to achieve a target electron mobility or range of electron mobilities through each intermediate layer.

[0302] Resistance Domain In one example, the membrane system includes a resistance domain located more distally from the electroactive surface than the enzyme domain. While the following description is directed to a resistance domain for a glucose sensor, the resistance domain can be modified to facilitate detection of other analyte and co-reactant concentration(s). In one example, the resistance domain is configured to control the flux of oxygen through the membrane. In another example, the resistance domain is configured to control the flux of an analyte or co-reactant other than oxygen through the membrane. In yet another example, the resistance domain is configured to control the flux of two or more analytes through the membrane.

[0303] Immobilized enzyme-based glucose sensors that use oxygen as a coreactant are supplied with a non-rate-limiting excess of oxygen so that the sensor responds linearly to changes in glucose concentration but not to changes in oxygen concentration. Specifically, when the glucose monitoring reaction is oxygen-limited, linearity is not achieved above a minimum concentration of glucose. Without a semipermeable membrane placed over the enzyme domain to control the flux of glucose and oxygen, a linear response to glucose levels can only be obtained for glucose concentrations up to about 40 mg / dL. However, in a clinical setting, a linear response to glucose levels up to at least about 400 mg / dL is desirable.

[0304] In one embodiment, the resistance domain includes a semipermeable membrane that controls the flux of oxygen and glucose to the underlying enzyme domain, making oxygen a non-rate-limiting excess. As a result, the upper linearity limit of glucose measurement is extended to values ​​much higher than that achieved without the resistance domain. In one embodiment, the resistance domain exhibits an oxygen-to-glucose permeability ratio of about 50:1 or less to about 400:1 or more. In a further embodiment, the oxygen-to-glucose permeability ratio is about 200:1.

[0305] In an alternative embodiment, a lower ratio of oxygen to glucose may be sufficient to provide excess oxygen by using a high oxygen solubility domain (e.g., a silicone or fluorocarbon-based material or domain) to enhance oxygen supply / transport to the conversion element domain. Also, if more oxygen is supplied to the enzyme, more glucose may be supplied to the conversion element without creating an oxygen-limiting excess. In an alternative embodiment, the resistance domain is formed from a silicone composition such as that described in U.S. Patent Application Publication No. 2005 / 0090607 to Tapsak et al.

[0306] In one embodiment, the resistance domain includes a polyurethane membrane having both hydrophilic and hydrophobic regions. The hydrophilic and hydrophobic regions may be used in combination to control the diffusion of one or more analytes (e.g., glucose, oxygen, ketones, lactate, uric acid, etc.) to the analyte sensor. A suitable hydrophobic polymer component is polyurethane or polyetherurethaneurea. Polyurethane is a polymer produced by the condensation reaction of a diisocyanate with a difunctional hydroxyl-containing material. Polyurethaneurea is a polymer produced by the condensation reaction of a diisocyanate with a difunctional amine-containing material. In polyurethane and polyurethaneurea polymers, either the hard or soft segments can include multiple different chemical structures; for example, the soft segment can include hydrophobic and hydrophilic segments.

[0307] Examples of diisocyanates useful as hard segment components of the polyurethane or polyurethaneurea polymers of the present disclosure include aliphatic diisocyanates containing about 4 to about 8 methylene units. Diisocyanates containing alicyclic moieties can also be useful in preparing the polymer and copolymer components of the membranes of the present disclosure. The material forming the base of the hydrophobic matrix of the resistance domain may be selected to be sufficiently permeable to allow relevant compounds to pass through it, for example, to allow oxygen molecules from the test sample to pass through the membrane to reach the active enzyme or electrochemical electrode. Examples of materials that can be used to prepare non-polyurethane-type membranes include vinyl polymers (including polyvinylimidazole and polyvinylpyridine), inorganic polymers such as polyethers, polyesters, polyamides, polysiloxanes, and polycarbosiloxanes, natural polymers such as cellulosic and protein-based materials, and mixtures or combinations thereof. In some examples, these non-polyurethane-type membranes contain a crosslinker in addition to the base polymer to improve mechanical properties and / or tailor mass transport of analytes or other species. In some embodiments, the resistive domain may be polyvinyl butyral (PVB). In other embodiments, the base polymer may be a segmented block copolymer. In another embodiment, the hard segment may be about 15% to about 75% by weight. In yet another embodiment, the hard segment may be about 25% to about 55% by weight. In yet another embodiment, the hard segment may be about 35% to about 45% by weight. For example, the base polymer may include polyurethane and / or polyurea segments and one or more of polycarbonate, polydimethylsiloxane (PDMS), polyether, fluorine-modified segments, perfluoropolyol, or polyester segments. In other embodiments, the base polymer may be a polyurethane copolymer selected from the group including polyether-urethane-urea, polycarbonate-urethane, polyether-urethane, polyester-urethane, and / or copolymers thereof.

[0308] In one embodiment, the hydrophilic polymer component of the resistance domain is polyethylene oxide (PEO). For example, one useful hydrophobic-hydrophilic copolymer component is a polyurethane polymer containing about 1% to about 50% by weight of polyethylene oxide. In one embodiment, the resistance domain contains 5% to about 30% by weight of polyethylene oxide (PEO). In another embodiment, the resistance domain contains about 10% to about 40% by weight of PEO. The polyethylene oxide portion of the copolymer is thermodynamically driven to separate from the hydrophobic portion of the copolymer and the hydrophobic polymer component. The polyethylene oxide-based soft segment portion of the copolymer used to form the final blend influences the water uptake and subsequent glucose permeability of the membrane.

[0309] In one embodiment, one or more of NBDI, IPDI, TDI, MPDI, HMDI, MDI, 1,3-H6XDI, 1,4-H6XDI, CHDI, PPDI, TODI, or HDI diisocyanate are used to form various polyurethanes and polyurethane-ureas for the resistor domain and / or other sensor domains. In one embodiment, the polyurethanes and polyurethane-ureas have a soft segment that is aliphatic or amphiphilic. In one embodiment, the soft segment comprises a diol, diamine, diester, or dicarbonate. In one embodiment, the soft segment comprises two or more of a diol, diamine, diester, or dicarbonate.

[0310] In one embodiment, one or more of NBDI, IPDI, TDI, MPDI, HMDI, MDI, 1,3-H6XDI, 1,4-H6XDI, CHDI, PPDI, TODI, and HDI are reacted with one or more dicarbonates, polyethers, polyesters, polyalkyl-diols, or polyalkyl-diamines.

[0311] In one embodiment, one or more of NBDI, IPDI, TDI, MPDI, HMDI, MDI, 1,3-H6XDI, 1,4-H6XDI, CHDI, PPDI, TODI, and HDI are reacted with a C5 or C6 dicarbonate, such as U90 OXYMER™, or polyhexamethylene carbonate glycol (PHA). In one embodiment, NBDI, IPDI, TDI, MPDI, HMDI, MDI, 1,3-H6XDI, 1,4-H6XDI, CHDI, PPDI, TODI, and HDI, or a mixture thereof, are reacted with a C5 or C6 dicarbonate, such as U90 OXYMER™, and one or more polyethers, polyesters, polyalkyl-diols, or polyalkyl-diamines. In one embodiment, the dicarbonate is sterically branched to increase the Tg of the soft segment, e.g., to provide a Tg near body temperature.

[0312] In one embodiment, one or more of the following hard segment diisocyanates are reacted with one or more of polyethers, such as polytetramethylene oxide (PTMO), polypropylene oxide (PPO), polyethylene glycol (PEG), and polybutadiene diol (PBU), either alone or in combination with polydimethylpolysiloxane (PDMS). In one embodiment, the same polyether with different molecular weights (Mw) is used. In one embodiment, two or more polyethers with the same or different Mw are used. In one embodiment, one or more polyethers with the same or different Mw are used in combination with one or more PDMS polymers with the same or different Mw. Without being bound by any particular theory, it has been observed that as the molecular weight of the soft segment decreases, phase mixing of the different soft segment components increases. In one embodiment, it has been observed that higher molecular weight soft segments result in the formation of a rich phase, likely due to, among other things, entropy contributions.

[0313] In one embodiment, one or more hard segment diisocyanates of NBDI, IPDI, TDI, MPDI, HMDI, MDI, 1,3-H6XDI, 1,4-H6XDI, CHDI, PPDI, TODI, HDI are reacted with one or more polyesters, such as polyethylene adipate glycol (PEA), polytetramethylene adipate glycol (PBA), alone or in combination with one or more polyethers, polyalkyl-diols, or polyalkyl-diamines.

[0314] In one embodiment, NBDI, IPDI, TDI, MPDI, HMDI, MDI, 1,3-H6XDI, 1,4-H6XDI, CHDI, PPDI, TODI, HDI, or mixtures thereof are reacted with one or more polyalkyl-diols, alone or in combination with one or more polycarbonates, polyethers, polyesters, or polyalkyl-diamines.

[0315] In one embodiment, the resistive domain is deposited directly onto the electrode surface or onto one or more layers of the enzyme domain to yield a resistive domain thickness of about 0.05 microns or less to about 20 microns or more. In another embodiment, the total resistive domain thickness is about 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 1, 1.5, 2, 2.5, 3, or 3.5 microns to about 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 19.5 microns. In another embodiment, the total resistive domain thickness is about 2, 2.5, or 3 microns to about 3.5, 4, 4.5, or 5 microns. In some embodiments, the resistive domain is deposited onto the enzyme domain by spray or dip coating, slot die coating, 3D printing, picojet printing, or piezo inkjet printing. In certain embodiments, spray coating is a deposition technique. Because the spraying process atomizes and mistizes the solution, most or all of the solvent evaporates before the coating material settles on the underlying domain, thereby minimizing contact between the solvent and the enzyme. One additional advantage of spray coating the resistive domain as described in this disclosure includes the formation of a membrane system that substantially blocks or resists ascorbate, a known electrochemical interferent in hydrogen peroxide-measuring glucose sensors. Without wishing to be bound by theory, it is believed that a structural morphology characterized by substantial impermeability to ascorbate is formed during the process of depositing the resistive domain as described in this disclosure.

[0316] Heterocyclic Resistance Domain and Cofactor Immobilization or Retention Domain In one embodiment, the cofactor and enzyme are present in an enzyme and / or resistor domain comprising a domain, e.g., polyvinylpyridine, polyvinylpyridine-co-styrene, polyvinylpyridine copolymers with vinyl and (meth)acrylic monomers, poly(styrene-co-acrylonitrile), polyvinylimidazole, or polyvinylimidazole copolymers with vinyl and (meth)acrylic monomers, and / or provided as a layer adjacent to the electrode domain or electrode surface. As used herein, "polyvinylpyridine" encompasses poly(2-vinylpyridine), 3-vinylpyridine, 3-vinylpyridine, and alkyl-substituted derivatives thereof. Blends and / or graphs of the above polymers can be used. Blends and / or graphs of the above polymers with chitosan, amphiphilic or aliphatic polyurethanes or polyurethaneureas, polyols (e.g., PEG, PTMO), or zwitterionic polymers can be used. In some embodiments, polystyrene copolymers with vinyl monomers containing electron-withdrawing groups, such as nitriles, can be used. In some embodiments, vinyl polymers with benzene and nitrile functional groups can be used.

[0317] In one embodiment, the cofactor and enzyme are present within domains, e.g., enzyme and / or resistance domains comprising at least partially crosslinked poly(4-vinylpyridine), polyvinylpyridine-co-styrene, polyvinylpyridine copolymers with vinyl and (meth)acrylic monomers, poly(styrene-co-acrylonitrile), polyvinylimidazole, or polyvinylimidazole copolymers with vinyl and (meth)acrylic monomers are used as resistance domains and / or provided as layers adjacent to electrode domains or electrode surfaces. In one embodiment, poly(4-vinylpyridine), polyvinylpyridine-co-styrene, polyvinypyridine copolymers with vinyl and (meth)acrylic monomers, poly(styrene-co-acrylonitrile), polyvinylimidazole, or polyvinylimidazole copolymers with vinyl and (meth)acrylic monomers, with or without crosslinking, provide for immobilization or retention of one or more cofactors in the resistance domain. In one embodiment, the immobilization or retention of the cofactor is via covalent bonding with functional groups of the polymer. In another embodiment, the immobilization or retention of the cofactor is achieved through non-covalent interactions, for example, through equilibration with functional groups on the polymer.

[0318] Examples of cofactor immobilization via non-covalent interactions with polymers include NAD+ with cationic polymers (e.g., chitosan, quaternized PVPy, polyzwitterionic polymers, etc.) and / or polymers containing boronic acid functional groups. Thus, in one example, the cofactor and enzyme are present in a domain, e.g., an enzyme and / or resistance domain, comprising a polymer with pendant boronic acid groups that provide strong, dynamic covalent binding with the diol functional group on a cofactor, e.g., NAD, at a specific pH, allowing for immobilization or retention of NAD(H). Thus, in one example, the 1,2 diol-containing ribose ring structure of the NADH and NAD+ structures is used to associate and / or bind to one or more boronic acid functional groups through at least covalent interactions, as shown in Scheme 1a and Scheme 1b for the NADH form. Similar covalent interactions are envisioned for NAD+ forms.

[0319] [ka]

[0320] In one embodiment, the boronic acid polymer structure and coating solution pH are adjusted to provide sufficient association of the NAD / NADH structures to reduce or eliminate migration from the polymer membrane. In one embodiment, the boronic acid polymer includes styrene polymers, styrene copolymers (e.g., with acrylics, acrylates, acrylamides, olefins, cyclic olefins), naphthyl, anthracenyl polymers, and copolymers thereof. In one embodiment, the boronic acid polymer is at least partially crosslinked.

[0321] In some embodiments, the domains are configured to repel cofactors, for example, the RL functions to "repel" NAD(H) from passing through, thereby attenuating its movement from the EZL.

[0322] In another embodiment, NAD is tethered to a domain or to an electrode surface. In another embodiment, NAD is directly tethered to a domain or directly bound to an electrode surface. In another embodiment, NAD is bound to an electrode surface using an electron transfer agent. In one embodiment, the free amine of the adenine group of NAD(H) is extended with an alkyl chain bearing a primary amine to provide EDC or (sulfo-)NHS coupling chemistry with the -COOH group on the mediator, as shown in Scheme 3, which shows a modified NAD cofactor with an extended free -NH2 bound to one of the -COOH groups on a PQQ (pyrroloquinoline quinone) mediator.

[0323] In another embodiment, the free amine of the adenine group of NAD(H) is extended with an alkyl chain bearing a primary amine to provide EDC or (sulfo-)NHS coupling chemistry with the HBDH enzyme.

[0324] In one example, the modified NAD+ cofactor has an extended free -NH2 that can be easily crosslinked to one of the -COOH groups in a PQQ (pyrroloquinoline quinone) mediator, which has another -COOH group that can then be crosslinked to a polymer backbone, an enzyme, or directly onto an electrode surface.

[0325] In one embodiment, the cofactor and enzyme are present in a domain, e.g., an enzyme and / or resistance domain, comprising an amphiphilic polyurethane or polyurethaneurea polymer as disclosed above for the biointerface / drug-release layer, where the aliphatic polyurethane or polyurethaneurea has about 20-40 wt. % hard segment content, about 10-30 wt. % polysiloxane segments, and about 15-40 wt. % polyglycol segments. In one embodiment, the amphiphilic polyurethane or polyurethaneurea also contains 0-25 wt. % polyvinylpyrrolidone polymer. In one embodiment, the amphiphilic polyurethane or polyurethaneurea also contains 0-25 wt. % polyvinylpyridine or alkylated or polyol-substituted pyridine polymer. Such amphiphilic polyurethane or polyurethaneurea polymer resistance domains provided acceptable sensitivity and stability for greater than two weeks. In one embodiment, the amphiphilic polyurethane or polyurethaneurea polymer is at least partially crosslinked. Such amphiphilic polyurethane or polyurethaneurea polymer resistance domains provided sensitivity and stability comparable to the PVPy resistance domains described above.

[0326] In one embodiment, the cofactor and enzyme are present in a domain, e.g., an enzyme and / or resistance domain, comprising an aliphatic polyurethane or polyurethane urea polymer having a hard segment content of about 40-60 wt. %, about 15-50 wt. % polytetrahydrofuran (PTMO) segments, and about 5-30 wt. % polysiloxane segments. In one embodiment, the aliphatic polyurethane or polyurethane urea polymer is at least partially crosslinked. In one embodiment, a polycarbodiimide crosslinker is used. Such aliphatic polyurethane or polyurethane urea resistance domains have demonstrated lower sensitivity and stability than amphiphilic polyurethane or polyurethane urea-based resistance domains.

[0327] In one embodiment, the cofactor and enzyme are present in a domain, e.g., an enzyme and / or resistance domain, comprising a block copolymer obtained by polycondensation of a carboxylic acid polyamide (e.g., PA6, PA11, PA12) with a polyether (e.g., polytetramethylene glycol, polyethylene glycol, PEG, polytetrahydrofuran PTMO). In one embodiment, the block copolymer obtained by polycondensation of a carboxylic acid polyamide with a polyether can be at least partially crosslinked.

[0328] In one embodiment, the cofactor and enzyme are present in a domain, e.g., an enzyme and / or resistance domain, comprising a polyvinylpyridine-poly(ethylene glycol) diglycidyl ether (PVPy-PEG-DGE) matrix. In one embodiment, the mass ratio of PEG-DGE to PVPy is about 1-10% by weight. In one embodiment, the polyvinylpyridine-PEG-DGE matrix is ​​at least partially crosslinked.

[0329] In one example, the cofactor and enzyme are present in a domain, e.g., an enzyme and / or resistance domain, comprising a water-dispersible polyurethane-zwitterionic polymer crosslinked with a carbodiimide or polycarbodiimide. Examples of such domains include those disclosed in U.S. Patent Application Publication Nos. 2017 / 0191955, 2017 / 0188922, and U.S. Patent No. 11,112,377 B2, the disclosures of which are incorporated herein by reference. In one example, the domain comprises an enzyme and a polymer comprising a polyurethane and / or polyurea segment and one or more zwitterionic repeat units. In one example, the domain comprises an enzyme and a blend of a polyurethane-based polymer and polyvinylpyrrolidone. In some examples, the enzyme domain is formed from a polyurethaneurea having carboxyl betaine groups and nonionic hydrophilic polyethylene oxide segments incorporated into the polymer, and the polyurethaneurea polymer is dissolved in an organic or non-organic solvent system according to a predetermined coating formulation and, optionally, crosslinked and / or cured. The domains described above can be from 0.01 μm to about 250 μm thick.

[0330] Depending on the embodiment, the resistance domain(s) discussed herein may be formed by any number of methods, including, but not limited to, dip coating or spray coating of any layer or layers, depending on the concentration of the solution, insertion rate, residence time, withdrawal rate, and / or desired thickness of the resulting film, or other factor or combination of factors.

[0331] Advantageously, sensors having the membrane system of the present disclosure, including an electrode domain and / or interference domain, an enzyme domain, and a resistance domain, provide a stable signal response to increasing glucose levels of about 40 to about 400 mg / dL and provide sustained functionality (at least 90% signal intensity) even at low oxygen levels (e.g., about 0.6 mg / L02). Without wishing to be bound by theory, it is believed that the resistance domain provides sufficient resistivity, or the enzyme domain provides sufficient enzyme, such that oxygen limitation is observed at much lower oxygen concentrations compared to prior art sensors.

[0332] In one example, the sensor signal has a current in the picoampere range, which is described in more detail elsewhere herein. However, the ability to generate a signal with a current in the picoampere range may depend on a combination of factors, including the electronic circuit design (e.g., A / D converter, bit resolution, etc.), the membrane system (e.g., analyte permeability through the resistive domain, enzyme concentration, and / or electrolyte availability for the electrochemical reaction at the electrode), and the exposed surface area of ​​the working electrode. For example, the resistive domain can be designed to be more or less restrictive to the analyte, depending on the design of the electronic circuit, the membrane system, and / or the exposed electroactive surface area of ​​the working electrode.

[0333] Thus, in one embodiment, the membrane system is designed with a sensitivity of about 1 pA / mg / dL to about 100 pA / mg / dL. In another embodiment, the sensitivity is about 5 pA / mg / dL to about 25 pA / mg / dL. In a further embodiment, the sensitivity is about 4 to about 7 pA / mg / dL. Without wishing to be bound by any theory, it is believed that membrane systems designed with sensitivities in the above ranges enable measurement of analyte signals in low analyte and / or low oxygen conditions. That is, conventional analyte sensors have exhibited reduced measurement accuracy in the low analyte range due to low analyte availability to the sensor and / or increased signal noise in the high analyte range due to insufficient oxygen required to react with the amount of analyte being measured. Without wishing to be bound by theory, it is believed that the membrane systems of the present disclosure, in combination with the electronic circuitry design and exposed electrochemically reactive surface area design, support the measurement of analytes in the picoampere range, which allows for improved levels of resolution and accuracy in both the low and high analyte ranges not seen in the prior art.

[0334] Although some example sensors described herein include an optional interference domain to block or reduce one or more interferents, sensors having the disclosed membrane system including an electrode domain, an enzyme domain, and a resistance domain have been shown to inhibit ascorbate without an additional interference domain. That is, the disclosed membrane system including an electrode domain, an enzyme domain, and a resistance domain has been shown to be substantially unresponsive to ascorbate within a physiologically acceptable range. Without wishing to be bound by theory, it is believed that the process of depositing the resistance domain by spray coating described herein results in a structural morphology that is substantially resistant to ascorbate.

[0335] Interference-free membrane system In general, it is believed that appropriate solvents and / or deposition methods can be selected for one or more domains of the membrane system to form one or more transition domains such that they are substantially impermeable to interferents. Thus, sensors can be constructed without separate or deposited interference domains that are unresponsive to interferents. Without wishing to be bound by theory, it is believed that simplified multilayer membrane systems, more robust multilayer fabrication processes, and reduced variability caused by the thickness of deposited micron-thin interference domains and associated oxygen and glucose sensitivity can be provided. Additionally, optional polymer-based interference domains that typically inhibit hydrogen peroxide diffusion are eliminated, thereby enhancing the amount of hydrogen peroxide that passes through the membrane system. In other examples, the interference domains can be configured to block or reduce the diffusion of one or more interfering species, including HO, acetaminophen, or other interferents or combinations of interferents.

[0336] Oxygen delivery tube As mentioned above, some sensors employ a transducer element within a membrane system through which a host's bodily fluid passes and in which analytes (e.g., glucose, ketones) in the bodily fluid react in the presence of a co-reactant (e.g., oxygen) to produce a product. This product is then measured using electrochemical methods, and the output of the electrode system thus serves as a measure of the analyte. For example, when the sensor is a glucose oxidase-based glucose sensor, the species measured at the working electrode is HO. The enzyme glucose oxidase catalyzes the conversion of oxygen and glucose to hydrogen peroxide and gluconic acid according to the following reaction: Glucose + O2 → gluconic acid + H2O2.

[0337] For each glucose molecule reacted, there is a proportional change in the product, HO, so the change in HO can be monitored to determine the glucose concentration. The oxidation of HO by the working electrode is balanced by the reduction of ambient oxygen, enzymatically generated HO, and other reducible species, for example, at the counter electrode. See Fraser, D.M., "An Introduction to In vivo Biosensing: Progress and Problems," in "Biosensors and the Body," D.M. Fraser, ed., 1997, pp. 1-56, John Wiley and Sons, New York.

[0338] In vivo, glucose concentrations are generally about 100 times higher than oxygen concentrations. As a result, oxygen is the limiting reactant in the electrochemical reaction, and when insufficient oxygen is supplied to the sensor, the sensor is unable to accurately measure glucose concentrations. Therefore, reduced sensor function or inaccuracy is believed to be the result of problems in the availability of oxygen to the enzyme and / or electroactive surface.

[0339] Thus, in an alternative embodiment, an oxygen conduit (e.g., a high oxygen solubility domain formed from silicone or a fluorochemical or perfluorocarbon compound) is provided that extends from the ex vivo portion of the sensor to the in vivo portion of the sensor to increase the availability of oxygen to the enzyme. The oxygen conduit can be formed as part of the coating (insulating) material or can be a separate conduit associated with the assembly of wire(s) that form the sensor.

[0340] In some embodiments, one or more domains of the sensing membrane are formed from materials such as silicone, polytetrafluoroethylene, ethylene tetrafluoroethylene copolymer, polyolefin, polyester, polycarbonate, biostable polytetrafluoroethylene, homopolymer, copolymer, terpolymer of polyurethane, polypropylene (PP), polyvinylchloride (PVC), polyvinylidene fluoride (PVDF), polybutylene terephthalate (PBT), polymethylmethacrylate (PMMA), polyether ether ketone (PEEK), polyurethane, cellulosic polymers, poly(ethylene oxide), poly(propylene oxide), and copolymers and blends thereof, polysulfone, and block copolymers thereof (e.g., diblock, triblock, alternating, random, and graft copolymers). US Patent Application Publication No. 2005 / 0245799 to Brauker et al., which is incorporated herein by reference in its entirety, describes biological interfaces and sensing membrane configurations and materials that may be applied to the sensors of the present disclosure.

[0341] The sensing membrane can be deposited on the electroactive surface of the electrode material using known thin-film or thick-film techniques (e.g., spraying, electrodeposition, dipping, etc.). Note that the sensing membrane surrounding the working electrode need not be of the same structure as the sensing membrane surrounding the reference electrode, etc. For example, the transducer domain deposited on the working electrode does not necessarily need to be deposited on the reference and / or counter electrodes.

[0342] In one example, as described in detail herein and understood by one of ordinary skill in the art, the sensor is an enzyme-based electrochemical sensor, where the working electrode measures hydrogen peroxide produced by an enzyme-catalyzed reaction of glucose to be detected, generating a measurable electronic current (e.g., glucose detection utilizing glucose oxidase produces hydrogen peroxide as a by-product, and HO reacts with the surface of the working electrode to release two protons (2H + ), two electrons (2e - ), and one molecule of oxygen (O), which generates an electronic current that is detected. In some embodiments, one or more potentiostats are used to monitor the electrochemical reaction at the electroactive surface of the working electrode. A potentiostat applies a constant potential to the working electrode and its associated reference electrode to determine the current produced at the working electrode. The current produced at the working electrode (and flows through the circuit to the counter electrode) is substantially proportional to the amount of HO that diffuses to the working electrode. The output signal is typically a raw data stream used to provide, for example, a host or physician with a useful value of the measured analyte concentration in the host, e.g., the raw signal processed by an algorithm before displaying the value.

[0343] Some alternative analyte sensors that can benefit from the systems and methods of the present disclosure are described, for example, in U.S. Pat. No. 5,711,861 to Ward et al., U.S. Pat. No. 6,642,155 to Vachon et al., U.S. Pat. No. 6,654,625 to Say et al., U.S. Pat. No. 6,565,509 to Say et al., U.S. Pat. No. 6,514,718 to Heller, U.S. Pat. No. 6,465,666 to Essenpreis et al., U.S. Pat. No. 6,465,666 to Offenbacher et al., U.S. Pat. No. 6,514,718 to Heller, U.S. Pat. No. 6,465,666 to Essenpreis et al., U.S. Pat. Nos. 6,214,185 to Cunningham et al., 5,310,469 to Shaffer et al., 5,683,562 to Bonnecaze et al., 6,579,690 to Say et al., 6,484,46 to Say et al., 6,512,939 to Colvin et al., 6,424,847 to Mastrototaro et al., and 6,424,847 to Mastrototaro et al. Each of the above patents is incorporated herein by reference in its entirety, and while it is not intended to be exhaustive of all applicable analyte sensors, it should be understood that the disclosed embodiments are generally applicable to a variety of analyte sensor configurations. In other embodiments of sensor systems including a biological interface / drug-releasing layer(s), the sensor may be a planar or substantially planar sensor.

[0344] Exemplary Multi-Analyte Sensor Membrane Configurations Continuous multi-analyte sensors are provided having various membrane configurations suitable for facilitating simultaneous, intermittent, and / or sequential signal transduction corresponding to analyte concentrations. In one embodiment, such sensors may be configured using signal transducers comprising one or more transducing elements ("TL"). Such continuous multi-analyte sensors may employ various transduction means, such as amperometric, voltammetric, potentiometric, and impedimetric methods, among other techniques.

[0345] In one embodiment, the transduction element comprises one or more membranes that can include one or more layers and / or domains, each of which can independently include one or more signal transducers, such as enzymes, RNA, DNA, aptamers, binding proteins, etc. As used herein, transduction element includes and is used interchangeably with enzymes, ionophores, RNA, DNA, aptamers, and binding proteins.

[0346] In one embodiment, the transducer element resides in one or more membranes, layers, or domains formed over the sensing region. In one embodiment, such a sensor may be constructed using a membrane domain containing one or more enzyme domains, e.g., enzyme domains also referred to as EZ layers ("EZLs"), each of which may contain one or more enzymes. References below to an "enzyme layer" are intended to include all or a portion of an enzyme domain, any of which may be all or a portion of a membrane system as discussed herein, e.g., as a single layer, as two or more layers, as a bilayer pair, or combinations thereof.

[0347] In one embodiment, the continuous multi-analyte sensor uses one or more of the following analyte-substrate / enzyme pairs, e.g., sarcosine oxidase in combination with creatinine amidohydrolase, creatinine amidohydrolase used for sensing creatinine. Other examples of analyte / oxidase enzyme combinations that can be used in the sensing region include, e.g., alcohol / alcohol oxidase, cholesterol / cholesterol oxidase, galactose-galactose / galactose oxidase, choline / choline oxidase, glutamate / glutamate oxidase, glycerol / glycerol-3-phosphate oxidase (or glycerol oxidase), bilirubin / bilirubin oxidase, ascorbic acid / ascorbic acid oxidase, uric acid / uric acid oxidase, pyruvate / pyruvate oxidase, hypoxanthine-xanthine / xanthine oxidase, glucose / glucose oxidase, lactate / lactate oxidase, L-amino acid oxidase, and glycine / sarcosine oxidase. Other analyte-substrate / enzyme pairs may be used, including those containing recombinant enzymes, immobilized enzymes, mediator-wired enzymes, dimerized enzymes, and / or fusion enzymes.

[0348] NAD-based multi-analyte sensor platform Nicotinamide adenine dinucleotide (NAD(P) + NAD(P)H is a coenzyme, e.g., a dinucleotide consisting of two nucleotides linked through their phosphate groups. One nucleotide contains an adenine nucleobase, and the other contains nicotinamide. NAD exists in two forms, e.g., oxidized (NAD(P)H) and oxidized (NAD(P)H). + ) and reduced form (NAD(P)H) (H = hydrogen). The reaction between NAD+ and NADH is reversible, so the coenzyme is essentially not consumed, and NAD(P) + / and NAD(P)H forms can be continuously cycled between.

[0349] In one example, one or more enzyme domains in the sensing region of a continuous multi-analyte sensor device of the present disclosure include an amount of NAD+ or NADH to provide transduction of a detectable signal corresponding to the presence or concentration of one or more analytes. In one example, one or more enzyme domains in the sensing region of a continuous multi-analyte sensor device of the present disclosure include an excess amount of NAD+ or NADH to provide enhanced transduction of a detectable signal corresponding to the presence or concentration of one or more analytes.

[0350] In one embodiment, NAD, NADH, NAD + , NAD(P) + ATP, flavin adenine dinucleotide (FAD), magnesium (Mg++), pyrroloquinoline quinone (PQQ), and their functionalized derivatives may be used in combination with one or more enzymes in a continuous multi-analyte sensor device. In one example, NAD, NADH, NAD + , NAD(P) + , ATP, flavin adenine dinucleotide (FAD), magnesium (Mg ++ ), pyrroloquinoline quinone (PQQ), and their functionalized derivatives are incorporated into the sensing region. In one embodiment, NAD, NADH, NAD + , NAD(P) + , ATP, flavin adenine dinucleotide (FAD), magnesium (Mg ++ ), pyrroloquinoline quinone (PQQ), and their functionalized derivatives are dispersed or distributed in one or more membranes or domains of the sensing region.

[0351] In one aspect of the present disclosure, continuous sensing of one or more analytes using NAD+-dependent enzymes is provided in one or more membranes or domains of the sensing region. In one example, the membranes or domains provide a mechanism for the retention and stable recycling of NAD+ and the conversion of NADH oxidation or NAD+ reduction into an amperometrically measurable current. In one example described below, continuous sensing of multiple analytes is provided, at least one of which is either reversibly coupled to or oxidized or reduced by an NAD+-dependent enzyme, such as ketones (beta-hydroxybutyrate dehydrogenase), glycerol (glycerol dehydrogenase), cortisol (11β-hydroxysteroid dehydrogenase), glucose (glucose dehydrogenase), alcohol (alcohol dehydrogenase), aldehydes (aldehyde dehydrogenase), and lactate (lactate dehydrogenase). In other examples described below, membranes are provided that allow for the continuous on-body sensing of multiple analytes that utilize FAD-dependent dehydrogenases, such as fatty acid (acyl-CoA dehydrogenase).

[0352] Exemplary configurations of one or more membranes or portions thereof, configured to provide NAD+ retention and recycling, are provided. Thus, the electrode surface of a conductive wire (coaxial) or a planar conductive surface is coated with at least one layer containing at least one enzyme, as depicted in FIG. 7A. Referring to FIG. 7B, one or more optional layers may be positioned between the electrode surface and one or more enzyme domains. For example, one or more interference domains (also referred to as "interferent-blocking layers") may be used to reduce or eliminate signal contributions from undesired species present, or one or more electrodes (not shown) may be used to assist with wetting, system equilibration, and / or start-up. As shown in FIGS. 6A and 6B, one or more membranes provide an NAD+ reservoir domain that provides a reservoir for NAD+. In one example, one or more interferent-blocking membranes are used, and a potentiostat is utilized to measure HO production or 0 consumption of an enzyme such as or similar to NADH oxidase, where the NAD reservoir and enzyme domain locations can be switched to facilitate better consumption of excess NAD and slower outward diffusion. Exemplary sensor configurations can be found in U.S. Provisional Patent Application Nos. 63 / 321,340, filed March 18, 2022, entitled "CONTINUOUS ANALYTE MONITORING SENSOR SYSTEMS AND METHODS OF USING THE SAME," and 63 / 291,726, filed December 20, 2021, entitled "MEDIATOR-TETHERED NAD(H) FOR KETONE SENSING," both of which are incorporated herein by reference in their entireties.

[0353] In one embodiment, one or more mediators suitable for NADH oxidation are incorporated into one or more electrode domains or enzyme domains. In one embodiment, organic mediators such as phenanthrolinedione or nitrosoaniline are used. In another embodiment, organometallic mediators such as ruthenium-phenanthrolinedione or osmium(bpy)2Cl, polymers containing covalently bound organic mediators or organometallic coordination mediator polymers, e.g., polyvinylimidizole-Os(bpy)2Cl, or polyvinylpyridine-organometallic coordination mediators (including ruthenium-phenanthrolinedione), are used. Other mediators may be used, as discussed further below.

[0354] In humans, serum levels of beta-hydroxybutyrate (BHB) are typically in the low micromolar range but can rise to approximately 6-8 mM. Serum levels of BHB can reach 1-2 mM after strenuous exercise, and consistent levels above 2 mM are achieved with a low-carbohydrate ketogenic diet. While other ketones, such as acetoacetate and acetone, are present in serum, the majority of the dynamic range in ketone levels is in the form of BHB. Therefore, monitoring BHB, for example, continuously, is useful for providing health information to users or healthcare providers.

[0355] Thus, an exemplary continuous ketone analyte detection method is provided using an electrode-associated mediator / NAD+ / dehydrogenase, e.g., beta-hydroxybutyrate dehydrogenase (HBDH), for continuous monitoring of BHB. In one example, a continuous ketone sensor configuration capable of monitoring BHB includes a mediator / NAD+ / dehydrogenase present adjacent to the electrode surface. Alternatively, for example, multiple enzyme domains can be used in an enzyme layer, with a mediator / NAD+-containing layer being more proximal to the electrode surface than an adjacent enzyme domain containing a dehydrogenase enzyme. In one example, NAD+ and / or HBDH are present in the same or different enzyme domains, both of which can be immobilized using, for example, an amine-reactive crosslinker (e.g., glutaraldehyde, epoxide, NHS ester, imidoester). In one example, NAD+ is conjugated to a polymer and present in the same or different enzyme domain as HBDH. In one embodiment, the molecular weight of NAD+ is increased to prevent or eliminate migration from the sensing region; for example, NAD+ is dimerized using its C6-terminal amine with any amine-reactive crosslinker, or NAD+ is immobilized to a polymer from its C6-terminal amine. In one embodiment, a mediator polymer containing an organic mediator or an organometallic coordination mediator polymer is used, covalently or otherwise operably coupled to an electrode. In other embodiments, NAD+ may be electrografted to an electroactive surface (e.g., a working electrode). In one embodiment, the electrografted NAD+ is enzymatically active. In another embodiment, the electrografted NAD+ is not enzymatically active.

[0356] In some embodiments, the flux of reactants / co-reactants, such as oxygen, through the sensing region has little, if any, effect on the transduced signal. In the above configuration, there is no oxygen consumption or hydrogen peroxide production; rather, there is a direct transfer of electrons from the enzyme to the electrode surface for signal transduction. Therefore, despite endogenous electroactive species such as ascorbate and urate, the need to preferentially attenuate the flux of analytes relative to other reactants, such as oxygen and peroxide, is reduced or eliminated. For example, a homogeneous polymer with a controlled mesh size may be used. In other embodiments, the sensing region includes one or more enzymes that are oxygen-dependent, and oxygen flux is maximized, including, for example, silicone, polysiloxane, or copolymers. Other membranes may be used, such as those described above, positioned between or above the EZL or NAD+ reservoir, e.g., the drug release layer and / or the biointerface layer.

[0357] In one embodiment, the diaphorase is electrically coupled to an electrode having an organometallic coordination mediator polymer. In another embodiment, the diaphorase is covalently coupled to an electrode having an organometallic coordination mediator polymer.

[0358] Alternatively, multiple enzyme domains can be used in the enzyme layer, for example, separating electrode-associated diaphorase (closest to the electrode surface) from more distal adjacent NAD+ or dehydrogenase enzymes to essentially separate NADH oxidation from analyte (ketone) oxidation. Alternatively, NAD+ can be closer to the electrode surface than adjacent enzyme domains containing dehydrogenase enzymes. In one example, NAD+ and / or HBDH are present in the same or different enzyme domains, both of which can be immobilized using, for example, amine-reactive crosslinkers (e.g., glutaraldehyde, epoxides, NHS esters, imidoesters). In one example, NAD+ is conjugated to a polymer and present in the same or different enzyme domain as HBDH. In one example, the molecular weight of NAD+ is increased to prevent or eliminate migration from the sensing region, for example, NAD+ is dimerized using its C6-terminal amine with any amine-reactive crosslinker. In one example, NAD+ can be covalently attached to an aspect of the enzyme domain that has a higher molecular weight than NAD+, which can improve the stability profile of NAD+ and improve the ability to retain and / or immobilize NAD+ within the enzyme domain, for example, dextran-NAD.

[0359] In yet another example, the transduction signal from the transduction element for a sequential ketone (and one or more other analytes) sensor configuration can be provided using the oxidation of NADH oxidase enzyme to form hydrogen peroxide at the electrode surface as the signal transduction species. In this configuration, an electrode surface, membrane, layer, or domain can be used that selectively reduces the flux of analyte and NAD+ while allowing high flux of oxygen to the sensing region. Thus, one or more interference domains, such as NAFION™ or alternating layers of polyallylamine and polyacrylate acid, can be used. In one example, NADH and one or more other analyte-specific oxidase enzymes can be present in the same or different enzyme domains, either of which can be immobilized.

[0360] In one embodiment, NAD+ may be bound to or physically trapped within a polymer and reside in the same or a different enzyme domain as HBDH. In one embodiment, the molecular weight of NAD+ is increased to prevent or eliminate migration from the sensing region; for example, NAD+ is dimerized using its C6-terminal amine with an optional amine-reactive crosslinker. In one embodiment, superoxide dismutase (SOD) may be included in the configuration, e.g., in the same enzyme domain as NADH, to scavenge free radicals generated by NADH oxidase, thereby improving signal stability and sensor performance. In the above configuration, the transduced signal is oxygen-dependent, and oxygen flux is maximized, e.g., by using a homogeneous polymer membrane with a controlled mesh size and / or by including silicone, polysiloxane, or copolymers in one or more enzyme domains. In one embodiment, if the signal is oxygen-independent, a resistance domain is used to attenuate the flux of analyte(s) into the EZL so that the sensor response remains linear throughout the physiological range of the target analyte(s). A "target" analyte, as discussed herein, is an analyte intended to be detected by a sensor system as discussed herein. One or more target analytes may be detected and analyzed using a sensor system as discussed herein.

[0361] In one embodiment, the sensing region comprises one or more NADH acceptor oxidoreductases and one or more NAD-dependent dehydrogenases. In one embodiment, the sensing region comprises one or more NADH acceptor oxidoreductases and one or more NAD(P)-dependent dehydrogenases having NAD(P)+ or NAD(P)H as a cofactor present in the sensing region. In one embodiment, the sensing region comprises a quantity of diaphorase.

[0362] In one embodiment, a ketone sensing configuration suitable for combination with another analyte sensing configuration is provided. Thus, an approximately 1-20 μm thick EZL layer is prepared by providing an EZL solution composition in 10 mM HEPES in water with about 20 μL 500 mg / mL HBDH, about 20 μL [500 mg / mL NAD(P)H, 200 mg / mL polyethylene glycol-diglycol ether (PEG-DGE) of about 400 MW], about 20 μL 500 mg / mL diaphorase, and about 40 μL 250 mg / mL polyvinylimidazole-osmium bis(2,2'-bipyridine) chloride (PVI-Os(bpy)2Cl) on a substrate such as a working electrode, so as to provide, after drying, about 15-40 wt% HBDH, about 5-30% diaphorase, about 5-30% NAD(P)H, about 10-50% PVI-Os(bpy)2Cl, and about 1-12% PEG-DGE (400 MW). Substrates discussed herein, which may include a working electrode, may be formed from gold, platinum, palladium, rhodium, iridium, titanium, tantalum, chromium, and / or alloys or combinations thereof, or carbon (e.g., graphite, glassy carbon, carbon nanotubes, graphene, or doped diamond, and combinations thereof...

Claims

1. 1. A monitoring system comprising: a continuous analyte sensor configured to generate an analyte measurement associated with an analyte level in a patient; a sensor electronics module coupled to the continuous analyte sensor and configured to receive and process the analyte measurements.

2. The continuous analyte sensor comprises: A substrate; a working electrode disposed on the substrate; 10. The monitoring system of claim 1, further comprising: a reference electrode disposed on the substrate; and wherein the analyte measurements produced by the continuous analyte sensor are determined using an electrochemical method based at least in part on a difference in current generated between the working electrode and the reference electrode.

3. the continuous analyte sensor is a multi-analyte sensor comprising a continuous glucose sensor and a continuous ketone sensor; The monitoring system of claim 1 , wherein the analyte measurements include a glucose measurement and a ketone measurement.

4. a memory containing executable instructions; in data communication with said memory; receiving the analyte measurements, including the glucose measurement and the ketone measurement, from the sensor electronics module; processing the analyte measurements to determine analyte metrics, including at least glucose metrics and ketone metrics; 4. The monitoring system of claim 3, further comprising: one or more processors configured to execute the executable instructions to generate a treatment recommendation based at least in part on the analyte metric.

5. The processor: The monitoring system of claim 4 , further configured to receive patient treatment data corresponding to the patient.

6. the patient treatment data includes medication information corresponding to a medication configured to reverse euglycemic diabetic ketoacidosis; The monitoring system of claim 5 , wherein the treatment recommendation comprises at least one of changing the dosage of the medication or changing the frequency of taking the medication.

7. 7. The monitoring system of claim 6, wherein the medication is an SGLT-2 inhibitor and the treatment recommendation includes at least a change in the frequency or the dosage of the SGLT-2 inhibitor.

8. The processor: The monitoring system of claim 5 , further configured to generate a euglycemic diabetic ketoacidosis prediction using the analyte metric and the patient treatment data.

9. The processor:

10. The monitoring system of claim 8, further configured to generate an alert or alarm based on at least one of the euglycemic diabetic ketoacidosis prediction or the treatment recommendation.

10. and further comprising one or more non-analyte sensors, wherein the processor:

5. The monitoring system of claim 4, further configured to receive non-analyte sensor data generated for the patient using one or more non-analyte sensors, and wherein the treatment recommendation is further based on the non-analyte sensor data.

11. 11. The monitoring system of claim 10, wherein the one or more non-analyte sensors comprise at least one of an insulin pump, an ECG sensor, a heart rate monitor, a blood pressure sensor, a respiration sensor, a thermometer, an oxygenated hemoglobin sensor, an activity tracker, a peritoneal dialysis machine, or a hemodialysis machine.

12. The monitoring system of claim 4 , wherein the processor is further configured to generate a euglycemic diabetic ketoacidosis prediction based at least in part on the analyte metric.

13. 13. The monitoring system of claim 12, wherein the euglycemic diabetic ketoacidosis prediction includes at least one of a likelihood or risk that the patient is experiencing euglycemic diabetic ketoacidosis, a likelihood or risk that the user will experience euglycemic diabetic ketoacidosis, the presence of euglycemic diabetic ketoacidosis experienced by the patient, or a severity of euglycemic diabetic ketoacidosis experienced by the patient.

14. 14. The monitoring system of claim 13, wherein the euglycemic diabetic ketoacidosis prediction is determined by a rule-based model, a machine learning model, a Kalman filter, a probability model, or a probabilistic model.

15. 5. The monitoring system of claim 4, wherein the analyte metrics include at least one of ketone baseline, maximum ketone level, minimum ketone level, rate of ketone level change, ketone clearance rate, ketone trend, time in ketone range, rate of glucose level change, glucose trend, blood glucose variability, glucose clearance, or time in glucose range.

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